Management in the Age of Digital Business Complexity [1 ed.] 2020055334, 2020055335, 9780367230746, 9781032011721, 9780429278211

Citation preview

Management in the Age of Digital Business Complexity

Management in the Age of Digital Business Complexity focuses on how the digital age is changing management and vastly speeding up complexity dynamics. The recent coevolution of technologies has dramatically changed in just a few years how people and firms learn, communicate, and behave. Consequently, the process of how firms coevolve and the speed at which they coevolve has been dramatically changed in the digital age, and managerial methods are lagging way behind. Combining his own expertise with that of a number of specialist and international co-authors, McKelvey conveys how companies that fall behind digitally can quickly be driven out of business. The book has been created for academics seeking to upgrade management thinking into the modern digital age and vastly improve the change capabilities of firms facing digital-oriented competition. Bill McKelvey was a Professor of Strategic Organizing and Complexity Science at the UCLA Anderson School of Management.

Routledge Studies in Innovation, Organizations and Technology

Developing Digital Governance South Korea as a Global Digital Government Leader Choong-sik Chung Digital Business Models Perspectives on Monetisation Adam Jabłoński and Marek Jabłoński Developing Capacity for Innovation in Complex Systems Strategy, Organisation and Leadership Christer Vindeløv-Lidzélius How is Digitalization Affecting Agri-food? New Business Models, Strategies and Organizational Forms Edited by Maria Carmela Annosi and Federica Brunetta Social Innovation of New Ventures Achieving Social Inclusion and Sustainability in Emerging Economies and Developing Countries Marcela Ramírez-Pasillas, Vanessa Ratten and Hans Lundberg Sustainable Innovation Strategy, Process and Impact Edited by Cosmina L. Voinea, Nadine Roijakkers and Ward Ooms Management in the Age of Digital Business Complexity Edited by Bill McKelvey with Renata Kaminska, María Paz Salmador, and Nadine Escoffier Citizen Activities in Energy Transition User Innovation, New Communities, and the Shaping of a Sustainable Future Sampsa Hyysalo For more information about this series, please visit: www​.r​​outle​​dge​.c​​om​/ Ro​​utled​​ge​-St​​udies​​-in​-I​​nnova​​tion-​​Organ​​izati​​ons​-a​​nd​-Te​​chnol​​ogy​/b​​ook​s​​eries​​/RIOT​

Management in the Age of Digital Business Complexity

Edited by Bill McKelvey with Renata Kaminska, María Paz Salmador, and Nadine Escoffier

First published 2021 by Routledge 2 Park Square, Milton Park, Abingdon, Oxon OX14 4RN and by Routledge 605 Third Avenue, New York, NY 10158 Routledge is an imprint of the Taylor & Francis Group, an informa business © 2021 selection and editorial matter, Bill McKelvey; individual chapters, the contributors The right of Bill McKelvey to be identified as the author of the editorial material, and of the authors for their individual chapters, has been asserted in accordance with sections 77 and 78 of the Copyright, Designs and Patents Act 1988. All rights reserved. No part of this book may be reprinted or reproduced or utilised in any form or by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying and recording, or in any information storage or retrieval system, without permission in writing from the publishers. Trademark notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe. British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library Library of Congress Cataloging-in-Publication Data Names: McKelvey, Bill, editor. Title: Management in the age of digital business complexity/edited by Bill McKelvey. Description: Milton Park, Abingdon, Oxon; New York, NY: Routledge, 2021. | Includes bibliographical references and index. Identifiers: LCCN 2020055334 (print) | LCCN 2020055335 (ebook) Subjects: LCSH: Electronic commerce–Management. | Organizational change. Classification: LCC HF5548.32 .M338 2021 (print) | LCC HF5548.32 (ebook) | DDC 658/.05–dc23 LC record available at https://lccn​.loc​.gov​/2020055334 LC ebook record available at https://lccn​.loc​.gov​/2020055335 ISBN: 978-0-367-23074-6 (hbk) ISBN: 978-1-032-01172-1 (pbk) ISBN: 978-0-429-27821-1 (ebk) Typeset in Bembo by Deanta Global Publishing Services, Chennai, India

Bill McKelvey was a complex Complexity scholar, a brilliant professor, a great intellectual, and an incredible researcher. We would like to express our utmost gratitude to Professor Bill McKelvey for sharing his passion about research and collaboration. The fact that he collaborated with people from all around the world with different cultures and backgrounds made his research projects interesting and exciting. He was incredibly generous with his time— always available to discuss a project, always challenging us to think in new ways and always open to new research ideas. His guidance shaped and continues to inform our work from how to write an introduction to what many of us referred to as the “McKelvey effect.” We still can hear him say, “are you adding a brick to the wall (doing incremental work) or are you building a new wall?” His contribution in pushing the research forward in our field will remain a legacy for future generations. Professor Bill McKelvey had a positive impact on our lives and we feel very lucky that somehow, somewhere we have crossed his path. He will be deeply missed and will always be remembered as an example of generosity, dedication, passion for his work and above all as an amazing and supporting mentor and friend. It has been an honor collaborating with him! Cera Oh Tammy L. Madsen María Paz Salmador Renata Kaminska Elena Goryunova Nadine Escoffier

Contents

List of contributors 1 Managing the consequences of digital networks and accelerated complexity dynamics in digital business ecosystems

ix 1

BILL MCKELVEY

2 Rescuing economics and management from Darwin’s evolution via death-and-replacement by the Baldwin Effect: learning during a lifetime

48

BILL MCKELVEY AND CERA OH

3 Why Lamarck dominates Darwin in explaining organizational change and evolution

110

TAMMY L. MADSEN AND BILL MCKELVEY

4 “Simple rules” for improving digital corporate IQ: basic lessons from complexity science

136

BILL MCKELVEY

5 Digital dynamic capabilities

153

MARÍA PAZ SALMADOR, RENATA KAMINSKA, AND BILL MCKELVEY

6 Understanding value conflict between business and society: a new perspective from neuro and complexity sciences

182

ELENA GORYUNOVA AND BILL MCKELVEY

7 A digital perspective about how complexity science pushes firms into the stochastic frontier MARÍA PAZ SALMADOR AND BILL MCKELVEY

216

viii Contents

8 Using Crowd wisdom via crowdsourcing: proof of concept of an efficient digital strategy in the age of digital business complexity 242 NADINE ESCOFFIER AND BILL MCKELVEY

Index

269

Contributors

Nadine Escoffier is an expert in crowdsourcing in the entertainment industry. She co-led with Professor Bill McKelvey a crowdsourcing research project in the entertainment industry which was mentioned in the Hollywood Reporter and allowed to publish four articles, among which one was first on the list of most read articles of 2017 in the International Journal of Innovation Management. This research project was also presented in conferences such as the World Conference on Mass Customization, Personalization, and Co-Creation (co-organized in 2011 by the University of California, Berkeley, US; Massachusetts Institute of Technology, US; The Hong Kong University of Science and Technology, Hong Kong; and RWTH Aachen University, Germany); the Academy of Management, US (2011); the UCLA Bruce Mallen Scholars and Practitioners in Motion Picture Industry Studies (coorganized by the University of California Los Angeles, US [2011]) and Open and User Innovation (co-organized in 2012 by Harvard Business School, US, and Massachusetts Institute of Technology, US). She is now working on a research project mixing crowdsourcing and Blockchain technology. Elena Goryunova has a PhD in strategy and sustainable development. Her research interests include CSR, sustainable development, innovation, complexity science and moral neuroscience. In her PhD thesis, she explored the link between CSR and innovation based on an integrative approach of complexity science. She has presented her research at the Academy of Management annual meetings in Orlando, US (2013), and Vancouver, Canada (2015), as well as at the International Association for Business and Society (IABS) Conference 2014 in Sydney, Australia, and at The Alliance for Research on Corporate Sustainability (ARCS) 2014 in London, Canada. She obtained an MRes in management science, strategy and sustainable development from Institute of Administration and Enterprises (IAE) Aix en Provence, France, and an MA in world economics from St. Petersburg’s Institute of International Economic Affairs, Economy and Law, Russia. She is a content creator for online education who is interested in the crossdisciplinary inquiry for sustainability. She believes that cross-fertilization between natural and social sciences will provide us with a deeper insight into how to design innovative and socially responsible organizations for a better, more just, and sustainable world.

x Contributors

Renata Kaminska is a Professor of Strategy and Innovation at SKEMA Business School, Sophia Antipolis campus, France. Renata’s research interests concern strategy process, organizational dynamics, and innovation. Her most recent research, developed in the frame of the EU-funded project ELSE, focuses on leadership for safety in complex, high-risk environments. Renata teaches strategy, innovation and creativity, and organizational design in the BBA, MSc, and Executive MBA programs. She is Scientific Director of M2/MSc in Research (Management and Innovation) jointly run by University Côte d’Azur’s ELMI Graduate School and SKEMA Business School. Tammy L. Madsen is the M. Keck Foundation Professor of Strategic Management and a former Associate Dean of the Leavey School of Business. She teaches in the areas of strategy, digital transformation and innovation in the MBA, Executive MBA, and Executive Development programs at SCU. Her research examines topics such as competitive heterogeneity, industry dynamics following a shock, and co-innovation in platform-based ecosystems. Her research has received various awards and appears in outlets such as Strategic Management Review, Strategic Management Journal, Organization Science, Journal of Management Studies, Industrial and Corporate Change, and Journal of Knowledge Management. She co-authored the fourth edition of Modern Competitive Strategy (2016) with Gordon Walker and has a forthcoming book, Co-Innovation Platforms, with David Cruickshank. Tammy serves on the Board of Governors of the Academy of Management and on the Board of Advisors, Global Innovation Institute; she also is the Director of the Strategy Research Foundation’s Dissertation Grant Program. Bill McKelvey was a Professor of Strategic Organizing and Complexity Science at the Anderson School of Management at the University of California Los Angeles (UCLA), US. He chaired the Building Committee that produced the $110,000,000 Anderson Complex at UCLA which opened in 1994. As Director of the Center for Rescuing Strategy and Organization Science (SOS), he initiated activities leading to the founding of UCLA’s Center for Human Complex Systems and Computational Social Science. He organized various agent-modeling speaker programs and conferences at UCLA and advised some 170 student consulting projects in firms. Cera Oh is a patent attorney in Portland, Oregon, US. Her area of focus is intellectual property law, especially in patent prosecution. She has a BSc in biology (2011) from the University of California Los Angeles, US. She received her JD from Lewis & Clark Law School, Portland, Oregon, US, in 2015. María Paz Salmador has a PhD in business administration and is a Professor and Head of the Department of Strategic Management at University Autónoma de Madrid, Spain. She has also been a Visiting Scholar at the Japan Advanced Institute of Science and Technology and Senior Postdoctoral Fulbright Scholar at Texas A&M University, US.

1

Managing the consequences of digital networks and accelerated complexity dynamics in digital business ecosystems Bill McKelvey

Introduction The first management-oriented Special Issue focusing on digital business (DB) was published in 2013 by the MIS Quarterly, titled Digital Business Strategy: Toward a Next Generation of Insights (all the articles are cited later). It’s about how a firm’s competitive environment and digital strategic posture influence DB strategy. Several additional information systems (IS)-oriented articles had also appeared before the Special Issue (Varian 1996; Katsikas et al. 2005; Damiani et al. 2007; Salam et al. 2008; Al-Debei and Avison 2010; Tsatsou et al. 2010; and Melville 2012). In addition, there are now many other articles and books writing more broadly about DB ecosystems, of which a few recent ones are: Choi 2017; Durand et al. 2017; Kshetri 2017; Ponce-Jara et al. 2017; Remane et al. 2017; Schallmo et al. 2017; Seo 2017; Thomson et al. 2017; Wokurka et al. 2017; Yager and Espada 2017; Qiu et al. 2018; Schallmo and Williams 2018; Weill and Woerner 2018. Earlier published articles and books are listed later. The complexity dynamics stemming from agent interactions often result in the formation of rare and extreme outcomes—often called Stochastic Frontiers by strategy theorists—such as dominant firms in industries (Scheinkman and Woodford 1994; Stanley et al. 1996; Lee et al. 1998; Stanley et al. 2000; Axtell 2001; Moss 2002; Jones 2005; Andriani and McKelvey 2007, 2009, 2011; Aoyama et al. 2009; Park et al. 2010; Glaser 2013; Chew 2015), entrepreneurial firms (Crawford et al. 2015; Crawford and McKelvey 2018; Salmador Sanchez and McKelvey 2018) and other kinds of network effects (cited previously), some of which show up as skew distributions on social media sites such as Facebook or Twitter (comments, photos, or stories “going viral” on the Internet) (Bedhead 2013). The Internet is now a dominant feature of all developed economies and is a growing feature in developing economies (Seigneur 2005; Malecki and Moriset 2008; Briscoe 2010; Li et al. 2012; El Sawy and Pereira 2013; Verdouw et al. 2013; Mullineux and Murinde 2014; Coeckelbergh 2015; Kim and Mauborgne 

2  Bill McKelvey

2015; Manyika et al. 2015; Mullineux 2015; Kshetri 2017; Majeed 2017; PonceJara et al. 2017). Networks form much more quickly in Internet-connected DB ecosystems (deCosta 2013; Kellmereit and Obodovski 2013; McEwen and Cassimally 2013; McQuivey 2013; Schmidt and Cohen 2014; Da Xu et al. 2014; Al-Fuqaha et al. 2015; Greengard 2015; Manyika et al. 2015; Miller 2015; Keerthana and Ashika Parveen 2017; Yager and Espada 2017; Hassan et al. 2018; Qiu et al. 2018). Many, if not most, networks tend to be skewdistributed (see the aforementioned studies). The Internet now allows firms to learn much more about their customers via “crowdsourcing” and firms can also get information from their customers about what customers like or don’t like about their products via crowdsourcing (Day et al. 2003; Mulhern 2009; Faed 2010; Strader 2010; McKormack 2011; Chaffey and Ellis-Chadwick 2012; Escoffier and McKelvey 2014, 2015, 2018; Holliman and Rowley 2014; Kim and Mauborgne 2015). Using the Internet also allows customers to learn details about a similar product produced by competing firms. Companies can also use the Internet to quickly learn about their competitors’ new products. The foregoing Internet-based, quickly gained knowledge then speeds up the coevolution of DB ecosystems. The coevolution of dinosaurs took some 180 million years. The coevolution of Apple’s iPhone and iPod, Samsung’s Galaxy phone, and Google’s Android have dramatically changed how people and firms learn, communicate, and behave in just thirteen years (the iPhone went to market in June of 2007). Consequently, the coevolutionary process of how firms coevolve and the speed at which they coevolve has been dramatically changed in the Digital Age. Many if not most managers and their firms have not kept up with the increased speed of coevolution in their now-digital ecosystems. The MBA programs that train students on how to be effective managers struggle to develop programs in response to the changed managerial world of the Digital Age. They still have to recognize that, because of the speeding up of information flows and increasingly pervasive digital access to information about customers and competing firms, the managerial skills they teach have to be realigned with the coevolutionary dynamics of the Digital Age. A recent story in The Economist (2018), which is titled “Automation and productivity: producing ideas,” makes no mention of digital information flows or Internet connections. And yet firms can learn about markets, customer preferences and complaints, and what their competitors are in the process of planning and creating by hacking (i.e. finding ways to get access to information in digital devices) into their internal digital-information flows about new products, new ideas, information about customer preferences, marketing and production techniques, etc. While, for sure, a new idea comes from an employee’s brain, in the Digital Age, digital information flows are what bring knowledge and existing ideas together such that new creative ideas emerge. DB firms, thus, have rapidly occurring opportunities for more creativity and change than firms that are stuck in traditional ways of doing business. Given

Managing the Consequences of Digital Networks  3

all the information available on Google, remembering information is, pretty much, irrelevant! The mention of “digital business” in the Academy of Management Review is only in two articles: Ancona et al. (2001) and Albert et al. (2015). The Strategic Management Journal has published two articles: one by Rosenbloom (2000) and a second by Durand et al. (2017). Organization Science has now published 32 articles mentioning “digital business.” But I don’t yet know how much “depth” is given to the topic in each article. Organization Studies has published only two articles. Worse, the European Management Review has published only one: Dum (2007). Several articles in the Harvard Business Review relate more or less vaguely to “digital business.” Circa July 2018, Amazon shows that 396 DB books have appeared since the first one in 1995 (Tapscott 1995: The Digital Economy [second “anniversary edition” published in 2015.]) Given the nearly 400 books available on Amazon and the 1000+ articles (in different disciplines) available via Google Scholar, it is clear that the Digital Age and DB are part of 21st century reality. But in 2015 I found only three digital MBA Programs via Google (all in Europe); as of July 2018, Google now shows that more than ten top-ranked digital-business MBA Programs exist in the US. It is both surprising and disturbing, however, that so little attention to this new reality that managers now face is offered by so few of the top-ranked American management-oriented journals or business schools. The development of coevolution (Kauffman 1993) in the DB age consists of four elements: first, I focus on IS because it is information that is now being transferred digitally among people and firms connected to the Internet. Second, I then focus on complexity dynamics that lead to new-order creation because they are vastly speeded up in the digital world. Third, I offer information about how the effect of digital complexity speeds up the coevolution of digitalbusinesses in DB ecosystems. Finally, I focus on implications for effective management in the Digital Age.

Digital-age effects on information systems Needless to say, digital effects obviously speed up information flows because modern information flowing between computers, smartphones, iPads, and iPods, etc. is the result of the digital connections, which allow information to flow at the speed of electricity and/or light vs. the speed of horses, postal service trucks, trains, or planes. Any business-related aspect that has some relation to what is transferred by digital-speed information flows is going to change very quickly: almost instantly if people are not involved; a bit or quite a bit slower if people are involved. Obviously, firms that have changed their equipment so as to deal with digital speeds, and have also managed to get their employees to more quickly change so as to respond to digital-speed information flows, have better survival chances.

4  Bill McKelvey Realities of the Digital Age

There are now many articles and books describing DB ecosystems, and how they are different from prior business ecosystems. Some of these are: Tapscott 1995, 2015; Ancona et al. 2001; Coupey 2001, 2016; Day et al. 2003; Dourmas et al. 2005; Seigneur 2005; Brousseau and Pénard 2007; Corallo et al. 2007; Muntaner-Perich and de la Rosa Esteva 2007; Nachira et al. 2007a,b; Pappas et al. 2007; Razavi et al. 2007; Tan et al. 2009; Stanley and Briscoe 2010; Tan and Macaulay 2011; Herdon et al. 2012; Li et al. 2012; Passerini and El Tarabishy 2012; El Sawy and Pereira 2013; Attour and Della Peruta 2014; Cojocaru et al. 2014; Jensen et al. 2014; Morabito 2014; Rong and Shi 2014; Turcoane 2014; Liu and Rong 2015; Tafti et al. 2015; Rogers 2016; Choi 2017: Durand et al. 2017; Kshetri 2017; Ponce-Jara et al. 2017; Remane et al. 2017; Schallmo et al. 2017; Seo 2017; Thomson et al. 2017; Wokurka et al. 2017; Yager and Espada 2017; Qiu et al. 2018; Schallmo and Williams 2018; Weill and Woerner 2018. The “Internet of Things” and other digital-world realities

The “Internet of Things” (deCosta 2013; Kellmereit and Obodovski 2013; McEwen and Cassimally 2013; McQuivey 2013; Schmidt and Cohen 2014; Greengard 2015; Miller 2015; Sawicki 2016; Keerthana and Ashika Parveen 2017; Yaqoob et al. 2017) consists of computers or embedded miniature computers in smartphones or other everyday objects that can connect with the Internet wirelessly. For example, because of lack of banks but their growing number of Internet and smartphone connections, more and more people in Africa use their smartphones rather than real money to buy things (Smith 2014; Brazzell 2017; Monks 2017). Thus: imagine you live in a small village in rural Kenya. Your daughter attends university in Nairobi and needs financial support to buy textbooks and pay her rent. How do you send her money if you, like many Kenyans, don’t have an electronic bank account or Internet access? In the US, the answer would be simple. In fact, you would have an abundance of options: PayPal, Venmo, online banking, checks, money orders, or good old-fashioned cash. Many people around the world, however, don’t have access to the financial services some of us Americans take for granted. Two billion ‘unbanked’ adults, mostly in developing countries, face barriers to tasks as simple as receiving wages or sending money to family members. Without access to banking services, their finances are unstable because they don’t have a good way to save for the future or borrow in times of need. (Brazzell 2017: 1) A powerful tool to achieve equitable development is promotion of economic empowerment for marginalized citizens by increasing formal financial services access and utilization. The provision of these services

Managing the Consequences of Digital Networks  5

via mobile phones has shown great promise in overcoming geographic, demographic, and institutional constraints to financial inclusion, especially in Africa and led by the mobile banking revolution in Kenya. This is exemplified by the extraordinary success since 2007 of Safaricom’s M-PESA, a mobile phone-based money transfer, payment, and banking service: as of June 2015, Safaricom had more than 22 million M-PESA subscribers served by over 90,000 M-PESA agents. The confluence of several factors has contributed to M-PESAs success, including Kenya’s political and economic context, demographics, telecommunications sector structure, lack of affordable consumer options, and enabling regulatory policies. Equally important have been Safaricom’s internal astute management and marketing of M-PESA. But M-PESA is now facing a strong new rival in Airtel Money, offered by Equity Bank, Kenya’s third largest bank. Now two different models for mobile financial services are competing vigorously in Kenya: Safaricom, an example of telecom-led mobile banking and Equity Bank, an example of bank-led mobile banking. There are three key challenges in Kenya to further promotion of financial inclusion via development of mobile financial services: facilitation of increased competition; transformation of non-digital microfinance institutions; and enactment of greater consumer protection. Where Kenya’s success factors might be present, many of Kenya’s lessons can be adapted. Where conditions are significantly different, the challenge becomes how best to nurture homegrown innovative solutions to address specific local constraints. (Abstract of a working paper [2016] written by Rosengard, lecture at the Harvard John F. Kennedy School of Government) The development of the “Internet Technology” roadmap is depicted in Figure 1.1. It shows the change in mobile devices and Internet technology starting in 2000. Needless to say, because there are so few bricks-and-mortar bank branches in Africa, people in Africa have learned how to take advantage of their mobile phones, the development of the Internet to use money to buy things, and their postal service to get them delivered to where they live. DB ecosystem effects

Many authors refer to the digital-business ecosystem. Dr. Ryan McCormack defines “digital ecosystems” as: “The digital ecosystem of a business is the combination of all relevant digital touchpoints, the people that interact with them, and the business processes and technology environment that support both” (McCormack 2011: 1). The “touchpoints” include: digital whatever, people, social media and the Internet, various interconnections, and other relevant business processes and relevant environmental forces. McCormack uses a “tree” as a complex system example based on “elements (roots, leaves, trunk), interconnections (chemical flows) and a function (to survive).”

Figure 1.1 Technology roadmap: The Internet of Things. ( Source: SRI Consulting Business Intelligence 2008)

6  Bill McKelvey

Managing the Consequences of Digital Networks  7

Gorbis (2013) describes the various ways in which social interactions, the social economy, governance, science, medicine, and all sorts of other societybased phenomena have changed because of the emergent digital technologies. Philips (2013) focuses on how companies can create value by better integrating people and business operations with digital analytical processes used to learn from all of the increasing amounts of Web-based data. Absent Internetoriented digital analytic skills, companies can’t compete in the modern Digital Age. Lanier (2013: 29–85) focuses on how businesses and various other kinds of social interactions are now influenced by the “cybernetic tempest” now impacting digital markets and networks, democratic processes, how people earn and spend money, humanistic realities, and morals. McQuivey (2013) discusses how digital disruptions affect how people will live in the Digital Age. Schmidt and Cohen (2014) worry about how the “New Age” of digital disruption will affect the future of governance. The Internet seems lawless, as the recent 2016 fight between Google and the FBI over access to a mobile phone illustrates, revolutions such as the Arab Spring and the effects of ISIL in Syria and Europe, terrorism, and other kinds of conflicts and/or combats, and the apparent digital invasions by various Russians to alter the outcome of the US Presidential election in 2016, or cause power outages in the US (Anderson and Bell 2012), and the stealing of information (Bai and Koong 2017; Chan and Yeoh 2017; McGeehan et al. 2017 ) among the many other publications listed by Google Scholar. Greengard (2015) discusses how specific elements of the Internet affect mobility, the digital storage cloud, digital tools, the industrial Internet, getting Internet phenomena to work better, and more broadly, the realities of the Digital Age. Because the Internet and all of its digital connections and digital speed have a tremendous impact on intelligence, Miller (2015) focuses on things like smart TVs, smart appliances, smart homes, smart clothing, smart shopping, smart cars, smart drones, smart warfare, smart medicine and more broadly smart businesses, smart cities, on and on. Tapscott (1995, 2015: 11) labels DB ecosystem as “The Age of Networked Intelligence,” based on the Internet. He notes that: labor markets have changed; privacy has been destructed; it has led to a bipolarization of wealth; “telework” has emerged, meaning that new jobs are related to Internet communications rather than workers operating machines; and “cyber-democracy” has emerged. Coupey (2016) focuses on how the digital Internet or the World Wide Web affects businesses: new technology; electronic commerce; speeding up buying, selling, and the effects of government policy changes; “dotcombat” (how digital companies can win by speeding up the buying and selling process so that customers can reduce their shopping time); emergent new business models and strategies; the new dominance of digital technology; and the totally changed basis of communication and advertising because of electronic commerce. From a more specific management perspective, a DB ecosystem is an industry in which firms compete with each other and more broadly some number of industries that compete against each other for sales, a good example of which are transportation industries, such as airlines, trains, subways, buses, and automobiles, etc. (Dourmas et al. 2005; Corallo et al. 2007; Muntaner-Perich and de la

8  Bill McKelvey

Rosa Esteva 2007; Tan et al. 2009; Briscoe 2010; Strader 2010; Herdon et al. 2012; Laudon and Laudon 2013; Cojocaru et al. 2014; Tafti et al. 2015). The impact of digital technology on other management concerns such as strategy, leadership, and organizational change is also discussed later in this chapter. Digital platform design

There are two kinds of “platforms” referred to in the literature: (1) product platforms; and (2) industry platforms. The former is “used with reference to a foundation or base of common components around which a company might build a series of related products” (Cusumano 2010: 32). Product platforms became a popular topic in the 1990s (ibid). In their book, Gawer and Cusumano (2002) identify “two essential differences” between product and industry platforms: (a) “an industry platform provides [a core technology] function as part of a technology ‘system’ whose components are likely to come from different companies, which we call ‘complementors’” (ibid: 32); and (b) “the industry platform has relatively little value to users without these complementary products or services” (ibid: 32). For example, a smartphone is just a little box without the technology of the touch screen, all the apps, the Internet, cell towers, and all the related technologies now supplied by various other companies in the industry. Furthermore, they point out that a single company is unlikely to have all the resources or capabilities to make its platform compelling to other users. For example, Microsoft’s original PC was a new technology platform that was made available for other companies to add additional complementary technologies and products, whereas the original Apple portable computer invented by Steve Wozniak and Steve Jobs was kept very unchanged by their company, Apple, and hence, was not nearly as successful as the PC produced by Microsoft. Cusumano (2010: 33) also notes that the critical distinguishing feature of an industry platform and ecosystem is the creation of “network effects.” These are positive feedback loops that can grow at geometrically increasing rates as adoption of the platform and the complements rise. The network effects can be very powerful, especially when they are “direct,” such as in the form of a technical compatibility or interface standard—which exists between the Windows–Intel PC and Windows-based applications. Cusumano also observes that network effects can also be “indirect,” and sometimes these are very powerful as well, such as when an overwhelming number of application developers, content producers, buyers and sellers, or advertisers adopt a particular platform that requires [producers of] complements to adopt a specific set of technical standards that define how to use or connect to the platform. (ibid: 33)

Managing the Consequences of Digital Networks  9

The PC eventually led to a much improved Apple computer and business model (Bergvall-Kåreborn and Howcroft 2013), which was followed by the iPod, iPhone, iPad, iTunes, eBay, Google, Amazon, and Facebook, etc., all of which added value to the original PC platform, which consequently became an “industry” platform (Gawer and Cusumano 2002, 2010). In recent years the platform concept has blossomed in the literature. Wells (2001) points out that while platforms can offer many engineering advantages, they can also create marketing disasters. Production advantages from a platform can lead to innovation and cost saving commonalities. But marketing advantages call for brand distinctions. Wells notes that companies collectively involved in a platform have to maintain a “delicate balance” between the two. Simula and Vuori (2012) discuss the benefits and barriers in the use of crowdsourcing platforms. Boudreau and Jeppesen (2014) find that network effects can be a “mirage” because they don’t always grow for platform owners, as presumed by Cusumano (2010). This means that managers can’t simply presume that all platforms automatically result in high-growth networks. On the other hand, competent platform creation and management does indeed result in the growth of networks, which I substantiate later on. In general, and more broadly, there are many articles showing the creation and effective structuring and management of DB platform strategies: e.g. Koufteros et al. 2002; Koufteros et al. 2005; Lin et al. 2007; Tee and Gawer 2009; Gawer and Cusumano 2010; Lee et al. 2010; Kenney and Pon 2011; Bondreau and Jeppesen 2014; Marion et al. 2015. Not surprisingly, the literature about platform design and strategy in the Digital Age has gone worldwide in various disciplines: e.g. Muffatto 1999; Koufteros et al. 2002; Koufteros et al. 2005; Lin et al. 2007; Lee et al. 2010; Simula and Vuori 2012; Bergvall-Kåreborn and Howcroft 2013; Delina 2015; Yoshida 2017; Ding et al. 2018; Ikävalko et al. 2018; Thapliyal 2018; Yager and Espada 2018. In the Digital Age the components of a company’s platform can coevolve and radically change in weeks or months, even though its supply chains and markets are spread all around the world. Information systems in the Digital Age

Needless to say, the Digital Age phenomena affect IS most obviously. People communicate and learn much more and much more quickly via digital connections to the Internet—such as iPhones, smartphones, iPods, iPads, and computers—than they could before the modern Internet-related technology was created. This is the key message from the MISQ Special Issue Digital Business Strategy: Toward a Next Generation of Insights which includes: Bharadwaj et al. 2013; Grover and Kohli 2013; Lucas et al. 2013; Markus and Loebbecke 2013; Mithas et al. 2013; Pagani 2013; Woodard et al. 2013. See also some articles published before MISQ’s Special Issue: Varian 1996; Damiani et al. 2008; Salam et al. 2008; Al-Debei and Avison 2010; Tsatsou et al. 2010; and Melville 2012.

10  Bill McKelvey IS basics

The six components that must come together to produce an IS are:1 1. Hardware: The term “hardware” refers to machinery. This category includes the computer itself, which is often referred to as the central processing unit (CPU), and all of its support equipment. Among the support equipment are input and output devices, storage devices, and communications devices; 2. Software: The term “software” refers to computer programs and the manuals (if any) that support them. Computer programs are machinereadable instructions that direct the circuitry within the hardware parts of the system to function in ways that produce useful information from data. Programs are generally stored on some input/output medium, often a disk or tape; 3. Data: Data are facts that are used by programs to produce useful information. Like programs, data are generally stored in machine-readable form on disks or flash-drives until the computer needs them; 4. Procedures: Procedures are the policies that govern the operation of a computer system. “Procedures are to people what software is to hardware” is a common analogy that is used to illustrate the role of procedures in a system; 5. People: Every system needs people if it is to be useful. Often the most over-looked element of the system are the people, probably the component that most influences the success or failure of information systems; 6. Feedback: This is another component of IS, that defines that an IS may be provided with feedback. To summarize: the primary ways that the Digital Age has changed IS are: 1. DB is mostly about changing IS networks—in and outside of a business or company; 2. The Internet of Things—laptop and desktop computers, iPods, smartphones, and iPads, etc.—connected to the Internet that enhance bi-directional information flows at digital speeds; 3. The Internet is mostly a network of networks; 4. Network effects are based on embedded computer components; 5. Complexity dynamics are vastly speeded up compared to those in biological systems and pre-Internet companies; 6. Skewed (i.e. non-normal) distributions in DB ecosystems are much more likely; 7. Interconnectivity effects on businesses and customers become more dominant; 8. IS technology effects, i.e. interactions of technology and Information;

Managing the Consequences of Digital Networks  11

9. Managing and/or coping with messages and images going viral on the Web; 10. New ways of learning and learning quickly via the Internet. IS is essentially about information flows within and between firms and within ecosystems. No change of anything can happen without information flows of one kind or another. People and firms only change because of some kind of new information—usually informing them about a “tension” they need to deal with—that motivates them to change. The faster they are informed by new kinds of information, the faster they can change—if they wish to. Over the past 70 years or so, as computers have progressed from IBM’s first commercial computer, the IBM 650, to the current laptop computers, iPods, iPads and various brands of smartphones, information flows have changed from personal voice-based to digit-based flows. New information can get lodged in a computer or iPad without a person knowing about it. As we have recently learned, both individuals and companies have suffered major changes caused by digital hacking—information flows, whether from the Russians, or Facebook, or other sources, causes, or changes. In the Digital Age, all information flows have sped up from the old days of horse-speed postal service, to telephones, to almost instant and constant digital connections. Because of hi-speed information flows, the coevolution of customer preferences, products, and firms has become vastly faster than its origins among animals, birds, and plants (Darwin 1859)—dealt with later in Chapter 3 and others. But, not only does information flow faster, many more kinds of information are quickly available to many more people and firms, whether friends or foes. Consequently, firms in a rapidly coevolving business ecosystem can take advantage of the hi-speed information flows— and hacking—or can be left behind and driven out of business. Since the complexity dynamics that give rise to new kinds of coevolving order have also vastly increased the speed of new-order creation, I define and discuss them next.

Digital Age effects on complexity dynamics Complexity theorists define systems in the emergent complexity category as being in a state “far from equilibrium” (Prigogine and Stengers 1984) or “at the edge of chaos” (Kauffman 1993). Prigogine and his colleagues observe that energy importing, self-organizing, open systems create structures that in the first instance increase negentropy,2 but nevertheless ever after become sites of energy or order dissipation. Consequently they were labeled “dissipative structures”; self-organized—and self-contained—dissipative structures, once formed, exhibit persistence and predictable qualities. Complexitycaused self-organizing structures are now seen as ubiquitous natural phenomena (Mainzer 2007) and broadly applicable to firms (Maguire and McKelvey 1999; Marion 1999).

12  Bill McKelvey New-order creation defined by complexity theory

The region of emergent complexity is defined by the first and second “critical values” (Mainzer 2007). Nothing is so basic to the definition of complexity science as the Bénard cell—two metal plates with a fluid in between (Bénard 1901). An energy (heat) differential between the plates—defined here as “adaptive tension,” T—creates a molecular motion of some velocity, R, as hotter molecules move toward the colder plate. The energy differential in the Bénard cell parallels that between the hot surface of the earth and cold upper atmosphere—hotter air molecules move upward and if they move fast enough, create storm cells that take on predictable structures and with occasional tornadoes, i.e. aperiodicity. The role T plays in defining the region of complexity “at the edge of chaos” is fundamental to complexity science. If T remains below the first critical value, a new structure does not emerge. If T increases beyond the second critical value, the agent system is forced into the region of chaotic complexity. Here the system is likely to oscillate between different basins of attraction. Suppose a large firm acquires another firm needing a turnaround and the acquiring firm allows T to stay below the first critical value—existing management stays in place with little incentive to make changes. There is, thus, little reason for people in the acquired firm to create new structures. However, if T goes above the second critical value, complexity theory predicts chaos. Suppose, by way of example, the acquiring firm changes several of the acquired firm’s top managers and sends in “MBA terrorists” to change the management systems “overnight”—new budgeting and information systems; new personnel procedures, new promotion approaches, new benefits packages; new production and marketing systems. In this circumstance, two basins of attraction could emerge: one basin defined around demands of the MBA terrorists and the other centered around the comfortable pre-acquisition ways of doing business and resistance to change. The activities of the system could start oscillating between these two basins. Between the first and second critical values lies the region of complexity, just below the edge of chaos that Brown and Eisenhardt (1998) aim at. Here, network structures emerge to solve T problems. Using the storm cell metaphor, in this region the “heat conduction” of interpersonal dynamics between sporadically communicating individuals is insufficient to reduce the observed T. To pick up the adaptive pace, the equivalent of organizational storm cells consisting of “bulk” adaptive workflows starts. Formal or informal structures emerge, such as new network formations, informal or formal group activities, departments, entrepreneurial ventures, and so on. Given that new-order creation in complex systems results from agent3 interactions and consequences, if all the agents (employees) are the same, there is no advantage to networking (Holland 1995). We have over four billion years of mutation and crossover creating biological diversity—Campbell (1960) called it “blind variation.” He argued that “blind” variation was much

Managing the Consequences of Digital Networks  13

more relevant for social innovation than “rational” variations. Furthermore, LeBaron (2000) shows that novelty, innovation, and learning all collapse as the attributes of agents collapse from heterogeneity to homogeneity. The definition of creativity favored by psychologists—i.e. “remote associates”— essentially holds that creativity emerges when agents having different ideas or concepts interact and, consequently, notions heretofore separated are joined to produce something new. Human capital is the basis of agent heterogeneity. The idea of networked idiots doesn’t offer much promise. The “human capital” idea dates back to Becker’s (1975) early work on the subject. He argued that the economists’ Cobb-Douglas production function needs a component to reflect the knowledge people hold, as well as capital and labor. This is especially true in today’s knowledge economy—the economic advantage of the US, today, is much more a function of human capital than financial capital or labor. Zucker and Darby (1996) find that one genius appropriately networked is superior to larger networks comprised of less talented agents. Basic complexity concepts (McKelvey 2016)

Next, I briefly describe the high-speed digital effects in the three phases of the development of complexity theory: Phase 1: The European School

1. Tension (force causing change and new-order creation): Like high heat on a stove that causes a rolling boil in a kettle (a phase transition), tension in complexity science is an imposed force of some kind that causes new order of some kind—a phase transition. In Prigogine’s view (1997), tension was imposed from outside a system. But an additional view is that some agents respond to self-imposed tension. Steve Jobs (a founder of Apple) was famous (or infamous) for self-imposed tension (but also for forcing it onto others). Needless to say, these two sources of tension may combine forces or work at odds with each other. Digital Age dynamics can speed up information flows and, therefore, can increase the number of tensions imposed on companies or their employees at any given time. This can greatly increase the probability of chaos occurring. Because of this likelihood of an increased number of tensions appearing all at the same time, the difficulty of identifying, and then focusing on, the most important tension (or the top two or three) is considerably increased. 2. First critical value (edge of order): Phase transitions typically occur after a tipping point is passed. This is called the “Firstt critical value” in thermodynamics. Complexity scientists call it the “edge of order”—where existing order is abandoned and replaced by a new order of some kind. 3. Dissipative structures (phase transitions): Nobel laureate Ilya Prigogine (1955) referred to new order emerging after the first critical is passed

14  Bill McKelvey

as “dissipative structures”—i.e. whatever order emerges after the phase transition occurs simply to reduce the imposed tension—it dissipates the tension. Needless to say, Digital Age dynamics speed up the occurrence of tensions and therefore the need to create dissipative structures, and perhaps one, two, or three at the same time, which, again, could increase the likelihood of creating chaos; i.e. especially if a company or its employees have trouble in deciding which of the chaotic number of tensions to focus on first.

Phase 2: The American School

4. Second critical value: Researchers at the Santa Fe Institute (where the American School began) started by focusing on change occurring just before what they called the “edge of chaos.” This edge occurs because of two different kinds of force: (1) when so many different kinds of tension imposed on an agent or system at the same time that it can’t respond effectively to any of them or (2) if there is so much of one kind of tension imposed on an agent or system that it becomes dysfunctional. Digital dynamics, for sure, are likely to increase the likelihood of creating chaos, simply because, in the Digital Age, companies or their employees can receive vastly increased numbers of messages from customers, producers, or competitors, any one of which can identify one or more tensions that appear to need to be immediately dealt with or one or more of them will cause dysfunctionality. 5. Region of emergence: The region of emergence lies between the edges of order and chaos. Stuart Kauffman (1993) calls it the “melting zone”— existing order melts away and is replaced by new order. Systems are more adaptive if the region is larger than smaller. This occurs when the edge of order occurs with less imposed tension and the agents or system can tolerate higher levels of tension or can respond effectively to more than one at the same time. Systems benefit by aiming for as wide a region as possible. Because of the likelihood of increased networking in the Digital Age, new kinds of order appear and need to be dealt with more quickly, such that the region of emergence becomes larger and includes more different kinds of tensions to be dealt with. While responding to an increased number of tensions causes companies or their employees to face more problems needing correction, it can also increase the likelihood of emergent chaos. 6. Agents: These may be entities of all kinds, mental processes, bacteria, ants, animals, concepts and ideas, people, groups, departments, organizations, economies, societies, and so on. They are “agents” because they have some level of ability to respond to forces, change, and/or self-organize. 7. Heterogeneous agents: Agents may be clones of each other, or forced to become more like each other by recombining each other’s behavioral rules into their own behavioral rules—this is what Granovetter (1973)

Managing the Consequences of Digital Networks  15

calls the “strong-tie” effect—agents connect and talk to each other frequently (e.g. once a week)—this can develop trust and efficiency, but also produce agents who think alike. Granovetter’s “weak-tie” effect occurs when agents meet less frequently (e.g. once a year); they may change and learn new things in between meetings and so when they do meet they learn new things from each other—weak-tie connections are more likely to produce innovation and successful entrepreneurship. If all the agents connecting in a system are clones of each other, they learn nothing new by connecting. Hence, for self-organization and new order to occur, the agents need to be “heterogeneous”—i.e. different from each other in various ways. In the Digital Age businesses, rapid and more frequent digital information flows can cause increased homogeneity or heterogeneity to happen more quickly. 8. Self-organization: “Self” organization is defined to occur only when agents themselves become motivated to change—there is no “global controller” as Holland (1988) put it—they don’t need to be told to start changing; they just do it. The minimum ingredients for self-organized new order to emerge are tension, connectivities among agents, and agents’ motivations to adapt to the imposed tension. In the Digital Age businesses and agents working in them can be influenced by more rapid information flows and new information from the Internet to start selforganizing more rapidly or frequently so as to more quickly start trying to solve emerging problems. 9. Tiny initiating events (butterfly events): There are many (tens, hundreds, thousands) of seemingly meaningless incidents or changes in any given firm over time. Most are just random events. But some repeat and start growing/re-repeating, thereby becoming the beginnings of networked behavior, agreements, groups, and so on. In the latter instances, the initially random-seeming events become what Holland called “tiny initiating events” that grow into significant changes, whether positive or negative from a firm’s perspective, i.e. they could be ideas that ramp up into new products, or mistakes, or rebellions. Because of a famous presentation by Lorenz (1972), they are sometimes called “butterfly events.” Such events are the beginnings of self-organized new-order creation. Needless to say, in the Digital Age, because of increased information flows, and the increased number of different kinds of tensions appearing, the number of tiny initiating events needing to be dealt with, can vastly increase. While solving more tensions more quickly is good, more imposed tensions because of digital connections and information flows can also lead to more chaos more often. 10. Connections; connectivities: Creativity is usually a mixing of existing ideas that gives rise to a new idea. If heterogeneous agents don’t connect and interact, novelty is unlikely to occur. In short, absent connectivities novelty, innovation, and new entrepreneurial ventures are much less likely. But, again, remember Granovetter’s strong- and weak-tie effects: interacting frequently with the same people creates trust and efficiency but

16  Bill McKelvey

not novelty; infrequent interactions and the mixing of ideas heretofore not connected are what can result in innovation and novel entrepreneurship. In the Digital Age, connections and connectivities are vastly more likely to occur. Of course, these increases can lead to increased interactions but can also more easily lead to chaos. 11. Motives to connect, survive, and grow: Connectivities are essential, but absent agents’ motivations to interact, connectivities don’t appear. Motives to survive and grow often lead to interactions by which agents learn, change, and adapt, etc. Ants are motivated to search for food, leave pheromone trails, bring food back to the colony, eat, reproduce, adapt to changing environmental conditions, and avoid predators or the colony doesn’t survive. Dogs like to eat, chase things, reproduce, and can be trained to sleep all day or attack. People have all kinds of motivations, but they can enter a firm and be trained or incentivized to become passivedependent, loners, and maintain the status quo or they can learn, change, interact, motivate others, innovate, and adapt to and survive changing competitive environments. Some people are strongly self-motivated but managers and/or fellow employees may lead or stimulate them in either direction (i.e. toward passive dependence or innovation and change). In the Digital Age, it is also easier to ignore the inflows of increased digital information via the Internet, whether from other friends, employees, or sources of information from customers, suppliers, competitors, or various other information sources, like governments, newspapers, etc., by ignoring the Internet. 1 2. Bottom-up emergence: Some people in a firm inevitably know bottom-up emergence has occurred. But there are lots of emergent behaviors and structures that managers don’t know about. In a classic Harvard case, a bunch of Sicilian cousins had totally changed the company’s product line because of changing technology and customer preferences—all totally unknown to upper management! It happens! True, newly emergent ideas and intellectual capital (IC) may be intangible and based on tacit knowledge, but even so, emergent developments in IC are there to be found. Emergent networks, groups, and hierarchies are more tangible and hence more easily observable or discovered kinds of emergence. Implicit in the foregoing is to what extent a firm tolerates, punishes, or rewards people generating emergent behaviors and structures. They may be treated as deviations from approved behavior or treated as developments at least worthy of further study and potentially worthy of value and further stimulation. Emergence can result in new ideas, new networks, new groups, and new hierarchies. In the Digital Age, needless to say, the vastly larger number of digital connections via the Internet among the many more lower level employees, than toplevel managers and the CEO, means that many more relevant new ideas coming off the Internet from other employees, customers, suppliers, competitors, or the broader population in general is more likely to motivate bottom-up emergence, unless toplevel managers indicate that they don’t want to pay attention to any new ideas other

Managing the Consequences of Digital Networks  17

than their own—or ignore other ideas already existing among lower-level employees in the company. 1 3. Network dominance: As a system composed of some number of agents starts tipping across the Edge of Order into the region of emergence, agents can actually determine the nature of the emergent new order—the nature of the phase transition. Suppose you are the key person at Microsoft who has authority to work with 100 engineers at Nokia to quickly come up with a digital phone design to compete effectively with iPhone or Android. OK, just imagine this…Following Haken’s (1983) logic, then, you see that—despite the apparent dominant motive to connect with Microsoft so as to create a mobile phone that could save Nokia from fading into oblivion is the dominant imposed tension—many of the 100 engineers have become more or less enslaved (Haken’s term) by other more personal or more immediate tensions: searching for new jobs, finding a better school for their kids, buying new cars, trying to leverage some other new project to get a promotion, getting ready for the annual ski trip as winter approaches, on and on. Hence, many engineers slowly become enslaved by various other (personal) tensions. Consequently, as the phase transition develops, Haken points out that there are usually only a few strongly networked individuals who actually determine the nature of new order. This could be good or bad. Generally, the most dominant network of agents ultimately drives the nature of the phase transition and emergent new order in organizations. In the Digital Age, needless to say, network dominance may occur much more often because information is digital and it is much easier for people to communicate with each other (as indicated by Swaminathan and Meffert 2017).

Phase 3: Fractals and power laws

14. Fractals: Consider the cauliflower. Cut off a “floret”; cut a smaller floret from the first floret; then an even smaller one; and then another even smaller one, and so on. Despite their increasingly small size, each lowerlevel floret performs the same function and has roughly the same design as the floret above and below it in size. The cause of repetitive formation is the same at each level and hence is explained by a “scale-free” theory— the same theory applies to multiple levels of a multi-level system. This feature defines it as “fractal.” Fractals have most often shown to result from mathematical formulas—as in Mandelbrot’s (1983) Fractal Geometry. However, fractal structures also originate from adaptive processes—like the cauliflower—in biological, social, and financial-economic contexts. In fractal structures the same adaptation dynamics appear at multiple levels. In the Digital Age fractal structures, networks, or influence patterns are most likely to be much larger because of digital information flows are much more likely to be network connected, as is obvious because of all the “viral” events occurring and

18  Bill McKelvey

growing on Internet-based social media, such as Facebook or Twitter, etc. A recent one on Facebook (March 2018) involved messages from more than 50 million Facebook users. 1 5. Power Laws: PLs often act as good indicators of fractal structures. A well-formed Pareto rank/frequency distribution plotted using doublelog scales appears as a PL distribution—an inverse sloping (more or less) straight line. PLs often take the form of rank/frequency expressions such as F ~ N –β, where F is frequency, N is rank (the variable) and β, the exponent, is constant. In a typical “exponential” function, e.g. p(y) ~ e(ax): the exponent is the variable and e is constant. The now famous PL “signature” dates back to the early 20th century (Auerbach 1913; Zipf 1929, 1949). Andriani and McKelvey (2007, 2009) list ~140 kinds of PL rank/ frequency distributions in physical, biological, social, and organizational phenomena, all of which are good indicators of fractal geometry. Others find that manufacturing firms in the U.S. show a fractal structure (Stanley et al. 1996, 2000; Axtell 2001; Newman 2005; Glaser 2013). McKelvey and Salmador Sanchez (2011) list another 60 or so specifically in financial economics. Again, in the Digital Age, because networks are more likely because of the much more readily available Internet connections available to people, network effects resulting in PL distributions are much more likely—as indicated by the many studies listed in the “Accelerated Network Interaction Effects” Section below. For a visual example, see the “Map of the global network of AT&T” in Malecki and Moriset (2008: 38).

More rapid and frequently developing new-order creations In the following section, I first summarize which and how the various complexity dynamics speed up and then I discuss how they cause (1) new-order creation much more quickly; (2) produce more frequent and more dramatic network effects; and (3) produce consequent skewed distributions because some firms coevolve and grow more quickly and become much larger. As noted earlier, Cusumano (2010: 33) notes that the most distinguishing feature of digital industry platforms and ecosystems is their “creation of ‘network effects’” and positive-feedback-based skew distributions. In short, digital complexity dynamics more quickly create networks and skew distributions. But, as Baum et al. (2014) find, the joint existence of open networking and a skewed industry distribution can result in reduced performance for most firms. Digital-speed effects on complexity dynamics and new-order creation

Some of the digital-speed effects are: ••

Peoples’ high-speed digital equipment: laptops, desktops, iPods, iPhones, iPads, etc.;

Managing the Consequences of Digital Networks  19

•• •• •• •• •• ••

A large number of digital connections; Many networks and skew distribution effects; Communication and knowledge sharing at digital speed; Speedy digital knowledge about customers’ preferences and competitors’ products; Competitors can change at digital speed; Co-evolution at digital speed.

Digital tension effects: •• ••

Phase transitions and emergence; Chaos.

Digital effects in the region of emergence: •• •• •• •• •• •• •• ••

Changing agents; Motives to connect, survive, and grow; Agent interactions; Upward and downward influences in organizations; Self-organized change in social systems; Emergent networks and hierarchies; Emergent nonlinearities; Emergent new order.

Digital effects on fractal and power-law distributions: ••

In the following section, I show many citations—from 2001 to 2018— using data showing the digital effect in creating power-law-distributed networks.

For a graphic description of how key elements of DB strategy influence business performance, see Figure 1.2. It shows that various causes influence key aspects of DB strategy, which then affects business performance. Accelerated network interaction effects

Based on Haken’s (1983) discussion, digital connections of agents to the Internet vastly speed up their interactions and network-formation effects, which produce accelerated complexity dynamics in DB ecosystems (as indicated in my previous discussion of basic complexity dynamics) that speed neworder formations, changes in agents’ attributes, their connectivities, new social orders in the form of networking, networks, and network-based groupings and departments within firms. Many studies show that network-based neworder formations are fractal and skew-distributed social phenomena (DeSolla Price 1965) in which one or a few agents have the most network interactions and connections. Some examples are: collaboration networks (Newman 2001);

Figure 1.2 Drivers of four key elements of digital-business strategy, which leads to better performance. Reproduced from Bharadwaj et al. (in MISQ’s 2013 Special Issue on IS in Digital Businesses)

20  Bill McKelvey

Managing the Consequences of Digital Networks  21

scale-free networks (Barabási 2002); organizational networks (Dodds et al. 2003; Watts 2003); social networks (Csányi and Szendrői 2004); success in an ecosystem (Tavlaki 2005; Briscoe and De Wilde 2006; Hamilton et al. 2007); alliance networks (Gay and Dousset 2005); network dynamics (Powell et al. 2005); company networks (Souma et al. 2006; Chmiel et al. 2007); peer-to-peer networks (Krishnan et al. 2007); online networks (Mislove et al. 2007); interfirm relationships (Saito et al. 2007); degrees of connectivities (Santiago and Benito 2008); communication ability (Zhai et al. 2013); economic sectors (Hu et al. 2013); firm performance (Baum et al. 2014); buyer-supplier networks (Mizuno et al. 2014); weighted receiving time (Dai et al. 2015); trapping time (Dai et al. 2016); emergence of fractal scaling (Wei and Wang 2016); entropy production (Mülken et al. 2017); receiving time (Ye et al. 2017); Italian industrial districts (Carbonara 2018). In the Digital Age, networks such as these can form online at digital speed, which makes their outcomeeffects also occur much more rapidly. Accelerated and more dramatic fractal and skew distributions

Fast-acting complexity dynamics often result in the more rapid formation of rare and extreme outcomes (PL-distributions) in DB ecosystems, such as dominant firms in industries (e.g.: Scheinkman and Woodford 1994; Stanley et al. 1996; Lee et al. 1998; Stanley et al. 2000; Axtel 2001; Moss 2002; Jones 2005; Andriani and McKelvey 2007, 2009, 2011; Aoyama et al. 2009; Park et al. 2010; Glaser 2013). Several authors have analyzed data about entrepreneurial firms. All of these show that the formation of entrepreneurial firms results in PL-distributed skew distributions based on the size or value of the firms (Crawford et al. 2015; Salmador Sanchez and McKelvey 2018). An analysis of Microsoft’s ecosystem—data published by Iansiti and Levien (2004)—shows that the thousands of firms comprising Microsoft’s ecosystem exist in a wellformed PL (skewed) distribution. Other kinds of network effects have shown up as skew distributions on Facebook and Twitter (e.g. comments, photos, or stories “going viral” on the Internet) (BBC News 2016; Indian EXPRESS 2017; Mirror News 2018). Accelerated coevolutionary consequences

In Figure 1.3, I show a graph that depicts coevolution between two firms multiple times. Coevolution occurs when a company changes one or more of its attributes in response to a change of one or more attributes of another firm or more likely a competitor. Thus, consider equivalent attributes in two firms, Alpha and Beta (Figure 1.3). Coevolution may occur among many, if not all, surviving firms in a competitive environment as they invent and/or copy each other. With dinosaurs, mammals, and humans, coevolution occurs as parents mate and produce offspring that have genetic differences, some of which lead to coevolutionary

Coevolution

22  Bill McKelvey Alpha introduces Strategy A Beta copies Strategy A; call it B Alpha evolves to A1 Beta evolves to B1 Alpha evolves to A2 Beta evolves to B2 Alpha evolves to A3 Beta evolves to B3 Etc.4 Etc. 5 Time

Etc. 6

Etc. 7+

Figure 1.3 Coevolution between two firms.

success and others lead to death in a biological niche or competitive industrial environment (Ciborra 1992; Kauffman 1993; Djelic and Ainamo 1999; Lewin et al. 1999; McKelvey 1999a,b, 2002; Vander Zee and De Jong 1999; Peppard and Breu 2003; Benbya and McKelvey 2006; Jiang et al. 2006; Pacheco et al. 2014; Tasselli et al. 2015; Reyes-Garcia et al. 2017; Carbonara 2018). In biology, coevolution is very slow: parents have to grow up (marry, maybe); mate; have offspring; offspring survive and adapt, or they don’t; survivors start the foregoing steps over again. The time between a modern mother’s birth and her children’s births can be from ~14 to ~45 years. Coevolution takes many years. Given that firms’ employees can search for customer preferences via the Internet (crowdsourcing: Escoffier and McKelvey 2014, 2015, 2018) and also find out about competitors’ product changes and new products very quickly via the Internet coevolution among firms can take place very quickly in the Digital Age—takes minutes to hours for a firm to learn about customers and competitors; usually takes somewhat longer for it to revise a product so as to keep up with a changing competitor. Needless to say, DB coevolution is obviously vastly much, much faster than biological coevolution. But of course, coevolution is not automatic, but firms that keep with and or improve competitors’ products have a higher probability of survival.

Managerial digital-business realities These DB-caused changes in business realities were identified by the McKinsey Quarterly: McKinsey Quarterly’s seven strategic principles for competing in the Digital Age

First, the McKinsey Quarterly identifies seven strategic principles for competing in the Digital Age (Hirt and Willmott 2014): 1. Pressure on prices: Digital technologies lead to knowledge about everything; it’s easy for customers to switch brands, retailers, and services with a few clicks;

Managing the Consequences of Digital Networks  23

2. Online customers: Digitization reduces barriers to entry; no need to build offices or hire local agents (e.g. banks allow customers access to digital, online bank accounts and now have ATMs everywhere, Frederick’s of Hollywood, Africans’ use of the Internet, etc.); 3. Online winner-takes-all dynamics: Transaction costs reduced; increased returns to scale; magnets for digital talent;. 4. Start-up costs for new DBs are minimized; online “storefronts”; rapid gap filling; 5. Software replaces labor: Brilliant machines—IBM’s Watson—will win; “normal” employees replaced by fewer digital employees (see lead story in The Economist [“Manhood,” May 30th, 2015]); 6. Converging global supply and demand: “Digital technologies know no borders”; 7. High-velocity business-model evolution: Examples: mobile apps, sensors in cars, the cloud, shop for a product and buy online; delivery by Fed Ex drones (in the future), etc. McKinsey Quarterly’s seven traits of successful DB managers

Next, it identifies seven traits of successful DB managers (Olanrewaju et al. 2014): •• •• •• •• •• •• ••

DB at the Board level; stretch vision; measure digital value; Acquire digital skills; move into adjacent markets; “Ring-fence” digital talent; don’t rely on existing HR models; Challenge everything; question the status quo; Accomplish quick wins; create a data-based view of each customer; Lower costs by investing in digital “back-office” functions; Learn from every interaction with a customer.

McKinsey Quarterly’s opportunities for managers

In another MQ article, Bughin et al. (2010) describe DB trends managers need to pay attention to: 1. Crowdsourcing: Learn what they like or don’t like from customer databases: customer-based product innovation and new customer services; 2. Building a networked organization and making it work; 3. Creating more interactions and collaborations among employees and also with other firms; 4. Taking advantage of “The Internet of Things” to enhance company intelligence and transactions; 5. Look for and test Web factors that take advantage of big data and use it in business experiments; 6. Wiring for a sustainable world; e.g. “green data center.” Both leaders and key functional players must understand sustainability’s growing importance to broader goals;

24  Bill McKelvey

7. Taking advantage of cloud computing; 8. Create and take advantage of multisided business and software “platforms.” DB effects on finance

Focus is on the various ways that DB finance is affected by high-speed complexity dynamics. 1. The security of online electronic banking is a growing problem (Claessens et al. 2002). While electronic banking systems provide people with easy access, banks have to correctly authenticate the person who wishes to take money from a bank account. Maintaining data confidentiality and clients’ authentication are growing problems as banking and money transfers become more and more online. DB is mostly about changing IS networks—in and outside a business; 2. Mobile devices such as smartphones, iPads, iPods, computers (mobile or not) and the convenient authorization of e-banking, online payments, and other kinds of online transactions, are increasing in volume; the consequence is banks closing many of their bricks-and-mortar branches (Herzberg 2003); users or customers don’t even have to go to an actual bank-building anymore to get money to pay bills, etc.; 3. In many African countries, there are so few nearby bricks-and-mortar bank-branches that people don’t go to branches anymore but simply buy whatever by using online access to their bank accounts (Smith 2014; Brazzell 2017; Monks 2017), and, as a result, “real” paper currency or metal coins are used much less; they use “digital” currency online; 4. Micro-payments (e.g. small purchases at kiosks and fast-food restaurants or purchases from machines such as candy, soda drinks, etc.) and macro-payments (e.g. more expensive services, more expensive restaurants, retail shopping for more expensive products) are increasingly online (Mallat et al. 2004); 5. Companies have to learn more about “digital consumer behavior” (Schuchmann and Seufert 2015: 15). In their Figure 2, these authors outline “Learning Services” and “Development Services.” Learning services involve “training,” “transfer support,” “performance support,” and “services for collaboration in working processes and networks (internal and external):” Development services include “enabling self-organization for the learning organization”; 6. See also: Banks 2001; Seitz and Stickel 2001; Brousseau and Pénard 2007; Kelly 2014; Mullineux and Murinde 2014; Schatt and Laplanche 2014; Coeckelbergh 2015; Mullineux 2015; Schulte 2015; Scott et al. 2017. DB effects on marketing

Our focus is on research relevant to how companies take advantage of digital marketing methods.

Managing the Consequences of Digital Networks  25

1. Parsons et al. (1998) (all three of whom worked at the McKinsey consulting company and not long after Tapscott published his 1995 book titled The Digital Economy, in which he concluded that “digital marketing presents some significant organizational challenges”): (a) “Formal vs. informal?” Is the digital impact relevant enough to require a “formal” management response, as opposed to waiting to see whether informal dynamics lead the charge; (b) “Centralized vs. decentralized?” The authors note that there are “compelling reasons” (p. 44) to favor centralizing or decentralizing digital marketing. I note that top management centralizing can be designed to stimulate decentralized digital marketing development throughout the lower levels of a firm; (c) In-house vs. outsourced? This becomes the classic “build vs. buy” question (p. 44). Although there are obviously more employees and companies able to quickly and effectively take advantage of digital marketing, the authors conclude, in their 1998 article, that “‘outsourcing’ digital marketing is quicker and more effective that trying to digitize an entire old-style company.” Nowadays, however, i.e. 2018 and beyond, it may be easier and more relevant for companies to totally revamp their functioning to fit the Digital Age; and (d) “Functionally focused vs. customer focused?” Should a newly designed digital business organization focus on its functional departments (i.e. internal production processes) vs. customer groups such as “frequent travelers,…home office workers, or remote-channel investors?” (p. 45). This choice affects where in a firm the digital market unit or skills are located; 2. Digital marketing includes “marketing via websites, search engines, online advertisements, e-mail, and social media channels” (Järvinen and Karjaluoto 2015: 117). They develop a method for collecting “clickstream data” that indicates the source of company’s “website traffic (e.g. e-mail, search engines, display ads, social links), navigation paths, and the behavior of visitors during their website visits” (p. 118); 3. Leeflang et al. (2014) survey 777 marketing executives all around the globe. Their findings reveal that “filling ‘talent gaps’, adjusting the ‘organizational design’, and implementing ‘actionable metrics’ are the biggest improvement opportunities for companies across sectors” (p. 1). They begin their article by noting that Wharton’s Professor George Day (2011) “identified the widening gap between the accelerating complexity of markets and the capacity of most marketing organizations to comprehend and cope with this complexity”; 4. Leeflang et al. (2014) also “believe that Internet usage is the main driver behind the widening gap” (p. 1). They focus on the following tensions: (a) “digital revolution and business models”; (b) “customer insights”; (c) “stifling creativity and innovation”; (d) “social media and brand health”; (e) “online targeting”; (f) “price transparency”; (g) “automated interactions”; (h) “online metrics”; (i) “talent gap”; and (j) “organizational challenges” (throughout the article). They conclude with: (1) “the use of

26  Bill McKelvey

customer insights and data to compete effectively”; (2) “the threatening power of social media for brands and customer relationships”; (3) “the omnipresence of new digital metrics and the subsequent assessment of the effectiveness of (digital) marketing activities”; and (4) “the increasing talent gap in analytical capabilities within firms” (pp. 9–10); 5. “To improve digital marketing engagement, marketers must focus on relationship-based interactions with their customers” (Tiago and Verĺssimo 2014: 703). These authors conclude that: to effectively utilize the advantages offered by the Internet, though, firms must adopt social media as a channel of providing information to customers; connecting with stakeholders; and, ultimately generating sales. As marketing communications become increasingly integrated with the digital space, marketers can use social media to create digital linkages with customers. There are two main methods for developing these linkages: (1) perform as a digital or interactive firm…or (2) adopt various kinds of social media interaction to increase usage of digital marketing (p. 708); 6. Royle and Laing (2014) develop a “digital marketer model.” It ranges from “business management skills” to digital “technical skills” (i.e. the model ranges from business skills such as “corporate communication principles” to “excellent client engagement skills” to “futuregazing foresighting and futureproofing” to “research” to “strategic integration of digital marketing skills” to “measurement monitoring and evaluation” to “technological knowledge” (their bold; p. 69). The most important problem in digital marketing their research identifies is the difficulty in finding employees with relevant strategic business knowledge about digital marketing. They note that a “business expert” told them that the kind of digital marketing expert she/he wants to hire “doesn’t exist” (p. 69). A “Digital Solutions Architect” told them (obviously before their article was published in 2014) that “there are not enough people around who have the breadth of knowledge to design a solution for clients’ needs and sell it to them” (p. 69); 7. See also: Tapscott 1995, 2015. DB effects on production

Focus is on how the “Digital Divide” (van Deursen and Van Dijk 2011) affects production skills, quality, and the ability of production designers to learn from customers. 1. The “Digital Divide” concept focuses on the difference between production designers who know how to learn from the Internet and their use of various devices such as computers and smartphones vs. those who don’t know how to connect with, or learn from, the Internet;

Managing the Consequences of Digital Networks  27

2. Connections with, and learning from what are called “POD” checks (POD stands for “production-oriented design”). Workers designing and producing automobiles need to learn from customers via their Internetconnection devices while designing automobile parts and also check in with customers to find out whether they like their automobiles with the newly designed parts, or not (Michalos et al. 2010); 3. In the modern Digital Age, employees involved in the production of complicated products, such as automobiles, need to avoid the Digital Divide, by learning how to take advantage of information via the Internet and digital communication with their customers before and after they design automobiles or one or more of the thousands of parts comprising automobiles (Kutin et al. 2016); 4. While much of the focus in the Digital Divide literature is focused on automobiles—because they have thousands of parts—Klinenberg (2005) focuses on the use of digital information in the production of “news” and/or journalistic types of media, which needless to say, don’t include the same mechanical kinds of components that automobiles consist of. Newspapers and journals also need to take advantage of the Internet and its related devices so as to learn as quickly as possible what the latest news is, or what readers like or don’t like or respect in what is produced by journals; 5. The reality is that people writing about digital production are very much like those writing about digital marketing: whether producing mechanical parts and products, or producing non-mechanical products involving information, companies can rapidly and greatly benefit by trying to learn as quickly and readily as possible from the people who buy or pay attention to their products. The basic ideas now are that companies exist in the Digital Age; they need to learn as quickly and accurately as possible from customers about how to improve their products. This is often called “crowdsourcing” (Escoffier and McKelvey 2014, 2015, 2018); 6. See also: Chituc et al. 2007; Stechert and Franke 2007. DB effects on IS

Focus is how to best re-frame a company’s information flows such that they enable, allow, and/or force employees to produce new information, data, and new interpretations at digital speeds (Tapscott 1995, 2015; Laudon and Laudon 2013): 1. DB is mostly about changing IS networks—in and outside a business; 2. The Internet of Things consists of personal computers and iPhones connected to the Internet that can cause bi-directional information flows; 3. The Internet is a network of networks; 4. Network effects; embedded computer components;

28  Bill McKelvey

5. Complexity dynamics; skew PL distributions in DB ecosystems; 6. Interconnectivity effects on businesses and customers; 7. IS technology effects; interactions of technology and information; 8. Managing and/or coping with messages and images going viral on the Web; 9. New ways of learning and learning quickly via the Internet. As Baum et al.’s (2014) findings support, firms that attempt to maintain open networks show poorer performance, presumably because they lose out in the coevolution process. In the Digital Age, their innovations are easily and quickly stolen by competitors, and, if the latter are clever in either (a) constantly changing quickly, and (b) effectively preventing competitors from quickly stealing their new ideas; they end up as superior performers. Much of this now has to do with taking advantage of digital information flows and/or preventing other firms from taking advantage of such flows to steal coevolutionary advantageous information. DB effects on strategy

Focus is on how best to change a company’s existing strategies to strategies more in tune with the high-speed coevolutionary competitive context of the Digital Age (Bones and Cohen 2013; Coupey 2016). 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11.

Digital Age: e-commerce; bricks-and-mortar stores; combination platforms; New business designs for the digital competitive environment; Keep up with the changing digital world; globalization; Strategies for global companies; companies with operations in developing and third-world countries; third-world companies; finding new strategies that work; E-commerce business models and value chains for DB ecosystems; E-Entrepreneurship strategies; Few stores in third-world countries and in many parts of developing countries, but people have iPhones and can do online banking and buying, etc.; Learning from the seven McKinsey Quarterly articles; Online strategies, marketing, sales, payment; High-speed coevolution calls for rapidly changing platform strategies; Digital strategy implementation.

As I note in previous sections, Baum et al.’s (2014) findings suggest that in the Digital Age the most effective strategy is to take advantage of, or steal from, other firms’ open networks as quickly as possible while at the same time trying to prevent them from the stealing process. In the Digital-Coevolution Age, therefore, firms have to learn how to keep up with digital-speed information flows, take advantage of them from other firms while at the same time, doing as much as possible in preventing other firms from capturing their own information flows. But of course, it is increasingly difficult to hide digital information. This is especially true with crowdsourcing. Since it is a combination of digital

Managing the Consequences of Digital Networks  29

information flows to and back from the crowd, it is really difficult to hide the implications of crowdsourcing questions and answers—it is all out on the Web! DB effects on leadership

Focus is on how to best motivate, change, and train company employees to better cope with high-speed change in the Digital Age (Lanier 2013; Westerman et al. 2014): 1. 2. 3. 4.

Recognizing the Digital Age and DB ecosystems; Identifying e-commerce and Internet opportunities; Leading Digital Age change; Changing CEO and personnel behaviors; finding and improving relevant knowledge and skills; 5. Forming e-commerce teams; 6. Hiring the right people; 7. Creating new e-commerce departments; 8. Changing existing departments; 9. Appropriately managing the interactions of bottom-up and top-down influence streams. Digital Age advantages can’t be left only to hackers. CEOs need to learn how best and most quickly get their managerial subordinates and employees to capture digital information from customers—of course—but more critically important, find ways to digitally search competitors’ digital sources so as to get ahead and win the coevolution game. The first thing to accomplish is for everyone in a firm to become more digitally competent and learn how to take advantage of the Digital Age. Then there is more detailed process of actually learning how to gain access to, and then learn from, customers and competitors. Then there is the equivalent of competitor hacking—i.e. using digital methods to learn more about, and more quickly about, what various competitors are changing and what they are aiming to do in the future, especially regarding product changes upon which coevolution is based. DB effects on organization change

Focus is on how best to change a company’s culture so that it is more prone to digital thinking and moves into the Digital Age of high-speed coevolution with its competitors and other aspects of its digital environment (Brynjolfsson and McAfee 2011, 2014; Gorbis 2013). 1. 2. 3. 4.

The reality of high-speed coevolution has to dominate; Basics about digital thinking in DB Ecosystems and their Internet realities; Basics about organizational and product co-evolution with competitors; Learning about Digital Age networks and network dynamics;

30  Bill McKelvey

5. Using top-down and bottom-up streams of influence and leadership; 6. Getting managers and employees re-trained and better able to cope with the Internet of Things, co-evolution, digital realities, digital learning, and high-speed thinking and change; 7. New recruiting policies; 8. Implementing organization change from top to bottom. Probably, this focus should come first. Firms need to update their culture from pre-e-mail days and e-mail days to the Digital Age. The latter has to become the dominant culture as quickly as possible to survive in the Digital Age. I rephrase some of McKinsey Quarterly’s key traits needed for managerial success in a DB ecosystem as follows: •• •• •• •• •• •• ••

Create digital visions throughout your firm; find ways to measure whether digital value exists or not; Acquire digital skills from top to bottom; they need to be up-to-date everywhere; move into adjacent markets; Make it clear to everyone that company-relevant digital skills are highly valued; don’t rely on existing old-school HR models; Challenge everything that isn’t already digital; Accomplish quick wins by creating a digital view of each customer and all of your competitors. Lower costs by investing in digital “back-office” functions; digital whatever should be everywhere; Learn from every interaction with customers—i.e. crowdsourcing. What do they want? What do they like about your and your competitors’ products or services? What don’t they like?

Conclusion The message throughout this chapter is that: (1) information now flows much faster, if not almost instantaneously, than it did before the Digital Age; (2) all of the complexity dynamics defined in the chapter occur much faster than they did before the Digital Age; and (3) these two effects vastly speed up how quickly all of the various “management” activities are affected and how much faster managers need to respond to newly occurring incidents in the Digital Age. Why is this important? Because, in the Digital Age, managers who don’t learn how to respond managerially-quickly will run the risk of their firms losing their competitive advantage, and discovering that their competitors are making appropriate changes much more rapidly because of digital business effects. In this chapter, I first focus on information systems because it is information that is now being transferred digitally among people and firms connected to the Internet. Second, I then focus on complexity dynamics that lead to new-order creation because they are vastly speeded up in the digital world. Third, I offer

Managing the Consequences of Digital Networks  31

information about how the effect of digital complexity speeds up the coevolution of digital-businesses in digital-business ecosystems. Finally, I focus on implications for effective management in the Digital Age. Absent the Internet, except for information flowing via phone calls within zones like the US or Europe, information 100+ years ago flowed at the rate of mail transported by the postal services in various countries and crossed oceans on ships moving at more or less 23 knots per hour. It took mail a minimum of six days to cross the Atlantic Ocean; even longer across the Pacific. In the 1800s information flowed at the speed of walking horses, if it was not on a train. Phone calls could not be made across oceans, and airplanes hadn’t been invented yet. Now, think about how different life would have been both personally and professionally if the pandemic had hit, let’s say, 20 years ago? In a conference organized in August 2020 by the Delegation of the European Union to the United States of America, Brad Smith, President of Microsoft, offers his perspective pertaining to shared digital futures in the post-COVID era: We just ended a decade where digital technology continually accelerated and really transformed the economy around the world and certainly among the democracies of the world. And then came 2020. We are facing a pandemic that none of us expected to be addressing this year. And interestingly enough, it accelerated digitization even more. What we quickly found was basically about two years of digitization taking place in the course of two months as everybody was forced to work virtually. And I think we should expect this faster rate of digitization to continue throughout the decade ahead. It does mean that there are a lot of opportunities especially for the countries, for the economies, the industries and the companies that really make the most of it. What the pandemic fundamentally shows is that in so many ways, a modern economy, whether we are talking about the public sector or the private sector or non-profits, really can benefit from, even need, better data to manage what they do in a more systemic way and that is one of the real insights to come out of 2020. This push of the digital economy to the next step will accelerate the applications of big data, cloud computing, and artificial intelligence in a significant way with a pressing need for governments and companies to train a new generation of workers along with a larger access to broadband in order to avoid a digital divide. If managed properly, the new order could create an ongoing driver for economic growth on both sides of the Atlantic and in the other market economies around the world. However, the digital economy should learn the lessons from the industrial one and be mindful of the risks that could have an impact on democracy, political freedom, and human rights. Following the Facebook-Cambridge Analytica data scandal4 and during her testimony before the British Parliament in 2018, Brittany Nicole Kaiser, former business development director for Cambridge Analytica, states that the company’s targeting tool used to be export-controlled by the British government,

32  Bill McKelvey

so that would mean that the methodology was considered a weapon: weaponsgrade communications tactics. In The Great Hack documentary (2019), she explains further: We bombarded those whose minds we thought we could change (“The Persuadables”) through blogs, websites, articles, videos, ads, every platform you can imagine until they saw the world the way we wanted them to. Until they voted for our candidate. It’s like a boomerang. You send your data out, it gets analyzed, and it comes back at you as targeted messaging to change your behavior. In their 2019 Worldwide Threat Assessment (Coats 2019), the US intelligence community asserts that some governments increasingly use cyber operations to threaten both minds and machines in an expanding number of ways to steal information, to influence citizens, or to disrupt critical infrastructure. They are now becoming more adept at using social media to alter how we think, behave, and decide. Carole Cadwalladr, British author, investigative journalist and features writer, declares in The Great Hack that there is also evidence that Russian intelligence created fake Black Lives Matter memes and when people clicked on them, they were taken to pages where they were actually invited to protests that were organized by the Russian government. At the same time, they were setting up pages targeting adversary groups, like Blue Lives Matter. She further explains that those tactics are about stoking fear and hate to turn the country against itself. Digital technology can create economic growth and make the democratic process more accessible. How can we protect our data and our democracy without interfering with the positive aspects of the digital economy? When asked by the British Parliament “What do you actually think the legislators should do in order to better protect people’s data?” Brittany Nicole Kaiser answered “I think that the best way to move forward is for people to really possess their data like their property.” In a report (Arbib and Seba 2020) published by RethinkX (an independent think tank whose mission is to facilitate a robust global conversation about the threats and opportunities of technology-driven disruptions, and highlight choices that could lead to a more equitable, healthy, resilient, and stable society), the authors recommend treating user data like intellectual property (IP). Whether we talk about data or digital democracy’s protection, blockchain technology5 seems to lead to promising results. In an article from Forbes titled “Want to Regain Control of Your Data? Blockchain’s Permission Management Is a Step in the Right Direction” (Spoke 2020), the author states that blockchain does provide a secure method of managing access to data. In an article from The New York Times called “Could we fight misinformation with blockchain technology?” (Reddy 2020), the research and development team at The New York Times concludes that when testing their blockchain prototype with users, they found that it effectively helped users make informed judgments about photos in a

Managing the Consequences of Digital Networks  33

social media feed and even though there is a long way to go before something like this can be fully realized, there is a large opportunity for using blockchain to help fight against misinformation in news photos. But as with any network, blockchain is only as powerful as the size of its participation. Therefore, even though blockchain technology could be part of the solution, more research and business applications are needed. In an era where we can influence a whole nation into voting for a specific candidate, where we can bring worldwide awareness to important societal and environmental issues, and where only the digital economy can still function during a pandemic, the question is not if a firm should be part of the digital business ecosystem but how efficiently and responsibly these potentially “weapons-grade communications tactics” should be managed.

Notes 1 Taken from Wikipedia: http:​/​/en.​​wikip​​edia.​​org​/w​​iki​/I​​nform​​atio​n​​_syst​​em (Accessed April 6, 2018). 2 Schrödinger (1944) coined “negentropy” to refer to energy importation. 3 The term “agent,” is defined in footnote #1. 4 Cambridge Analytica collapsed after details of its misuse of Facebook data (https​:/​/en​​ .wiki​​pedia​​.org/​​wiki/​​Faceb​​ook​%E​​2​%80%​​93Cam​​bridg​​e​_Ana​​lytic​​​a​_dat​​a​_sca​​ndal) were revealed to have potentially impacted voting in the UK Brexit referendum (https​:/​/ en​​.wiki​​pedia​​.org/​​wiki/​​2016_​​Unite​​d​_Kin​​gdom_​​Europ​​ean​_U​​nion_​​membe​​rs​hip​​_refe​​ rendu​m) and the 2016 US presidential election (https​:/​/en​​.wiki​​pedia​​.org/​​wiki/​​2016_​​ Unite​​d​_Sta​​tes​_p​​resid​​ent​ia​​l​_ele​​ction​). 5 For a definition, refer to Chapter 8 (“Using crowd-wisdom via crowdsourcing: proof-of concept of an efficient digital-strategy in the age of digital business complexity”).

References Al-Debei, M.M. and Avison, D. (2010) “Developing a unified framework of the business model concept,” European Journal of Information Systems, 19 (3): 359–376. Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M. and Ayyash, M. (2015) “Internet of Things: a survey on enabling technologies, protocols, and applications,” IEEE Communications Surveys & Tutorials, 17 (4): 2347–2376. Albert, D., Kreutzer, M. and Lechner, C. (2015) “Resolving the paradox of interdependency and strategic renewal in activity systems,” Academy of Management Review, 40 (2): 210–234. Ancona, D.G., Goodman, P.S., Lawrence, B.S. and Tushman, M.L. (2001) “Time: a new research lens,” Academy of Management Review, 26 (4): 645–663. Anderson, G.B. and Bell, M.L. (2012) “Lights out: impact of the August 2003 power outage on mortality in New York, NY,” Epidemiology (Cambridge, Mass.), 23 (2): 189. Anderson, P. (1999) “Complexity theory and organization science,” Organization Science, 10 (3): 216–232. Andriani, P. and McKelvey, B. (2007) “Beyond Gaussian averages: redirecting organization science toward extreme events and power laws,” Journal of International Business Studies, 38 (7): 1212–1230. Andriani, P. and McKelvey, B. (2009) “From Gaussian to Paretian thinking: causes and implications of power laws in organizations,” Organization Science, 20 (6): 1053–1071.

34  Bill McKelvey Andriani, P. and McKelvey, B. (2011) “From skew distributions to power-law science,” in P. Allen, S. Maguire and B. McKelvey (eds) The SAGE Handbook of Complexity and Management: Ch. 15, London: SAGE, 254–273. Aoyama, H., Fujiwara, Y., Souma, W., Ikeda, Y. and Iyetomin, H. (2009) Pareto Corporations: Statistical Life and Death in Complex Business Networks, Kyoto: Kyoto University, June. Arbib, J. and Seba, T. (2020) Rethinking humanity: five foundational sector disruptions, the lifecycle of civilizations, and the coming age of freedom, San Francisco, CA: RethinkX. Arthur, W.B., Durlauf, S.N. and Lane, D.A. (eds) (1997) The Economy as an Evolving Complex System, Proceedings of the Santa Fe Institute, Vol. XXVII. Reading, MA: AddisonWesley, 1–14. Attour, A. and Della Peruta, M. (2014) “Architectural knowledge: key flows and processes in designing an inter-organisational technological platform,” Knowledge Management Research & Practice, 14 (1): 27–34. Auerbach, F. 1913) “Das Gesetz der Bevolkerungskoncentration,” Petermanns Geographische Mitteilungen, 59: 74–76. Axtell, R.L. (2001) “Zipf distribution of U.S. firm sizes,” Science, 293 (5536): 1818–1820. Bai, S. and Koong, K.S. (2017) “Financial and other frauds in the United States: a panel analysis approach,” International Journal of Accounting & Information Management, 25 (4): 413–433. Banks, E. (2001) e-Finance: The Electronic Revolution in Financial Services, Hoboken, NJ: Wiley. Barabási, A. (2002) Linked: The New Science of Networks, Cambridge, MA: Perseus. Baum, J.A.C., Cowan, R. and Jonard, N. (2014) “Does evidence of network effects on firm performance in pooled cross-section support prescriptions for network strategy,” Strategic Management Journal, 35 (5): 652–667. BBC News (2016) “How to make something go viral on the Internet,” http:​/​/www​​.bbc.​​ com​/n​​ews​/b​​usine​​ss​-​35​​80420​1 (Accessed March 18, 2018). Becker, G.S. (1975) Human Capital (2nd ed), Chicago, IL: University of Chicago Press. Bedhead, A. (2013) “Hot chick with a douchebag: Kate Upton’s idiot boyfriend told her to lose weight,” Pajiba Love, October. http:​/​/www​​.paji​​ba​.co​​m​/paj​​iba​_l​​ove​/h​​ot​-ch​​ick​-w​​ ith​-a​​-douc​​hebag​​-kate​​-upto​​ns​-id​​iot​-b​​oyfri​​end​-t​​ol​d​-h​​er​-to​​-lose​​-weig​​ht​.ph​p (Accessed March 18, 2018). Bénard, H. (1901) “Les tourbillons cellulaires dans une nappe liquide transportant de la chaleur par convection en régime permanent,” Annales de Chimie et de Physique, 23: 62−144. Benbya, H. and McKelvey, B. (2006) “Using coevolutionary and complexity theories to improve IS alignment: a multi-level approach,” Journal of Information Technology, 21 (4): 284–298. Bergvall-Kåreborn, B. and Howcroft, D. (2013) “The apple business model: crowdsourcing mobile applications,” Accounting Forum, 37 (4): 280–289. Bharadwaj, A., El Sawy, O.A., Pavlou, P.A. and Venkatraman, N. (2013) “Digital business strategy: toward a next generation of insights,” MIS Quarterly, 37 (2): 471–482. Bones, C. and Cohen, J. (2013) Leading Digital Strategy, London: Kogan Page Limited. Boudreau, K.J. and Jeppesen, L.B. (2014) “Unpaid crowd complementors: the platform network effect mirage,” Strategic Management Journal, 36 (12): 1761–1777. Brazzell, D. (2017) “How mobile banking is transforming Africa,” HUFFPOST Blog, November 14. https​:/​/ww​​w​.huf​​fingt​​onpos​​t​.com​​/foot​​note/​​how​-m​​obile​​-bank​​ing​-i​​s​-tra​​​ _b​_12​​94443​​8​.htm​l (Accessed March 18, 2018).

Managing the Consequences of Digital Networks  35 Briscoe, G. (2010) “Complex adaptive digital ecosystems,” in Proceedings of the International Conference on Management of Emergent Digital EcoSystems, AC Med, 39–46. Briscoe, G. and De Wilde, P. (2006) “Digital ecosystems: evolving service-orientated architectures,” in Proceedings of the 1st International Conference on Bio Inspired Models of Network, Information and Computing Systems, Madonna di Campigilo: ACM, Italy, 1–6. December 11–13. Brousseau, E. and Pénard, T. (2007) “The economics of digital business models: a framework for analyzing the economics of platforms,” Review of Network Economics, 6 (2): 81–114. Brown, S.L. and Eisenhardt, K.M. (1998) Competing on the Edge: Strategy as Structured Chaos, Boston, MA: Harvard Business School Press. Brynjolfsson, E. and Mcafee, A. (2011) Race Against Machines, Lexington, MA: Digital Frontier Press. Brynjolfsson, E. and Mcafee, A. (2014) The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies, New York: W.W. Norton & Co. Bughin, J., Chui, M. and Manyika, J. (2010) “Clouds, big data, and smart assets: ten techenabled business trends to watch,” McKinsey Quarterly, 4: 26–43. Campbell, D.T. (1960) “Blind variation and selective retentions in creative thought as in other knowledge processes,” Psychological Review, 67 (6): 380–400. Carbonara, N. (2018) “Competitive success of Italian industrial districts: a network-based approach,” Journal of Interdisciplinary Economics, 30 (1): 78–104. Chaffey, D. and Ellis-Chadwick, F. (2012) Digital Marketing, Upper Saddle River, NJ: Pearson Higher Educ. Chan, C.K. and Yeoh, A.W.T. (2017) “Development of a platform to explore network intrusion detection system (NIDS) for cybersecurity,” Journal of Computer and Communications, 6 (1), 1–11. Chew, E.K. (2015) “Digital organizations of the future,” in J. Collin, K. Hiekkanen, J.J. Korhonen, M. Halen, T. Itala, and M. Helenius (eds) IT Leadership In Transition: The Impact of Digitalization on Finnish Organizations, Helsinki: Aalto University School of Science, Dept. of Computer Science, 13–27. Chituc, C.M., Toscano, C. and Azevedo, A. (2007) “Towards the creation of a digital business ecosystem for the shoe manufacturing domain,” in The IEEE Digital EcoSystems and Technologies Conference: DEST’07, Inaugural IEEE-IES, 88–93. Chmiel, A., Sienkiewicz, J. Suchecki, K. and Hołyst, J. (2007) “Networks of companies and branches in Poland,” Physica A, 383 (1): 134–138. Choi, Y. (2017) Digital Business and Sustainable Development: Asian Perspectives, London: Routledge. Ciborra, C.U. (1992) “From thinking to tinkering: the grassroots of strategic information systems,” The Information Society: An International Journal, 8 (4): 297–309. Claessens, J., Dem, V., De Cock, D., Preneel, B. and Vandewalle, J. (2002) “On the security of today’s online electronic banking systems,” Computers & Security, 21 (3): 253–265. Coats, D.R. (2019) “Worldwide threat assessment of the US intelligence community,” Statement for the Record, January 29. Coeckelbergh, M. (2015) Money Machines: Electronic Financial Technologies, Distancing, and Responsibility in Global Finance, Burlington, VT: Ashgate Publishing, Ltd. Cojocaru, L.E., Sarraipa, J., Jardim-Golcalves, R. and Stanescu, A.M. (2014) “Digital business ecosystem framework for the agro-food industry,” in K. Mertins, F. Bénaben, R. Poler and J.P. Bourrières (eds) Enterprise Interoperability VI: Interoperability for Agility,

36  Bill McKelvey Resilience and Plasticity of Collaborations, Cheltenham: Springer International Publishing, 285–296. Corallo, A., Passiante, G. and Prencipe, A. (eds) (2007) The Digital Business Ecosystem, Cheltenham: Edward Elgar Publishing. Coupey, E. (2001, 2nd edn 2005) Digital Business: Concepts and Strategy, Upper Saddle River, NJ: Pearson Education, Inc., Pearson Prentice Hall. Coupey, E. (2016) Digital Business: Concepts and Strategies (3rd ed), New York: Routledge. Cowan, G.A., Pines, D. and Meltzer, D. (eds) (1994) Complexity: Metaphors, Models, and Reality, Proceedings of the Santa Fe Institute, Vol. XIX, Reading, MA: Addison-Wesley. Crawford, G.C. and McKelvey, B. (2018) “Using maximum likelihood estimation methods and complexity science concepts to research power-law distributed phenomena,” in E. Mitleton-Kelly (ed) Handbook of Research Methods in Complexity Science, Cheltenham: Edward Elgar. Crawford, G.C., Aguinis, H., Lichtenstein, B., Davidsson, P. and McKelvey, B. (2015) “Power law distributions in entrepreneurship: implications for theory and research,” Journal of Business Venturing, 30 (5): 696–713. Csányi, G. and Szendrői, B., (2004) “Structure of a large social network,” Physical Review. Part E, 69 (3): 036131-1–5. Cusumano, M. (2010) “Technology strategy and management: the evolution of platform thinking,” Communications of the ACM, 53 (1): 32–34. Da Xu, L., He, W. and Li, S. (2014) “Internet of Things in industries: a survey,” IEEE Transactions on Industrial Informatics, 10 (4): 2233–2243. Dai, M., Sun, Y., Shao, S., Xi, L. and Su, W. (2015) “Modified box dimension and average weighted receiving time on the weighted fractal networks,” Scientific Reports, 5: 18210. Dai, M., Ye, D., Hou, J., Xi, L. and Su, W. (2016) “Average weighted trapping time of the node-and edge-weighted fractal networks,” Communications in Nonlinear Science and Numerical Simulation, 39: 209–219. Damiani, E., Ceravolo, P., Fugazza, C. and Reed, K. (2007) “Representing and validating digital business processes,” in Web Information Systems and Technologies Proceedings, Berlin: Springer, 19–32. Damiani, E., Ardagna, C.A. and El Ioini, N. (2008) Open Source Systems Security Certification, New York: Springer Science+Business Media. Darwin, C. (1859) On the Origin of Species By Means of Natural Selection, London: Murray. [A facsimile of the 1st ed., with an Introduction by E. Mayr, was published in 1964 by the Harvard University Press, Cambridge, MA.] Day, G.S. (2011) “Closing the marketing capabilities gap,” Journal of Marketing, 74 (5): 183–195. Day, G.S., Fein, A.J. and Ruppersberger, G. (2003) “Shakeouts in digital markets: lessons from B2B exchanges,” California Management Review, 45 (2): 131–150. De Solla Price, D.J. (1965) “Networks of scientific papers,” Science, 149 (3683): 510–515. deCosta, F. (2013) Rethinking the Internet of Things: A Scalable Approach to Connecting Everything, New York: Apress. Delina, R. (2015) “Trust building service model for digital business platforms,” Systémová Integrace, 22 (4): 3–18. Ding, G., Wu, Q., Zhang, L., Lin, Y., Tsiftsis, T.A. and Yao, Y.D. (2018) “An amateur drone surveillance system based on the cognitive Internet of Things,” IEEE Communications Magazine, 56 (1): 29–35. Djelic, M.-L. and Ainamo, A. (1999) “The coevolution of new organizational forms in the fashion industry: a historical and comparative study of France, Italy, and the United States,” Organization Science, 10 (5): 622–637.

Managing the Consequences of Digital Networks  37 Dodds, P., Watts, D. and Sabel, C. (2003) “Information exchange and the robustness of organizational networks,” Proceedings of the National Academy of Sciences of the United States of America, 100 (21): 12516–12521. Dourmas, G., Nikitakos, N. and Lambrou, M. (2005) “The concept of digital business ecosystems applied to the shipping industry,” in Proceedings of the International Association of Maritime Economists’ 2005 Conference, 22–25. Dum, R. (2007) “Science of complex systems for tackling challenges of the 21st century: a brief overview,” European Management Review, 4 (2): 73–76. Durand, R., Grant, R.M. and Madsen, T.L. (2017) “The expanding domain of strategic management research and the quest for integration,” Strategic Management Journal, 38 (1): 4–16. El Sawy, O.A. and Pereira, F. (2013) “Anticipating game changers for ‘enterprise 2020’ in a digitally-intensive world,” in O.A. El Sawy, and F. Pereira (eds) Business Modelling in the Dynamic Digital Space [El Sawy and Pereira are also the book’s authors], Heidelberg: Springer, 1–12. Escoffier, N. and McKelvey, B. (2014) “Chapter: Using ‘Crowd-wisdom strategy’ to co-create market value: proof-of-concept from the movie industry,” in International Perspectives on Business Innovation and Disruption in the Creative Industries, Cheltenham, UK; Northampton, MA, USA: Edward Elgar Publishing, pp. 200–222. Escoffier, N. and McKelvey, B. (2015) “The wisdom of crowds in the movie industry: toward new solutions to reduce uncertainties,” International Journal of Arts Management Special Issue, 17 (2): 52–63. Escoffier, N. and McKelvey, B. (2018) “Using crowdsourcing to increase new product’s market value and positive comments for both the crowd involved and customers,” International Journal of Innovation Management, 22 (2), https​:/​/ww​​w​.wor​​ldsci​​entif​​ic​.co​​m​/ doi​​/abs/​​10​.11​​42​/S1​​36391​​​96185​​00330​. Faed, A. (2010) “A conceptual framework for e-loyalty in digital business environment,” in Digital Ecosystems and Technologies. 4th IEEE International Conference Proceedings, 547–552. Gabaix, X. (2016) “Power laws in economics: an introduction,” Journal of Economic Perspectives, 30 (1): 185–205. Gatautis, R. and Medziausiene, A. (2014) Digital business ecosystems for regional development: evidences from EU countries’ pilots,” in the Proceedings of the 13th Wuhan International Conference on e-Business—Innovation Management and IT Business Value. Paper #32. Gawer, A. and Cusumano, M.A. (2002) Platform Leadership: How Intel, Microsoft, and Cisco Drive Industry Innovation. Boston, MA: Harvard Business School Press. Gawer, A. and Cusumano, M.A. (2010) “The organization of technological platforms,” Research in the Sociology of Organizations, 29: 287–296. Gay, B. and Dousset, B. (2005) “Innovation and network structural dynamics study of the alliance network of a major sector of the biotechnology industry,” Research Policy, 34 (10): 1457–1475. Gell-Mann, M. (2002) “What is complexity?,” in A.Q. Curzio and M. Fortis (eds) Complexity and Industrial Clusters, Heidelberg: Physica-Verlag, 13–24. Glaser, P. (2013) “Inequality in an increasing returns world: US stock market capitalizations from 1930 to 2008,” in B. McKelvey (ed) Complexity: Crucial Concepts, Vol. 5: Power-law Distributions In Society and Business, London: Routledge, 410–423. Gorbis, M. (2013) The Nature of the Future: Dispatches from the Social Structured World, New York: Free Press. Granovetter, M. (1973) “The strength of weak ties,” American Journal of Sociology, 78 (6): 1360–1380.

38  Bill McKelvey Greengard, S. (2015) The Internet of Things, Cambridge, MA: MIT Press. Grover, V. and Kohli, R. (2013) “Revealing your hand: caveats in implementing digital business strategy,” MIS Quarterly, 37 (2): 655–662. Haken, H. (1983) Synergetics, An Introduction (3rd ed), Berlin: Springer-Verlag. Hamilton, M., Milne, B., Walker, R., Burger, O. and Brown, J. (2007) “The complex structure of hunter-gatherer social networks,” Proceedings of the Royal Society of London, Series B, 274 (1622): 2195–2203. Hassan, Q.F., Rehman Khan, A. and Madani, S.A. (eds) (2018) Internet of Things: Challenges, Advances, and Applications, Boca Raton, FL: CRC Press. Herdon, M., Várallyai, L. and Péntek, Á. (2012) “Digital business ecosystem prototyping for SMEs,” Journal of Systems and Information Technology, 14 (4): 286–301. Herzberg, A. (2003) “Payments and banking with mobile personal devices,” Communications of the ACM, 46 (5): 53–58. Hirt, M. and Willmott, P. (2014) “Strategic principles for competing in the digital age,” McKinsey Quarterly, May, 5(1), 1–13. Holland, J.H. (1975) Adaptation in Natural and Artificial Systems, Ann Arbor, MI: University of Michigan Press. Holland, J.H. (1988) “The global economy as an adaptive system,” in P.W. Anderson, K.J. Arrow and D. Pines (eds) The Economy as an Evolving Complex System, Reading, MA: Addison-Wesley, 117–124. Holland, J.H. (1995) Hidden Order, Reading, MA: Addison-Wesley. Holliman, G. and Rowley, J. (2014) “Business to business digital content marketing: marketers’ perceptions of best practice,” Journal of Research in Interactive Marketing, 8 (4): 269–293. Hu, S., Yang, H., Cai, B. and Yang, C. (2013) “Research on spatial economic structure for different economic sectors from a perspective of a complex network,” Physica A, 392 (17): 3682–3697. Iansiti, M. and Levien, R. (2004) “Strategy as ecology,” Harvard Business Review, 82 (3): 68–78. Ikävalko, H., Turkama, P. and Smedlund, A. (2018) “Value creation in the Internet of Things: mapping business models and ecosystem roles,” Technology & Innovation Management Review, 8 (3): 5–15. Indian Express (2017) “2017  rewind: 16 photos that went viral on the internet,” http:​/​/ind​​ ianex​​press​​.com/​​photo​​s​/tre​​nding​​-gall​​ery​/2​​017​-t​​op​-16​​-phot​​os​-th​​at​-we​​nt​-vi​​ral​-o​​n​​-the​​ -inte​​rnet-​​50013​​81/ (Accessed March 18, 2018). Järvinen, J. and Karjaluoto, H. (2015) “The use of web analytics for digital marketing performance measurement,” Industrial Marketing Management, 50: 117–127. Jensen, T., Tan, Y.H. and Bjørn-Andersen, N. (2014) “Unleashing the IT potential in the complex digital business ecosystem of international trade: the case of fresh fruit import to European Union,” in the Proceedings the 27th eConference: eEcosystems, Bled, Slovenia, June 1–5, 14–27. Jiang, J., Klein, G. and Chen, H.G. (2006) “The effects of user partnering and user nonsupport on project performance,” Journal of the Association of Information Systems, 7 (2): 68–90. Jianqiu, Z. and Mengke, Y. (2015) “Internet plus and networks convergence,” Communications, China, 12 (4): 42–49. Johnson, J.L. and Burton, B.K. (1994) “Chaos and complexity theory for management,” Journal of Management Inquiry, 3 (4): 320–328. Jones, C. (2005) “The shape of production functions and the direction of technical change,” Quarterly Journal of Economics, 120 (2): 517–549.

Managing the Consequences of Digital Networks  39 Katsikas, S.K., Lopez, J. and Pernul, G. (2005) “Trust, privacy and security in e-business: requirements and solutions,” in Proceedings of the 10th Panhellenic Conference on Advances in Informatics, Berlin, Heidelberg: Springer, 548–558. Kauffman, S.A. (1993) The Origins of Order: Self-Organization and Selection in Evolution, New York: Oxford University Press. Keerthana, M. and Ashika Parveen, S. (2017) “Internet of Things,” International Journal of Advanced Research Methodology in Engineering and Technology, 1 (2): 105–108. Kellmereit, D. and Obodovski, D. (2013) The Silent Intelligence: The Internet of Things. Warwick, NY: DND Ventures LLC. Kelly, G. (2014) “The digital revolution in banking,” Occasional Papers #39, Washington, DC: Group of Thirty. Kelly, S. and Allison, M.A. (1999. The Complexity Advantage, New York: McGraw-Hill. Kenney, M. and Pon, B. (2011) “Structuring the smartphone industry: is the mobile internet OS platform the key?,” Journal of Industry, Competition and Trade, 11 (3): 239–261. Kim, W.C. and Mauborgne, R. (2015) Blue Ocean Strategy: How to Create Uncontested Market Space and Make the Competition Irrelevant, Boston, MA: Harvard Business Review Press Klinenberg, E. (2005) “Convergence: news production in a digital age,” Annals of the American Academy of Political and Social Science, 597 (1): 48–64. Koufteros, X.A., Vonderembse, M.A. and Doll, W.J. (2002) “Integrated product development practices and competitive capabilities: the effects of uncertainty, equivocality, and platform strategy,” Journal of Operations Management, 20 (4): 331–355. Koufteros, X.A., Vonderembse, M.A. and Jayaram, J. (2005) “Internal and external integration for product development: the contingency effects of uncertainty, equivocality, and platform strategy,” Decision Sciences, 36 (1): 97–133. Krishnan, R., Smith, M.D., Tang, Z. and Telang, R. (2007) “Digital business models for peer-to-peer networks: analysis and economic issue,” Review of Network Economics, 6 (2): 194–213. Kshetri, N. (2017) “The economics of the Internet of Things in the global south,” Third World Quarterly, 38 (2): 311–339. Kutin, A., Dolgov, V. and Sedykh, M. (2016) “Information links between product life cycles and production system management in designing of digital manufacturing,” Procedia Cirp, 41: 423–426. Kutsikos, K., Konstantopoulos, N., Sakas, D. and Verginadis, Y. (2014) “Developing and managing digital service ecosystems: a service science viewpoint,” Journal of Systems and Information Technology, 16 (3): 233–248. Lanier, J. (2013) Who Owns the Future, New York: Simon & Schuster. Laudon, K.C. and Laudon, J.P. (2013) Management Information Systems: Managing the Digital Firm (13th ed) Upper Saddle River, NJ: Pearson/Prentice Hall. LeBaron, B. (2000) “Agent-based computational finance: suggested readings and early research,” Journal of Economic Dynamics and Control, 24 (5–7): 679–702. Lee, S.M., Kim, T. and Noh, Y. (2010) “Success factors of platform leadership in web 2.0 service business,” Service Business, 4 (2): 89–103. Lee, Y., Amaral, L.A.N., Canning, D., Meyer, M. and Stanley, H.E. (1998) “Universal features in the growth dynamics of complex organizations,” Physical Review Letters, 81 (15): 3275–3278. Leeflang, P.S.H., Verhoef, P.D., Dahlström, P. and Freundt, T. (2014) “Challenges and solutions for marketing in a digital era,” European Management Journal, 32 (1): 1–12.

40  Bill McKelvey Lewin, A.Y., Long, C.P. and Carroll, T.N. (1999) “The coevolution of new organizational forms,” Organization Science, 10 (5): 535–550. Lewin, R. (1992, 2nd edn 1999) Complexity: Life at the Edge of Chaos, Chicago, IL: University of Chicago Press. Li, W., Badr, Y. and Biennier, F. (2012) “Digital ecosystems: challenges and prospects,” in Proceedings of the International Conference on Management of Emergent Digital EcoSystems, ACM Digital Library, Association for Computing Machinery, 117–122. Lin, L.H., Tanyavutti, A. and Jindrapacha, S. (2007) “Analyzing eBay platform strategies: an application of Meyer’s product platform strategy model,” in Portland International IEEE Conference on Management of Engineering and Technology Proceedings, 125–142. Liu, G. and Rong, K. (2015) “The nature of the co-evolutionary process: complex product development in the mobile computing industry’s business ecosystem,” Group & Organization Management, 1059601115593830. Lorenz, E.N. (1972) “Predictability: does the flap of a butterfly’s wings in Brazil set off a tornado in Texas?,” Paper presented at the 1972 meeting of the American Association for the Advancement of Science, Washington, DC. Lucas, H.C., Agarwal, R., Clemons, E.K., El Sawy, O.A. and Weber, B. (2013) “Impactful research on transformational information technology: an opportunity to inform new audiences,” MIS Quarterly, 37 (2): 371–382. Maguire, S. and McKelvey, B. (1999) “Complexity and management: from fad to firm foundations,” Emergence, 1 (2): 19–61. Mainzer, K. (2007, 1st edn 1994) Thinking in Complexity: The Complex Dynamics of Matter, Mind, and Mankind (1st ed), New York: Springer-Verlag. Majeed, A. (2017) “Developing countries and Internet-of-Everything (IoE),” in the Computing and Communication Workshop and Conference Proceedings, 7th Annual IEEE Conference, 1–4. Malecki, E.J. and Moriset, B. (2008) The Digital Economy: Business Organization, Production Processes and Regional Developments, New York: Routledge. Mallat, N., Rossi, M. and Ruunainen, V.K. (2004) “Mobile banking services,” Communications of the ACM, 47 (5): 42–46. Mandelbrot, B.B. (1983, 2nd edn) The Fractal Geometry of Nature, New York: Freeman. Manyika, J., Chui, M., Bisson, P., Woetzel, J., Dobbs, R., Bughin, J. and Aharon, D. (2015) Unlocking the Potential of the Internet of Things, New York: McKinsey Global Institute. Marion, R. (1999) The Edge of Organization: Chaos and Complexity Theories of Formal Social Systems. Los Angeles: Sage Publications. Marion, T.J., Meyer, M.H. and Barczak, G. (2015) “The influence of digital design and IT on modular product architecture,” Journal of Product Innovation Management, 32 (1): 98–110. Markus, M.L. and Loebbecke, C. (2013) “Commoditized digital processes and business community platforms: new opportunities and challenges for digital business strategies,” MIS Quarterly, 37 (2): 649–654. McCormack, R. (2011) “Digital ecosystems: a framework for online business,” http:​/​/ bit​​strat​​egist​​.com/​​2011/​​06​/di​​gital​​-ecos​​ystem​​s​-a​-f​​ramew​​ork​-f​​or​-​on​​line-​​busin​​ess/ (Accessed March 18, 2018). McEwen, A. and Cassimally, H. (2013) Designing the Internet of Things, Chichester: Wiley. McGeehan, R., Popov, L.T., Palow, C.W., Read, R.J. and Keyani, P. (2017) U.S. Patent # 9,576,119, Washington, DC: U.S. Patent and Trademark Office. McKelvey, B. (1999a) “Avoiding complexity catastrophe in coevolutionary pockets: strategies for rugged landscapes,” Organization Science, 10 (3): 294–321.

Managing the Consequences of Digital Networks  41 McKelvey, B. (1999b) “Complexity vs. selection among coevolutionary firms,” in M.P. Cunha and C.A. Marques (eds) Readings in Organization Science, Lisbon: Instituto Superior de Psicologia Aplicada, 241–296. McKelvey, B. (2001) “What is complexity science? It’s really order-creation science,” Emergence, 3: 137–157. McKelvey, B. (2002) “Managing coevolutionary dynamics,” Working paper, UCLA Anderson School of Management, Los Angeles, CA. McKelvey, B. (2004a) “Complexity science as order-creation science: new theory, new method,” Emergence: Complexity and Organization, 6 (4): 2–27. McKelvey, B. (2004b) “Toward a 0th law of thermodynamics: order-creation complexity dynamics from physics & biology to bioeconomics,” Journal of Bioeconomics, 6 (1): 65–96. McKelvey, B. (2008) “Emergent strategy via complexity leadership: using complexity science & adaptive tension to build distributed intelligence,” in M. Uhl-Bien and R. Marion (eds) Complexity and Leadership, Vol. I: Conceptual Foundations, Charlotte, NC: Information Age Publishing, 225–268. McKelvey, B. (ed.) (2013a) Routledge Great Works Series: Complexity: Critical Concepts: Origins Of Order-Creation Science: Complexity Science From Basic Disciplines, Oxford: Routledge. McKelvey, B. (2013b) Self-organization, Emergence & Self-Organized Criticality, Oxford: Routledge. McKelvey, B. (2013c) Organization & Management Complexity Dynamics, Oxford: Routledge. McKelvey, B. (2013d) Power-Law Distributions in Society & Business, Oxford: Routledge. McKelvey, B. (2016) “Complexity ingredients required for entrepreneurial success,” Entrepreneurship Research Journal, 6 (1): 53–73. McKelvey, B. and Salmador Sanchez, M.P. (2011) “Explaining the 2007 bank liquidity crisis: lessons from complexity science and econophysics,” Working paper, UCLA Anderson School of Management, Los Angeles, CA. McKelvey, B., Lichtenstein, B.B. and Andriani, P. (2012) “When organizations and ecosystems interact: toward a law of requisite fractality in firms,” International Journal of Complexity In Leadership & Management, 2 (1): 104–136. McKormack, R. (2011) “Digital ecosystems: a framework for online business,” http:​/​/ bit​​strat​​egist​​.com/​​2011/​​06​/di​​gital​​-ecos​​ystem​​s​-a​-f​​ramew​​ork​-f​​or​-​on​​line-​​busin​​ess/ (Accessed March 18, 2018). McQuivey, J. (2013) Digital Disruption: Unleashing the Next Wave of Innovation, Las Vegas: Amazon Publishing. Melville, N. (2012) “Information systems, business and the natural environment: can digital business transform environmental sustainability?,” The Oxford Handbook on Business and the Natural Environment, 327–345. Michalos, G., Makris, S., Papakostas, N., Mourtzis, D. and Chryssolouris, G. (2010) “Automotive assembly technologies review: challenges and outlook for a flexible and adaptive approach,” CIRP Journal of Manufacturing Science and Technology, 2 (2): 81–91. Miller, M. (2015) The Internet of Things, 800 E. 96th Street, Indianapolis, IN: QUE. Mirror News (2018) “VIRAL,” https://www​.mirror​.co​.uk​/all​-about​/viral (Accessed March 18, 2018). Mislove, A., Marcon, M., Gummadi, K., Druschel, P. and Bhattacharjee, B. (2007) “Measurement and analysis of online networks,” in 7th ACM SIGCIMM Conference on Internet Measurement, IMC’07, San Diego, CA, USA. Mithas, S., Tafti, A. and Mitchell, W. (2013) “How a firm’s competitive environment and digital strategic posture influence digital business strategy,” MIS Quarterly, 37 (2): 511–536.

42  Bill McKelvey Mizuno, T., Souma, W. and Watanabe, T. (2014) “The structure and evolution of buyersupplier networks,” Working paper: Faculty of Economics, University of Tokyo. Monks, K. (2017) “M-Pesa: Kenya’s mobile money success story turns 10,” CNN. http:​ /​/www​​.cnn.​​com​/2​​017​/0​​2​/21/​​afric​​a​/mpe​​sa​-10​​th​-an​​niver​​​sary/​​index​​.html​ (Accessed March 18, 2018). Morabito, V. (2014) Trends and Challenges in Digital Business Innovation, New York: Springer International Publishing. Moss, S. (2002) “Policy analysis from first principles,” Proceedings of the National Academy of Sciences of the United States of America, 99 (Suppl. 3): 7267–7274. Muffatto, M. (1999) “Introducing a platform strategy in product development,” International Journal of Production Economics, 60 (1): 145–153. Mulhern, F. (2009) “Integrated marketing communications: from media channels to digital connectivity,” Journal of Marketing Communications, 15 (2–3): 85–101. Mülken, O., Heinzelmann, S. and Dolgushev, M. (2017) “Information dimension of stochastic processes on networks: relating entropy production to spectral properties,” Journal of Statistical Physics, 167 (5): 1233–1243. Mullineux, A. (2015) “Global banking after the GFC: lessons for India,” Journal of Indian Business Research, 7 (2): 196–204. Mullineux, A.W. and Murinde, V. (2014) “Financial sector policies for enterprise development in Africa,” Review of Development Finance, 4 (2): 66–72. Muntaner-Perich, E. and de la Rosa Esteva, J.L. (2007) “Using dynamic electronic institutions to enable digital business ecosystems,” in Coordination, Organizations, Institutions, and Norms in Agent Systems II, Berlin: Springer, 259–273. Nachira, F., Dini, P. and Nicolai, A. (2007a) “A network of digital business ecosystems for Europe: roots, processes and perspectives,” Introductory Paper, European Commission, Brussels. Nachira, F., Dini, P., Nicolai, A., Le Louarn, M. and Rivera Lèon, L. (2007b) “Digital business ecosystems: the results and the perspectives of the digital business ecosystem research and development activities in FP6,” Office for Official Publications of the European Community, Luxembourg. Newman, M.E.J. (2001) “The structure of scientific collaboration networks,” Proceedings of the National Academy of Sciences of the United States of America, 98 (2): 404–409. Newman, M.E.J. (2005) “Power laws, Pareto distributions and Zipf’s law,” Contemporary Physics, 46 (5): 323–351. Olanrewaju, T., Smaje, K. and Willmott, P. (2014) “The seven traits of effective digital enterprises,” McKinsey, May, http:​/​/www​​.mcki​​nsey.​​com​/b​​usine​​ss​-fu​​nctio​​ns​/or​​ganiz​​ ation​​/ouri​​nsigh​​ts​/th​​e​-sev​​en​-tr​​aits-​​of​-ef​​fecti​​ve​​-di​​gital​​-ente​​rpris​​es. Pacheco, D.F., York, J.G. and Hargrave, T.J. (2014) “The coevolution of industries, social movements, and institutions: wind power in the United States,” Organization Science, 25 (6): 1609–1632. Pagani, M. (2013) “Digital business strategy and value creation: framing the dynamic cycle of control points,” MIS Quarterly, 37 (2): 617–632. Pappas, N., Kazasis, F.G., Anestis, G., Gioldasis, N. and Christodoulakis, S. (2007) “A knowledge management platform for supporting digital business ecosystems based on P2P and SOA technologies,” in Digital EcoSystems and Technologies Conference Proceedings. DEST’07. Inaugural IEEE-IES, 196–202. Park, J.W., Morel, B. and Madhavan, R. (2010) “Riding the wave: self-organized criticality in M &A waves,” Academy of Management Proceedings, 1: 1–6.

Managing the Consequences of Digital Networks  43 Parsons, A., Zeisser, M. and Waitman, R. (1998) “Organizing today for the digital marketing of tomorrow,” Journal of Interactive Marketing, 12 (1): 31–46. Passerini, K. and El Tarabishy, A. (2012) Information Technology for Small Business: Managing the Digital Enterprise, New York: Springer. Patriarca, M., Heinsalu, E., Chakraborti, A. and Kaski, K. (2017) “The microscopic origin of the Pareto law and other power-law distributions,” in F. Abergel, H. Aoyama, B. Charkrabarti, A. Chakraborti, N. Deo, D. Raina and I. Vodenska (eds) Econophysics and Sociophysics: Recent Progress and Future Directions, New York: Springer International Publishing, 159–176. Peppard, J. and Breu, K. (2003) “Beyond alignment: a coevolutionary view of the information systems strategy process,” Proceedings of the 24th International Conference on Information Systems, Seattle, WA, December, 743–750. Phillips, J. (2013) Digital Analytics Primer. Upper Saddle River, NJ: Pearson FT Press. Ponce-Jara, M.A., Ruiz, E., Gil, R., Sancristóbal, E., Pérez-Molina, C. and Castro, M. (2017) “Smart grid: assessment of the past and present in developed and developing countries,” Energy Strategy Reviews, 18: 38–52. Powell, W., White, D., Koput, K. and Owen-Smith, J. (2005) “Network dynamics and field evolution,” American Journal of Sociology, 110 (4): 1132–1205. Prigogine, I. (1955) An Introduction to Thermodynamics of Irreversible Processes, Springfield, IL: Thomas. Prigogine, I. (with I. Stengers) (1997) The End of Certainty, New York: Free Press. Prigogine, I. and Stengers, I. (1984) Order Out of Chaos, New York: Bantam. Qiu, T., Qiao, R. and Wu, D.O. (2018) “EABS: an event-aware backpressure scheduling scheme for emergency Internet of Things,” IEEE Transactions on Mobile Computing, 17 (1): 72–84. Razavi, A.R., Moschoyiannis, S.K. and Krause, P.J. (2007) “A coordination model for distributed transactions in digital business ecosystems,” in Digital EcoSystems and Technologies Conference Proceedings. DEST’07. Inaugural IEEE-IES, 159–164. Reddy, P. (2020) “Could we fight misinformation with blockchain technology?” The New York Times, July 6. Remane, G., Hanelt, A., Nickerson, R.C. and Kolbe, L.M. (2017) “Discovering digital business models in traditional industries,” Journal of Business Strategy, 38 (2): 41–51. Reyes-García, V., Zurro, D., Caro, J. and Madella, M. (2017) “Small-scale societies and environmental transformations: coevolutionary dynamics,” Ecology and Society, 22 (1): 15–20. Rogers, D.L. (2016) The Digital Transformation Playbook, New York: Columbia University Press. Rong, K. and Shi, Y. (2014) Business Ecosystems: Constructs, Configurations, and the Nurturing Process, London: Palgrave Macmillan. Rosenbloom, R.S. (2000) “Leadership, capabilities, and technological change: the transformation of NCR in the electronic era,” Strategic Management Journal, 21 (10–11): 1083–1103. Rosengard, J.K. (2016) “A quantum leap over high hurdles to financial inclusion: the mobile banking revolution in Kenya,” Faculty research working paper series, Cambridge, MA: Harvard University. Royle, J. and Laing, A. (2014) “The digital marketing skills gap: developing a digital marketer model for the communication industries,” International Journal of Information Management, 34 (2): 65–73.

44  Bill McKelvey Saito, Y., Watanabe, T. and Iwamura, M. (2007) “Do larger firms have more interfirm relationships?,” Physica A, 383 (1): 158–163. Salam, M., Steenkamp, A.L. and Khoury, F. (2008) “The evolution of small and medium enterprise in digital business ecosystem; accelerating the evolution and the need for web 2.0 and visualization,” in Information and Communication Technologies: From Theory to Applications, ICTTA 2008. 3rd International IEEE Conference, 1–6. Salmador Sanchez, M.P. and McKelvey, B. (2018) “Competitive market dynamics: using complexity science and self-organized criticality to push firms to the stochastic frontier,” in B. McKelvey (ed) Management in the Age of Digital-Business Complexity, London: Routledge. Santiago, A. and Benito, R., (2008) “Connectivity degrees in the threshold preferential attachment model,” Physica A, 387 (10): 2365–2376. Sawicki, A. (2016) “The Internet of Things,” World Scientific News, 48: 89–96. Schallmo, D.R. and Williams, C.A. (2018) “Roadmap for the digital transformation of business models,” in Digital Transformation Now! Cham: Springer, 41–68. Schallmo, D.R., Williams, C.A. and Boardman, L. (2017) “Digital transformation of business models—best practice, enablers, and roadmap,” International Journal of Innovation Management, 21 (08): 1740014-1–17. Schatt, D. and Laplanche, R. (2014) Virtual Banking: A Guide to Innovation and Partnering, Hoboken, NJ: Wiley. Scheinkman, J. and Woodford, M. (1994) “Self-organized criticality and economic fluctuations,” American Economic Review, 84 (2): 417–421. Schmidt, E. and Cohen, J. (2014) The New Digital Age: Transforming Nations, Businesses, and Our Lives, New York: Vintage Books. Schrödinger, E. (1944) What is Life? The Physical Aspect of the Living Cell, Cambridge: Cambridge University Press. Schuchmann, D. and Seufert, S. (2015) “Corporate learning in times of digital transformation: a conceptual framework and service portfolio for the learning function in banking organizations,” International Journal of Advanced Corporate Learning, 8 (1): 31–39. Schulte, P. (2015) The Next Revolution in our Credit-Driven Economy: The Advent of Financial Technology, Singapore: Wiley. Scott, S.V., Reenen, J.V. and Zachariadis, M. (2017) “The long-term effect of digital innovation on bank performance: an empirical study of SWIFT adoption in financial services,” Research Policy, 46 (4): 984–1004. Seigneur, J.M. (2005) “Demonstration of security through collaboration in the digital business ecosystem,” in Workshop of the 1st International IEEE Conference on Security and Privacy for Emerging Areas in Communication Networks, 108–109. Seitz, J. and Stickel, E. (2001) “Internet banking—An overview,” in SCN Education B.V. (eds) Electronic Banking: The Ultimate Guide to Business and Technology of Online Banking, Vieweg+ Teubner Verlag: Springer, 67–77. Seo, D. (2017) “Digital business convergence and emerging contested fields: a conceptual framework,” Journal of the Association for Information Systems, 18 (10): 687–702. Simula, H. and Vuori, M. (2012) “Benefits and barriers of crowdsourcing in B2B firms: generating ideas with internal and external crowds,” International Journal of Innovation Management, 16 (6): 1240011-1–19. Smith, D. (2014) “Internet use on mobile phones in Africa predicted to increase 20-fold,” The Guardian, June. https​:/​/ww​​w​.the​​guard​​ian​.c​​om​/wo​​rld​/2​​014​/j​​un​/05​​/inte​​rnet-​​use​ -m​​obile​​-phon​​es​-af​​rica-​​predi​​​cted-​​incre​​ase​-2​​0​-fol​d (Accessed March 18, 2018).

Managing the Consequences of Digital Networks  45 Souma, W., Aoyama, H., Fujiwara, Y., Ikeda, Y., Iyetomi, H. and Kaizoji, T. (2006) “Correlation in business networks,” Physica A, 370 (10): 151–155. Spoke, M. (2020) “Want to regain control of your data? Blockchain’s permission management is a step in the right direction,” Forbes, March 10. SRI Consulting Business Intelligence/National Intelligence Council. (2008) Disruptive Technologies Global Trends 2025. Srinivas, V. and Mitra, P. (2016) Link Prediction in Social Networks: Role of Power Law Distribution, New York: Springer International Publishing. Stacey, R.D. (1995) “The science of complexity: an alternative perspective for strategic change processes,” Strategic Management Journal, 16 (6): 477–495. Stanley, H.E., Amaral, L.A.N. and Plerou, V. (2000) “Scale invariance and universality of economic fluctuations,” Physica A, 283 (1–2): 31–41. Stanley, J. and Briscoe, G. (2010) “The ABC of digital business ecosystems,” arXiv preprint arXiv:1005.1899. Stanley, M.H.R., Amaral, L.A.N., Buldyrev, S.V., Havlin, S., Leschhorn, H., Maass, P., Salinger, M.A. and Stanley, H.E. (1996) “Scaling behavior in the growth of companies,” Nature, 379 (1–2): 804–806. Stechert, C. and Franke, H.-J. (2007) “Requirement-oriented configuration of parallel robotic systems,” in CIRP Design Conference 2007 - The Future of Product Development, Berlin, 259–268. Strader, T.J. (2010) Digital Product Management, Technology and Practice: Interdisciplinary Perspectives, Hershey, PA: IGI Global. Swaminathan, A. and Meffert, J. (2017) Digital @ Scale: How You Can Lead Your Business to the Future with Digital@scale, Hoboken, NJ: Wiley. Tafti, S.F., Kordnaeij, A., Hoseini, S.H.K. and Jamali, M. (2015) “Business ecosystem as a new approach in strategy,” Management and Administrative Sciences Review, 4 (1): 198–205. Tan, B., Pan, S.L., Lu, X. and Huang, L. (2009) “Leveraging digital business ecosystems for enterprise agility: the tri-logic development strategy of Alibaba. Com,” in 30th ICIS Proceedings. Paper 171. Tan, Y.L. and Macaulay, L.A. (2011) “Factors affecting regional SMEs progression to digital business ecosystems,” presented at the 17th Americas Conference on Information Systems (AMCIS). Tapscott, D. (1995) The Digital Economy, New York: McGraw-Hill Education. Tapscott, D. (2015) The Digital Economy: Rethinking Promise and Peril in the Age of Networked Intelligence (20th Anniversary ed), New York: McGraw-Hill Education. Tasselli, S., Kilduff, M. and Menges, J.I. (2015) “The microfoundations of organizational social networks: a review and an agenda for future research,” Journal of Management, 41 (5): 1361–1387. Tavlaki, E. (2005) “Business model: a prerequisite for success in the network economy,” in the Proceedings of the 18th Bled e-Conference: E-Integration in Action, Bled, Slovenia, June 6–8. Tee, R. and Gawer, A. (2009) “Industry architecture as a determinant of successful platform strategies,” European Business Review, 6 (4): 217–232. Thapliyal, H. (2018) “Internet of Things-based consumer electronics: reviewing existing consumer electronic devices, systems, and platforms and exploring new research paradigms,” IEEE Consumer Electronics Magazine, 7 (1): 66–67. The Economist (2015) “Manhood,” May 30, 2015. Available at https​:/​/ww​​w​.eco​​nomis​​t​ .com​​/essa​​y​/201​​5​/05/​​28​/m​e​​n​-adr​​ift.

46  Bill McKelvey The Economist (2018) “Automation and productivity: producing new ideas,” June 26, 2016. Available at https​:/​/ww​​w​.eco​​nomis​​t​.com​​/spec​​ial​-r​​eport​​/2016​​/06​/2​​3​/aut​​omati​​o​ n​-an​​d​-anx​​iety. Thomson, P., Johnson, M. and Devlin, J.M. (eds) (2017) Conquering Digital Overload: Leadership Strategies That Build Engaging Work Cultures, Cham: Springer International Publishing. Tiago, M.T.P.M.B. and Verĺssimo, J.M.C. (2014) “Digital marketing and social media: why bother?” Business Horizons, 57 (6): 703–708. Tomasi, D.G., Shokri-Kojori, E. and Volkow, N.D. (2017) “Brain network dynamics adhere to a power law,” Frontiers in Neuroscience, 11: 1–9. Tsatsou, P., Elaluf-Calderwood, S. and Liebenau, J. (2010) “Towards a taxonomy for regulatory issues in a digital business ecosystem in the EU,” Journal of Information Technology, 25 (3): 288–307. Turcoane, O. (2014) “Digital democracy in knowledge society: a proposed architecture based on cloud and complementary technologies,” Informatica Economica, 18 (4): 73–90. Van Deursen, A. and Van Dijk, J. (2011) “Internet skills and the digital divide,” New Media & Society, 13 (6): 893–911. Van Mierlo, T., Hyatt, D. and Ching, A.T. (2015) “Mapping power law distributions in digital health social networks: methods, interpretations, and practical implications,” Journal of Medical Internet Research, 17 (6): e160. Vander Zee, T.J.M. and De Jong, B. (1999) “Alignment is not enough: integrating business and information technology management with the balanced business scorecard,” Journal of Management Information Systems, 16 (2): 137–156. Varian, H. (1996) “The information economy: the share nature of information technology raises questions about doing digital business,” Educom Review, 31: 44–46. Verdouw, C.N., Beulens, A.J.M. and van der Vorst, J.G.A.J. (2013) “Virtualisation of floricultural supply chains: a review from an Internet of Things perspective,” Computers and Electronics in Agriculture, 99 (1): 160–175. Watts, D. 2003. Six Degrees: The Science of a Connected Age, New York: Norton. Wei, Z.W. and Wang, B.H. (2016) “Emergence of fractal scaling in complex networks,” Physical Review. Part E, 94 (3): 032309. Weill, P. and Woerner, S.L. (2018) “Is your company ready for a digital future?,” MIT Sloan Management Review, 59 (2): 21–25. Wells, P. (2001) “Platforms: engineering panacea, marketing disaster?” Journal of Materials Processing Technology, 115 (1): 166–170. Westerman, G., Bonnet, D. and McAfee, A. (2014) Leading Digital: Turning Technology into Business Transformation, Boston, MA: Harvard Business Review Press. Wokurka, G., Banschbach, Y., Houlder, D. and Jolly, R. (2017) “Digital culture: why strategy and culture should eat breakfast together,” in Oswald, G., & Kleinemeier, M. (eds.), Shaping the Digital Enterprise, London: Springer International Publishing, 109–120. Woodard, C.J., Ramasubbu, N., Tschang, F.T. and Sambamurthy, V. (2013) “Design capital and design moves: the logic of digital business strategy,” MIS Quarterly, 37 (2): 617–632. Yager, R.R. and Espada, J.P. (eds) (2018) New Advances in the Internet of Things, Cham: Springer International Publishing. Yaqoob, I., Ahmed, E., Hashem, I.A.T., Ahmed, A.I.A., Gani, A., Imran, M. and Guizani, M. (2017) “Internet of Things architecture: recent advances, taxonomy, requirements, and open challenges,” IEEE Wireless Communications, 24 (3): 10–16.

Managing the Consequences of Digital Networks  47 Ye, D., Dai, M., Sun, Y. and Su, W. (2017) “Average weighted receiving time on the nonhomogeneous double-weighted fractal networks,” Physica A, 473: 390–402. Yoshida, K. (2017) “Development and promotion of application technologies for digital business platforms,” FUJITSU Scientific & Technical Journal, 53 (1): 67–70. Zhai, L., Yan, S. and Zhang, G. (2013) “A centrality measure for communication agility in weighted network,” Physica A, 392 (23): 6107–6117. Zipf, G.K. (1929) “Relative frequency as a determinant of phonetic change,” Harvard Studies in Classical Philology, 40: 1–95. Zipf, G.K. (1949) Human Behavior and the Principle of Least Effort, New York: Hafner. Zucker, L.G. and Darby, M.R. (1996) “Star scientists and institutional transformation: patterns of invention and innovation in the formation of the biotechnology industry,” Proceedings of the National Academy of Sciences of the United States of America, 93 (23): 12709–12716. Yager, R.R. and Espada, Jordán-Pascual (eds) (2017) New Advances in the Internet of Things, New York, NY: Springer International Publishing.

2

Rescuing economics and management from Darwin’s evolution via death-and-replacement by the Baldwin Effect: learning during a lifetime Bill McKelvey and Cera Oh

Introduction The “Baldwin Effect” is the label Simpson (1953) used to collectively characterize an idea originated simultaneously by Baldwin (1896a), Morgan (1896a,b), and Osborn (1896). Briefly defined, the Baldwin Effect is a phenotypic change resulting from observation and learning during an organism’s lifetime that enhances its chances of survival when confronted by a significant environmental change during its lifetime. Darwin is quoted as saying “It is not the strongest of the species that survives, nor the most intelligent that survives. It is the one that is most adaptable to change” (quoted in Kreutzer and Land 2014: v). But, in fact, Baldwinian evolution is a much more change-oriented evolutionary theory than Darwin’s. Since 1985, American biologists, following Simpson, have come to refer to the Baldwin Effect as “phenotypic plasticity,” and UK biologists usually refer to it as “developmental plasticity.” While the Baldwin Effect is now prevalent in biology (see Appendices 1 and 2), it is generally ignored or, if briefly mentioned, it is misinterpreted by almost everyone applying evolutionary theorizing to the study of socioeconomic and organizational phenomena (e.g. Hannan and Freeman 1989; Singh 1990; Baum and Singh 1994; Baldwin and Rafiquzzaman 1995; Carroll and Hannan 1995; Nelson 1995; Aldrich 1999; Baum and McKelvey 1999; Aldrich and Martinez 2003; Hodgson 2004, 2006a, 2010; Madsen and McKelvey 2005). Campbell (1965, 1974) did cite Baldwin’s psych works (Baldwin 1900, 1906) but he ignored the Baldwin Effect. The Baldwin Effect does not appear at all in the Journal of Evolutionary Economics, though “phenotypic plasticity” appears in footnotes in Hodgson and Knudsen (2006) and is misused by Khalil (2012). However, in contrast to the aforementioned, Nozick (1994) uses the Baldwin Effect to explain the emergence of rationality in economists’ “rational actors,” and Federici (2003) applies it to the development of culture. It is mentioned problematically by Levinthal and Marino (2010). Surprisingly, while the Journal of Evolutionary Psychology has a couple of reviews of books that focus on the 

The Baldwin Effect  49

Baldwin Effect, it has no articles about the Baldwin Effect. However, there is an excellent article published by Scarfe (2009) in the Journal of Natural and Social Philosophy that compares Baldwin vs. Whitehead on “organic selectivity.” Studying the interactions between Baldwin’s phenotypic plasticity and Darwinian theory applied to evolving genetic structures is increasingly present in bio-evolutionary theory and research (see especially, Appendix 2). This research focuses on if, or how much, and how the causal impacts of (a) rapid phenotypic adaptation to near-term jolts in rapidly changing environments vs. (b) Darwinian evolutionarily created genetic structure, affect each other. Needless to say, interactions between (a) short-term adaptive responses by individual socioeconomic institutions or business organizations vs. (b) their longer-term resilience and survival in changing niches, are topics of increasing interest (e.g. Weick and Sutcliffe 2001; Coutu 2002; Hamel and Välikangas 2003; Star et al. 2003; Olsson et al. 2004; Norman et al. 2005; Sundström and Hollnagel 2006; Korhonen and Seager 2008; McKelvey and Andriani 2010; McKelvey and Yalamova 2011; Sundström and Hollnagel 2011). Even so, how institutional and organizational plasticity and resilience interact with Darwinian evolution via variation, selection, death, and replacement is missing from evolutionary economics and only vaguely present in a few organizational evolution studies (e.g. Child and Kieser 1981; March 1994; Doz 1996; White et al. 1997; Burgelman 2002). Madsen and McKelvey (2005), Witt (2005), and Murmann (2012) offer the most explicit theorizing about the interaction between intra-firm adaptation and Darwinian evolutionary theory. We begin with a brief definition and review of the Baldwin Effect and follow-on studies in biology, offering evidentiary data demonstrating the recent growth of biologists’ focus on the Baldwin Effect, phenotypic plasticity (see Appendix 1), and how it affects evolving genetic structures (see Appendix 2). We then shift our discussion from phenotypic plasticity vs. genes to organizational plasticity and memes. Given that phenotypic plasticity affects Darwinian evolutionary processes in organisms with one minuscule brain and genes dominating the rest of the organisms’ physiological hierarchy, we begin a discussion of some of the changes required in applying Darwinian evolutionary theorizing to socioeconomic institutions and organizations, given that they are comprised of one or more humans (often at multiples levels in a hierarchy) who have large and more complex brains, longer lives, and human learning and communication capabilities. Next we note that research by Chesbrough (1999) offers evidence of evolutionary change in the same industry via both intra-organizational adaptive plasticity (in Japan) and Darwinian death-andreplacement (in the US)—but he doesn’t offer any explanation as to why. Because Miller’s book The Icarus Paradox (1990) describes the many instances of firms dying off or going adaptively dormant because they get better and better at a skill that is increasingly irrelevant in a changing environment—which suggests Darwinian death-and-replacement—we conclude that economists and organizational researchers now face pronounced evidence that both Baldwinian phenotypic plasticity and Darwinian evolution have to be jointly

50  Bill McKelvey and Cera Oh

applied to validly theorize about institutional and organizational adaptation and evolution because only firms showing phenotypic plasticity can survive in a changing competitive ecosystem. Any attempt to focus solely on one or the other is clearly wrong and misleading. We conclude by emphasizing Miller’s (1993) propositions offering explanations about why organizations comprised of humans with large brains, long lives, and good learning and communication skills frequently cannot overcome Darwin’s evolutionary death-and-replacement process. Emphasizing the apparent on-and-off nature of Darwinian vs. Baldwinian evolution is one consequent approach. Alternatively, given that biological research about phenotypic plasticity over the past 30+ years now fully supports the evolutionary importance of Baldwin’s phenotypic plasticity, and given that humans have vastly better brains, communication, and learning abilities, the time has come for organizational and socioeconomic theorists and researchers to put much more emphasis on enhancing firms’ plasticity so as to improve their chances of surviving in changing ecosystems, rather than waiting to explain organizational and socioeconomic deaths (and-replacement) via Darwinian evolutionary theorizing. What is the Baldwin Effect? Background

Baldwin was a child psychologist by training, as is obvious from his writings about infant psychology (1890); imitation (1894); mental development (1895, 1897b); consciousness (1896b); heredity and instinct (1896c,d); organic selection (1897a); and human development (1902). Small children and even babies have incredible, quick-learning brains. It is not too surprising, then, that Baldwin raised key questions about Darwin’s theory of evolution in his 1896a article “A new factor in evolution.” First, since humans, from children to adults, have large brains, great learning skills, and live for decades, isn’t it more likely that they learn, adapt, and improve their survival chances during their lifetimes? Second, children live long enough with their parents to learn from them. Hence: can enhanced survival capabilities and behaviors pass directly from parents to children during their parents’ lifetimes? In 1896 Baldwin, Morgan, and Osborn each independently originated what Simpson (1953) eventually labeled the “Baldwin Effect.” In what follows, we draw more specifically on Baldwin’s work on phenotypic modification—now termed “phenotypic plasticity”—while also recognizing that Waddington (1941, 1942, 1952a,b,c, 1953a,b,c,d, 1957, 1961) made the first research contribution with his idea and early empirical tests of “genetic assimilation,” which has often been assumed to be the same as Baldwin’s “phenotypic accommodation,” but they are now seen as different (Longa 2006; Crispo 2007). Species, however, appear as a giant rank/frequency distribution: countless bacteria (with no brains) at the high-frequency end that multiply rapidly and

The Baldwin Effect  51

have very short lifetimes, but even so, quickly adapt to changing environments (e.g. as indicated by constantly emerging new antibiotics, and bacteria’s increasing resistance to antibiotics); and vastly smaller numbers of much larger species, such as hippos, rhinos, elephants, and whales at the low-frequency end, which also have larger bodies, bigger brains, and longer lives. Gorillas, chimps, and people have the most superior learning capabilities and fairly long lives. Baldwin’s third question follows: how far down the rank/frequency can phenotypes learn well enough and fast enough to improve their survival chances during their lifetimes given significant environmental changes? If chimps and people can do it, can elephants, hippos, elks, tigers, wolves, sheep, foxes, birds, mice, tadpoles, or ants and fleas do it? Does Baldwin’s (1896a) “new factor” alter Darwin’s theory of evolution for just the smart species with big brains or does it apply all the way down the rank/frequency distribution to bacteria? If not, where does it stop? Biologists now accept the fact that Darwin’s (1859) selectionist theory applies to all organisms. Baldwin’s view was that as organisms live longer and have larger brains—like people—they have increased ability to learn and adapt to changing environments during their lifetimes and pass their learning to their offspring. Thereby they improve the viability of themselves and their offspring and increase the probability of passing on genes that support such learning to future generations, thereby giving their follow-on genetic structure an evolutionary advantage. Since 1896 we have had 122 years of discussion, avoidance, debate, and finally—in the past 30+ years or so—empirical research and more agreement on just what is meant by the concept “phenotypic plasticity” and to which species it applies. But just what is the Baldwin Effect? Baldwin Effect defined

Darwin’s (1859) theory of natural selection holds that (1) a male and female mate and produce some number of offspring having genetic variance (i.e. blind variation [Campbell 1965]); (2) some offspring survive and propagate while some don’t (selection of genes occurs); (3) in a stable niche (e.g. grass, rabbits, foxes, say, 500 years ago in Wyoming) the genetic structure of the grass, prey, and predators evolves toward genetic make-ups supporting the optimal survival of all three species; and (4) in a changing niche/environment the selection of the fittest offspring varies as the environment changes. Baldwin (1896a) added to Darwin’s theory of natural selection by introducing the idea of “intelligent” adaptation. He considered three factors that affect an organism’s phenotypic plasticity: (1) environment and physical factors such as weather, location, chemical agents, etc., to which an organism can adapt instinctively because of its genetic structure; (2) “rise to occasion” types of spontaneous activities that occur during the normal innate functioning of an organism to which it can adapt to neurologically; and (3) what he called “intelligent” or “psycho-genetic” factors to which an organism adapts

52  Bill McKelvey and Cera Oh

consciously via imitations, maternal instruction, pleasure/pain lessons, and experiences. The intelligent, psycho-genetic factors are what he emphasized an organism must take advantage of and put into practice to enhance its probability of survival during its lifetime, given changing environmental conditions, which includes changing predators, changing kinds of infections and diseases, and changing kinds of food. Adaptation based on intelligence allows an organism to change via changing neuronal connections and brain functions (i.e. learning via experience), which is more effective for fast adaptation and less costly (i.e. fewer “negatives”). These learned adaptations (mostly from the maternal parent) are then inherited by the offspring since those who acquire these adaptations directly from their parents during both their lifetimes are more likely to survive and produce subsequent offspring that also have the neurological capacity to learn the adaptations their parents learned during their lifetimes. In short, parental plasticity may continue as offspring plasticity such that, eventually, the plasticity may become embedded in the species’ genetic structure. But here is where debate and research proliferate: under what conditions, and how much, and how quickly, if at all, does phenotypic plasticity actually become embedded in a species’ genetic structure? Review of phenotypic plasticity in biology: a more detailed view Conceptual

Beginning with a short review of 100 years of prior thinking, arguing, and rethinking about how best to connect the Baldwin Effect with Darwin’s evolutionary theory, Robinson and Dukas (1999) show that many recent but disparate empirical studies are showing more clearly and consistently that phenotypic plasticity, indeed, has a major positive influence on the direction and rate of adaptive divergence in changing environments (e.g. Hinton and Nowlan 1987; Matsuda 1987; Maynard-Smith 1987; Sultan 1987; Stearns 1989; Wcislo 1989; West-Eberhard 1989; Stephens 1991; Papaj 1994; Parisi and Nolfi 1996; Turney 1996a,b; Turney et al. 1996). Robinson and Dukas relate the findings of these empirical works to 1999-era thinking about how phenotypic modifications may influence evolution. Their summary of Baldwin’s theory is as follows (publishing in the UK rather than the US, they use “accommodation” not “plasticity”): 1. Phenotypic accommodations reduce the likelihood of species extinction stemming from environmental change; 2. Phenotypic accommodations increase the rate of evolution via natural selection; 3. Phenotypic accommodations steer adaptive divergence in positive directions. We find that Badyaev (2009) offers the best overall logic about how basic phenotypic plasticity fits in with Darwinian selectionist theory, coupled with

The Baldwin Effect  53

an excellent empirical study of emerging plasticity observed in the house finch of North America: 14 generations over 70 years. He begins by noting that phenotypes’ responses to changing environments “play an important role in an organism’s survival and functioning” (pp. 1125–1126). Response to drastic environmental changes within its lifetime is key to a phenotype’s survival— otherwise it could die immediately and leave no offspring. How, then, does this fit with evolutionary lineages based on genetic structures? Badyaev offers several observations (most of his citations are not included here, though many are included in Appendix 2): 1. Parental plasticity offers a clear illustration of the Baldwin Effect; parental plasticity is learned by their offspring, which helps the offspring improve their adaptivity; 2. Weakening of natural selection effects can coincide with drastic environmental change such as domestication (Van Valen 1965) or, e.g. emerging drought conditions in a rain forest. We see this effect in the Los Angeles area where urban invasion into neighboring mountainous wild areas has resulted in the killing off of predators (e.g. the bears, coyotes, mountain lions, and rattlesnakes in LA’s 100-mile wide urban area, which includes many square miles of areas where people can’t build houses, which are the areas where the animals now survive; however, because of the drought conditions in the Los Angeles wild area, the bears, coyotes, and mountain lions, etc. are more and more searching for food in humaninhabited areas); 3. Novel within-generation plasticity can allow previously suppressed genes to have greater impact in the offspring (Whyte 1965). Some bears, coyotes, and mountain lions have learned that there is food in peoples’ backyards, garbage cans, and in residential neighborhoods; 4. Novel maternal influences within a generation can also induce successful follow-on genetic variation in subsequent offspring (Badyaev 2008), which he validates in his studies of house finches and summarizes in his 2009 article; 5. To the extent that maternal plasticity carries over into the offspring, the parental plasticity (female, male, or both) may actually be amplified in the offspring and in follow-on generations (Müller and Newman 2003). Badyaev (2009) reduces the Baldwin Effect to three key follow-on evolutionary components: 1. Random variations created via phenotypic plasticity can have good or bad impact on an organism’s longer-term survival, i.e. plasticity can create long-term genetic advantage or it can offer temporary responses to temporary drastic environmental changes that are useless or even detrimental to long-term evolutionary adaptivity;

54  Bill McKelvey and Cera Oh

2. Previously existing underlying genetic elements activated by plasticity may be what assure the retention and influence of the initial set of phenotypic responses to environmental changes, i.e. plasticity may reflect preexisting genetic structure rather than create novel follow-on genetic structure; 3. A subsequent natural selection process emerges that “both favors the ability to accommodate novel inputs when they increase an organism’s fitness and sorts among the resultant developmental variants based on their survival value” (p. 1126). Since phenotypic plasticity may improve or hinder subsequent fitness, it is left to subsequent selection and retention processes to sort this out. Our quick review of the biological “plasticity” literature identified 210+ references focusing on “phenotypic plasticity” and another 150+ focusing on “developmental plasticity,” the latter in the UK. The foregoing reviews are critical since most of the debate over the past 100+ years pertains to how phenotypic plasticity and Darwinian selectionist dynamics interact. Their conclusions may be combined as follows: 1. Phenotypic plasticity allows organisms to adapt to changing environments during their lifetime, thus protecting a population from extinction during its lifetime; 2. First generation offspring have time to learn directly from their parents; 3. There is a danger, however, that short-term phenotypic adaptation to drastic, but possibly temporary non-repeating environmental changes, could be disadvantageous over the longer term; 4. Later generations can benefit from having both improved phenotypic plasticity and improved genetic structure and therefore have both improved short- and long-term adaptive capability; 5. Parental plasticity may reflect inherited genetic elements; the latter elements may also support the creation of follow-on genetic structure; 6. The subsequent genetic structures aid selectionist Darwinian adaptation and co-evolution; 7. Offspring have improved survival capability because: a. They have phenotypic plasticity acquired from their parents, if needed because drastic environmental changes continue; b. Their plasticity allows them to readapt if the drastic changes don’t persist; c. Phenotypic plasticity fosters enhanced evolution and coevolution, whatever environmental conditions prevail. Confirmation via experiments using live species

The Baldwin Effect was pretty much ignored for some 50 years because of plain avoidance or the dominance of various opinions about theoretical terms and perspectives among biologists arguing for or against it. For a recent collection

The Baldwin Effect  55

of arguments for and against the Baldwin Effect, see Evolution and Learning: The Baldwin Effect Reconsidered (Weber and Depew 2003).1 After the works by Waddington (1941 to 1961) and Simpson (1953), research and theorizing pertaining to how phenotypic changes could become embedded in genetic structure has proliferated. Appendices 1 and 2 and our Reference section include 210 articles (not counting duplicates) by way of offering some evidentiary data showing the increasing volume of research pertaining to phenotypic plasticity.2 In Appendices 1 and 2 we list some of the studies about phenotypic plasticity cited in articles by Robinson and Dukas (1999); Crispo (2007); Ghalambor et al. (2007); Lande (2009); and Moczek et al. (2011), as well as some others. The actual experimental studies have mostly appeared since the mid1980s. In the experiments,3 researchers were able to explicitly change the environment—i.e. change climate effects or insert various predators or prey into the experimental situation—so as to find out whether the maternal parents (mostly females) changed their behaviors, and whether their offspring then changed their behaviors. In these experiments, researchers could explicitly test how phenotypic plasticity and phenotypic accommodation to changing environments are best interrelated to Darwin’s theory of descent with modification via blind (random) variation, selection, and genetic retention; coevolution; and niche construction. Note, however, that almost all of these studies involve very small species with very short life spans, really tiny brains, and only a few days or weeks of time that an offspring is able to learn anything from its maternal parent; examples are flies, other insects, tadpoles, snails, beetles, guppies, toads, lizards, and various species of birds; a few studies involved larger species such as turkeys, chimps, apes and in one experiment, even children. The experiments primarily used small animals with tiny brains because they died within a few years after birth and therefore the experimenters could study multiple generations so as to find out whether the learning effects of phenotypic plasticity actually showed up in the genetic structure of later generations. Since very small species don’t live very long, a single experimenter can study the effects of phenotypic plasticity over multiple generations before she/he retires.4 Such species, however, come with tiny brains. Baldwin, the psychologist, was targeting the human species that seemingly has the best brains and lives decades longer than the tiny animals studied in the foregoing experiments. What is rather amazing is that these species—with tiny brains, short lives, minimal communication skills, and limited time to learn from (mostly) the female parent—are all experimentally shown to exhibit phenotypic plasticity. Baldwin the psychologist—if he had still been alive—could easily have argued that such experiments would be totally unfair tests, given minimal communication skills between offspring and parent and little time to actually observe, learn from, and copy the parent. Even so, the experimental manipulation effects all serve to confront the parent with some kind of drastic environmental change to which she has to change her behavior so as to survive

56  Bill McKelvey and Cera Oh

(Waddington 1952a,b,d; Yeh and Price 2004; Crispo 2007; Badyaev 2009). Frequently, even among these species, the offspring learn from the plasticity of the maternal parent and also become behaviorally changeable themselves (West-Eberhard 2003, 2005; Crispo 2007). Plasticity may also evolve (Windig et al. 2004; Danielson-François et al. 2006; Suzuki and Nijhout 2006). The offspring’s plasticity capability may be carried forward by subsequent offspring such that it eventually alters the offspring’s genetic structure (Price et al. 2003; Müller and Newman 2003; Ghalambor et al. 2007; Badyaev 2009): and eventually becomes an instinctive plasticity capability (Turney 1996a). The latter is especially useful if the offspring continue to face precipitous environmental changes (Waddington 1957; Gibson and Dworkin 2004; Flatt 2005). Offspring’s genetic changes resulting from plasticity, however, can be dysfunctional, not adaptive, and therefore not embedded in the follow-on genetic structure if said environmental changes do not persist (Waddington 1953a,b; Scharloo 1991; Grether 2005; Moczek et al. 2011). Confirmation via computational simulation studies

As noted previously, existing experiments on many living species have limitations: given their tiny brains, the mothers have limited plasticity capabilities and their offspring, with limited learning capabilities—i.e. minimal language and communication abilities—have limited ability and time to learn from parental plasticity. Furthermore, studying the effect of phenotypic plasticity on genetic structure takes time. Fourteen generations of house finches required ~30 years (Badyaev 2009). Fourteen generations of larger and smarter species extend much longer in time. Langergraber et al. (2012) calculate average generation times for chimpanzee females to be 25 years (14 generations = 350 years); gorilla females = 18 years (14 generations = 252 years); and human females = 27 years (14 generations = 387 years). Longer life spans with one female producing multiple offspring over some number of years (a human mother could have four babies two or three or four years apart; or multiple babies born at the same birth time; “Octomom” had eight babies at the same birth time) could result in offspring appearing before and then after her plasticity response to a drastic environmental change. Later-born offspring in subsequent generations could benefit from increased parental plasticity, which could increase the likelihood of plasticity effects on genetic structure, but the generation time-length would increase. Given this, it is logical for scientists to turn to agent-based5 computational models (ABMs) to experiment in ways not possible on many living species. ABMs allow scientists to study variables—like the relative cost of phenotypic plasticity vs. the cost of letting evolution dominate—that are difficult to measure and manipulate in living species, or variables more representative of species that have much longer lifetimes that the species listed in Table 2.2. Since ABMs study “computer” agents (animals), the “biology” of the species’ attributes is translated into computational variables, which appear as stylized,

The Baldwin Effect  57

abstract replications of the originating biological variable. Since ABM variables and results are “stylized”—variables and findings lack biological realism (e.g. cells, nerves, organs, blood, food, etc.)—they inadvertently have the advantage of a level of abstraction that makes them more easily moved over and applied to social phenomena like organizations and institutions. Some ABM studies are by Hinton and Nowlan (1987); Mayley (1997); Turney (1996a); Bullinaria (2001); Price et al. (2003); Curran et al. (2007); Ge et al. (2016); and Dominguez-Isidro and Mezura-Montes (2017). Suzuki and Arita (2007) take on the study of the cost and benefits of learning in the context of dynamic evolution. They confirm the Baldwin Effect as follows: 1. Learning by many phenotypes aids the population’s search toward finding the best fitness peaks; 2. Learning costs from having to interact with multiple phenotypes limit the size of the search area; 3. Learning among fewer isolated phenotypes, however, can increase benefits stemming from a greater number adaptive interactions among plastic phenotypes; 4. Higher phenotype interactions and combinations overcome the learning costs of individual phenotypes leading to improved genetic combinations and ultimately genetic assimilation. Some of the agent rules in the foregoing ABMs and several others not described in the foregoing paragraphs are listed in Appendix 3. They illustrate in a bit more depth the nature of key rules and/or variables in the ABMs that are abstracted out from the biological nature of the species so as to work in computational models, which also makes the results stylized enough to then be reasonably applied to understand the relation between organizational plasticity and evolution among humans in organizations and other social systems.

Baldwin Effect in socioeconomic and organizational evolutionary theory? Barely In organizational evolutionary theory: Baldwin Effect missing

It is reasonable to conclude that, between the origin of the idea by Baldwin, Morgan, and Osborn in 1896 and the beginning of Waddington’s works (1941 to 1961), the biologists largely misinterpreted the essence of the Baldwin Effect, confused it with Lamarkianism, and offered little if any empirical research for or against the idea; see some reviews and commentaries (e.g. Simpson 1953; Dobzhansky 1970; Mayr 1982; Turney 1996b; Robinson and Dukas 1999; Longa 2006; Crispo 2007; Ghalambor et al. 2007; Badyaev 2009; Lande 2009; Sznajder et al. 2012; Weber 2013; Kull 2014; Kirschner 2015; Ge et al. 2016; Bull 2017; Scheiner et al. 2017). A variety of pro and con views also appear in Weber and Depew (2003).

58  Bill McKelvey and Cera Oh

As one can see from our Appendices 1 and 2, meaningful empirical work other than the various studies by Waddington (1942 to 1961) didn’t start appearing until after 1985. Indeed, much has changed in biology about the relevance of the Baldwin Effect and its interaction with Darwinian selectionist evolutionary thinking. The 182 studies listed in Appendices 1 and 2 (not counting duplicates) are not there just as “references” but rather to offer demonstrable empirical evidence that, indeed, more consistent theorizing and much more empirical support for the Baldwin Effect now exists in biology. We can also mention the 122+ studies (2003–2017) that have been published in the Proceedings of the Royal Society B: Biological Sciences on “developmental plasticity”—the UK version of the Baldwin Effect. Even if the early non-biological evolutionists (e.g. Campbell 1960, 1974, 1982, 1994; Hannan and Freeman 1977, 1989; Aldrich 1979, 1999; McKelvey 1980, 1982; Nelson and Winter 1982; McKelvey and Aldrich 1983; Baum and Singh 1994; Baum and McKelvey 1999) had tried harder to look for non- or less-Darwinian thinking after what was called the “Grand Synthesis”—i.e. the Baldwin Effect was integrated into Darwin-based evolutionary theory—they would have found nothing but confusing arguments about “organic selection,” “orthoplasy,” “plasticity,” “phenotypic accommodation,” “Lamarckianism,” “genetic accommodation,” “genetic assimilation,” and various other phrases attributed to Baldwin and Waddington. Knowing what sort of “plasticity theory” to apply to organizational evolution would have been difficult. Boyd and Richerson (1985) mention phenotypic plasticity and adaptation (pp. 82, 83, & 130) and develop an elaborate evolutionary adaptation math model, but don’t support its development by any reference to Baldwin’s or Waddington’s plasticity research—though in fairness we note that studies other than Waddington’s had not appeared before their book went to press. Our review of later books more broadly focusing on social evolution (e.g. Durham 1991; Shennan 2002; Pluciennik 2005; Richerson and Boyd 2005; Sanderson 2007; Royle et al. 2012; Gaus and Thrasher 2014; Hubbard 2015; Read 2016) shows no mention of Baldwin or phenotypic (or developmental) plasticity, nor any discussion of how phenotypes’ learning and adaptation to changing environments might affect the “social” equivalent of genetic structure. Although Campbell (1974) associated the Baldwin Effect with the learned adaptive patterns that precede instincts, he didn’t mention the Baldwinian essence—phenotypic plasticity—in his (Campbell’s) works, nor is it mentioned in the 1999 review by Aldrich and Kenworthy. Reflecting the early 20th century misunderstanding of Baldwin’s idea, Breslin and Jones (2012) equate Baldwin’s idea to a “neo-Lamarckian form of inheritance,”6 and “the acquisition of behaviors through reinforcement learning” (p. 5). In fact, Baldwin’s idea was that phenotypes can use learning to survive in changing environments and thereby both (1) extend their lifetime and (2) improve the inherited plasticity and survival ability of their offspring in changing environments, which eventually can become embedded in the species’ genetic structure (unless the prolonged environment stops changing, the niche stabilizes, and the plasticity

The Baldwin Effect  59

genes become dysfunctional for longer-term survival). Baldwin’s thinking was very much non-Lamarckian. Our search of the large collection of electronic subscriptions UCLA pays for—i.e. “Business Source Complete”—includes ~8500 journals pertaining to economics, psychology, as well as all of the other business-, management-, and organization-related disciplines. We find no evidence that any of the early writers—or any other business-related authors—applying evolutionary theory to population dynamics, organizations, or management had discovered the Baldwin Effect. The one application of evolutionary theory to leadership that we found—Van Vugt’s (2006): “Evolutionary origins of leadership and followership” in the Personality and Social Psychology Review—associates phenotypic plasticity with situational leadership and behavioral flexibility, a correct interpretation; i.e. leaders can learn to cope successfully with changing conditions and this learning eventually can become embedded in the memetic nature of a firm.7 John Child recently published an edited book titled The Evolution of Organizations (2012). It reproduces 32 prior works (works actually dealing with evolution date from 1984 to 2008). Though Child begins this book with Baldwin’s 1896 article “A New Factor in Evolution,” there is no mention of Baldwin, or the “Baldwin Effect,” or “phenotypic plasticity,” or Simpson (1953) in any of the included works. Whereas the biologists had to deal with how phenotypic plasticity relates to genetic structure (studies in our Appendix 2), none of the works that Child includes pick up on this aspect of modern evolutionary biology. Although there are discussions of organizational change via adaptation (e.g. Child and Kieser 1981; Tushman and Romanelli 1985; Kogut and Zander 1993; March 1994; Doz 1996; Usher and Evans 1996; McKelvey 1997; White et al. 1997; Huygens et al. 2001; Burgelman 2002; Witt 2005) and Darwinian death-and-replacement (e.g. Langton 1984; Winter 1990; Hannan and Freeman 1977; Nelson 1994; Carroll 1997; Hodgson and Knudsen 2006, 2008), it is obvious that most of the studies just cited were published after the Baldwin Effect emerged as an important newly recognized aspect of biological evolutionary theory.8 None of them show some kind of organizational evolution over time that clearly identifies individual organizational (phenotypic) plasticity, adaptation, and prolonged existence in a dramatically changing competitive niche vs. Darwinian death-and-replacement as they are so obviously distinguished and described by Chesbrough (1999)—as we will illustrate later on. We could conclude that, since many of the authors in Child’s Evolution book discuss organizational adaptation in the context of environmental change, this could be evidence that allows most organizational theorists and researchers to essentially ignore biological evolutionary theory and thinking. And, since managers and employees can change organizations in response to environmentally imposed tensions, self-organized emergent new order and other complexity dynamics are also sources of organizational plasticity (e.g. White et al. 1997—in Child’s book—as well as other complexity science

60  Bill McKelvey and Cera Oh

applications to organizations: McKelvey 1997, 1999, 2004, 2010, 2013a,b,c; Maguire et al. 2006; Allen et al. 2011; Mitleton-Kelly et al. 2018). What is missing in evolutionary organization theory, however, are the details about just how intra-organizational adaptive plasticity gets embedded in routines and memes or other aspects of organizational cultures that foster efficiency and persistence over time as employees come and go in organizations9—that is, how emergent plasticity negates Miller’s (1990) Icarus Paradox. This issue is highlighted in Chesbrough’s (1999) research. Why is it that we see Baldwinian plasticity in Japanese laptop computer companies but Darwinian death-andreplacement in US firms? This is what current organizational evolutionary theory should be focusing on. Baldwinian in Japan? Darwinian in the US? Is it a cultural difference? Is it characteristic of only the hard disk drive industry? We discuss this in more detail later in the chapter. In evolutionary economics: mostly missing or wrongly interpreted

In their classic book of 1982, Nelson and Winter make no mention of the Baldwin Effect nor phenotypic/developmental plasticity. Nor does Nelson (1995) mention the Baldwin Effect in his 43-page review article “Recent evolutionary theorizing about economic change.” In their introduction to a special issue of the journal Industrial and Corporate Change (Dosi and Malerba 2002), which focuses on the 20 years of theorizing in evolutionary economics since Nelson and Winter’s 1982 book, and in the Special Issue itself, there is no mention of the Baldwin Effect or anything equivalent to phenotypic plasticity—nor in any other related kinds of “economics” journals, e.g. the Journal of Evolutionary Economics. As Witt (2005) notes, Penrose (1959) focused on endogenous organizational change as “a process of development…akin to natural biological processes in which an interactive series of internal changes leads to increases in size accompanied by changes in the characteristics of the growing object” (quoted in Witt 2005: 347). Witt (2005) focuses on the “entrepreneurial role”— which we interpret as equivalent to organizational phenotypic plasticity. In his 2007 article, however, Nelson does say that “the selection mechanism in a market setting may involve business judgment and decision making, and the shifting of what firms do, as much as it involves the birth and death of firms” (p. 88). In this sentence he puts faith in the effect of organizational phenotypic plasticity though he doesn’t mention the phrase “phenotypic plasticity,” nor the Baldwin Effect. The most prolific institutional economist and defender of Darwinismapplied-to-economics is Hodgson (1991, 1997, 2004, 2006a, 2007, 2009, 2010, 2012); Aldrich et al. (2008); Hodgson and Knudsen (2006, 2008, 2010). First, we note that there is no mention of Baldwin or the organizational equivalent of phenotypic plasticity in the recent articles (Aldrich, et al. 2008; Hodgson and Knudsen 2006, 2008, 2010), of which he is a co-author. Recognizing Baldwin as a psychologist, Hodgson (2006a) cites Baldwin’s 1909 book, Darwin and the

The Baldwin Effect  61

Humanities, saying “Darwinism brought…a new understanding of the human mind and consciousness onto the agenda of science” (p. 24). In his book, Darwin’s Conjecture, Hodgson (2010) mentions Baldwin (1909) in a footnote (p. 13) that also mentions other authors writing in 1902, 1913, and 1914.10 Hodgson has the most commentary about Baldwin and the Baldwin Effect in his book The Evolution of Institutional Economics (2004). Before describing Hodgson’s view, however, we start with Simpson’s classic article “The Baldwin Effect” (1953) by way of background. Simpson noted that “Baldwin, Lloyd Morgan, and Osborn all explicitly postulated the Baldwin Effect as a way out of the neo-Darwinian-neo-Lamarckian dilemma” (p. 110). He then said that “Baldwin called the effect in question ‘organic selection’ and defined it as follows: ‘Organic Selection: The process of individual accommodation considered as keeping organisms alive, and so, by also securing the accumulation of variations, determining evolution in subsequent generations’” (p. 111). Simpson continued: “[the] ambiguities and the difficulty of finding an apt descriptive term for so complex a process led Huxley (1942) to speak of the ‘Baldwin and Lloyd Morgan principle’11 and Hovasse (1950) [1943] called it ‘Baldwin’s principle’” (p. 112). Simpson then said: “Whether the mechanism or process in question is really a ‘principle’ remains debatable, and I prefer the expression ‘Baldwin Effect’” (p. 112), which is the term that dominates to this day. However, given all of the subsequent empirical research and computational modelling that supports the Baldwin concept, it could now be called the “Baldwin Principle.” Simpson defined the Baldwin Effect as “a complex sequence of events. The effect may be analyzed as involving three distinct (but partly simultaneous) steps” (p. 112): which we quote as follows: ••

•• ••

“Individual organisms interact with the environment in such a way as systematically to produce in them behavioral, physiological, or structural modifications that are not hereditary as such but that are advantageous for survival, i.e. are adaptive for the individuals having them”; “There occur in the population genetic factors producing hereditary characteristics similar to the individual modifications referred to in (1) or having the same sorts of adaptive advantages”; “The genetic factors of (2) are favored by natural selection and tend to spread in the population over the course of generations. The net result is that adaptation originally individual and non-hereditary becomes hereditary.”

At the time of his writing, i.e. 1953, Simpson said: “At this point it need not be taken for granted that the effect actually occurs or has an essential role in evolution. It may be taken as a hypothesis subject to investigation” (p. 112). Today, more than 60 years later, however, there is substantial research support for what is now called phenotypic or developmental plasticity. Simpson cites Waddington (1952a), but we also have Waddington (1953a,b,c,d, 1957, 1961),

62  Bill McKelvey and Cera Oh

all of the pre-1999 and post-2003 studies listed in Appendices 1 and 2, plus over 100 post-1999 studies on “developmental plasticity” published in the UK. With the foregoing additional Simpson-based background on the Baldwin Effect and its modern offshoot, phenotypic plasticity, we turn to what Hodgson says (or doesn’t say) about the Baldwin Effect. To do this, we list various quotes from Hodgson’s 2004 book: 1. “Morgan addressed the problem of explaining a sufficiently rapid pace of evolution within a Darwinian framework. As noted above, this was a pressing problem at that time because a prominent Lamarckian objection to Darwinism was that evolution would happen too slowly and haphazardly without the inheritance of acquired characters” (p. 111); and “Baldwin (1896) and Morgan (1896a, 1896b) both published arguments that attempted to show how biological evolution could be hastened without the inheritance of acquired characters. Morgan was relatively unlucky, for the phenomenon acquired the name of the ‘Baldwin Effect’” (p. 111); 2. “Eventually, Baldwin was unlucky too, for as Darwinism became ascendant after the 1930s, some thinkers dismissed the Baldwin-Morgan arguments because they seemed to smack of Lamarckian heresy. However, in fact the Baldwin-Morgan theories were devised to rebut Lamarckism and rescue Darwinism” (pp. 111–112)—especially as it could be applied to humans; 3. “Natural selection did not simply privilege beneficial inherited characteristics, but also inherited propensities [like phenotypic learning and plasticity] to acquire beneficial characteristics” (p. 112); 4. “In the Lamarckian view, acquired habits could be accumulated and passed on by human genetic inheritance, as well as by imitation or learning. Following Weismann [1889], Morgan rightly denied that the human genetic endowment was evolving so rapidly” (p. 136). [Meaning that Morgan was not advocating Lamarckianism, since he didn’t accept Lamarck’s idea that a phenotype’s habits were immediately incorporated into its offspring’s genetic structure]; 5. “It is primarily the social system that would preserve or develop the capacity for change, not significantly the human genotype” (pp. 137–138); 6. “One of Baldwin’s key innovations was to extend the theory of natural selection to the theory of human development and learning”;12 7. “Baldwin considered the basis of social integration and solidarity, and emphasized the importance of self-reflective behavior at the social level. But although he saw social groups as possible units of selection, he had no developed notion of institutions or social structures. Essentially, he applied Darwinism to the natural but not to the social world…Overall, Baldwin’s contribution to the Darwinian theory of social evolution was less important than that of Ritchie (1896) or Veblen (1899)” (p. 235). Of course, we recognize that Hodgson is an institutional economist and hence focuses mostly on social and institutional behaviors rather than individual

The Baldwin Effect  63

human or organizational behaviors.13 And, needless to say, we haven’t quoted the entire pages of the book where “Baldwin” appears or is discussed. We note the following, however: 1. Hodgson never mentions the concept of “phenotypic plasticity,” even though our Appendix 1 shows many “classic” works (not counting Waddington’s or Simpson’s works) on plasticity that had appeared many years before Hodgson’s 2004 book was published. And, since Appendix 1 jumps from the pre-1999 works to the post-2003 works there are many others that also had been published before Hodgson started his 2004 book. Hodgson’s book had apparently gone to the copy-editors before he likely could have become aware of Weber and Depew (2003)—it’s not cited. 2. In the parts of the book discussing Baldwin, Hodgson focuses on social institutions rather than individual human learning. The term “learning” is only mentioned twice on the pages pertaining to Baldwin, and there is no evidence that Hodgson makes any use of “inherited propensities” (see bullet #3 previously: Hodson’s emphasis) in his application of Baldwin’s, or Morgan’s focus on phenotypic or developmental learning (later termed plasticity by biologists). Even though Hodgson does recognize the concept of “inherited propensities,” he doesn’t appear to recognize plasticity and learning as key elements of human propensities—even though, and very obviously, social institutions are comprised of individual human brains more or less interacting, communicating, influencing, and/or learning from each other. 3. Furthermore, though Hodgson recognizes that Baldwin extended “natural selection theory to the theory of human development and learning” (see bullet #6 previously), there is little, if any, evidence anywhere in his 2004 book that the change and development of social groups or institutions may also happen if the members themselves learn new things about their group or its norms and values or its external environment (i.e. evidence of phenotypic plasticity) and then make changes in their individual behaviors that are in response to social, normative, or environmental changes or, alternatively, cause these changes—which eventually instigate institutional changes. In addition to evolutionary change via the death-and-replacement of an institution, it seems hard to believe that institutional change could not also occasionally result from changes in individual members’ learning, knowledge, attributes, behaviors, or connections with other members, i.e. phenotypic plasticity effects. How can an institution change if one or more human members don’t change it? Summing up: most of Hodgson’s works ignore the Baldwin Principle. All of his writing ignores phenotypic plasticity, which is obviously the newest aspect of evolutionary theory applied by biologists in their research. In the one book (2004) where he discusses Baldwin somewhat more, there is no evidence that Hodgson is aware of the evolutionary effects of phenotypic

64  Bill McKelvey and Cera Oh

plasticity (peoples’ learning during their lifetimes and their children’s learning about their parents’ plasticity) and its effects in enhancing survivability given changing environmental conditions, and then how this interacts with ongoing genetic structure (routines, memes, and social structures in institutions)—which is an obvious concern in Waddington’s 1941–1961 studies and in Simpson’s 1953 article, and in much of the subsequent plasticity theory and research—as our previous summaries of recent reviews by biologists indicate. Khalil (2012) tries to apply the Baldwin Principle to “routines,” which he defines as “habits or what economists regard as units of ‘technology’ or ‘institutions’ that amount to inflexible patterns of action” (p. 2). Basically, he has it backwards. He says: “The Baldwin Effect basically pays attention to the possibility that organisms seek out new environmental conditions…[They] might stumble into a new condition that favors them over the non-explorers that lack the desired genetic makeup” (p. 35). In fact, biologists now consistently interpret the Baldwin Principle to hold that phenotypes with phenotypic plasticity are able to learn how to adapt and survive significantly to changing environments during their lifetime. They don’t select new environments; they have the plasticity ability to learn how to survive changing environments impinging upon them. Yes, biologists agree that an organism might be able to adapt because some inherited inactive genes it already has give it the sensitivities and ability to learn and change to cope with a significant changing environment and hence foster the phenotype’s survival. But let’s be clear, the Baldwin Principle pertains strictly to plasticity that allows a phenotype to learn and change to adapt to survival-threatening changing environmental conditions during its lifetime; i.e. humans learning during their lifetime and kids learning from their parents. In an article titled “Strategic choice of preferences: The persona model,” Wolpert et al. (2011: 3) refer to “endogenously determined preferences as ‘personas.’” Their claim is that the persona phenomenon often contributes to determining the behavioral preferences that people adopt…The kind of learning of personas across a lifetime that we posit can be seen as an instance of the Baldwin Effect (Dennett 2003). In the Baldwin Effect, natural selection does not provide individuals of a species with a particular behavior, but rather with the ability to learn over their lifetimes what behavior to adopt. (ibid: 3–4) Applying plasticity to strategic choices in games, Wolpert et al. observe that a person would then learn, based on their interactions with their (social) environment, which behavioral preferences are best to adopt for different kinds of games…This use of the Baldwin Effect provides natural selection a way to optimize behavioral preferences. (ibid: 4)

The Baldwin Effect  65

This is the only correct application of the Baldwin Principle to humans that we can find in any of the existing management or economics journals.

The Baldwin Effect: from miniscule brains to people and firms Small birds are the most frequently studied species in phenotypic-plasticity experiments—as indicated in Appendix 2—and brains in small birds are tiny.14 But even smaller species with many fewer neurons in their brains have also been experimentally studied: insects, tadpoles, guppies, snails, and moths; and a few much larger species as well: chimps, apes, and children. Some have very short life expectancies (in years), e.g. guppies = 2–3; sunfish, tadpoles, and house finches = 5; snails = 5–10; moths = 1–2 weeks (some insects can live up to two years). Researchers study species with short life spans so they can study how plasticity connects to genetic restructuring across multiple generations. Furthermore, as species get larger and more like humans, conducting experiments that have an outcome of death for various test samples is hard to justify in many cultures—another reason to experiment only with really small animals. Generally, big animals have larger brains with many more neurons and live longer. Hence large phenotypes have vastly longer times to learn and change during their lifetime than do really small species. Consequently, their offspring have much longer time spans to learn from their mothers/parents. It follows then, that the larger the species, the more likely phenotypic plasticity is a reality. Additionally, humans usually have better brains than any other species, live longer than most, have much more sophisticated linguistic communication abilities, and usually get better education. Thus, human offspring have more relevant skills and a longer time to learn from their parents, though “rebellions” often occur, in which the children try really hard not to learn from their parents; needless to say, however, children may exhibit even more plasticity when rebelling—i.e. they can readily learn from teachers or other friends or from the Web—and, of course in the Digital Age, they can really learn more and much faster from the Internet. Studying 14 generations of larger animals extends well beyond a scientist’s working lifetime, so the difficulty in studying plasticity effects on genetic structure becomes difficult, if not impossible, for one scientist to do it as species become larger, live longer, have bigger brains, and are smarter. This is another reason why most phenotypic plasticity experiments have been done with species that have short life spans, such as birds and tadpoles, and consequently have small brains and minimal plasticity capabilities. Given all of the above, we, therefore, conclude that applying only Darwinian (deathand-replacement) theory to explain human institutional and organizational evolutionary changes has significant limitations. And, given all of the small animal studies showing that phenotypic plasticity does indeed exist and, as many experiments show, helps both maternal parent and offspring survive drastic environmental changes, the Baldwin Principle

66  Bill McKelvey and Cera Oh

surely explains much of human survival and consequent genetic restructuring since humans became a species—though emergent cultural effects were also present (Boyd and Richerson 1985; Shennan 2002; Pluciennik 2005; Richerson and Boyd 2005; Sanderson 2007). Obvious (recent) evidence of human plasticity in action was dramatically illustrated during the Dark Ages (primarily from 476 to 1000AD but sometimes extended to the Renaissance in 1500AD), when variations in parental ability to save their children during the transitory— within lifetime—raids of the Huns, Ostrogoths, and Visigoths, as well as the “Alans, Burgundians, Thuringians, Frisians, Gepidae, Suevi, Alemanni, Angles, Saxons, Jutes, Lombards, Heruli, Quadi, and Magyars” (Manchester 1992: 5), which led to mass murders, rapes, pillage, and destruction of unprotected small communities here and there throughout the forests of Europe. Darwinian theory—strictly interpreted—is even more questionably applied to evolving (human) organizations, as we detail next.

Chesbrough’s findings about organizational evolution Chesbrough (1999) studies the coevolution of firms during the development of the hard disk drive (HDD) industry from 1973 to 1996 in the US and Japan. The US firms behaved more Darwinian; the Japanese firms showed more Baldwinian plasticity. The HDD memory devices went from 14-inch to 8-inch to 5.25-inch to 3.25-inch and finally to 2.5-inch diameters during this time period. But we really don’t know why firms survived this change in Japan, but not in the US! Evolution of the HDDs in the US

IBM was the giant incumbent (the elephant in the living room) when the hard disk data storage industry started so as to supply HDDs for smaller sized computers, i.e. not “mainframes.” Chesbrough (1999: 291) asks the question: was the growth of new firms “incumbent inertia or startup strength?” IBM “dominated the storage market in the 1970s”; it held 89.5% of the hard disk storage market share; even by 1995 it still controlled 88.5% of market share (Chesbrough 1999: 290); its ThinkPad brand was famous worldwide; but IBM sold its personal computer business and its ThinkPad brand to Lenovo in 2005. Chesbrough points out that: •• ••

Smaller firms responded to new markets—for minicomputers, workstations, microcomputers (PCs) more quickly. IBM was not the first mover for any of these new product concepts; IBM “consistently refused to sell any of its HDDs (or their key components…) to other computer firms” as an original equipment manufacturer and supplier (p. 291). Chesbrough cites Christensen (1993: 569) as pointing out firms like IBM “listened too attentively to their established

The Baldwin Effect  67

•• ••

customers and ignored new product architectures whose initial appeal was in remote markets”; Many of the more creative engineers became oppressed from being constrained by IBM’s unwillingness to venture in new directions and therefore left IBM; Taking advantage of IBM’s reluctance, startups moved into the new markets but they had to overcome several difficulties (Chesbrough: 291–292). They had to: •• Acquire the appropriate kinds of new technologies; •• Attract business away from the incumbents; •• Manufacture at competitive costs; •• Design HDDs into their own computer systems to demonstrate that they worked to new customers; •• Raise and consume millions of dollars before making any profits from sales. For this they greatly benefited from the rise of the Venture Capital industry at the same time; •• Many if not most of the startups were founded by engineers who left IBM. If they weren’t actually founded by an IBM engineer, they had to attract the oppressed engineers from IBM; •• IBM eventually had to buy all of its ThinkPad HDDs from the startups.

In our Figure 2.1, we reproduce what Chesbrough calls “a partial genealogy” (in his Figure 2). The entire genealogy starts with four firms started by engineers who left IBM: 1. The startups interacted with their environment in such a way as to systematically produce behavioral and structural modifications in them that that were advantageous for survival, i.e. were adaptive for the engineers with this kind of learning; 2. IBM never showed the kind of phenotypic plasticity that would have undermined the survival of its offspring, shown in our Figure 2.1; 3. Engineers also kept being “spawned” out of the startups—leaving one startup to start up another startup; 4. IBM itself did not show the plasticity to produce its own HDDs as they shrunk in size; by 1986 it had to buy millions of the 5.25-inch drives from the startups. In this evolutionary happening, “Mother” IBM didn’t show plasticity in HDDs, didn’t change, and eventually didn’t make them at all. Engineers are the “spawned” equivalents of a mother’s “eggs” who gave rise to the startups. Needless to say, as shown in Figure 2.1, there was much variation in the startups; of the 26 newly created firms, only seven survived to keep making the HDDs. This shows classic Darwinian evolution, and no HDD-relevant phenotypic plasticity was developed within IBM from the beginning to the end of the evolutionary period of HDD development. Furthermore, many

68  Bill McKelvey and Cera Oh IBM Memorex

Systems Industries

Storage Tech

Pertec

Shugart Associates Seagate Technology

Priam

Conner

Kalok

Micropolis

Tandon

Vertex

Syquest

Century

Quantum

Maxtor

Western Digital

Orca Technology

Miniscribe

Brand Technologies

Lapine

Prairie Tek Ecol. 2

Still making HDDs as of 12/96

Computer Memories

JTS

Integral

Figure 2.1 Shows all the new firms appearing and disappearing as the HDD industry grew. Reproduction of Figure 2 in Chesbrough (1999, p. 293) with permission from Springer Science and Business Media, May 11, 2013.

of the startups also didn’t survive the transition from the 14- down to 2.5inch disks. Evolution of the HDDs in Japan

Instead of the one giant IBM in the US, in Japan “there were four relatively similar-sized Japanese firms: Fujitsu, Hitachi, Nippon Electric Corporation… and Toshiba” (Chesbrough 1999: 294). Although the Japanese copied IBM rather carefully, each of the Japanese firms moved into HDD production. Each firm made the HDD devices both for its own use and also to sell to other customers. Chesbrough notes that as the disk size diminished from 14 inches to 2.5 inches in diameter, these four firms stayed in the HDD business, selling to customers. In short, each firm shows the plasticity necessary to keep up with the new technologies, attract competent engineers, and redesign their production skills and equipment to keep up with the shrinking HDD disk diameters and designs. As shown in Appendix 1 (Chesbrough’s Table 2), by the time the 5.25-inch and 3.5-inch HDDs were dominant, all four firms were heavily involved in producing them; by the time the 2.5-inch disk dominated the industry, three of the four firms were still involved .

The Baldwin Effect  69 Table 2.1 Percent of Japanese firms’ total revenues from HDD devices* Company

14” HDD

8” HDD

5.25” HD

3.5” HDD

2.5” HDD

Fujitsu Hitachi NEC Toshiba

80% 5% 0% 0%

67% 70% 20% 80%

100% 80% 80% 80%

100% 90% 60% 80%

100% 90% 0% 65%

* Reproduction of Table 2 in Chesbrough (1999, p. 295) with permission from Springer Science and Business Media, May 11, 2013.

In Japan we see an evolutionary progression dominated by firms’ plasticity. Each of the four firms adapted to keep up with changing technology and customer demand as HDDs shrank from 14 to 2.5 inches. Furthermore, as our Table 2.2 (Chesbrough’s Table 5) indicates, there was almost no spawning of engineers as “eggs” going out of, or coming into, the four top firms mentioned above, even though “there were 28 other firms that entered the Japanese market, some of whom were parts of very large, successful firms” (Chesbrough 1999: 297, footnote 12) that tried to enter and make an impact on the HDD market. As Table 2.3 (Chesbrough’s Table 4) shows, in the US there were mostly new firms appearing for each new technology as the disks shrank from 14 to 2.5 inches—i.e. death-and-replacement. In Japan a very different reality appeared: Fujitsu rode the new-tech wave from 14 down to 2.5 inches; other firms are shown as leading firms in more than one disk size—some firms don’t appear as “dominant” in this table, but they still rode the new-tech wave from 14- down to 2.5-inch disk diameter since our Table 2.3 shows that each disk size continued to be a high-percentage contributor to their total revenues. In Japan, phenotypic plasticity dominated and the “egg” spawning of engineers to take the genetic equivalent of new technology from larger firms to the startups was almost nonexistent. The result is that the coevolution story in Japan is dramatically different from the one in the US. In the US, Darwinism dominated the HDD producers; in Table 2.2 Number of engineers hired from or lost to competitor firms in Japan* Company

# HDD engineers in Cumulative # of engineers the firm in 1997 hired into the firm over the past 20 years

Cumulative # of engineers lost to competitor firms the past 20 years

1 2 3 4

225 250 400 250

0 1 1–2 1–2

0 0 1 0

* Reproduction of Table 5 in Chesbrough (1999, p. 299) with permission from Springer Science and Business Media, May 11, 2013.

70  Bill McKelvey and Cera Oh Table 2.3 New firms for each shrinking disk size in the US; same firms continuing as disk size shrinks in Japan* Drive size (Year)

14” Drives (1978)

8” Drives (1981)

5.25” Drives (1983)

3.5” Drives (1987)

2.5” Drives (1991)

Miniscibea PrairieTek Tandon Connera Conner WDa Rodimea Quantuma Plus/ IBM Quantuma JVC NEC Toshiba JVC Nippon Fujitsu Fuji Fujitsu Peripherals Electric Fujitsu NEC

Seagate Leading US CDC Storage Shugart Miniscribe producers Tech CDC Tandon Memorex IMI IMIa CMI Century Leading NPL Fujitsu Japanese Hitachi producers NEC

Fujitsu Hitachi NEC

* Reproduction of Table 4 in Chesbrough (1999, p. 297) with permission from Springer Science and Business Media, May 11, 2013. a incumbent firms that were HDD startup firms in a previous generation.

Japan, Baldwin’s phenotypic plasticity dominated. But of course, IBM showed plasticity in its other production operations—just not in its HDD activities.

Darwin vs. Baldwin in firms: Miller’s findings We draw on Chesbrough’s research (1999) to make the point that the evolution of the hard disk drive (HDD) industry exemplified Darwinism in the US and the Baldwin Principle in Japan. Where there is a clear pattern of death-and-replacement—or prolonged adaptive dormancy—as organizations decline in effectiveness while an industry develops, there is reason to believe that Darwinian variation, selection, and retention prevails. Evidence of this appears in Miller’s book The Icarus Paradox (1990). It is about all the American firms that: ••

••

••

Have become complacent, careless, and out of touch. They pursue shortterm, bottom-line targets and bury themselves in technical or financial intricacies, while they neglect the substance—the products and the customers—of their business. They stop making the right stuff for the right markets and pay the price: earnings plummet, stock prices collapse, and managers are dismissed (p. 1); The roots of decline are extremely complicated and insidious, and they run very deep into the fabric of outstanding organizations. Indeed, it is the central paradox of this book that success itself and the things that cause it seem very much to contribute to decline (p. 2); It is ironic that many of the most dramatically successful organizations are so prone to failure. The histories of outstanding companies demonstrate

The Baldwin Effect  71

this time and time again. In fact, it appears that when taken to excess the very factors that drive success—focused tried-and-true strategies, confident leadership, galvanized corporate cultures, and especially the interplay among all these things—can also cause decline. Robust, superior organizations evolve into flawed purebreds; they move from rich character to exaggerated caricature as all subtlety, all nuance, is gradually lost (p. 3). What happened in most of the firms Miller studied is that they tried to get better and better at what they already did best. In a changing competitive niche, however, while getting better and better at what they did best, they also became more and more obsolete, technically out of date, and increasingly disconnected from changing customer tastes. As happened with IBM, their best talent eventually left the dying firm and moved to, or started up, a more ecologically competitive firm. Darwinian evolution prevailed: lack of relevant variation, then death;15 and then replacement or reconstructing themselves after their adaptive dormancy. Some firms, however, avoided death because plasticity finally was allowed to dominate. Again, IBM provides a good example: Louis Gerstner became CEO in 1993 and saved it from going out of business. Much of IBM died off and was replaced by all the startups that took over everything but the supercomputer market. While IBM kept developing its supercomputer product line and its servers, under Gerstner the rest of it morphed into an IT-service consulting organization. Turney (1996a) argues that species may be biased toward learning or toward instinct; one of the motives of all the experiments in Appendix 2 may be phrased as proving that plasticity-fostered learning translates into instinct as the learning advantage is captivated by the genetic structure. Translated into the organizational context described by Miller (1990): firms that tried to get better and better at what they knew how to do best, essentially moved toward—were biased toward—instinctive behavior, got trapped by instinct, and eventually went adaptively dormant. But sometimes, firms can reverse the sequence (presumably much more than can animals); like IBM, firms can bring back learning by bringing in a new CEO (e.g. Gerstner; or: Marchionne at Chrysler; Jobs coming back to Apple; Schultz returning as CEO of Starbucks); or learning from a newly joined merger partner, starting new research projects, learning more from customers (Escoffier and McKelvey 2013, 2015, 2018) and especially prospective new customers. In principle, firms should be able to shift bias from instinct to learning much more, much faster, more effectively, and more appropriately than can animals like insects, tadpoles, birds, reptiles, and even apes. Since the Baldwin Principle can dominate human behavior because of big brains, long life, learning, and linguistic communication, surely the time has come for the Baldwin Principle to be brought into evolutionary theorizing in economics, organization science, and management, to better explain change over time. Truth be told, however, the Baldwin Principle should be dominating management and socioeconomic evolutionary theorizing. After all,

72  Bill McKelvey and Cera Oh

firms can live for decades, they can have hundreds of human brains at all levels of their hierarchies, employees can form “strong-tie” networks that foster trust and efficiency and/or “weak ties” that foster innovation and entrepreneurship (Granovetter 1973; McKelvey et al. 2013); and/or employees can leave or be fired and be replaced with more competent, more innovative, differently motivated employees who, needless to say, have big brains as well as human learning and communication abilities. In shifting from biospecies to firms, six dramatic differences become apparent: 1. Life span: The life spans of firms can range from months to centuries. Some startups come and go very quickly. Many last a year or so. Others survive some number of decades, with a few lasting a century, or more. Religious organizations can last for centuries; the Holy Roman Empire (now the Roman Catholic Church) has survived some 2000 years. Many government organizations have survived many centuries—monarchies, parliaments, armies, and all sorts of other organizations and agencies. Institutions (defined variously by Hodgson 2000, 2006b) of various kinds can also survive one or more centuries; 2. Multiple brains: Biospecies have one brain, from tiny to human, with vastly different capabilities. Organizations (and institutions) have one-tomany large brains (some even well educated, well trained, and intellectually brilliant) at all levels of the hierarchy (if there is one); 3. Brain change: Consequently, organizations have brains instead of genes dominating the hierarchy below the top (i.e. one brain in the head of an animal or the CEO of a firm).16 While genes slowly lose their capability over a lifetime or become cancerous, the many organizational brains can change more dramatically for good or bad—by actually changing or by people coming and going. Employees can be focused, changed, and motivated by superiors (Uhl-Bien et al. 2007; McKelvey 2010) or de-motivated and made “passive dependent” such that they just sit and wait till the boss tells them what to do (Argyris 1957). Or superiors can be changed and motivated by their lower-level employees; 4. Bias: While animals can’t stop the transition of phenotypic plasticity into less plastic instinctive genetic structure, firms can. Not that they all do; as noted above, many firms do everything they can to move toward the equivalent of instinct (Miller 1990). Many other firms, however, show a pattern of continuous innovation and novelty. Porter (1996) recognizes these opposing biases with his “efficiency frontier”: a firm may locate at the low cost-efficiency end of the curve or oppositely at the highproduct-quality and novelty end—instinct dominates at the low-cost end; plasticity dominates at the novelty end; 5. Shift: There is much evidence that many firms have been able to shift their bias from instinct to new learning, dramatic change, and recovery from

The Baldwin Effect  73

near death—besides IBM being rescued by Gerstner, another is GE when Welch was CEO (McKelvey 2010); Apple, Google, Amazon, Samsung, Toyota, Starbucks, Alcoa, Ford, Johnson & Johnson, and McDonalds are recent examples. Francisco (2012) argues that good strategy is to avoid being stuck at any given point on Porter’s efficiency curve; in a changing competitive niche, firms must be able to change their bias; they need to learn and change as appropriate. McDonalds shows adaptive plasticity here and there when it introduces a new kind of hamburger (or more recently competing for the coffee and breakfast trade); but it also shows tremendous instinctive behavior in its ability to produce most of its products with the same look and taste worldwide with relatively short-term employees; 6. Digital Age: The newest development affecting firms, but not nonhuman species, is the creation of the Internet and the emergence of digital business (Tapscott 1995, 2015; Corallo et al. 2007; Passerini and Tarabishy 2012; El Sawy and Pereira 2013; McQuivey 2013; Brynjolfsson and Mcafee 2014; Westerman et al. 2014; Greengard 2015; Tafti et al. 2015; Coupey 2016; Wokurka et al. 2017; McKelvey 2018a,b). New ideas can emerge and go viral on the Internet very quickly, even overnight. As more and more marketing and selling occur via the Internet and via digital devices like smart phones and computers, the effects of the Digital Age are causing major changes in many firms, such as going out of business (Borders Group—a bookstore chain) or moving their business online (e.g., Frederick’s of Hollywood), etc.

Discussion Adding plasticity to evolutionary socioeconomic and organization sciences

Phenotypic plasticity findings and effects are increasingly abundant in biology (some are listed in Appendix 1). What is rather amazing is that biologists have found that even very small life forms—insects, birds, and fish, i.e. species with minuscule brains—can exhibit phenotypic plasticity in very short lives, and then its transfer into their offsprings’ genetic structure (many of these experimental tests are listed in Appendix 2). Whereas genes and organs with no brains comprise the hierarchy of components in all biological life forms, hierarchical layers of people with the best brains of all species populate socioeconomic institutions and organizations. Given that plasticity effects appear in animals with tiny brains and short life spans, we can’t avoid accepting the idea that plasticity is a key element in socioeconomic institutions and organizations that evolutionists need to account to. Therefore, evolutionary economists and sociological and organizational evolutionists, cannot and should not blithely presume that Darwinian theory universally explains how socioeconomic institutions and organizations change: it is mostly inappropriate! In reality, Darwinism is an on-and-off phenomenon in social systems.

74  Bill McKelvey and Cera Oh

Biologists deal with non-human species, i.e. populations of fish, birds, animals, and snakes, etc., phenotypes, cells, and genes. Economists, managers, and organization/management researchers deal with organizational populations (industries): organizations, people, and routines/competencies (Singh 1990; Baum and Singh 1994; Carroll and Hannan 1995; Nelson 1995; Aldrich 1999; Baum and McKelvey 1999; Hodgson 2004, 2006, 2010). Following Durham (1991); Gabora (1997); and Dennett (2006); Shepard and McKelvey (2009) add memes. We highlight people because they have the best brains, have superior language abilities, live decades longer than fleas, ants, tadpoles, frogs, and small birds, etc. Peoples’ phenotypic plasticity is vastly greater than that of all the non-human species that biologists have explicitly researched (though we list one study that does focus on children). Adding the impact of peoples’ phenotypic plasticity to evolutionary economics and organization theory much more dramatically reorients the outcome and contribution of evolutionary theorizing in these disciplines than it does in biology. In a recent issue of the Financial Times, Hill (2013: 10) has a commentary titled “The best strategy must build on the past.” Yet he ends with: Any institution that clings too firmly to past glories, without reflecting on their relevance to the present, will itself end up as history. As reviewed earlier, Miller’s (1990) book offers substantial evidence in support of this quote. In contrast, however, Chesbrough’s (1999) research shows that some firms (in Japan) can adapt to and thrive in the context of dramatic imposing changes whereas others cannot—depending on corporate culture. Since the beginning of life on Earth some 3.5 billion years ago (Schidlowski 1988), millions of species have gone extinct but many others have existed millions of years with evolutionary changes in genetic structure occurring over time. Until the works of Baldwin, Morgan, and Osborn in 1896, and then the later contributions of Waddington (1941 to 1961) and Simpson (1953), Darwinism dominated biological thinking about evolution—it still does. Darwinism is about change via death-and-replacement; however, parents die; offspring appear with different genetic structures, some of which offer survival advantages. It is unfortunate that institutional and organization scientists publishing after the beginning of plasticity research—circa 1985 (e.g. Singh 1990; Baum and Singh 1994; Carroll and Hannan 1995; Nelson 1995; Baum and McKelvey 1999; McKelvey 1982; Hodgson 2004, 2006a,b, 2010; Aldrich, et al. 2008; Hodgson and Knudsen 2006, 2008, 2010; Dosi and Malerba 2000; Child 2012; Hodgson 2013; Abatecola 2014; Abatecola et al. 2016a,b; Zollo et al, 2016)— did not pick up on the importance and relevance of the Baldwin Principle as its emphasis in biology strengthened after 1985 (see studies listed at the top of Appendix 1). Focus on the Baldwin Principle is also missing in the Journal of Evolutionary Economics, the Journal of Bioeconomics, and mostly also in the journal Evolutionary Psychology. While there is some scientific credence in producing

The Baldwin Effect  75

historical records of the death, replacement, and evolution of biological species and human institutions and organizations in the past, so as to keep building the record of what doesn’t thrive and survive over time, the fact is that Darwinian evolutionary theory focuses on failure followed by creation—organisms and species die off and are replaced with better-adapted ones. At the time of Lamarck and Darwin, the origin of species—creation—was mostly attributed to some god. Darwin is famous for offering an alternative explanation. In the past 150+ years evolutionary science has shown the truth of Darwinian evolution as a cause and explanation of adapted species after the failure of prior forms. Economists and those of us in management schools, however, are supposed to offer more than just documenting the historical record of death-and-replacement of organizations or institutions. This is why it is so important for the Baldwin Principle to enter into the socioeconomic, public policy, organization and management sciences and research, and teaching about management. We need to transform the study of change over time from a recording of dying socioeconomic institutions and organizations to an emphasis on how phenotypic plasticity can help institutions, economies, and firms change so as to prolong their lives and effectiveness in a changing world. Researching and teaching methods for improving economies’ and firms’ effectiveness—which requires organizational (phenotypic) plasticity in institutions, governments, and firms—is a dominant objective in public policy and management schools and, of course, for consulting firms. It surely is the mission of some government officials, CEOs, and other people in economic organizations and firms, though as Miller (1990) points out, many CEOs focus on leading/forcing their firms toward getting better at what they are already doing—which can lead to Darwinian death-and-replacement—rather than creating the kind of organizational plasticity that supports effective adaptation to changing niches and their changing competitive ecosystems. Though various management authors (e.g. Burgelman 1991; Tushman and O’Reilly 1996; Teece et al. 1997; Zollo and Winter 2002; Feldman and Pentland 2003; Richerson and Boyd 2005; Witt 2005; Loasby 2007; Breslin 2008, 2012; O’Reilly and Tushman 2008; Stoelhorst 2008; Pentland et al. 2010) have recognized the difference between pure Darwinian evolution via deathand-replacement vs. change via human learning and change, none of them explicitly refer to the Baldwin Principle’s impact on biological evolutionary theory. However, Breslin (2014) does cite the 2013 working-paper version of this chapter and uses an agent-based computational model to show how organizational plasticity and learning can enhance an organization’s chance of winning the co-evolutionary race (Kauffman 1993) toward survival. Change via plasticity and resilience and Darwinian death-and-replacement

Starting with Holling (1973) and Rollo and Shibata (1991): various biologists have found that phenotypic plasticity can improve the resilience of both an

76  Bill McKelvey and Cera Oh

organism and its species in changing niches (e.g. Walker et al. 2004; Waples et al. 2009; Nicotra et al. 2010; Reed et al. 2011; Canale and Henry 2010; Canale et al. 2011; Macri et al. 2011; Rymer et al. 2013). Plasticity and resilience dynamics have also been researched in the socioecological-geographical context (e.g. Berkes and Folke 1998; Peterson et al. 1998; Adger 2000; Folke et al. 2003; Rose 2004; Tompkins and Adger 2004, 2005; Walker et al. 2004; Folke et al. 2005; Folke 2006; Paton and Johnston 2006; Zhou et al. 2010; Reed et al. 2011; Park et al. 2014; Witham and Sayer 2015; Shen et al. 2016). Some organizational and institutional plasticities have also been connected to creating resilience in socioeconomic, business, and management contexts (e.g. Tierney 1997; Horne and Orr 1998; Tinch 1998; Weick and Sutcliffe 2001; Coutu 2002; Hamel and Välikangas 2003; Star et al. 2003; Olsson et al. 2004; Norman et al. 2005; Sundström and Hollnagel 2006; Korhonen and Seager 2008; Gulati 2010; McKelvey and Andriani 2010; McKelvey and Yalamova 2011; Sundström and Hollnagel 2011; Boin and Van Eeten 2013; Chewning et al. 2013; Limnios et al. 2014; Olsson et al. 2015; Meneghel et al. 2016; Ortiz-de-Mandojana and Bansal 2016; Linnenluecke 2017). Biologists generally don’t have much reason to care about the extinction of specific individual insect, bird, reptile, and animal species over the millions of years, whether they have lifetime plasticity and resilience or not.17 Darwinian evolution is the most valid long-term explanatory theory. Even for human socioeconomic and political systems—and their typically less than 100-year life spans—Darwinian evolutionary theory mostly applies. Mostly but not totally: yes, most socioeconomic institutions and firms die off and are replaced with new-improved alternatives within decades—but exceptions are the Holy Roman Empire/Roman Catholic Church and the English Monarchy: they have each survived longer than 1000 years. There is no doubt that how the Empire was managed during the Dark Ages (Manchester 1992) was quite different from current Vatican behavior. The English Monarchy has also shown much change and resilience from King Alfred the Great to Queen Elizabeth II. Exceptions noted, however—for most socioeconomic entities, death-and-replacement dominates plasticity and resilience within just a century let alone over a 1000-year time span. Darwin wins. As we shift perspective from insect, fish, bird, reptile, and animal organisms and species to currently existing social, socioeconomic, political, government, as well as business institutions and organizations (all with people involved) the Baldwin Principle can and should become more relevant than Darwinism, given that people have better brains, better learning and communication skills, and often have greater prolonged personal interests in keeping the systems they are involved with from dying off. This because the Baldwin Principle: (1) is a more valid theoretical explanation of change and resilience in people-populated entities surviving long enough to face multiple niche and environmental changes; and (2) can take advantage of peoples’ everyday thinking about how to actually improve real-time institutional and organizational plasticity and change so as to foster resilience and survival in the face of threatening niche competitors

The Baldwin Effect  77

and broader environmental changes—presuming that they are not forced into passive dependence.

Conclusion What is both interesting and scary in human institutions and organizations is that, depending on the people involved, either Baldwinian or Darwinian effects may apply—they may be on or off as appropriate, or as necessary, or quite inappropriately. As we described earlier, Argyris’s (1957) and Miller’s (1990) research shows that managers and employees in firms can, indeed—knowingly or unknowingly—create conditions leading to Darwinian evolution—deathand-replacement, or long periods of adaptive dormancy. Chesbrough’s (1999) research, however, shows that Darwinism explains evolution in the American HDD (hard disk drive) industry, whereas Baldwinism explains the change over time of HDD firms in Japan. It appears that the corporate culture of IBM vs. the startups, or in Japan vs. the US, can influence whether Darwin’s death-and-replacement or Baldwin’s phenotypic plasticity applies in industries and firms. As Shepard and McKelvey (2009) show, plasticity in the form of memetic changes is not necessarily produced in organizations even though many are trying to be innovative. The existence of Baldwinian vs. Darwinian evolutionary change is also not just a function of taking long views (e.g. 100+ years) vs. short views. Human brains and behaviors within institutions and organizations can have a prevailing influence on whether Darwinian or Baldwinian effects apply— people in institutions and organizations can create either the Baldwinian or Darwinian effect. Either effect may last for a longer or shorter time period. Also, either effect may be the result of top-down “leadership” influence (e.g. Norman et al. 2005; Hazy et al. 2007; Uhl-Bien et al. 2007; Uhl-Bien and Marion 2009; McKelvey 2010; Nohria and Khurana 2010; Marion and UhlBien 2011; Hurst 2012; Kellerman 2012; Colbert et al. 2014; Mihalache et al. 2014; Nahavandi 2016; Bolman and Deal 2017) or the bottom-up emergent behavior featured in complexity science (e.g. Kauffman 1993; Holland 1995; Pascale et al. 2000; Lissack 2002; McKelvey 1999, 2004, 2010, 2013a,b,c; Maguire et al. 2006; Allen et al. 2011; Nooteboom and Termeer 2013; Baltaci and Baci 2017; Mitleton-Kelly et al. 2018). Given human capacities for plasticity, what is both amazing, sad, and scary is that for the most part Darwinian death-and-replacement appears to dominate Baldwinian plasticity in academic evolutionists’ thinking. The brutal fact is that Darwinian evolution describes both subtle success and dramatic failure: as a niche slowly changes, parents produce offspring that have enough genetic variance to survive in conditions of slow change. Phenotypic plasticity allows parents and their offspring to survive more drastic environmental changes. But many species disappear because they can’t cope with changes beyond the limits of their genetic variance: dodo birds, dinosaurs, passenger pigeons, giant rodents, saber-toothed tigers, mammoths, etc. Many

78  Bill McKelvey and Cera Oh

institutions and industries have gone the way of the dodo bird: knights in armor, steam engines, windmills for grinding, coal furnaces for homes, gas lighting, sailing ships, typewriters, slide rulers, etc. No amount of plasticity or resilience could have kept these industries alive. Darwinian death-andreplacement clearly applies. But of course humans are also capable of creating far more drastic industrial changes—like guns, steamships, light bulbs, assembly lines and cars, jet engines, rockets, laptop computers, the Web, iPads, iPhones, cloud-computing—which are beyond most institutions’ or firms’ adaptive capabilities—just consider what Research in Motion (Blackberry) and Nokia are struggling with these days or bricks-and-mortar stores coping with the growth of Web-based shopping and more broadly, digital-business ecosystems (Tapscott 1995/2015; Slywotzky and Morrison 2001; Day et al. 2003; Coupey 2016; Brousseau and Penard 2007; Corallo et al. 2007; Briscoe et al. 2007; Krishnan et al. 2007; Brynjolfsson and McAfee 2011, 2014; McQuivey 2013; Rong and Shi 2014; Schmidt and Cohen 2013; Bones and Hammersley 2015; Kreutzer and Land 2015; Tafti et al. 2015; Wang 2015; Choi 2017; Kshetri 2017; Ponce-Jara et al. 2017; Remane et al. 2017; Schallmo et al. 2017; Scott et al. 2017; Seo 2017; Swaminathan and Meffert 2017; Wokurka et al., 2017; Qiu et al. 2018; Schallmo and Williams 2018; Weill and Woerner 2018). Given human brains, long lives, and learning and communication abilities, however, why do we see so much of Miller’s Icarus Paradox in firms, socioeconomic institutions, and governments? Miller (1993) (now over 25 years ago) offers explanations about why Darwinism wins in firms full of people fully capable of using Baldwinian plasticity to negate death-and-replacement. He presents ten propositions summarizing a variety of theories about why human intelligence and plasticity capabilities mostly give way to Darwinian evolutionary death-and-replacement. He theorizes that “In successful organizations”: 1. “Primary goals, values, and strategies will be pursued more aggressively whereas secondary ones will be increasingly neglected” (p. 118); 2. “All varieties of simplicity will increase as a function of the duration and degree of success in achieving the goals of the dominant coalition” (p. 118); 3. “Managerial world views will become more homogeneous and will focus on ever fewer objectives, issues, and cues from the environment” (p. 122); 4. “Values will become more homogeneous, reducing subunit differentiation; a single department or elite will become more dominant; and the skill set of the organization will narrow. These changes will contribute to the formation of monolithic cultures and strategies” (p. 123–124); 5. “Systems, structures, and processes will become tailored to a narrower set of tasks; for example, routines will become more specialized, information systems will be honed to monitor a smaller set of concerns, and the power distribution will become more skewed. Departments and executives that

The Baldwin Effect  79

6. 7. 8. 9.

are credited with past success will become more powerful and influential” (p. 129); “Simplicity in managerial world views, goals, culture, strategy, skills, and the power structure will be mutually reinforcing and, therefore, highly correlated” (p. 130); “Simplicity in managerial world views, goals, culture, strategy, and skills will promote the use of more inertial routines, systems, and processes. But the latter aspects will, in turn, reinforce simplicity in the former” (p. 130); “At first, increases in all varieties of simplicity will lead to an increase in organizational performance”; but, “Simplicity over long periods of time will eventually lead to lower organizational performance, especially in competitive and changing environments” (p. 131).

The foregoing descriptions are correct only for organizations in which their current strategy remains successful—usually because they become more efficient and keep winning in unchanging competitive ecosystems (environments). Given the above, some firms last a very long time, but for many others it is a prolonged path to death-and-replacement. Organizational-oriented evolutionary theorizing, teaching, and research needs to move beyond the simple recording of Darwinian death-andreplacement—whether from one individual to another, or from one organization to another, or from one industry or another, or from one institutional component or design to another. For better management, we need to refocus the application of Darwinian evolutionary theory to an approach that emphasizes the Baldwin Principle, plasticity, and resilience. We need to modernize socioeconomic evolutionary thinking so as to emphasize plasticity and resilience rather than only (there are very few exceptions) theorizing about Darwinian death-and-replacement. Miller’s tenth proposition captures some of the elements necessary for plasticity to survive: 10. “Simplicity will be less prevalent, even under conditions of success, where (a) new top managers, especially outsiders, have just risen to power; (b) a generalist strategy is being pursued; (c) cultural and structural heterogeneity and participativeness are cherished; (d) the environment is turbulent; and (e) there are few institutional constraints” (p. 132). Miller concludes that over time “most successful organizations become simpler, not more complex.” They “behave less like organisms [with plasticity] and more like machines so that surprise and randomness, the sources of much knowledge, are lost” (p. 134). The “Architecture of Simplicity” (the title of Miller’s 1993 article) needs to be replaced by “The Architecture of Complexity” if Baldwinian plasticity is to win out over Darwinian death-and-replacement— as many complexity scientists have already proposed (e.g. Holland 1995; McKelvey 1999, 2004, 2010, 2013a,b,c; White et al. 1997; Pascale et al. 2000;

80  Bill McKelvey and Cera Oh

Weick and Sutcliffe 2001; Dopazo and Perazzo 2002; Lissack 2002; Chu et al. 2003; Folke et al. 2003; Maguire et al. 2006; Allen et al. 2011; Zia et al. 2014; Crawford and Kreiser 2015; Giannoccaro 2015; Oyama et al. 2015; Ciemins et al. 2016). Our discussion and conclusion begin by emphasizing the apparent on-andoff nature of Darwinian vs. Baldwinian evolution. Ironically, socioeconomic and organizational evolutionary theorizing essentially focuses on the failure of managers, organizations, governments, and institutions to create the kinds of plasticity that would prevent Darwinian evolutionary dynamics. And, yes, since the foregoing do fail now and then, we do need Darwinian evolutionary theory to explain such outcomes. But, if you will pardon the analogy, wouldn’t it better to stop wars before the fact rather than explain them after the fact? Analogously, wouldn’t it be better for managers, organizations, governments, and institutions—and academic theorists and researchers—to recognize, develop, and apply the phenotypic-plasticity component of evolutionary theory to speed up innovation and enhance adaptation to changing competitive environments, rather than waiting to explain the death-and-replacement component? If the species is already extinct—or if the many current species approaching extinction could think and learn like humans—don’t you think they would want to enhance their phenotypic plasticity rather than wait for a Darwinian explanation? Of course, hardcore evolutionists can just wait till the time has arrived for applying Darwinian evolutionary theorizing to dying firms and possible followon startups. For all the rest of us, however, it is better to explicitly emphasize the Baldwin Principle so as to diminish the relevance of Darwinian evolutionary thinking—except when organizations, governments, and socioeconomic institutions are negated by novelties such as castles, swords, guns, steam engines, steamships, light bulbs, piston engines, airplanes, assembly lines and cars, turbines, jet engines, rockets, X-ray machines, computers, microchips, laptop computers, the Web, iPads, iPhones, and cloud-computing and digital-business ecosystems, among many other industry-changing new inventions. Given that biological research about phenotypic plasticity over the past 30+ years (see Appendices 1 and 2) now fully supports the vintage 1896 idea of Baldwin, Morgan, and Osborn, and given that humans have vastly better brains, communication, and learning abilities, the time has come for organizational and socioeconomic theorists and researchers to redefine and redevelop the basis of their evolutionary thinking. Given recent biological research, we could end by saying socioeconomic and organizational scholars should modernize their views of evolutionary theory. But since the Baldwin Effect is over 100 years old, “modernize” doesn’t seem like quite the right term to use. Sadly and ironically, most evolutionary thinking by economists and organization and management theorists are studies of economic and organizational failures (deaths followed by replacements). Focusing on phenotypic plasticity puts the focus on studying and enhancing how economies and organizations can become more change-oriented, resilient, agile (read Chapter 4), and innovative.

The Baldwin Effect  81

Evolutionary theory is about the past, and dead or dying, firms. The science of Baldwinian plasticity thinking is about how social systems can most quickly and effectively change, adapt, and cope with the pressures of rapidly changing digitalbusiness ecosystems, within which most organizations and economies are now embedded (Brown and Agnew 1982; Tan et al. 2009; Prange 2016; McKelvey 2018a,b). Evolution is about the past; plasticity is about how firms need to and/ or can change so as cope with changing ecosystems and survive into the future. Appendix 1: Biological articles pertaining to phenotypic plasticity Some empirical studies before Robinson & Dukas (1999) (1965) Bradshaw, A.D. “Evolutionary significance of phenotypic plasticity in plants,” Advances in Genetics, 13: 115–155. (1979) Lande, R. “Quantitative genetic analysis of multivariate evolution applied to brain: body size allometry,” Evolution, 33: 401–416. (1985) Via, S. and Lande, R. “Genotype–environment interaction and the evolution of phenotypic plasticity,” Evolution, 39: 505–522. (1986) West–Eberhard, M.J. “Alternative adaptations, speciation, and phylogeny,” Proc. Nat. Academy of Sciences USA, 83: 1388–1392. (1987) Bull, J.J. “Evolution of phenotypic variance,” Evolution, 41: 303–315. (1987) Hinton, G.E. and Nowlan, S.J. “How learning can guide evolution,” Comp. Systems, 1: 495–502. (1987) Matsuda, R. Animal evolution in changing environments, New York: Wiley. (1987) Maynard-Smith, J. “When learning guides evolution,” Nature, 329: 761–762. (1989) Stearns, S.C. “The evolutionary significance of phenotypic plasticity,” BioScience, 39: 436–445. (1989) Wcislo, W.T. “Behavioral environments and evolutionary change,” Annual Review of Ecological Systems, 20: 137–169. (1989) West-Eberhard, M.J. “Phenotypic plasticity and the origins of diversity,” Annual Review of Ecological Systems, 20: 249–278. (1990) Rollo, C.D. and Shibata, D.M. “Resilience, robustness, and plasticity in a terrestrial slug…,” Canadian J. of Zoology, 69: 978–87. (1991) Brakefield, P.M. and Reitsma, N. 1991. “Phenotypic plasticity, seasonal climate and the population biology of Bicyclus butterflies (Satyridae) in Malawi,” Ecol. Entomology, 16: 291–303. (1993) Gavrilets, S. and Scheiner, S.M. “The genetics of phenotypic plasticity,” J. Evol. Biology, 6: 31–48. (1993) Scheiner, S.M. “Genetics and evolution of phenotypic plasticity,” Annual Rev. Ecol. Systematics, 24: 35–68. (1993) Scheiner, S.M. “Plasticity as a selectable trait: A reply to Via,” American Naturalist, 142: 371–373. (1994) Emery, R. et al. “Phenotypic plasticity of stem elongation in 2 ecotypes of Stellaria longipes…,” Plant Cell Environ., 17: 691–700. (1994) Papaj, D.R. “Optimizing learning and its effect on evolutionary change in behavior,” in L.A. Real (ed): Behavioral Mechanisms in Evolutionary Ecology,” Chicago, IL: U. Chicago Press, 133–153. (1995) Doughty, P. “Testing the ecological correlates of phenotypic plasticity within a phylogenetic framework,” Oecologia, 16: 519–524.

82  Bill McKelvey and Cera Oh (1995) de Jong, G. “Phenotypic plasticity as a product of selection in a variable environment,” American Naturalist, 145: 493–512. (1995) Gotthard, K. and Nylin, S. “Adaptive plasticity and plasticity as an adaptation…,” Oikos, 74: 3–17. (1995) Jablonka, E. et al. “The adaptive advantage of phenotypic memory in changing environments,” Philos Trans Roy Soc B, 350: 133–141. (1995) Schmitt, J. et al. “A test of the adaptive plasticity hypothesis using transgenic and mutant plants disabled in phytochrome–mediated elongation responses to neighbors,” American Naturalist, 146: 937–953. (1995) Sultan, S.E. “Phenotypic plasticity and plant adaptation,” Acta Botanica Neerlandica, 44: 363–383. (1995) Via, S. et al. “Adaptive phenotypic plasticity: consensus and controversy,” Trends in Ecology & Evolution, 10: 212–217. (1996) Robinson, B.W. and Wilson, D.S. “Genetic variation and phenotypic plasticity in a trophically polymorphic population of pumpkinseed sunfish (Lepomis gibbosus),” Evol. Ecology, 10: 631–652. (1996) Potvin, C. and Tousignant, D. “Evolutionary consequences of simulated global change: Genetic adaptation or adaptive phenotypic plasticity,” Oecologia, 108: 683–693. (1996) Turney, P. et al. “Evolution, learning, and instinct: 100 years of the Baldwin effect,” Evolutionary Computation, 4: iv–viii. (1997) van Tienderen, P.H. “Generalists, specialists, and the evolution of phenotypic plasticity in sympatric populations of distinct species,” Evolution, 51: 1372–1380. (1998) DeWitt, T.J. et al. “Costs and limits of phenotypic plasticity,” Trends in Eco. & Evolution, 13: 77–81. (1998) Dukas, R. “Evolutionary ecology of learning,” in R. Dukas (ed) Cognitive Ecology: the evolutionary ecology of information processing and decision making, Chicago, IL: University of Chicago Press, 129–174. (1998) Stearns, S.C. “The evolutionary significance of phenotypic plasticity,” Bioscience, 39: 436–445. Articles published after the chapters in Weber and Depew (2003) were written (2003) Federici, D. “Culture and the Baldwin effect,” in W. Banzhaf, T. Christaller, P. Dittrich, J.T. Kim and J. Ziegler (eds): Proceedings of European Conference on Artificial Life, Berlin: Springer-Verlag, 309–318. (2003) Lee, C.E. et al. “Evolution of physiological tolerance and performance during freshwater invasions,” Integrative and Comparative Biology, 43: 439–449. (2003) Price, T.D. et al. “The role of phenotypic plasticity in driving genetic evolution,” Proceedings of the Royal Society of London, Series B., 270: 1433–1440. (2003) Reale, D. et al. “Genetic and plastic responses of a northern mammal to climate change,” Proc. Royal Soc. London B, 270: 591–596. (2003) Schmitt, J. et al. “The adaptive evolution of plasticity: phytochrome-mediated shade avoidance responses,” Integrative and Comparative Biology, 43: 459–469. (2004) Adams, C.E. and Huntingford, F.A. “Incipient speciation driven by phenotypic plasticity? Evidence from sympatric populations of Arctic charr,” Biological Journal of the Linnean Society, 81: 611–618. (2004) DeWitt, T.J. and Scheiner, S.M. Phenotypic plasticity: functional and conceptual approaches, Oxford, UK: Oxford Univ. Press. (2004) David, J.R. et al. “Evolution of reaction norms,” in T.J. DeWitt and S.M. Scheiner (eds) Phenotypic plasticity, New York: Oxford Univ. Press, 50–63.

The Baldwin Effect  83 (2004) Ernande, B. and Dieckmann, U. “The evolution of phenotypic plasticity in spatially structured environments: implications of intraspecific competition, plasticity costs and environmental characteristics,” Journal of Evolutionary Biology, 17: 613–628. (2004) Sarkar, S. “From the Reaktionsnorm to the evolution of adaptive plasticity: a historical sketch 1909–1999,” in T.J. DeWitt and S.M. Scheiner (eds): Phenotypic Plasticity: Functional and Conceptual Approaches, Oxford, UK: Oxford University Press, 10–30. (2004) Schlichting, C.D. “The role of phenotypic plasticity in diversification,” in T.J. DeWitt and S.M. Scheiner (eds) Phenotypic Plasticity, Oxford.UK: Oxford University Press, 191–200. (2004) Stauffer, J.R. and van Snick Gray, E. “Phenotypic plasticity: its role in trophic radiation and explosive speciation in cichlids…,” Animal Biology, 54: 137–158. (2004) Windig, J.J. et al. “Genetics and mechanics of plasticity,” in T.J. DeWitt and S.M. Scheiner, (eds) Phenotypic Plasticity, Oxford, UK: Oxford University Press, 31–49. (2004) Yeh, P.L. and Price, T.D. “Adaptive phenotypic plasticity and the successful colonization of a novel environment,” American Naturalist, 164: 531–542. (2005) Badyaev, A.V. “Stress-induced variation in evolution: from behavioral plasticity to genetic assimilation,” Proc. Royal Society of London B Biological Sciences, 272: 877–886. (2005) de Jong, G. “Evolution of phenotypic plasticity…,” New Phytologist, 166: 101–118. (2005) Donohue, K. “Niche construction through phenological plasticity,” New Phytologist, 166: 83–92. (2005) Dybdahl, M.F. and Kane, S.L. “Adaptation vs. phenotypic plasticity in the success of a clonal invader,” Ecology, 86: 1592–1601. (2005) Gianoli, E. and González-Teuber, M. “Environmental heterogeneity and population differentiation in plasticity to drought in Convolvulus chilensis (Convolvulaceae),” Evolutionary Ecology, 19: 603–613. (2005) Grether, G.F. “Environmental change, phenotypic plasticity, and genetic compensation,” American Naturalist, 166: E115–E123. (2005) Griffith, T. and Sultan, S, ‘Shade tolerance plasticity in response to neutral vs. green shade cues in Polygonum species of contrasting ecological breadth,” New Phytologist, 166: 141–147. (2005) Kolbe, J.J. and Losos, J.B. “Hind-limb length plasticity in Anolis carolinensis,” J. Herpetol, 39: 674–678. (2005) Nussey, D.H. et al. “Selection on heritable phenotypic plasticity in a wild bird population,” Science, 310: 304–306. (2005) Sultan, S.E. and Stearns, S.C. “Environmentally contingent variation: phenotypic plasticity and norms of reaction,” in B. Hall and B. Hallgrimsson (eds) Variation: a central concept in biology, Burlington, MA: Elsevier Academic Press, 303–332. (2005) van Kleunen, M. and Fisher, M. “Constraints on the evolution of adaptive phenotypic plasticity in plants,” New Phytologist, 165: 49–60. (2005) West-Eberhard, M.J. “Phenotypic accommodation: adaptive innovation due to developmental plasticity,” J. Experimental Zoology Part B: Molecular and Developmental Evolution, 304: 610–618. (2006) Amarillo-Suarez, A.R. and Fox, C.W. “Population differences in host use by a seed beetle: local adaptation, phenotypic plasticity and maternal effects,” Oecologia, 150: 247–258. (2006) Borenstein, E. et al. “The effect of phenotypic plasticity on evolution in multipeaked fitness landscapes,” J. Evo. Bio., 19: 1555–70.

84  Bill McKelvey and Cera Oh (2006) Danielson-François, A.M. et al. “Genotype x environment interaction for make attractiveness in an acoustic moth: evidence for plasticity and canalization,” J. Evolutionary Biology, 19: 532–542. (2006) Garland, T., Jr. and Kelly, S.A. “Phenotypic plasticity and experimental evolution,” J. Experimental Biology, 80: 287–316. (2006) Gomez-Mestre, I. and Buchholz, D.R. “Developmental plasticity mirrors differences among taxa in spadefoot toads linking plasticity and diversity,” Proc. Nat. Acad. Sciences, 104: 19021–19026. (2006) Kelly, S.A. et al. “Experimental evolution and phenotypic plasticity of hindlimb bones…,” J. Morphology, 267: 360–374. (2006) Longa, V.M. “A misconception about the Baldwin effect: implications for language evolution,” Folia Linguistica, 4: 305–318. (2006) Parsons, K.J. and Robinson, B.W. “Replicated evolution of integrated plastic responses during early adaptive divergence,” Evolution, 60: 801–813. (2006) Pigliucci, M. et al. “Phenotypic plasticity and evolution by genetic assimilation,” J. Exp. Biology, 209: 2362–2367. (2006) Price, T.D. “Phenotypic plasticity, sexual selection and the evolution of colour patterns,” J. Exp. Biology, 209: 2368–2376. (2006) Richards, C.L. et al. “Jack of all trades, master of some? On the role of phenotypic plasticity…,” Ecol. Lett., 9: 981–993. (2006) Richter-Boix, A. et al. “A comparative analysis of the adaptive developmental plasticity hypothesis in six Mediterranean anuran species along a pond permanency gradient,” Evolutionary Ecology Research, 8: 1139–1154. (2007) Buckley, C.R. et al. “Testing the persistence of phenotypic plasticity after incubation in the western fence lizard, Sceloporus occidentalis,” Evolutionary Ecol. Research, 9: 169–183. (2007) Aubret, F. et al. “The role of adaptive plasticity in a major evolutionary transition,” Functional Ecology, 21: 1154–1161. (2007) Aubret, F. et al. “Evolutionary biology: Adaptive developmental plasticity in snakes,” Nature, 431: 261–262. (2007) Chun, Y.J. et al. “Phenotypic plasticity of native vs. invasive purple loosestrife…,” Ecology, 88: 1499–1512. (2007) Crispo, E. “The Baldwin effect and genetic assimilation: Revisiting two mechanisms of evolutionary change meditated by phenotypic plasticity,” Evolution, 61: 2469–2479, (2007) Geng, U-P. et al. “Phenotypic plasticity rather than locally adapted ecotypes allows the invasive alligator weed to colonize a wide range of habitats,” Biological Invasions, 9: 245–256. (2007) Ghalambor, C.K. et al. “Adaptive versus non-adaptive phenotypic plasticity and the potential for contemporary adaptation in new environments,” Functional Ecology, 21: 394–407. (2007) Gutteling, E.W. et al. “Mapping phenotypic plasticity and genotype-environment interactions affecting life-history traits in Caenorhabditis elegans,” Heredity, 98: 28–37. (2007) Nussey, D.H. et al. “The evolutionary ecology of individual phenotypic plasticity in wild populations,” J. Evol. Bio., 20: 831–844. (2007) Paenke, I. et al. “Influence of plasticity and learning on evolution under directional selection,” American Naturalist, 170: E47–E58. (2007) Shaw, K. et al. “Ancestral plasticity and the evolutionary diversification of courtship behavior in threespine sticklebacks,” Animal Behavior, 73: 415–422.

The Baldwin Effect  85 (2008) Badyaev, A.V. “Maternal effects as generators of evolutionary change: A Reassessment,” in C.D. Schlichting and T.A. Mousseau, (eds): The year in evolutionary biology, New York: Wiley-Blackwell, 151–161. (2008) Callahan, H.S. et al. “Phenotypic plasticity, costs of phenotypes, and costs of plasticity,” Ann. NY Acad. Sciences, 1133: 1144–1166. (2008) Cano, L. et al. “Increased fitness and plasticity of an invasive species in its introduced range: a study using Senecio pterophorus,” J. Ecology, 96: 468–476. (2008) Crispo, E. “Modifying effects of phenotypic plasticity on interactions among natural selection, adaptation and gene flow,” J. Evolutionary Biology, 21: 1460–1469. (2008) Debat, V. et al. “Plasticity, casnalization, and developmental stability of the Drosophila wing,” Evolution, 63: 2864–2876. (2008) Hendry et al. “Human influence on rates of phenotypic change in wild animal populations,” Molecular Ecology, 17: 20–29. (2008) Lardies, M.A., Bozinovic, F. “Genetic variation for plasticity in physiological and life-history traits among populations of an invasive species, the terrestrial isopod Porcellio laevis,” Evolutionary Ecology Research, 10: 747–762. (2008) Lombaert, E. et al., ‘Phenotypic variation in invasive and biocontrol populations of the harlequin ladybird,” Harmonia axyridis. Biocontrol, 53: 89–102. (2008) Price, T.D. et al. “Phenotypic plasticity and the evolution of a socially selected trait following colonization of a novel environment,” American Naturalist, 172: S49–S62. (2008) Uller, T. “Developmental plasticity and the evolution of parental effects,” Trends in Ecology & Evolution, 23: 432–438. (2008) Wund, M. et al. “A test of the ‘Flexible stem’ model of evolution: ancestral plasticity, genetic accommodation, and morphological divergence in the threespine stickleback radiation,” American Naturalist, 172: 449–462. (2009) Aubin-Horth, N., Renn, S.C.P. “Genomic reaction norms: Using integrative biology to understand molecular mechanisms of phenotypic plasticity,” Mol. Ecology, 18: 3763–3780. (2009) Badyaev, A.V. “Evolutionary significance of phenotypic accommodation in novel environments: an empirical test of the Baldwin effect,” Phil. Trans. Royal Society B: Biological Sciences, 364: 1125–1141. (2009) Megalhaes, I.S. et al. “Divergent selection and phenotypic plasticity during incipient speciation in Lake Victoria cichlid fish,” J. Evolutionary Biology, 22: 260–274. (2009) Van Buskirk, J., Steiner, U.K. “The fitness costs of developmental canalization and plasticity,” J. Evol. Biology, 22: 852–860. (2010) Bomken, S. et al. “Understanding the cancer stem cell,” Br. J. Cancer, 103: 439–445. (2010) Matesanz, S. et al. “Global change and the evolution of phenotypic plasticity in plants,” Ann NY Acad of Sciences, 1206: 35–55. (2010) McCairns, R.J.S., Bernatchez, L. “Adaptive divergence between freshwater and marine sticklebacks,” Evolution, 64: 1029–1047. (2010) Moczek, A.P. “Phenotypic plasticity and diversity in insects,” Phil. Tran. Royal Society B: Biological Sciences, 365: 593–603. (2010) Otaki, J. et al. “Phenotypic plasticity in the range–margin population of the lycaenid butterfly…,” BMC Evolu. Biology, 10: 252 (2010) Pfennig, D.W. et al. “Phenotypic plasticity’s impacts on diversification and specification,” Trends in Ecol & Evol., 25: 459–467.

86  Bill McKelvey and Cera Oh (2010) Pigliucci, M. “Phenotypic plasticity,” in M. Pigliucci and G.B. Muller (eds) Evolution: the extended synthesis, Cambridge, MA: MIT Press, 355–378. (2010) Scoville, A. and Pfrender, M. “Phenotypic plasticity facilitates recurrent rapid adaptation to introduced predators,” Proc. Nat. Acad. Sciences, 107: 4260–4263. (2010) Snell-Rood, E.C. et al. “Toward a population genetic framework of developmental evolution: costs, limits, and consequences of phenotypic plasticity,” BioEssays, 32: 71–81. (2010) Specchia, V. et al. “Hsp90 prevents phenotypic variation by suppressing the mutagenic activity of transposons,” Nature 46: 662–625. (2010) Sultan, S.E. “Plant developmental responses to the environment,” Current Opinion in Plant Biology, 13: 96–101. (2010) Nicotra, A.B. et al. “Plant phenotypic plasticity in a changing climate,” Trends in Plant Science, 15: 684–692. (2011) Davidson, A.M. et al. “Do invasive species show higher phenotypic plasticity than native species…,” Ecology Letters, 14: 419–431. (2011) Moczek, A.P. et al. “The role of developmental plasticity in evolutionary innovation,” Proc. Royal Soc. B: Bio. Sci., 278: 2705–2713. (2011) Chevin, L.-M. and Lande, R. “Adaptation to marginal habitats by evolution of increased phenotypic plasticity,” J. Evol. Biology, 24: 1462–1476. (2011) Davidson, A.M. et al. “Do invasive species show higher phenotypic plasticity that native and, if, so, is it adaptive? A metal-analysis,” Ecology Letters, 14: 419–431. (2011) Franks, S.J. “Plasticity and evolution in drought avoidance and escape…,” New Phytologist, 190: 249–257. (2011) Espinosa-Soto, C. et al. “Phenotypic plasticity can facilitate adaptive evolution in gene regulatory circuits,” BMC Evolutionary Biology, 11: 1–14. (online) (2011) Fierst, J.L. “A history of phenotypic plasticity accelerates adaptation to a new environment,” J. Evol. Biology, 24: 1992–2001. (2011) Hunta, B.G. et al. “Relaxed selection is a precursor to the evolution of phenotypic plasticity,” Proc. Nat. Acad. of Sciences. Early Edition. www​.p​​nas​.o​​rg​/cg​​i​/ doi​​/10​.1​​073​/p​​nas​.1​​10482​​5108 Accessed January 25, 2018 (2011) Lind, M. et al. “Gene flow and selection on phenotypic plasticity in an island system of Rana temporaria,” Evolution, 65: 684–697. (2011) Lind, M. and Johansson, F. “Testing the role of phenotypic plasticity for local adaptation,” J. Evolutionary Biology, 24: 2696–2704. (2011) Scheel, C. and Weinberg, R.A. “Phenotypic plasticity and epithelialmesenchymal transitions in cancer and normal stem cells?” Int. J. Cancer, 129: 2310–2314. (2011) Tanaka, K. “Phenotypic plasticity of body size in an insular population of a snake,” Herpetologica, 67: 46–57. (2011) Thibert-Plante, X. and Hendry, P. “Consequences of phenotypic plasticity for ecological speciation,” J. Evol. Biology, 24: 326–342. (2012) Draghi, J.A. and Whitlock, M.C. “Phenotypic plasticity facilitates mutational variance, genetic variance, and evolvability along the major axis of environmental variation,” Evolution, 66: 2891–2902. (2012) Herman, J.J. et al. “Adaptive transgenerational plasticity in an annual plant,” Integrative & Comparative Biology 52, 77–88. (2012) Leichty, A.R. et al. “Relaxed genetic constraint is ancestral to the evolution of phenotypic plasticity,” Integrative and Comparative Biology, 52: 16–30.

The Baldwin Effect  87 (2012) Pichancourt, J.-B. and van Klinken, R.D. “Phenotypic plasticity influences the size, shape and dynamics of the geographic distribution of an invasive plant,” Plos ONE 7: e32323–1–12 [online]. (2012) Wund, T.D. et al. “Adaptive phenotypic plasticity and the successful colonization of a novel environment,” Amer. Naturalist, 164: 531–42. (2015) Hoang, K., Matzkin, L. M. and Bono, J. M. “Transcriptional variation associated with cactus host plant adaptation in Drosophila mettleri populations,” Molecular Ecology, 24: 5186–5199. Appendix 2: Some empirical studies of phenotypic plasticity in biology Species

References

Waddington, C.H. (1953) “Genetic assimilation of an acquired character,” Evolution, 7: 118–126. Grouse Etches, R.J. et al. (1979) “Plasma concentrations of prolactin during egg laying and incubation in the ruffed grouse (Bonasa umbellus),” Canadian Journal of Zoology, 57: 1624–1627. Birds Clark, A.B. and Wilson, D.S. (1981) “Avian breeding adaptations: Hatching asynchrony, brood reduction, and nest failure,” Q. Rev. Biol., 56: 253–277. Sparrows Mead, P.S. and Morton, M.L. (1985) “Hatching asynchrony in the mountain white-crowned sparrow (Zonotrichia leucophyris oriantha); A selected or incidental trait?’ The Auk, 102: 781–792. Birds Bortolotti, G.R. (1986) “Influence of sibling competition on nestling sex ratios of sexually dimorphic birds,” Am. Nat., 127: 495–507 Coal tits Alatalo, R.V. and Gustafsson, L. (1988) “Genetic component of morphological differentiation in coal tits under competitive release,” Evolution, 42: 200–203. Sparrow brood Dijkstra, C. (1990) “Adaptive seasonal variation in the sex ratio of kestrel broods,” Functional Ecology, 4: 143–147. Tadpoles Pfennig, D.W. (1990) “The adaptive significance of an environmentally-cued developmental switch in an anuran tadpole,” Oecologia, 85: 101–107. Insects Mousseau, T.A. and Dingle, H. (1991) “Maternal effects in insects: examples, constraints, and geographical variation,” in E.C. Dudley (ed) The unity of evolutionary biology, Portland, OR: Dioscorides Press, 745–761. Altricial birds Ricklefs, R.E. (1993) “Sibling competition, hatching asynchrony, incubation period, and lifespan in altricial birds,” Curr. Ornithol., 11: 199–276. Guppies Resnick, D. and Yang, A.P. (1993) “The influence of fluctuating resources on life history patterns of allocation and plasticity in female guppies,” Ecology, 74: 2011–20. Children, Tomasello, M. et al. (1993) “Imitative learning of actions on objects Chimps by children, chimpanzees, and enculturated chimpanzees,” Child Development, 64: 1688–1705. Flies

88  Bill McKelvey and Cera Oh Species

References

Stoleson, S.H. and Beissinger, S.R. (1995) “Hatching asynchrony and the onset of incubation in birds, revisited: When is the crucial period?” Curr. Ornithol., 12: 191–270. Domestic Tabibzadeh, C. et al. (1995) “Modulation of ovarian cytochrome turkeys P 450 17a-hydroxylase and cytochrome aromatase messenger ribonucleic acid by prolacin in the domestic turkey,” Biology of Reproduction, 52: 600–608. Snails DeWitt, T.J. (1998) “Costs and limits of phenotypic plasticity,” Trends in Ecology & Evolution, 13: 77–81. Beetles Moczek, A.P. (1998) “Horn polyphenism in the beetle Onthophagus taurus: Larval diet quality and plasticity in parental investment determine adult body size and male horn morphology,” Behavioral Ecology, 9: 636–641. Canaries Sockman, K.W. and Schwabl, H. (1999) “Daily estradiol and progesterone levels relative to laying and onset of incubation in canaries,” Gen. comp. Endocrinol., 114: 257–268. Finches Badyaev, A.V. and Hill, G.E. (2000) “The evolution of sexual dimorphism in the house finch. I. Population divergence in morphological covariance structure,” Evolution, 54: 1784–1794. Finches Badyaev, A.V. and Martin, T.E., (2000) “Individual variation in growth trajectories: phenotypic and genetic correlations in ontogeny of the house finch (Carpodacus mexicanus),” Journal of Evolutionary Biololgy, 13: 290–301. Apes Bering, J.M. et al. (2000) “Deferred imitation of object-related actions in human-reared juvenile chimpanzees and orangutans,” Developmental Psychobiology, 36: 218–232. Passerine Order Conway, C.J. and Martin, T.E. (2000) “Evolution of passerine incubation behavior: influence of food, temperature, and next predation,” Evolution, 54: 670–685. Birds Johnson, A.L. (2000) “Reproduction in the female,” in G.C. Whittow, (ed) Sturkie’s Avian Physiology, San Diego, CA: Academic Press, 569–596. Starlings Cordero, P.J. et al. (2001) “Seasonal variation in sex ratio and sexual egg dimorphism favouring daughters in first clutches of the spotless starling,” Journal of Evolutionary Biology, 14: 829–834. Sparrows Sockman, K.W. et al. (2001) “Regulation of yolk-androgen concentrations by plasma prolactin in the American Kestrel,” Horm. Behav., 40: 462–471. Scops owl Blanco, G. et al. (2002) “Sex-biased initial eggs favour sons in the slightly size-dimorphic scops owl (Otus scops),” Biological Journal of the Linnean Society, 76: 1–7. Birds Hébert, P.N. (2002) “Ecological factors affecting initiation of incubation behavior,” in D.C. Deeming (ed) Avian Incubation: Behaviour, Environment, and Evolution, Oxford, UK: Oxford University Press: 745–761. Mustard Queitsch, C. et al. (2002) “Hsp90 as a capacitor of phenotypic variation,” Nature, 417: 618–624. Birds

The Baldwin Effect  89 Species

References

Vleck, C.M. (2000) “Morphological control of incubation behavior,” in D.C. Deeming (ed): Avian incubation: Behaviour, environment, and evolution, Oxford, UK: Oxford University Press, 54–62. Sister species Andersson, M. et al. (2003) “Adaptive seasonal trend in brood sex ration: test in two sister species with contrasting breeding systems,” J. Evol. Biol., 16: 510–515. Egg yolks Eising, C.M. et al. (2003) “Maternal androgens in egg yolks: Relation with sex, incubation time and embryonic growth,” General and Comparative Endocrinology, 132: 241–247. Plants Steinger, T. et al. (2003) “Evolution in stressful environments II: Adaptive value and costs of plasticity in response to low light in Sinapis arvensis,” Journal of Evolutionary Biology, 16: 313–323. Arctic charr Adams, C.E. and Huntingford, F.A. (2004) “Incipient speciation driven by phenotypic plasticity? Evidence from sympatric populations of Arctic charr,” Biol. J. Linn Soc., 81: 611–618. Snakes Aubret, F. et al. (2004) “Evolutionary biology: adaptive developmental plasticity in snakes,” Nature, 431: 261–262. Red squirrels Berteaux, D. et al. (2004) “Keeping pace with fast climate change: can arctic life count on evolution?” Integrative and Comparative Biology, 44: 140–151. Water fleas Laforsch, C. and Tollrian, R. (2004) “Inducible defenses in multipredator environment: Cyclomorphosis in Daphnia Cucullata,” Ecology, 85: 2302–2311. Juncos Yeh, P.L. and Price, T.D. (2004) “Adaptive phenotypic plasticity and the successful colonization of a novel environment,” American Naturalist, 164: 531–542. People Brakefield, P.M. et al. (2005) “What are the effects of maternal and pre-adult environments on ageing in humans, and are there lessons from animal models?” Mechanisms of Ageing and Development, 126: 431–438. Fungi Cowen, L.D. and Lindquist, S. (2005) “Hsp90 potentiates the rapid evolution of new traits: drug resistance in diverse fungi,” Science, 309: 2185–2189. Fruit flies Dworkin, I. (2005) “A study of canalization and developmental stability in the sternopleural bristle system of Drosophila melanogaster,” Evolution, 59: 1500–1509. Lizards Kolbe, J.J. and Losos, J.B. (2005) “Hind-limb length plasticity in Anolis carolinensis,” J. Herpetology, 39: 674–678. Birds Nussey, D.H. et al. (2005) “Selection on heritable phenotypic plasticity in a wild bird population,” Science, 310: 304–306. Zebra finches Williams, T.D. et al. (2005) “Laying-sequence-specific variation in yolk oestrogen levels, and…oestrogen in female…finches (Taeniopygia guttata),” Proc. R. Soc. B, 272: 173–177. Gaudy Braendle, C. and Flatt, T. (2006) “A role for genetic accommodation Commodore in evolution?” Bioessays, 28: 868–467. Birds

90  Bill McKelvey and Cera Oh Species

References

Frogs

Gomez-Mestre, I. and Buchholz, D.R. (2006) “Developmental plasticity mirrors differences among taxa in spadefoot toads linking plasticity and diversity,” Proc. Nat. Acad. Sciences, 104: 19021–19026. Mommer, L. et al. (2006) “Photosynthetic consequences of phenotypic plasticity in response to submergence: Rumex palustris as a case study,” J. Experimental Botany, 57: 283–290. Parsons, K.J. and Robinson, B.W. (2006) “Replicated evolution of integrated plastic responses during early adaptive divergence,” Evolution, 60: 801–813. Sockman, K.W. et al. (2006) “Orchestration of avian reproductive effort: An integration of the ultimate and proximate bases for flexibility of clutch size, incubation behavior…,” Biol. Rev., 81: 629–666. Suzuki, Y. and Nijhout, H.F. (2006) “Evolution of a polyphenism by genetic accommodation,” Science, 311: 650–652. Buckley, C.R. et al. (2007) “Testing the persistence of phenotypic plasticity after incubation in the western fence lizard, Sceloporus occidentalis,” Evol. Eco. Research, 9: 169–183. Martin, T.E. et al. (2007) “Geographic variation in avian incubation periods and parental influences on embryonic temperature,” Evolution, 61: 2558–2569. Badyaev, A.V. and Oh, K.P. (2008) “Environmental induction and phenotypic retention of adaptive maternal effects,” BMC Evol. Biol., 8: 3. Charmantier, A. et al. (2008) “Adaptive phenotypic plasticity in response to climate change in a wild bird population,” Science, 320: 800–803. Ledón-Rettig, C.C. et al. (2008) “Ancestral variation and the potential for genetic accommodation in larval amphibians…,” Evol. Development, 10: 316–325. Lema, S. C. (2008) “The phenotypic plasticity of Death Valley’s pupfish,” American Scientist, 96: 28–36. http:​/​/www​​.amer​​icans​​cient​​ ist​.o​​rg​/is​​sues/​​num2/​​the​-p​​henot​​ypic-​​plast​​icity​​-of​-d​​eath-​​​valle​​ys​-pu​​ pfish​/3 Accessed Feb. 10, 2013. Rutkowskam, J. and Badyaev, A.V. (2008) “Meiotic drive and sex determination: molecular mechanisms of sex ratio adjustment in birds,” Phil. Trans. R. Soc., 363: 1675–1686. Duckworth, R.A. (2009) “Maternal effects and range expansion: a key factor in a dynamic process?” Phil. Trans. R. Soc., 364: 1075–1086. Hunt, B.G. et al. (2010) “Sociality is linked to rates of protein evolution…,” Mol. Bio. Evolution, 27: 497–500. Scoville, A. and Pfrender, M. (2010) “Phenotypic plasticity facilitates recurrent rapid adaptation to introduced predators,” PNAS, 107: 4260–4263.

White waterbuttercup Sunfish

Birds

Moths Lizards

Birds

Finches

Birds

Amphibians

Pup fish

Birds

Finches Insects Water fleas

The Baldwin Effect  91 Species

References

Connallon, T. and Clark, A.G. (2011) “Association between sexbiased gene expression and mutations with sex-specific phenotype consequences in Drosophila,” Genome Bio. Evolution, 3: 151–155. Toads Ledón-Rettig, C.C. and Pfennig, D.W. (2011) “Emerging model systems in eco-evo-devo: The environmentally responsive spadefoot toad,” Evol. Development, 13: 391–400. Beetles Snell-Rood, E.C., et al. (2011) “Developmental decoupling of alternative phenotypes: Insights from the transcriptomes of hornpolyphenic beetles,” Evolution, 65: 231–245. Tiger Fitzpatrick, B.M. (2012) “Underappreciated consequences of salamanders phenotypic plasticity for ecological speciation,” Int. J. of Ecology, Article ID 256017): 1–12. http:​/​/www​​.hind​​awi​.c​​om​/jo​​urnal​​s​/ije​​ co​/20​​​12​/25​​6017/​ Accessed Feb. 10, 2013. Polygonum Herman, J.J. et al. (2012) “Adaptive transgenerational plasticity in an plants annual plant…,” Integrative and Comparative Biology, 52: 77–88. Dung beetles Valena, S. and Moczek, A.P. (2012) “Epigenetic mechanisms underlying developmental plasticity in horned beetles,” Genetics Research International, (Article ID 576303): 1–14. http:​/​/www​​.hind​​ awi​.c​​om​/jo​​urnal​​s​/gri​​/201​2​​/5763​​03/ Accessed Feb. 10, 2013. Insects

Appendix 3: Agent rules from agent-based computational models Authors

Agent Rules

Each agent (gene) has 3 rules about whether to connect to 20 other agents (genes): (1) Connect; (2) Disconnect; (3) “specifies a connection containing a switch which can be opened or closed” (p. 496) Behera, Builds from the Hinton and Nowland model. Fitness values based on Nanjundiah random draws. Mating is random; at least one “crossover” required; (1995) one offspring results. The nature of each agent attribute is the consequence of a random draw. The model applies to any kind of species. They correct a weakness in Hinton and Nowland (1987). Mayley (1997) Uses Kauffman’s NK fitness landscape. Agents choose to copy another agent’s fitness if it is higher. These choices may be cost-free, lowcost, or high-cost. Turney (1997) Uses a genetic algorithm. Builds from the Hinton and Nowland model by introducing the concept of bias. A correct bias steers an agent toward a learning concept more quickly. A strong bias focuses an agent’s search process. There is a population average of bias correctness, bias strength, and fitness. These are given numerical values in the model. Hinton, Nowland (1987)

92  Bill McKelvey and Cera Oh Authors

Agent Rules

Uses a neural network to model human learning and evolution. Model consists of five learnable parameters. To simulate an evolutionary process, the model takes “a whole population of individual instantiations of the model and allows them to learn, procreate and die in a manner approximating these processes in real (living) systems” (p. 232). Learning is indicated by five adjustable “learning weights,” and learning rates; model starts with these set at zero. Model also includes birth, death, and mutation rates. The environment has stable to changing conditions. Price et al. Uses an additive genetic Matlab model. Plasticity is defined by the (2003) standard deviation of a Gaussian distribution. Behera, Builds from their earlier models (1995, 1996, 1997). This model Nanjundiah includes two environments—one normal, one stressful. Mating is (2004) random; 45 single-pair matings produces 900 progeny. Model applies to any species. They measure mean fitness in each environment. The ature of each agent attribute is the consequence of a random draw. Red'ko et al. Uses a neural network model of adaptive self-learning agents (2005) representing stockbrokers. Consists of two models; one predicts changes in a stock market; the other estimates the difference in value of a current stock price and the estimated future price. An evolving population consists of learning agents who live, give birth, learn, and die. Key variables are pure learning, pure evolution, and a combination of both. Curran et al. Uses Kauffman’s NK model. Agents may or may not have lifetime (2007) learning ability. Fitness initially defined by a random draw. Agents with learning ability can improve their fitness. The diversity of agent genomes (attributes) is defined by strings of 1s and 0s randomly drawn. As these agent strings become more similar, diversity is lost. Both fitness and diversity may be changed in the experiments. Suzuki, Arita Uses Kauffman’s NK model. Each agent has N traits. Fitness is based (2007) on combination of K=4 interacting binary traits randomly assigned values between 0.0 and 1.0. Each agent also has another N-length chromosome that defines its learning capability and plasticity. Agents can adjust their own plastic phenotypes to increase fitness. Interactions among agents (epistasis) have a cost attached. Bullinaria (2001)

Notes 1 Depew and Weber are also authors of a prior well-known book, Darwinism Evolving: Systems Dynamics and the Genealogy of Natural Selection (1995). 2 Appendix 1 lists 124 biological “Baldwin Effect” articles; Appendix 2 lists 64 empirical studies by biologists of phenotypic plasticity; Appendix 3 shows the agent rules created in 10 biological agent-based computational models. Many additional biophenotypic plasticity articles and books are listed as References. 3 The advantage of using experiments and agent-based computational-model experiments is that only experiments can validly and authentically test what philosophers of science call “counterfactual conditionals,” i.e. truly test the following: if A exists, B will exist; if A is removed, B will disappear (Fillenbaum 1974; Pearl 2000).

The Baldwin Effect  93 4 Badyaev (2009) could study 14 generations of house finches because there were traitbased descriptions of finches dating back to the 1970s. Badyaev only needed to collect trait measurements from later generations—1995–2007—to study how plasticity effects became embedded in the finches’ various genetic structures. 5 Agents may be genes, animals, species, humans, departments, organizations, economies, societies, or any other analytical unit of interest. 6 Lamarckianism and neo-Lamarckianism both hold that traits in species appear because of increased usage and disappear if not used. 7 For a more detailed study of innovation and memetic change in organizations see Shepard and McKelvey (2009), though they do not explicitly refer to memetic change as evidence of phenotypic plasticity. 8 Note that all the articles cited in this paragraph are chapters in Child’s book, but we include all of the original articles in our References section. 9 Note that although Boyd and Richerson (1985), Shennan (2002), Pluciennik (2005), Richerson and Boyd (2005) all focus on how cultural evolution differs from many of the more specific biological elements, they remain unaware of the interaction of biological phenotypic plasticity and underlying genetic structure. However, they do focus on how the genetic counterparts supporting the evolution of cultural continuity over time and across changing generations do so even though they ignore the reality that human phenotypic plasticity, communication, and learning abilities are vastly greater than in animals. 10 The “Baldwin Effect” doesn’t appear in the book’s Index. On pages 13 (footnote) and 20 (text): where J. M. Baldwin is referred to, he is mistakenly listed in the Index as “Carliss Y. Baldwin,” a current female Harvard B-school professor. 11 In a footnote Simpson noted that “Osborn thought of it as early as Lloyd Morgan and independently. When the coincidence was discovered, both Osborn and Lloyd Morgan deferred to Baldwin” (p. 112). 12 In his footnote #8 at this point Hodgson says: “[T]he idea of applying Darwinian principles to non-biological evolving systems…is also found in the nineteenth-century works of Bagehot (1872): James (1890): Alexander (1892): Drummond (1894): Ritchie (1896): Veblen (1899) and others. Ritchie and Veblen went much further than Baldwin by stressing the selection of social institutions.” (p. 235) 13 For more background on what Hodgson means by “institutions,” see Hodgson (2000, 2006b). 14 However, many studies show that some birds’ brains—especially those of parrots—have much higher neuron density; their brains are as smart as those of much larger animals, i.e. chimpanzees; see: https​:/​/ww​​w​.sci​​enced​​aily.​​com​/r​​eleas​​es​/20​​16​/06​​/1606​​​13153​​411​. h​​tm and http:​/​/www​​.dail​​ymail​​.co​.u​​k​/new​​s​/art​​icle-​​36400​​17​/Bi​​rd​-br​​ains-​​No​-pa​​r rots​​just​​-brig​​ht​-ap​​es​-Bi​​rds​-b​​rain-​​cells​​-​desp​​ite​-o​​rgan-​​small​​er​.ht​​ml (Accessed July 11, 2018). However, the parrot species—with its exceptional brain capability; parrots have the same brain capability as chimps—has not been experimentally studied. Only small birds with minimal brain capabilities have been experimentally studied to test whether they show evidence of the Baldwin Principle—which they do. 15 Technically, the firms did not actually go bankrupt (i.e. “die” as is implied by the Icarus analogy): but they failed in terms of declining earnings and shrinking size. Their lack of plasticity sent them into an adaptive “coma” in that they were adaptively dormant for many years—but many eventually came out of their coma and recovered. 16 Though some CEOs value brains lower in the hierarchy, Henry Ford is famous for once asking: “Why is it that every time I ask for a pair of hands a brain comes attached?” Quoted by Ricardo Semler (2004) in his book, The Seven-Day Weekend. 17 This said, however, there are many of us who would love to see living dinosaurs, sabertoothed tigers, mammoths, and other extinct species (but probably not in our backyards).

94  Bill McKelvey and Cera Oh

References Abatecola, G. (2014) “Research in organizational evolution. What comes next?,” European Management Journal, 32 (3): 434–443. Abatecola, G., Belussi, F., Breslin, D. and Filatočev, I. (2016a) “Darwinism, organizational evolution and survival: key challenges for future research,” Journal of Management & Governance, 20(1): 1–17. Abatecola, G., Belussi, F., Breslin, D. and Filatočev, I. (eds) (2016b) Special Issue: Darwinism, Organizational Evolution and Survival: Key Challenges for Future Research, London: Springer. Adger, W.N. (2000) “Social and ecological resilience: are they related?,” Progress in Human Geography, 24 (3): 347–364. Alatalo, R.V., Gustafsson, L., Linden, M. and Lundberg, A. (1985) Interspecific competition and niche changes in tits (Paridae): evaluation of non-experimental data. American Naturalist, 127 (6): 819–834. Aldrich, H.E. (1979) Organizations and Environments, Englewood Cliffs, NJ: Prentice-Hall. Aldrich, H.E. (1999) Organizations Evolving. Thousand Oaks, CA: SAGE. Aldrich, H.E. and Kenworthy, A.L. (1999) “The accidental entrepreneur: Campbellian antinomies and organizational foundings,” In J.A.C. Baum and B. McKelvey (eds) Variations in Organization Science: In Honor of Donald T. Campbell, Thousand Oaks, CA: SAGE, 19–33. Aldrich, H.E. and Martinez, M. (2003) “Entrepreneurship as social construction: a multilevel evolutionary approach,” in Z.J. Acs and D.B. Audretsch (eds) Handbook of Entrepreneurship Research, London: Kluwer, 359–399. Aldrich, H.E., Hodgson, G.M., Hull, D.L., Knudsen, T., Mokyr, J. and Vanberg, V.J. (2008) In defense of generalized Darwinism. Journal of Evolutionary Economics, 18 (5): 577–596. Alexander, S. (1892) “Natural selection in morals,” International Journal of Ethics, 2 (4): 409–439. Allen, P., Maguire, S. and McKelvey, B. (eds) (2011) The SAGE Handbook of Complexity and Management, London: SAGE. Argyris, C. (1957) Personality and Organization, New York: Harper. Badyaev, A.V. (2008) “Maternal effects as generators of evolutionary change: a reassessment,” in C.D. Schlichting and T.A. Mousseau (eds) The Year in Evolutionary Biology, New York: Wiley-Blackwell, 151–161. Badyaev, A.V. (2009) “Evolutionary significance of phenotypic accommodation in novel environments: an empirical test of the Baldwin Effect,” Philosophical Transactions of the Royal Society of London Series B Biological Sciences, 364(1520): 1125–1141. [Just so you know: I have capitalized the word “Effect” whenever it follows the word “Baldwin”]. Bagehot, W. (1872) Physics and Politics, or, Thoughts on the Application of the Principles of Natural Selection’ and ‘Inheritance’ to Political Society, London: Henry King. Baldwin, J.M. (1890) “Infant psychology,” Science, 16 (412): 351–353. Baldwin, J.M. (1894) “Imitation: a chapter in the natural history of consciousness,” Mind, 3 (9): 26–55. Baldwin, J.M. (1895) Mental Development in the Race and the Child: Methods and Processes, London: MacMillan. Baldwin, J.M. (1896a) “A new factor in evolution,” The American Naturalist, 30 (354): 441– 451, 536–553. Baldwin, J.M. (1896b) “Consciousness and evolution,” Psychological Review, 3 (3): 300–309.

The Baldwin Effect  95 Baldwin, J.M. (1896c) “Heredity and instinct,” Science, 3 (64, 67): 438–441, 558–561. Baldwin, J.M. (1896d) “Physical and social heredity,” American Naturalist, 30 (353): 422–428. Baldwin, J.M. (1897a) “Organic selection,” Science, 5 (121): 634–636. Baldwin, J.M. (1897b) Social and Ethical Interpretations in Mental Development: A Study of Social Psychology, London: Macmillan. Baldwin, J.M. (1900, 2nd edn) Mental Development in the Race and the Child: Methods and Processes (2nd ed), London: MacMillan. Baldwin, J.M. (1902) Development and Evolution, London: MacMillan. Baldwin, J.M. (1906, 2nd edn) Social and Ethical Interpretations in Mental Development: A Study of Social Psychology, London: Macmillan. Baldwin, J.M. (1909) Darwin and the Humanities, Baltimore, MD: Review Publishing Co. Baldwin, J.R. and Rafiquzzaman, M. (1995) “Selection versus evolutionary adaptation: learning and post-entry performance,” International Journal of Industrial Organization, 13 (4): 501–522. Baltaci, A. and Balci, A. (2017) “Complexity leadership: a theoretical perspective,” International Journal of Educational Leadership and Management, 5 (1): 30–58. Baum, J.A.C. and McKelvey, B. (eds) (1999) Variations in Organization Science: In Honor of Donald T. Campbell, Thousand Oaks, CA: SAGE. Baum, J.A.C. and Singh, J.V. (eds) (1994) Evolutionary Dynamics of Organizations, New York: Oxford University Press. Behera, N. and Nanjundiah, V. (1995) “An investigation into the role of phenotypic plasticity in evolution,” Journal of Theoretical Biology, 172 (3): 225–234. Behera, N. and Nanjundiah, V. (2004) “Phenotypic plasticity can potentiate rapid evolutionary change,” Journal of Theoretical Biology, 226 (2): 177–184. Berkes, F. and Folke, C. (eds) (1998) Linking Social and Ecological and Ecological Systems: Management Practices and Social Mechanisms for Building Resilience, Cambridge: Cambridge University Press. Boin, A. and Van Eeten, M.J. (2013) “The resilient organization,” Public Management Review, 15 (3): 429–445. Bolman, L.G. and Deal, T.E. (2017) Reframing Organizations: Artistry, Choice, and Leadership, Hoboken, NJ : John Wiley & Sons. Bones, C. and Hammersley, J. 2015. Leading Digital Strategy: Driving Business Growth Through Effective E-Commerce, London: Kogan Page. Boyd, R. and Richerson, P.J. (1985) Culture and the Evolutionary Process, Chicago, IL: University of Chicago Press. Breslin, D. (2008) “A review of the evolutionary approach to the study of entrepreneurship,” International Journal of Management Reviews, 10 (4): 399–423. Breslin, D. (2012) “The evolving organizational routine,” in F. Belussi and U. Staber (eds) Managing Networks of Creativity, London: Routledge, 46–62. Breslin, D. (2014) “Calm in the storm: simulating the management of organizational co-evolution,” Futures, 57: 62–77. Breslin, D. and Jones, C. (2012) “The evolution of entrepreneurial learning,” International Journal of Organizational Analysis, 20 (3): 294–308. Briscoe, G., Sadedin, S. and Paperin, G. (2007) “Biology of applied digital ecosystems,” Inaugural IEEE International Conference on Digital Ecosystems and Technologies Conference Proceedings, 458–463. Brousseau, E. and Penard, T. 2007. “The economics of digital business models: a framework for analyzing the economics of platforms,” Review of Network Economics, 6 (2): 81–114.

96  Bill McKelvey and Cera Oh Brown, J. and Agnew, N. (1982) “Corporate agility,” Business Horizons, 25 (2): 29–33. Brynjolfsson, E. and Mcafee, A. 2011. Race Against Machines, Lexington, MA: Digital Frontier Press. Brynjolfsson, E. and Mcafee, A. 2014. The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies, New York: W. W. Norton & Co. Bull, L. (2017) “The evolution of sex through the Baldwin Effect,” Artificial Life, 23 (4): 481–492. Bullinaria, J.A. (2001) “Exploring the Baldwin Effect in evolving adaptable control systems,” in R.F.French and J.P..Sougné (eds) Connectionist Models of Learning, Development and Evolution, London: Springer, 231–242. Burgelman, R.A. (1991) “Intraorganizational ecology of strategy making and organizational adaptation: theory and field research,” Organization Science, 2 (3): 239–262. Burgelman, R.A. (2002) “Strategy as vector and the inertia of coevolutionary lock-in,” Administrative Science Quarterly, 47 (2): 323–357. Campbell, D.T. (1960) “Blind variation and selective retention in creative thought as in other knowledge processes,” Psychological Review, 67 (6): 380–400. Campbell, D.T. (1965) “Variation and selective retention in socio-cultural evolution,” in H.R. Barringer, G.I. Blanksten and R.W. Mack (eds) Social Change in Developing Areas: A Reinterpretation of Evolutionary Theory, Cambridge, MA: Schenkman, 19–48. Campbell, D.T. (1974) “Evolutionary epistemology,” in P.A. Schilipp (ed): The Philosophy of Karl R. Popper, LaSalle, IL: Open Court Publishing, 413–463. Campbell, D.T. (1982) “The ‘blind-variation-and-selective-retention’ theme,” in J.M. Broughton and D.J. Freeman-Moir (eds) The Cognitive-Developmental Psychology of James Mark Baldwin: Current Theory and Research in Genetic Epistemology, Norwood, NJ: Ablex Publishing, 87–96. Campbell, D.T. (1994) “How individual and face-to-face group selection undermine firm selection in organization evolution,” in J.A.C. Baum and J.V. Singh (eds) Evolutionary Dynamics of Organizations, New York: Oxford University Press, 23–38. Canale, C.I. and Henry, P.-Y. (2010) “Adaptive phenotypic plasticity and resilience of vertebrates to increasing climatic unpredictability,” Climate Research, 43 (1–2): 135–147. Canale, C.I., Perret, M., Théry, M. and Henry, P.-Y. (2011) “Physiological flexibility and acclimation to food shortage in a heterothermic primate,” Journal of Experimental Biology, 214 (4): 551–560. Carroll, G.R. (1997) “Long-term evolutionary change in organizational populations: theory, models and empirical findings in industrial demography,” Industrial and Corporate Change, 6 (1): 119–143. Carroll, G.R. and Hannan, M.T. (eds) (1995) Organizations in Industry, New York: Oxford University Press. Chesbrough, H. (1999) “Arrested development: the experience of European hard disk drive firms in comparison with US and Japanese firms.,” Journal of Evolutionary Economics, 9 (3): 287–329. Chewning, L.V., Lai, C.H. and Doerfel, M.L. (2013) “Organizational resilience and using information and communication technologies to rebuild communication structures.,” Management Communication Quarterly, 27 (2): 237–263. Child, J. (ed) (2012) The Evolution of Organizations, Cheltenham: Edward Elgar. Child, J. and Kieser, A. (1981) “Development of organizations over time,” in P.C. Nystrom and W.H. Starbuck (eds) Handbook of Organizational Design, Vol. 1, Oxford: Oxford University Press, 28–64.

The Baldwin Effect  97 Choi, Y. (2017) Digital Business and Sustainable Development: Asian Perspectives, London: Routledge. Christensen, C. (1993) “The rigid disk drive industry: a history of commercial and technological turbulence,” Business History Review, 67 (4): 531–588. Chu, D., Strand, R. and Fjelland, R. (2003) “Theories of complexity,” Complexity, 8 (3): 19–30. Ciemins, E.L., Brant, J., Kersten, D., Mullette, E. and Dickerson, D. (2016) “Why the interdisciplinary team approach works: insights from complexity science,” Journal of Palliative Medicine, 19 (7): 767–770. Colbert, A.E., Barrick, M.R. and Bradley, B.H. (2014) “Personality and leadership composition in top management teams: implications for organizational effectiveness,” Personnel Psychology, 67 (2): 351–387. Corallo, A., Passiante, G. and Prencipe, A. (eds) (2007) The Digital Business Ecosystem, Cheltenham, UK; Northampton, MA: Edward Elgar Publishing. Coupey, E. (2016) Digital Business: Concepts and Strategies, London: Routledge. Coutu, E.L. (2002) “How resilience works,” Harvard Business Review, 80 (5): 46–51. Crawford, G.C. and Kreiser, P.M. (2015) “Corporate entrepreneurship strategy: extending the integrative framework through the lens of complexity science,” Small Business Economics, 45 (2): 403–423. Crispo, E. (2007) “The Baldwin Effect and genetic assimilation: revisiting two mechanisms of evolutionary change meditated by phenotypic plasticity,” Evolution, 61 (11): 2469–2479. Curran, D., O’Riordan, C. and Sorensen, H. (2007) “An analysis of the effects of lifetime learning on population fitness and diversity in an NK fitness landscape,” in F. Almeida e Costa, L.M. Rocha, E. Costa, I. Harvey and A. Coutinho (eds) Advances in Artificial Life, 9th European Conference, ECAL, Lisbon, Portugal, September, 273–283. Danielson-François, A.M., Kelly, J.K. and Greenfield, M.D. (2006) “Genotype × environment interaction for male attractiveness in an acoustic moth: evidence for plasticity and canalization,” Journal of Evolutionary Biology, 19 (2): 532–542. Darwin, C. (1859) The Origin of Species by Means of Natural Selection, London: Murray. Day, G.S., Fein, A.J. and Ruppersberger, G. 2003. “Shakeouts in digital markets: lessons from B2B exchanges,” California Management Review, 45 (2): 131–150. Dennett, D.C. (2003) “The Baldwin Effect: a crane, not a skyhook,” in B.H. Weber and D.J. Depew (eds) Evolution and Learning: The Baldwin Effect Reconsidered, Cambridge, MA: Bradford/MIT Press, 69–79. Dennett, D.C. 2006. “From typo to thinko: when evolution graduated to semantic norms,” in S.C. Levinson and P. Jaisson (eds) Evolution and Culture, Cambridge, MA: Bradford/ MIT Press, 133–145. Depew, D.J. and Weber, B.H. (1995) Darwinism Evolving, Cambridge, MA: Bradford. DeWitt, T.J. (1998) “Costs and limits of phenotypic plasticity: tests with predator-induced morphology and life history in a freshwater snail,” Journal of Evolutionary Biology, 11 (4): 465–480. Dobzhansky, T. (1970) Genetics of the Evolutionary Process, New York: Columbia University Press. Dominguez-Isidro, S. and Mezura-Montes, E. (2017) “The Baldwin Effect on a memetic differential evolution for constrained numerical optimization problems,” in Proceedings of the Genetic and Evolutionary Computation Conference Companion, AC Med, 203–204. Dopazo, H.J. and Perazzo, R.P.J. (2002) “Mutual influence of learning and evolution,” Complexity International, 9: 1–11.

98  Bill McKelvey and Cera Oh Dosi, G. and Malerba, F. (2002) “Interpreting industrial dynamics twenty years after Nelson and Winter’s evolutionary theory of economic change: a preface,” Industrial and Corporate Change, 11 (4): 619–622. Doz, E. (1996) “The evolution of cooperation in strategic alliances: initial conditions or learning processes?” Strategic Management Journal, 14 (S1): 55–83. Drummond, H. (1894) The Ascent of Man, London: Hodder and Stoughton. Durham, W.H. (1991) Coevolution: Genes, Culture, and Human Diversity, Stanford, CA: Stanford University Press. El Sawy, O.A. and Pereira, F. (2013) “Anticipating game changers for ‘Enterprise 2020’ in a digitally-intensive world,” in A. Bounfour (ed) Business Modelling in the Dynamic Digital Space, Heidelberg: Springer Berlin Heidelberg, 1–12. Ernande, B. and Dieckmann, U. (2004) “The evolution of phenotypic plasticity in spatially structured environments: implications of intraspecific competition, plasticity costs and environmental characteristics,” Journal of Evolutionary Biology, 17 (3): 613–628. Escoffier, N. and McKelvey, B. (2013) “Using ‘crowd-wisdom strategy’ to reduce market failure: proof-of-concept from the movie industry,” in R. DeFillippi and P. Wikstrom (eds) Business, Innovation and Disruption in Creative Industries, Lisbon: Formalpress, 200–222. Escoffier, N. and McKelvey, B. (2015) “The wisdom of crowds in the movie industry: toward new solutions to reduce uncertainties,” International Journal of Arts Management Special Issue, 17 (2): 52–63. Escoffier, N. and McKelvey, B. (2018) “Using crowdsourcing to increase new product’s market value and positive comments for both the crowd involved and customers,” International Journal of Innovation Management, 22 (2): pages [we don’t have them yet] Federici, D. (2003) “Culture and the Baldwin Effect,” in W. Banzhaf, T. Christaller, P. Dittrich, J.T. Kim, and J. Ziegler (eds) Proceedings of European Conference on Artificial Life, Berlin: Springer-Verlag, 309–318. Feldman, M.S. and Pentland, B.T. (2003) “Reconceptualizing organizational routines as a source of flexibility and change,” Administrative Science Quarterly, 48 (1): 94–118. Fillenbaum, S. (1974) “Information amplified: memory for counterfactual conditionals,” Journal of Experimental Psychology, 102 (1): 44–49. Flatt, T. (2005) “The evolutionary genetics of canalization,” Quarterly Review of Biology, 80 (3): 287–316. Folke, C. (2006) “Resilience: the emergence of a perspective for social-ecological systems analyses,” Global Environmental Change, 16 (3): 253–267. Folke, C., Colding, G. and Berkes, F. (2003) “Synthesis: building resilience and adaptive capacity in socio-ecological systems,” in F. Berkes, J. Colding and C. Folke (eds) Navigating Social-Ecological Systems: Building Resilience for Complexity and Change, Cambridge: Cambridge University Press, 352–387. Folke, C., Hahn, T., Olsson, P. and Norberg, J. (2005) “Adaptive governance of socialecological systems,” Annual Review of Environment and Resources, 30 (1): 441–473. Francisco, Y.O. (2012) “Is positioning too static a concept for today’s hyper-competitive world?” http:​/​/ygo​​rfran​​cisco​​.blog​​spot.​​com​/2​​012​/0​​8​/is-​​posit​​ionin​​g​-too​​-stat​​ic​-​co​​ncept​​ -for.​​html (Accessed July 11, 2018). Gabora, L. (1997) “The origin and evolution of culture and creativity,” Journal of Memetics: Evolutionary Models of Information Transmission, 1 (1): 1–28. Gaus, G. and Thrasher, J. (2014) “Social evolution,” in: G.F. Gaus and F. D’Agostino (eds) The Routledge Companion to Social and Political Philosophy, New York: Routledge, 643–655.

The Baldwin Effect  99 Ge, X., Bian, W.H., Jiang, X.L., Liu, W.S. and Wang, X.F. (2016) “The underlying driver for the C IV Baldwin Effect in QSOs with 0< z< 5,” Monthly Notices of the Royal Astronomical Society, 462 (1): 966–976. Ghalambor, C.K., McKay, J.K., Carroll, S.P. and Reznick, D.N. (2007) “Adaptive versus non-adaptive phenotypic plasticity and the potential for contemporary adaptation in new environments,” Functional Ecology, 21 (3): 394–407. Giannoccaro, I. (2015) “Adaptive supply chains in industrial districts: a complexity science approach focused on learning,” International Journal of Production Economics, 170 (Part B): 576–589. Gibson, G. and Dworkin, I. (2004) “Uncovering cryptic genetic variation,” Nature Reviews. Genetics, 5 (9): 681–690. Granovetter, M. (1973) “The strength of weak ties,” American Journal of Sociology, 78 (6): 1360–1380. Greengard, S. (2015) The Internet of Things. Cambridge, MA: MIT Press. Grether, G.F. (2005) “Environmental change, phenotypic plasticity, and genetic compensation,” American Naturalist, 166 (4): E115–E123. Gulati, R. (2010) Reorganize for Resilience: Putting Customers at the Center of Your Business, Boston, MA: Harvard Business Press. Hamel, G. and Välikangas, L. (2003) “The quest for resilience,” Harvard Business Review, 81 (9): 52–63. Hannan, M.T. and Freeman, J. (1977) “The population ecology of organizations,” American Journal of Sociology, 82 (5): 929–964. Hannan, M.T. and Freeman, J. (1989) Organizational Ecology, Cambridge, MA: Harvard University Press. Hazy, J.K., Goldstein, J.A. and Lichtenstein, B.B. (2007) Complex Systems Leadership Theory, Mansfield, MA: ISCE. Hill, A. (2013) “The best strategy must build on the past,” Financial Times, (Feb. 13: #38,158): 10. Hinton, G.E. and Nowlan, S.J. (1987) “How learning can guide evolution,” Complex Systems, 1 (1): 495–502. Hodgson, G.M. (1991) Rethinking Economics: Markets, Technology, and Economic Evolution, London: Edward Elgar. Hodgson, G.M. (1997) Economics and Evolution: Bringing Life Back Into Economics, Ann Arbor, MI: University of Michigan Press. Hodgson, G.M. (2000) “What is the essence of institutional economics,” Journal of Economic Issues, 34 (2): 317–329. Hodgson, G.M. (2004) The Evolution of Institutional Economics, New York: Routledge. Hodgson, G.M. (2006a) Economics in the Shadows of Darwin and Marx: Essays on Institutional and Evolutionary Themes, Cheltenham: Edward Elgar. Hodgson, G.M. (2006b) “What are institutions?,” Journal of Economic Issues, 40 (1): 1–25. Hodgson, G.M. (ed) (2007) The Evolution of Economic Systems: A Critical Reader, Cheltenham: Edward Elgar. Hodgson, G.M. (ed) (2009) Darwinism and Economics, Cheltenham: Edward Elgar. Hodgson, G.M. (2010) Darwin’s Conjecture: The Search for General Principles of Social & Economic Evolution, Chicago, IL: University of Chicago Press. Hodgson, G.M. (2012) Economics Without Homo Economicus, Chicago, IL: University of Chicago Press. Hodgson, G.M. (2013) “Understanding organizational evolution: toward a research agenda using generalized Darwinism,” Organization Studies, 34 (7): 973–992.

100  Bill McKelvey and Cera Oh Hodgson, G.M. and Knudsen, T. (2006) “Why we need a generalized Darwinism, and why generalized Darwinism is not enough,” Journal of Economic Behavior & Organization, 61 (1): 1–19. Hodgson, G.M. and Knudsen, T. (2008) “In search of general evolutionary principles: why Darwinism is too important to be left to the biologists,” Journal of Economic Behavior & Organization, 10 (1): 51–69. Hodgson, G.M. and Knudsen, T. (2010) Generative replication and the evolution of complexity. Journal of Economic Behavior & Organization, 75 (1): 12–24. Holland, J.H. (1995) Hidden Order: How Adaptation Builds Complexity, Reading, MA: Addison Wesley. Holling, C.S. (1973) “Resilience and stability of ecological systems,” Annual Review of Ecological Systems, 4 (1): 1–23. Horne, J.F. and Orr, J.E. (1998) “Assessing behaviors that create resilient organizations,” Employee Relations Today, 24 (4): 29–39. Hovasse, R. (1943) De l’Adaptation à l’Évolution par la Selection, Paris: Hermann. Hovasse, R. (1950) Adaptation et Evolution, Paris: Hermann. Hubbard, B.M. (2015) Conscious Evolution-Revised Edition: Awakening the Power of our Social Potential, Novato, CA: New World Library. Hurst, D.K. (2012) The New Ecology of Leadership, New York: Columbia University Press. Huxley, J.S. (1942) Evolution: The Modern Synthesis, New York: Harper. Huygens, M., Baden-Fuller, C., Van Den Bosch, F.A.J. and Volberda, H.W. (2001) “Co-evolution of firm capabilities and industry competition: investigating the music industry,” Organization Studies, 22(6): 971–1011. James, W. (1890) The Principles of Psychology (2 vols): London: Macmillan. Kauffman, S.A. (1993) The Origins of Order, New York: Oxford University Press. Kellerman, B. (2012) The End of Leadership, New York: Harper Collins. Khalil, E.L. (2012) “Are instincts hardened routines? A radical proposal,” Discussion paper 25/12, Department of Economics, Monash University, Clayton, Australia. Kirschner, M.W. (2015) “The road to facilitated variation,” in A.C. Love (ed) Conceptual Change in Biology: Scientific and Philosophical Perspectives on Evolution and Development, , Vol. 307, Dordrecht: Springer, 199–217. Kogut, B. and Zander, U. (1993) “Knowledge of the firm and evolutionary theory of the multinational corporation,” Journal of International Business Studies, 24 (4): 625–645. Korhonen, J. and Seager, T.P. (2008) “Beyond eco-efficiency: a resilience perspective,” Business Strategy and the Environment, 17 (7): 411–419. Kreutzer, R.T. and Land, K.-H. (2014) Digital Darwinism: Branding and Business Models in Jeopardy, Berlin: Springer-Verlag. Krishnan, R., Smith, M.D., Tang, Z. and Telang, R. (2007) “Digital business models for peer-to-peer networks: analysis and economic issues,” Review of Network Economics, 6 (2): 194–213. Kshetri, N. (2017) “The economics of the Internet of Things in the global south,” Third World Quarterly, 38 (2): 311–339. Kull, K. (2014) “Adaptive evolution without natural selection,” Biological Journal of the Linnean Society, 112 (2): 287–294. Lande, R. (2009) “Adaptation to an extraordinary environment by evolution of phenotypic plasticity and genetic assimilation,” Journal of Evolutionary Biology, 22 (7): 1435–1446. Langergraber, K.E., Prüfer, K., Rowney, C., Boesch, C., Crockford, C., Fawcett, K., InoueMuruyama, M., Mitani, J.C., Muller, M.N. and Robbins, M.M. (2012) “Generation times in wild chimpanzees and gorillas suggest earlier divergence times in great ape and

The Baldwin Effect  101 human evolution,” Proceedings of the National Academy of Sciences of the United States of America, 109 (39): 15716–15721. Langton, J. (1984) “The ecological theory of bureaucracy: the case of Josiah Wedgwood and the British pottery industry,” Administrative Science Quarterly, 29 (3): 330–354. Levinthal, D.A. and Marino, A. (2010) “From dynamic capabilities to dynamic organizations: the internal ecology organizational evolution,” Working paper, Wharton School, University of Pennsylvania, PA. Limnios, E.A.M., Mazzarol, T., Ghadouani, A. and Schilizzi, S.G. (2014) “The resilience architecture framework: four organizational archetypes,” European Management Journal, 32 (1): 104–116. Linnenluecke, M.K. (2017) “Resilience in business and management research: a review of influential publications and a research agenda,” International Journal of Management Reviews, 19 (1): 4–30. Lissack, M.P. (ed) (2002) The Interaction of Complexity and Management, Westport, CT: Quorum Books. Loasby, B.J. (2007) “A cognitive perspective on entrepreneurship and the firm,” Journal of Management Studies, 44 (7): 1078–1106. Longa, V.M. (2006) “A misconception about the Baldwin Effect: implications for language evolution,” Folia Linguistica, 4 (3–4): 305–318. Macri, S., Zoratto, F. and Laviola, G. (2011) “Early-stress regulates resilience, vulnerability and experimental validity in laboratory rodents through mother-offspring hormonal transfer,” Neuroscience & Biobehavioral Reviews, 35 (7): 1534–1543. Madsen, T. and McKelvey, B. 2005. “Dynamic capabilities and knowledge-driven microevolution: performance effects of intrafirm variation, selection and retention processes,” Research in Competence-based Management, 2: 3–38. Maguire, S., McKelvey, B., Mirabeau, L. and Öztas, N. (2006) “Complexity science and organization studies,” in S. Clegg, C. Hardy and T. Lawrence (eds) Handbook of Organizational Studies (2nd ed), London: SAGE, 165–214. Manchester, W. (1992) A World Lit Only By Fire: The Medieval Mind and the Renaissance, Boston, MA: Little, Brown & Co. March, J.G. (1994) “The evolution of evolution,” in J.A.C. Baum and J.V. Singh (eds) Evolutionary Dynamics of Organizations, Oxford: Oxford University Press, 39–49. Marion, R. and Uhl-Bien, M. (2011) “Implications of complexity science for the study of leadership,” in P. Allen, S. Maguire and B. McKelvey (eds) The SAGE Handbook of Complexity and Management, London: SAGE, 385–399. Matsuda, R. (1987) Animal Evolution in Changing Environments, New York: Wiley. Mayley, G. (1997) “Landscapes, learning costs, and genetic assimilation,” Evolutionary Computation, 4 (3): 213–234. Maynard-Smith, J. (1987) “When learning guides evolution,” Nature, 329 (6142): 761–762. Mayr, E. (1982) The Growth of Biological Thought, Cambridge, MA: Belknap/Harvard University Press. McKelvey, B. (1980) “Organizational speciation,” in C. Pinder and L. Moore (eds) Middle Range Theories of Organizations and Organizational Behavior, Boston, MA: Martinus Nijhoff, 169–186. McKelvey, B. (1982) Organizational Systematics: Taxonomy, Evolution, Classification, Berkeley, CA: University of California Press. McKelvey, B. (1997) “Quasi-natural organization science,” Organization Science, 8 (4): 352–280.

102  Bill McKelvey and Cera Oh McKelvey, B. (1999) “Complexity theory in organization science,” Emergence, 1 (1): 5–32. McKelvey, B. (2004) “Toward a complexity science of entrepreneurship,” Journal of Business Venturing, 19 (3): 313–341. McKelvey, B. (2010) “Complexity leadership: the secret of Jack Welch’s success,” International Journal of Complexity In Leadership & Management, 1 (1): 4–36. McKelvey, B. (ed) (2013a) Origins of Order-Creation Science—Routledge Major Works Series: Complexity: Critical Concepts, Vol. I, London: Routledge. McKelvey, B. (ed) (2013b) Self-organization, Emergence and Self-Organized Criticality— Routledge Major Works Series: Complexity: Critical Concepts, Vol. II, London: Routledge. McKelvey, B. (ed) (2013c) Organization and Management Complexity Dynamics—Routledge Major Works Series: Complexity: Critical Concepts, Vol. III, London: Routledge. McKelvey, B. (2018a) “Managing the consequences of digital IS networks & accelerated complexity dynamics in digital-business ecosystems,” Ch. 1 in B. McKelvey (ed) Management in the Age of Digital-Business Complexity, London: Routledge. McKelvey, B. (2018b) “Agility & accelerated complexity dynamics in digital-business ecosystems,” Ch. 4 in B. McKelvey (ed) Management in the Age of Digital-Business Complexity, London: Routledge. McKelvey, B. and Aldrich, H. (1983) “Populations, natural selection, and applied organizational science,” Administrative Science Quarterly, 28 (1): 101–128. McKelvey, B. and Andriani, P. (2010) “Using scale-free theory from complexity science to better manage risk,” Risk Management: An International Journal, 12 (1): 54–82. McKelvey, B. and Yalamova, R. (2011) “Financial resilience engineering: toward automatic action formulas against risk and reckless endangerment,” in G. Sundström and E. Hollnagel (eds) Governance and Control of Financial Systems: A Resilience Engineering Perspective, Farnham: Ashgate, 133–148. McKelvey, B., Li, M., Xu, H. and Vidgen, R. (2013) “Re-thinking Kauffman’s NK fitness landscape: going from artifact & groupthink to weak-tie effects,” Human Systems Management, 32 (1): 17–42. Mcquivey, J. (2013) Digital Disruption: Unleashing the Next Wave of Innovation, Las Vegas: Amazon Publishing. Meneghel, I., Salanova, M. and Martínez, I.M. (2016) “Feeling good makes us stronger: how team resilience mediates the effect of positive emotions on team performance,” Journal of Happiness Studies, 17 (1): 239–255. Mihalache, O.R., Jansen, J.J., Van den Bosch, F.A. and Volberda, H.W. (2014) “Top management team shared leadership and organizational ambidexterity: a moderated mediation framework,” Strategic Entrepreneurship Journal, 8 (2): 128–148. Miller, D. (1990) The Icarus Paradox, New York: HarperBusiness. Miller, D. (1993) “The architecture of simplicity,” Academy of Management Review, 18 (1): 116–138. Mitleton-Kelly, E., Paraskevas, A. and Day, C. (eds) (2018) Handbook of Research Methods in Complexity Science: Theory & Application, London: Edward Elgar. Moczek, A.P., Sultan, S., Foster, S., Ledon-Rettig, C., Dworkin, I. and Nijhout, H.F., Abouheif, E. and Pfennig, D.W. (2011) “The role of developmental plasticity in evolutionary innovation,” Proceedings of the Royal Society of London. Series B, Biological Sciences, 278 (1719): 2705–2713. Morgan, C.L. (1896a) Habit and Instinct, London: Edward Arnold. Morgan, C.L. (1896b) “On modification and variation,” Science, 4 (99): 733–740.

The Baldwin Effect  103 Mousseau, T.A. and Dingle, H. (1991) “Maternal effects in insects: examples, constraints, and geographical variation,” in E.C. Dudley (ed) The unity of evolutionary biology, Portland, OR: Dioscorides Press, 745–761. Müller, G.B. and Newman, S. (2003) Origination of Organismal Form: Beyond the Gene in Developmental and Evolutionary Biology, Cambridge, MA: MIT Press. Murmann, J.P. (2012) “The coevolution of industries and important features of their environments,” Organization Science, 24 (1): 58–78. Nahavandi, A. (2016) The Art and Science of Leadership—Global Edition, New York: Pearson Education. Nelson, R.R. (1994) “Evolutionary theorizing about economic change,” in N.J. Smelser and R. Swedberg (eds) The Handbook of Economic Sociology, Princeton, NJ: Princeton University Press, 108–136. Nelson, R.R. (1995) “Recent evolutionary theorizing about economic change,” Journal of Economic Literature, 33 (1): 48–90. Nelson, R.R. (2007) “Universal Darwinism and evolutionary social science,” Biology and Philosophy, 22 (1): 73–94. Nelson, R.R. and Winter, S.G. (1982) An Evolutionary Theory of Economic Change, Cambridge, MA: Belknap-Harvard University Press. Nicotra, A.B., Atkin, O.K., Bonser, S.P., Davidson, A.M., Finnegan, E.J. and Mathesius, U. (2010) “Plant phenotypic plasticity in a changing climate,” Trends in Plant Science, 15 (12): 684–692. Nohria, N. and Khurana, R. (2010) Handbook of Leadership Theory and Practice, Boston, MA: Harvard Business Press. Nooteboom, S.G. and Termeer, C.J. (2013) “Strategies of complexity leadership in governance systems,” International Review of Public Administration, 18 (1): 25–40. Norman, S., Luthans, B. and Luthans, K. (2005) “The proposed contagion effect of hopeful leaders on the resiliency of employees and organizations,” Journal of Leadership & Organizational Studies, 12 (2): 55–64. Nozick, R. (1994) “Invisible-hand explanations,” American Economic Review, 84 (2): 314–318. O’Reilly, C.A. and Tushman, M.L. (2008) “Ambidexterity as a dynamic capability: resolving the innovator's dilemma,” Research in Organizational Behavior, 28: 185–206. Olsson, L., Jerneck, A., Thoren, H., Persson, J. and O’Byrne, D. (2015) “Why resilience is unappealing to social science: theoretical and empirical investigations of the scientific use of resilience,” Science Advances, 1 (4): e1400217-1–11. Olsson, P., Folke, C. and Berkes, F. (2004) “Adaptive co-management for building resilience in social-ecological systems,” Environmental Management, 34 (1): 75–90. Ortiz-de-Mandojana, N. and Bansal, P. (2016) “The long-term benefits of organizational resilience through sustainable business practices,” Strategic Management Journal, 37 (8): 1615–1631. Osborn, H.F. (1896) “A mode of evolution requiring neither natural selection nor the inheritance of acquired characters,” Transactions of the New York Academy of Sciences, 15 (April 13): 141–142, 148. Oyama, K., Learmonth, G. and Chao, R. (2015) “Applying complexity science to new product development: modeling considerations, extensions, and implications,” Journal of Engineering and Technology Management, 35: 1–24. Papaj, D.R. (1994) “Optimizing learning and its effect on evolutionary change in behavior,” in L.A. Real (ed) Behavioral Mechanisms in Evolutionary Ecology, Chicago, IL: University of Chicago Press, 133–153.

104  Bill McKelvey and Cera Oh Parisi, D. and Nolfi, S. (1996) “The influence of learning on evolution,” in R.K. Below and M. Mitchell (eds) Adaptive Individuals in Evolving Populations: Models and Algorithms, Reading, MA: Addison-Wesley, 419–428. Park, E., Lallani, M.N. Park, K.J. and Baker, A.J. (2014) “Remote-ischemic preconditioning as a prophylactic method to increase biological resilience to mild traumatic brain injury,” Journal of Neurotrauma, 31 (12): 49–50. Pascale, R.T., Millemann, M. and Gioja, L. (2000) Surfing the Edge of Chaos: The Laws of Nature and the new Laws of Business, New York: Three Rivers Press. Passerini, K. and El Tarabishy, A. (2012) Information Technology for Small Business: Managing the Digital Enterprise, New York: Springer. Paton, D. and Johnston, D. (2006) Disaster Resilience: An Integrated Approach, Springfield, IL: Charles Thomas. Pearl, J. (2000) Causality: Models, Reasoning, and Inference, New York: Cambridge University Press. Penrose, E.T. (1959) The Theory of the Growth of the Firm, New York: Wiley. Pentland, B.T., Haerem, T. and Hillison, D. (2010) “Comparing organizational routines as recurrent patterns of action,” Organization Studies, 31 (7): 917–940. Peterson, G., Allen, C.R. and Holling, C.S. 1998. “Ecological resilience, biodiversity, and scale,” Ecosystems, 1 (1): 6–18. Pluciennik, M. (2005) Social Evolution, London: Duckworth & Co. Ponce-Jara, M.A., Ruiz, E., Gil, R., Sancristóbal, E., Pérez-Molina, C. and Castro, M. (2017) “Smart grid: assessment of the past and present in developed and developing countries,” Energy Strategy Reviews, 18: 38–52. Porter, M. (1996) “What is strategy?’ Harvard Business Review, 74 (6): 61–78. Prange, C. (2016) “Engaging with complex environments: why agility involves more than running hard,” International Journal of Complexity in Leadership and Management, 3 (3): 182–197. Price, T.D., Qvarnström, A. and Irwin, D.E. (2003) “The role of phenotypic plasticity in driving genetic evolution,” Proceedings of the Royal Society of London. Series B, Biological Sciences, 270 (1523): 1433–1440. Qiu, T., Qiao, R. and Wu, D.O. (2018) “EABS: an event-aware backpressure scheduling scheme for emergency Internet of Things,” IEEE Transactions on Mobile Computing, 17 (1): 72–84. Read, D.W. (2016) How Culture Makes Us Human: Primate Social Evolution and the Formation of Human Societies, New York: Routledge. Red’ko, V.G., Mosalov, O.P. and Prokhorov, D.V. (2005) “A model of Baldwin Effect in populations of self-learning agents,” Neural Networks, 3: 1355–1360. Reed, R.E., Schindler, D.E. and Waples, R.S. (2011) “Interacting effects of phenotypic plasticity and evolution on population persistence in a changing climate,” Conservation Biology, 25 (1): 56–63. Remane, G., Hanelt, A., Nickerson, R.C. and Kolbe, L.M. (2017) “Discovering digital business models in traditional industries,” Journal of Business Strategy, 38 (2): 41–51. Richerson, P.J. and Boyd, R. (2005) Not by Genes Alone, Chicago, IL: University of Chicago Press. Richie, D.G. (1896) “Social evolution,” International Journal of Ethics, 6 (2): 165–181. Robinson, B.W. and Dukas, R. (1999) “The influence of phenotypic modifications on evolution: the Baldwin Effect and modern perspectives,” Oikos, 85 (3): 582–589. Rollo, C.D. and Shibata, D.M. (1991) “Resilience, robustness, and plasticity in a terrestrial slug, with particular reference to food quality,” Canadian Journal of Zoology, 69 (4): 978–987.

The Baldwin Effect  105 Rong, K. and Shi, Y. (2014) Business Ecosystems: Constructs, Configurations, and the Nurturing Process, London: Palgrave Macmillan. Rose, A. (2004) “Defining and measuring economic resilience to disasters,” Disaster Prevention and Management, 13 (4): 307–314. Royle, N.J., Smiseth, P.T. and Kölliker, M. (2012) The Evolution of Parental Care, Oxford: Oxford University Press. Rymer, T.L., Pillay, N. and Schradin, C. (2013) “Extinction or survival? Behavioral flexibility in response to environmental change in the African striped mouse,” Sustainability, 5 (1): 163–186. Sanderson, S.K. (2007) Evolutionism and Its Critics: Deconstructing and Reconstructing an Evolutionary Interpretation of Human Society, London: Paradigm Publications. Scarfe, A.C. (2009) “James Mark Baldwin with Alfred North Whitehead on organic selectivity: the ‘Novel’ factor in evolution,” Journal of Natural and Social Philosophy, 5 (2): 40–107. Schallmo, D., Williams, C.A. and Boardman, L. (2017) “Digital transformation of business models—best practice, enablers, and roadmap,” International Journal of Innovation Management, 21 (08): 1740014-1–17. Schallmo, D.R. and Williams, C.A. (2018) “Roadmap for the digital transformation of business models,” in Digital Transformation Now! Cham: Springer, 41–68. Scharloo, W. (1991) “Canalization: genetic and developmental aspects,” Annual Review of Ecological Systematics, 22, 65–93. Scheiner, S.M., Barfield, M. and Holt, R.D. (2017) “The genetics of phenotypic plasticity, XV: genetic assimilation, the Baldwin Effect, and evolutionary rescue,” Ecology and Evolution, 7 (21): 8788–8803. Schidlowski, M. (1988) “A 3,800-million-year isotopic record of life from carbon in sedimentary rocks,” Nature, 333 (6171): 313–318. Schmidt, E. and Cohen, J. (2013) The New Digital Age: Transforming Nations, Businesses, and our Lives, New York: Vintage Books. Scott, S.V., Reenen, J.V. and Zachariadis, M. (2017) “The long-term effect of digital innovation on bank performance: an empirical study of SWIFT adoption in financial services,” Research Policy, 46 (4): 984–1004. Semler, R. (2004) The Seven-Day Weekend, New York: Penguin. Seo, D. (2017) “Digital business convergence and emerging contested fields: a conceptual framework,” Journal of the Association for Information Systems, 18 (10): 687–702. Shen, C., Shi, H., Zheng, W., Li, F., Peng, S. and Ding, D. (2016) “Study on the cumulative impact of reclamation activities on ecosystem health in coastal waters,” Marine Pollution Bulletin, 103 (1): 144–150. Shennan, S. (2002) Genes, Memes and Human History: Darwinian Archaeology and Cultural Evolution, London: Thames & Hudson. Shepard, J. and McKelvey, B. (2009) “An empirical investigation of organizational memetic variation,” Journal of Bioeconomics, 11 (2): 135–164. Simpson, G.G. (1953) “The Baldwin Effect,” Evolution, 7 (2): 110–117. Singh, J.V. (ed) (1990) Organizational Evolution: New Directions, Newbury Park, CA: SAGE. Slywotzky, A. and Morrison, D. (2001) “Becoming a digital business: it's not about technology,” Strategy & Leadership, 29 (2): 4–9. Snell-Rood, E.C., Van Dyken, J.D., Cruickshank, T., Wade, M.J. and Moczek, A.P. (2010) “Toward a population genetic framework of developmental evolution: costs, limits, and consequences of phenotypic plasticity,” BioEssays, 32 (1): 71–81.

106  Bill McKelvey and Cera Oh Star, R., Newfrock, J. and Delurey, M. (2003) “Enterprise resilience: managing risks in the networks economy,” Strategy+business, 30: 1–13. Stearns, S.C. (1989) “The evolutionary significance of phenotypic plasticity,” BioScience, 39 (7): 436–445. Stephens, D.W. (1991) “Change, regularity, and value in the evolution of animal learning,” Behavioral Ecology, 2 (1): 77–89. Stoelhorst, J.W. (2008) “The explanatory logic and ontological commitments of generalized Darwinism,” Journal of Economic Methodology, 15 (4): 343–363. Sultan, S.E. (1987) “Evolutionary implications of phenotypic plasticity in plants,” Evolutionary Biology, 21, 127–178. Sundström, G.A. and Hollnagel, E. (2006) “Learning how to create resilience in business systems,” in E. Hollnagel, D.D. Woods and N. Leveson (eds) Resilience Engineering: Concepts and Precepts, Boca Raton, FL: Taylor & Francis Group, 235–252. Sundström, G.A. and Hollnagel, E. (2011) “The importance of functional interdependencies in financial services systems,” in E. Hollnagel, J. Pariès, D.D. Woods and J. Wreathall (eds) Resilience Engineering: Concepts and Precepts, Boca Raton, FL: Taylor & Francis Group, 171–192. Suzuki, R. and Arita, T. (2007) “The dynamic changes in roles of learning through the Baldwin Effect,” Artificial Life, 13 (1): 31–43. Suzuki, Y. and Nijhout, H.F. (2006) “Evolution of a polyphenism by genetic accommodation,” Science, 311 (5761): 650–652. Swaminathan, A. and Meffert, J. (2017) Digital @ Scale: How you can Lead Your Business to the Future with Digital@scale, Hoboken, NJ: Wiley. Sznajder, B., Sabelis, M.W. and Egas, M. (2012) “How adaptive learning affects evolution: reviewing theory on the Baldwin Effect,” Evolutionary Biology, 39 (3): 301–310. Tafti, S.F., Kordnaeij, A., Hoseini, S.H.K. and Jamali, M. (2015) “Business ecosystem as a new approach in strategy,” Management and Administrative Sciences Review, 4 (1): 198–205. Tan, B., Pan, S.L., Lu, X. and Huang, L. (2009) “Leveraging digital business ecosystems for enterprise agility: the tri-logic development strategy of Alibaba​.co​m,” 30th ICIS Proceedings. Paper 171. Tapscott, D. (1995) The Digital Economy, New York: McGraw-Hill Education. Tapscott, D. (2015) The Digital Economy: Rethinking Promise and Peril in the age of Networked Intelligence [20th Anniversary Edition], New York: McGraw-Hill Education. Teece, D.J., Pisano, G. and Shuen, A. (1997) “Dynamic capabilities and strategic management,” Strategic Management Journal, 18 (7): 509–533. Tierney, K. (1997) “Impacts of recent disasters on businesses: the 1993 Midwest floods and the 1994 Northridge earthquake,” in B. Jones (ed) Economic Consequences of Earthquakes: Preparing for the Unexpected, Buffalo, NY: National Center for Earthquake Engineering Research, 189–222. Tinch, R. (1998) Resilience and Resource Management Under Risk, Norwich: School of Environmental Science, University of East Anglia. Tompkins, E.L. and Adger, W.N. (2004) “Does adaptive management of natural resources enhance resilience to climate change,” Ecology and Society, 9 (2): 1–14. Tompkins, E.L. and Adger, W.N. (2005) “Defining response capacity to enhance climate change policy,” Environmental Science & Policy, 8 (6): 562–571. Turney, P. (1996a) “How to shift bias: lessons from the Baldwin Effect,” Evolutionary Computation, 4 (3): 271–295. Turney, P. (1996b) “Myths and legends of the Baldwin Effect,” in Proceedings of the 13th International Conference on Machine Learning, Bari, Italy, 135–142.

The Baldwin Effect  107 Turney, P. (1997) “How to shift bias: lessons from the Baldwin Effect,” Evolutionary Computation, 41 (1): 73–85. Turney, P., Whitley, D. and Anderson, R. (1996) “Evolution, learning, and instinct: 100 years of the Baldwin Effect,” Evolutionary Computation, 4 (3): iv–viii. Tushman, M.L. and O'Reilly, C.A. (1996) “The ambidextrous organizations: managing evolutionary and revolutionary change,” California Management Review, 38 (4): 8–30. Tushman, M.L. and Romanelli, E. (1985) “Organizational evolution: a metamorphosis model of convergence and reorientation,” in L.L. Cummings and B.M. Staw (eds) Research in Organizational Behavior, , Vol. 2, Greenwich, CT: JAI Press, 171–222. Uhl-Bien, M. and Marion, R. (2009) Complexity Leadership, Part 1: Conceptual Foundations, Charlotte, NC: Information Age Publishing. Uhl-Bien, M., Marion, R. and McKelvey, B. (2007) “Complex leadership: shifting leadership from the industrial age to the knowledge era,” Leadership Quarterly, 18 (4): 298–318. Usher, J.M. and Evans, M.G. (1996) “Life and death along gasoline alley: Darwinism and Lamarckian processes in a differentiating population,” Academy of Management Journal, 39 (5): 1428–1466. Van Buskirk, J. and Steiner, U.K. (2009) “The fitness costs of developmental canalization and plasticity,” Journal of Evolutionary Biology, 22 (4): 852–860. Van Valen, L. (1965) “Morphological variation and width of the ecological niche,” American Naturalist, 94 (908): 377–390. Van Vugt, M. (2006) “Evolutionary origins of leadership and followership,” Personality and Social Psychology Review, 10 (4): 354–371. Veblen, T.B. (1899) The Theory of the Leisure Class: An Economic Study in the Evolution of Institutions, New York: Macmillan. Waddington, C.H. (1941) “Evolution of developmental systems,” Nature, 147 (3717): 108–110. Waddington, C.H. (1942) “Canalization of development and the inheritance of acquired characters,” Nature, 150 (3811): 563–565. Waddington, C.H. (1952a) “Selection of the genetic basis for an acquired character,” Nature, 169 (4294): 278–278. Waddington, C.H. (1952b) “Selection of the genetic basis for an acquired character: reply to Begg,” Nature, 169 (4302): 625–626. Waddington, C.H. (1952c) “The evolution of developmental systems,” in D.A. Herbert and A.H. Tucker (eds) Twenty-Eighth Meeting of the Australian and New Zealand Association for the Advancement of Science, Brisbane: Government Printer Brisbane, 155–159. Waddington, C.H. (1953a) “Epigenetics and evolution,” Symposia of the Society for Experimental Biology, 7: 186–199. Waddington, C.H. (1953b) “Genetic assimilation of an acquired character,” Evolution, 7 (2): 118–126. Waddington, C.H. (1953c) “The ‘Baldwin Effect,” ‘genetic assimilation,” and ‘homeostasis,” Evolution, 7(4): 386–387. Waddington, C.H. (1953d) “The evolution of adaptations,” Endeavour, 12 (47): 134–139. Waddington, C.H. (1957) The Strategy of the Genes, London: Allen & Unwin Ltd. Waddington, C.H. (1961) “Genetic assimilation,” in E.W. Caspari and J.M. Thoday (eds) Advances in Genetics, Vol. 10, New York: Academic Press, 257–290. Walker, B., Holling, C.S., Carpenter, S.R. and Kinzig, A. (2004) “Resilience, adaptability and transformability in social-ecological systems,” Ecology and Society, 9 (2): 1–9.

108  Bill McKelvey and Cera Oh Wang, R. 2015. Disrupting Digital Business, Boston, MA: Harvard Business Review Press. Waples, R.S., Beechie, T. and Pess, G.R. (2009) “Evolutionary history, habitat disturbance regimes, and anthropogenic changes: what do these mean for resilience of pacific salmon populations?,” Ecology and Society, 14 (1): article #3 Wcislo, W.T. (1989) “Behavioral environments and evolutionary change,” Annual Review of Ecological Systems, 20 (1): 137–169. Weber, B.H. (2013) “The Baldwin Effect in an extended evolutionary synthesis,” in B.G. Henning and A.C. Scarfe (eds) Beyond Mechanism: Putting Life Back Into Biology, Plymouth: Lexington Books, 251–258. Weber, B.H. and Depew, D.J. (eds) (2003) Evolution and Learning: The Baldwin Effect Reconsidered, Cambridge, MA: Bradford/MIT Press. Weick, K.E. and Sutcliffe, K.M. (2001) Managing the Unexpected: Assuring High Performance in an age of Complexity, San Francisco, CA: Jossey-Bass. Weill, P. and Woerner, S.L. (2018) “Is your company ready for a digital future?,” MIT Sloan Management Review, 59 (2): 21–25. Weismann, A. (1889) The Germ-Plasm: A Theory of Heredity (translated by W.N. Parker and H.R. Ronnfeldt), London: Walter Scott and Scribner’s. West-Eberhard, M.J. (1989) “Phenotypic plasticity and the origins of diversity,” Annual Review of Ecological Systems, 20, 249–278. West-Eberhard, M.J. (2003) Developmental Plasticity and Evolution, Oxford: Oxford University Press. West-Eberhard, M.J. (2005) “Phenotypic accommodation: adaptive innovation due to developmental plasticity,” Journal of Experimental Zoology. Part B – Molecular and Developmental Evolution, 304B (6): 610–618. Westerman, G., Bonnet, D. and McAfee, A. (2014) Leading Digital: Turning Technology Into Business Transformation, Boston, MA: Harvard Business Review Press. White, M.C., Marin, D.B., Brazeal, D.V. and Friedman, W.H. (1997) “The evolution of organizations: suggestions from complexity theory about the interplay between natural selection and adaptation,” Human Relations, 50 (11): 1383–1401. Whyte, L.L. (1965) Internal Factors in Evolution, New York: George Braziller. Windig, J.J., de Kovel, C.G.F. and de Jong, D. (2004) “Genetics and mechanics of plasticity,” in T.J. DeWitt and S.M. Scheiner (eds) Phenotypic Plasticity, Oxford: Oxford University Press, 31–49. Winter, S.G. (1990) “Survival, selection, and inheritance in evolutionary theories of organization,” in J.V. Singh (ed): Organizational Evolution: New Directions, London: SAGE, 269–297. Witham, M.D. and Sayer, A.A. (2015) “Biological resilience in older people–a step beyond frailty?,” European Geriatric Medicine, 6 (2): 101–102. Witt, U. (2005) “The evolutionary perspective on organizational change and the theory of the firm,” in K Dopfer (ed) The Evolutionary Foundations of Economics, Cambridge: Cambridge University Press, 339–364. Wokurka, G., Banschbach, Y., Houlder, D. and Jolly, R. (2017) “Digital culture: why strategy and culture should eat breakfast together,” in G. Oswald and M. Kleinemeier (eds) Shaping the Digital Enterprise, London: Springer International Publishing, 109–120. Wolpert, D., Jamison, J., Newth, D. and Harre, M. (2011) “Strategic choice of preferences: the persona model,” B.E. Journal of Theoretical Economics, 11 (1; article 18): 1–37. Yeh, P.L. and Price, T.D. (2004) “Adaptive phenotypic plasticity and the successful colonization of a novel environment,” American Naturalist, 164 (4): 531–542.

The Baldwin Effect  109 Zhou, H., Wang, J., Wan, J. and Jia, H. (2010) “Resilience to natural hazards: a geographic perspective,” Natural Hazards, 53 (1): 21–41. Zia, A., Kauffman, S., Koliba, C., Beckage, B., Vattay, G. and Bomblies, A. (2014) “From the habit of control to institutional enablement: re-envisioning the governance of socialecological systems from the perspective of complexity sciences,” Complexity, Governance & Networks, 1 (1): 79–88. Zollo, M. and Winter, S.G. (2002) “Deliberate learning and the evolution of dynamic capabilities,” Organization Science, 13 (3): 339–351. Zollo, M., Bettinazzi, E.L., Neumann, K. and Snoeren, P. (2016) “Toward a comprehensive model of organizational evolution: dynamic capabilities for innovation and adaptation of the enterprise model,” Global Strategy Journal, 6 (3): 225–244.

3

Why Lamarck dominates Darwin in explaining organizational change and evolution Tammy L. Madsen and Bill McKelvey

Introduction The dynamic capabilities of firm form a central pillar of the resource-based view of competitive strategy (Helfat et al, 2009; Teece 1984; Wernerfelt 1984; Winter, 2003). The dynamic capabilities of firms are those processes that contribute toward “appropriately adapting, integrating, and re-configuring internal and external organizational skills, resources, and functional competences” in changing environments (Teece et al. 1994: 12). Dynamic capabilities, therefore, play an important part in (1) determining the coevolutionary success of firms competing against each other in an industry population, and (2) the evolution of these populations. A fundamental debate remains, however, (1) as to the organizational processes underlying dynamic capabilities and (2) whether industry populations evolve because member firms have dynamic capabilities or whether populations change as a result of the death and replacement of member firms suffering from inertia. Two competing theories of evolution figure in this debate. Lamarckian (adaptation) perspectives focus on the intrafirm level of analysis and emphasize that the evolution of industry populations reflects changes in the strategy, structure, and capabilities of member firms in response to environmental pressures and opportunities (Cyert and March 1963; Lawrence and Lorsch 1967; Thompson 1967; Child 1972; Pfeffer and Salancik 1978; Nelson and Winter 1982; Teece et al. 1994). Darwinian (selection) views emphasize the structural inertia (Hannan and Freeman 1977, 1984; Astley 1985; Gopalakrishnan and Dugal 1998; Abatecola 2014; Caldart et al. 2014; Abatecola et al. 2016) present in firms that mitigates against dynamic capabilities, arguing instead that industry evolution occurs via the death and replacement of member firms based on external selection forces (Brittain and Freeman 1980; Carroll 1984). This chapter focuses on both elements of the debate. We hypothesize that Darwinian principles of natural selection may operate inside firms to create dynamic capabilities. These in turn act to thwart the effects of internal inertia, thereby improving firm performance and reducing the effect, via death and replacement, of external natural selection forces on industry evolution. Directing attention to the intrafirm adaptive processes associated with firm performance 

Lamarck vs. Darwin for explaining change  111

illuminates the mechanisms underlying organizational evolution and suggests a rapprochement between selection and adaptation perspectives. Weick (1979) defines internal natural selection, but little work has been done to examine the relationship between this process and firm performance. Firms have been observed to adjust the balance between adaptive processes of internal variation, selection, and retention (VSR) to address external selection pressures (Burgelman 1991; Miner 1994). The concept of balanced continuity, introduced here, suggests that a particular balance among continuous internal natural selection processes is necessary to sustain performance in a competitive environment. This chapter examines the relationship between internal VSR processes and firm performance and provides a partial test of the concept of balanced continuity. In subsequent sections, we discuss organizational evolution, internal natural selection processes, describe the research design employed, and present the results. We conclude with a discussion of the implications of this research for the study of organizational evolution.

Evolution—two processes of population change Organizational evolution is an explanation in which performance differences among industry populations and their member firms are attributed to a continuous process of slight or dramatic change over a long period of time (Aldrich 1979; McKelvey 1982; Nelson and Winter 1982). An industry population’s ecosystem is defined as a group of evolutionarily interacting organizations embodying similar combinations of key competencies (McKelvey 1982; Baum and Singh 1994a: 10). Darwinian coevolutionary change involves a change in the “blueprints” (Hannan and Freeman 1977), “competencies” (McKelvey 1982), or “routines” (Nelson and Winter 1982) held by members of a firm, that ultimately are diffused throughout an industry population. Routines and competencies reflect a firm’s experience-based knowledge, skills, and learning capabilities (McKelvey 1982; Nelson and Winter 1982). In the resource-based view, firms have at their disposal configurations of routines and competencies temporarily embodied in tacit, or not so tacit, knowledge held by their employees at any one time (Teece et al. 1994; Mosakowski and McKelvey 1997). For the most part, evolutionary perspectives take a company-level analysis, focusing on the adaptation, or death and replacement, of firms with respect to an exogenous competitive context (Hannan and Freeman 1977; Aldrich 1979; McKelvey 1982; Nelson and Winter 1982). Researchers also began applying Darwinian principles to intrafirm parts a while ago (Burgelman 1991, 1994; Baum and Singh 1994b; Brittain 1994; Rosenkopf and Tushman 1994; and Van de Ven and Garud 1994; McKelvey 1996). We begin by defining the Darwinian and Lamarckian views of evolution as they typically apply at the firm level of analysis. We then move to an intrafirm level of focus. We focus on Baldwin’s (1896) (Chapter 2) and Lamarck’s (1908) emphasis of evolution

112  Tammy L. Madsen and Bill McKelvey

based on learning during a parent’s lifetime that may be passed on to offspring. Toward the end of this chapter, we highlight some of the emerging literature on digital business ecosystems. Darwinian evolution—natural selection

Most research in organizational evolution applies the Darwinian view of population dynamics, where organizations are severely constrained by inertial forces, and change occurs within a population via environmental selection forces rather than internal adaptation (Aldrich and Pfeffer 1976; Hannan and Freeman 1977, 1984; Carroll and Hannan 1989). Ecological theory argues that inertia limits a firm’s ability to institute adaptive changes (Hannan and Freeman 1984). Variations at the population level1 of member firms only arise by chance as entrepreneurs start up new firms—based on Darwin’s theory of evolution. Failing firms are locked into obsolete capabilities while replacement firms survive because they have advantageous capabilities, given the present environmental conditions. External selection forces, in the form of competitors and environmental constraints, provide a context in which some firms thrive and are selected favorably while others fail and are replaced (Hannan and Freeman 1989; Singh 1990; Singh and Lumsden 1990). Darwinian evolution only occurs when all four principles of natural selection are simultaneously in effect (Dobzhansky et al. 1977; Lewontin 1978; McKelvey 1982): 1. Principle of Variation: Differences in competencies and fitness occur across organizations; 2. Principle of Selection: Environmental forces selectively discriminate against some organizational variations and favor others within a population; 3. Principle of Retention and Diffusion: Favored variations are retained and diffused throughout the population; 4. The Struggle for Existence: The competitive context is such that organizations holding a larger proportion of favored competencies will deprive organizations holding fewer favored competencies of required resources, leading to the eventual failure of the latter. The fourth principle emphasizes the role of competitive tensions in organizational evolution. Favored competencies and resources are those that are idiosyncratic2 and generate rents (Wernerfelt 1984; Barney 1991). In addition, idiosyncratic resources and competencies generate rents only as long as they remain relevant to the environment (Barney 1991). Favored competencies may also work to assure survival, but without rents (Mosakowski and McKelvey 1997; Mosakowski 1998). In Darwin’s theory of evolution, “variations” occur in the offspring of mated female and male members of a species, because the mix of DNA from

Lamarck vs. Darwin for explaining change  113

the female and male parents is randomly distributed among the offspring, whether there is only one or several. This causes some offspring to survive and pass on their genetic structure, whereas others die because they inherited a dysfunctional mix of DNA. In Darwin’s theory, the DNA mix among the offspring has a dominant effect in causing them to survive or not. The DNA differences led to what he referred to as “variations.” In 1896 Baldwin and others separately introduced learnings during a lifetime (in biology it is mostly the mother’s learning that has impact on the offspring since the male is usually not around to help the offspring grow up to become adult members of a species). Darwin-oriented evolutionary biologists ignored Baldwin and later also ignored Lamarck and their (Baldwin’s and Lamarck’s) emphasis of learnings during a lifetime that, in fact, modern biological research after 1980 shows also affects species evolution. Lamarckian evolution—organizational adaptation

Lamarck (1908, and later) also focused on learnings, but biologists disregarded his writings because they—the biologists—concluded that Lamarck’s theory also held that acquired characteristics resulting from a mother’s learnings during her lifetime could be directly passed on to her offspring—as opposed to via DNA.3 Other than Waddington’s early research and writing (1941, 1942, 1952a,b,c, 1953a,b,c,d), only after 1980 did other biologists start doing research pertaining to whether mostly very small species had mothers who could learn and change their behavior because of changes in the surrounding environment—they focused mostly on very small species (that also had tiny brains) because they died more quickly, which allowed the researchers to study multiple generations of a species during their own (the human researchers’) lifetimes. They studied whether the mother’s learning was then passed on to her offspring, i.e. could the offspring inherit the acquired characteristics of their mother (which were not in her DNA). See Appendices 1 and 2 in Chapter 2 for lists of relevant biologists’ research and published articles. Lamarck (1809) defined the earliest complete theory of evolution, which is especially relevant to organization evolution. His view, however, has since been discredited in biology (Mayr 1982), but it offers a useful and relevant alternative to Darwin’s theory for better understanding of organizational evolution. Applying Lamarckian theory to firms, internal organizational adaptive changes arise purposefully in response to shifting environmental changes and consequent tensions motivating change observed and/or learned by members of the firm, and not by blind chance (i.e. Darwin’s (1859) VSR. Darwin’s theory argues that variations only arise by chance and are “blind” as to their adaptive efficacy, and it is selection forces that reflect environmental constraints. Sixty-six years ago Penrose (1952) argued that organizational adaptation is often too perfect to be accounted for simply by chance and some changes arise via managerial responses to environmental pressures, i.e. from their learning.

114  Tammy L. Madsen and Bill McKelvey

Goldschmidt (1976) also suggested it also is likely that a certain portion of internal organizational changes may be purposefully responsive to environmental needs—managers can learn: they study their competitive ecosystem and, if necessary, make change. Lamarckian (adaptation) perspectives suggest that people in organizations take a purposeful role in searching for alternatives, and firms—unlike most biological species—are consequently able successfully adapt to shifting environmental conditions so as to ensure performance and continued survival (Lawrence and Lorsch 1967; Thompson 1967; Pfeffer and Salancik 1978; Andrews 1980; Cheng et al. 2014; Chakrabarti 2015; Levinthal and Marino 2015; Dattée and Barlow 2017)—see Chapter 2 in this book.4 A variety of theories exist in the management-relevant literature that emphasize different internal factors that influence organizational change and adaptation: •• •• •• •• •• •• •• •• ••

Structural Contingency Theory (Burns and Stalker 1961; Lawrence and Lorsch 1967; Thompson 1967; Wadongo and Abdel-Kader 2014; Pratono 2016; Shirokova et al. 2016); Goals, Expectations, Choice and Control (Cyert and March 1963; Scott 2014; Gerdenitsch et al. 2015; Król 2016); Strategic Management Theory (Chandler 1962; Child 1972; Miles and Snow 1978; Andrews 1980; Jauch and Glueck 1988; Trigeorgis and Reuer 2016; Aguinis et al. 2017); Organizations as Institutions (Zucker 1977, 1983, 1987; Greenwood et al. 2014; Scott 2014; Bachmann et al. 2015; Annosi and Brunetta 2017; Klüppel et al. 2017); Resource Dependence Theory (Pfeffer and Salancik 1978; Coupet and McWilliams 2017); Organizational Learning (Fiol and Lyles 1985; Levitt and March 1988; March 1991; Liao et al. 2017; Starbuck 2017); Organizational Change (Bennis et al. 1976; Goodman et al. 1982; Kanter 1983; Aarons et al. 2015; Al-Haddad and Kotnour 2015: Lord et al. 2015; Hornstein 2015; Hughes 2017; Kuusela et al. 2017; Yousef 2017); Organizational Development (French et al. 1994; Hoobler et al. 2014; Flamholtz and Brzezinski 2016; Sanders 2016); Resource-Based View (Teece et al. 1994; Henderson and Cockburn 1994; Lin and Wu 2014; Wu and Chiu 2015; Backman et al. 2017).

As additional background, in much of the relevant literature over the past fifty years, biologists reduced Lamarck’s concept of species’ change over time to the following: “Lamarck suggested that traits acquired during life were passed on ‘to a mother’s offspring.’ In contrast Darwin…suggested that biological traits were passed from parent to offspring, but these were unrelated to what was acquired during life” (Clark 2017: Ch. 2). Only after 1980 did biologists start creating experiments showing that species’ mothers could learn new things during their lifetimes as their environment changed, and that these

Lamarck vs. Darwin for explaining change  115

new learnings could be passed on to offspring—and also these experimental biologists started studying whether, or not, the newly learned behaviors during a mother’s lifetime would eventually end up in a species’ genetic structure. Many of these experiments are described in articles that are included in the Appendices of Chapter 2, which is about the Baldwin Effect—which focuses on parental learning during their lifetimes. As also noted in Chapter 2, most of the experiments cited were based on very small animals (with tiny brains), that had very short life-spans (two to three years) so the researchers could study multiple generations over time to experimentally test whether the newly learned concepts, actually, eventually appeared in the species’ genetic structure. Note, however, that our primary learning about the Baldwin Effect (hereinafter called the Baldwin Principle) is that when it is applied to humans, who have vastly larger brains than the tiny brains of insects, snails and tadpoles, etc., and who (humans) can also communicate among each other by sophisticated languages, their learning is vastly greater than learning by the tiny brains of insects, tadpoles, and snails, etc. The implication is that the lifetime learning among organizational employees and managers has a vastly greater effect on organizational coevolution over time in changing industry ecosystems (Havas et al. 2014; Kashan and Mohannak 2017; Tsujimoto et al. 2017) than the Baldwin Principle has among species with tiny brains. Selection vs. adaptation

The earliest rapprochement between Darwinian and Lamarckian approaches was developed by Weick (1979). In Weick’s view, purposeful adaptive outcomes are achieved via the processes of internal natural selection, i.e. VSR, by people who can (or are allowed to) learn. Managers, having studied the constraints of the external environment, and understanding the adaptive needs of their firms, “enact” programs of action which are Weick’s equivalent to Darwin’s variations—“Enactment is to organizing as variation is to natural selection” (1979: 130). March (1991) says that firms adapt to their surroundings by exploring new variations and environments, i.e. learning, thereby selecting alternative courses of action, implementing adjustments to environmental changes, and exploiting the existing environment and organizational competencies in novel ways. Other authors suggest that selection and adaptation are interrelated processes of change (Singh et al. 1986; Burgelman 1991; Levinthal 1991). First, in examining organizational change and mortality, Singh et al. consider whether adaptation arguments are more consistent with the empirical relationships between organizational change and mortality than ecological arguments. Their findings indicate that all changes are not adaptive with respect to survival and that organizational change does not always decrease organizational death rates. Second, Levinthal argues that adaptation and selection are interrelated through processes of learning and inertia. Third, Burgelman suggests that adaptive

116  Tammy L. Madsen and Bill McKelvey

processes of internal selection may combine with death and replacement processes to explain change in populations. Our previous empirical research (Madsen and McKelvey 2005) directs attention to the need for simultaneous modeling of selection and adaptation processes for a complete (and correct) theory of evolutionary change by organizations.

Intra-organizational evolution The study of evolution may span multiple levels of analysis (intrafirm, firms, industry populations, and multiple population communities) nested within a hierarchy5 (McKelvey 1982; Astley 1985; Tushman et al. 1986; Fombrun 1988; Kauffman 1993; Barnett 1994; Baum and Singh 1994b; Brittain 1994; Pianka 1994; Rosenkopf and Tushman 1994; Van de Ven and Garud 1994; McKelvey 1996; Abatecola 2014; Abatecola et al. 2016; Levinthal and Marino 2016; Boukhedouma et al. 2017). This literature indicates the several ways in which each level of the hierarchy interacts with other levels, influencing change processes and forming the patterns of organizational evolution. Given that in natural selection theory, selection is a contextual (or external) property affecting variation and retention, confusion may result once we think of evolution as a multilevel process. Plotkin (1993) uses the notion, “Darwin machine,”6 to represent the effect of the four Darwinian principles at a particular level of analysis (though selection may operate from a different level, i.e. an organization’s competitive ecosystem). Thus, an organism or firm may have Darwin machines operating at several internal levels as well as at its niche or environmental level. In a minimal hierarchical view, variation and selection processes exist where variations are weeded out at two levels, that is, two Darwin machines: (1) at the intrafirm level—firms develop a portfolio of variations from which managers select, and (2) at the firm level—external agents select some firms over others based on the variations retained by the firms. Two levels of retention also exist: (1) at the intrafirm level —variations selected by managers and retained by the firm are diffused throughout the firm, and (2) at the firm level—variations, retained by a firm that is favored by external selection agents, are diffused throughout the population. The following subsections define internal VSR and identify organizational mechanisms that facilitate each part of the internal natural selection process. Learning in companies relevant to Darwin’s variation

The heart of internal natural selection involves variation as trial-and-error learning events (Campbell 1969; McKelvey 1994). Intrafirm variations occur via (1) intentional or unintentional learnings; (2) focused or unfocused learnings; or (3) direct/indirect incentive systems to induce learning (Miner 1994). Whether intentional or unintentional, intrafirm learnings may often be guesses as to what

Lamarck vs. Darwin for explaining change  117

is relevant (i.e. mostly blind) as managers do not necessarily know, under conditions of uncertainty and competition, which of their learnings or learning events (Darwin’s variations) will actually become successful adaptive outcomes and consequently enhance their firm’s performance. Learnings (i.e. Darwin’s [1859] variations) may occur purposefully in response to environmental changes, they might be planned but not necessarily aimed at a particular environmental condition or, as variations, they might just happen (Campbell 1969; McKelvey 1994). Variations based on employee or management learnings also might arise from combinations of old and new routines that are not currently recognized as distinct competencies for a firm (Nelson and Winter 1982; Teece 1982). Some firms acknowledge the value of unfocused experimental learning and encourage boundary-spanning activities such as the learning about new environments, new ideas, and new improvements of competence (March 1991; Kanter et al. 1992; Ulrich et al. 1993). Firms often create havens for “safe learning” such as “skunkworks,” which facilitate informal work on new ideas (Gwynne 1997; Galbraith 1982; Peters and Waterman 1982; Miller 1993; Brown 2004; Palczewski 2017). Firms also promote variation via learnings from experimentation activities that include: (1) formalized research and development (Miner 1994); (2) identifying “champions of change” who shape a vision within firms and lead focused experimentation efforts (Kanter et al. 1992); (3) creating parallel projects where several teams work on the same problem, generating learning pertaining to competition around creating potential new product or technology variations/changes (Miner 1994); (4) design thinking practices (Martin 2009); and (5) crowdsourcing. Reward systems that provide direct and indirect incentives to individuals also facilitate variation (Lawler 1991; Kanter et al. 1992; Miner 1994; Badcock et al. 2017; Darbonnens and Zurawska 2017). Efforts to produce variations are motivated by incentives that reinforce useful innovation as part of standard responsibilities, compensate individuals for patents or innovative work, or allocate limited resources based on competition among employees. Organizations differ based on how they structure the variation process, that is, by how much they promote focused and unfocused experimentation and incentive systems. Increasing the level of variation promotes the learning process, which diffuses throughout the organization and leads to more adaptive forms of behavior (Aldrich 1979; Weick 1979). Developing a portfolio of variation alternatives from which to draw new ideas and test learning positions a firm more effectively to respond in the event of environmental change, as compared to firms that lack alternative courses of action and experience with experimentation (Nadler and Tushman 1988; Kanter et al. 1992). In addition, Tushman and Anderson (1986) show that firms that engage in change grow more rapidly than other firms. Firms that fail to learn new things and then to adopt changes in behavior—and continue to invest in obsolete practices—may lose touch with the competitive environment and risk failure. On the other hand, while learning, novel change, or “non-institutionalized innovation,” increases the potential for achieving competitive advantage, it also increases the risk of firm failure

118  Tammy L. Madsen and Bill McKelvey

(Zucker 1987), because some new ideas and resultant changes may induce failure. Consequently, firms need to achieve a balance between learning new ways of working (variations) vs. reinforcing past experience. Too much variation activity caused by learning may have adverse implications for firms. First, excessive variation in organizational form, based on new learning, threatens the preservation of complexly adaptive forms (Campbell 1969) and is disruptive to the firm as a whole (Hannan and Freeman 1984). In Amburgey et al.’s (1993) study of the Finnish newspaper industry, changes in product content and frequency of publication result in an immediate increase in the hazard rate of firm failure. Second, the cost of excessive variation may be detrimental to a firm’s performance in the long run and place the firm at risk of losing market share. Third, frequent change may result in random drift rather than performance enhancement when a firm’s operations are altered prior to the firm fully understanding the competitive environment (Lounamaa and March 1987). While high levels of learning and consequent variation are necessary in order to provide a sufficient amount of requisite variety in the event of environmental change (McKelvey and Aldrich 1983), too much variation may send the organization into a downward spiral (Hambrick and D’Aveni 1988; Hannan and Freeman 1984) or lead to suboptimal equilibria (March 1991). Further, the cost of excessive focus on learning and variation may be detrimental to the firm. Thus, trying out too many new ideas all the time causes too much variation, which could cause low performance and/or what complexity theorists label “chaos.” But, if the level of variation is too low, excessive controls and reinforcement of previously retained variations override learning and experimentation, constrain the selection of new variations and, thus, contribute to inertia. Learning in companies relevant to Darwin’s internal selection

Internal selection is management’s choice of learning and change that lead to Darwin’s variations (Weick 1979). Organizations facilitate internal selection primarily through administrative and cultural control mechanisms (McKelvey and Aldrich 1983; Burgelman 1991; Miner 1994). Administrative control mechanisms include strategic planning, goal setting, and rules governing research allocation (Weick 1979; Burgelman 1991) as well as project evaluation criteria, schedules or basic pre-screening criteria for projects, competition for resources or standards within the organization, and informal competition within an organization (Miner 1994). Organizations also promote selection by defining goals—and consequent kinds of learning needed to achieve the goals—but may not explicitly identify actions needed to achieve the goals. In this scenario, lower level employees may use goals to guide their learning and selection of variations so as to determine their course of action (Miner 1994). In addition, goals set premises, which are accepted by organizational members as a basis for future decisions (Simon

Lamarck vs. Darwin for explaining change  119

1976). However, goals and cultural control mechanisms can cause behavioral norms. Organizations are held together in part by a normative glue (Blau and Scott 1962). Allegiance to behavioral norms or status quo may underlie selection processes—norms against offering suggestions, against learning or experimenting or against taking initiatives may select out beneficial variations (Weick 1979). In the absence of learning, variations are not selected or retained, and previous firm behavior plays a larger role than new variations in defining firm performance. Consequently, absent relevant learning, the firm’s core characteristics and/or its organizational competencies may progressively become less fit with the environment over time as the reinforcement of past behavior contributes to inertia and precludes attention to environmental change (Stinchcombe 1965; Hannan and Freeman 1984; Meyer and Zucker 1989). Additionally, other organizational characteristics may also influence behavior in the absence of new learning relevant to administrative and cultural control processes (Meyer 1994). For example, political coalitions or a few tight, small, networks may govern the selection of alternatives. Consequently, changes may be selected regardless of whether they benefit the firm as a whole. In addition, a firm’s members often resist adopting change when they may be at risk of losing private gains. Firm members’ many divergent interests often drive the preservation of existing patterns of firm behavior despite the need for change (Meyer and Zucker 1989). Inordinately high levels of selection, or too high a pressure within a firm for control by top managers, may also adversely impact a firm’s performance (Weick 1979; McKelvey and Aldrich 1983). First, excessive procedures, such as rigorous project-evaluation criteria, as well as deep-set behavioral norms, may limit the amount of new learning and, thus, the number of variations managers evaluate. Second, managers may hesitate to evaluate and select particular variations when maintaining a previous course of action requires less effort than pursuing a learning process and then adopting of a new variation. Obviously, over time, learning and consequent variation activity may disappear if firms allow tight control practices (Campbell 1969). Learning in companies relevant to Darwin’s retention

Retention involves the diffusion and reinforcement of chosen learning and consequent variations. It represents the firm’s memory and its experiencebased knowledge: stored information from the firm’s history that can influence present and future decisions (Walsh and Ungson 1991). Firms facilitate retention through (1) implementation processes focused on maintaining consistency between actions instigated via internal selection and the actual implementation behavior of individuals; (2) leadership that serves as the driving force behind change and establishes a commitment to change efforts (Kanter et al. 1992); and (3) organizational design which facilitates communication and augments the transfer of information across units to share results of previously retained

120  Tammy L. Madsen and Bill McKelvey

variations and facilitate the diffusion process (Nadler and Tushman 1988). In addition, a continuous review of change efforts through management information systems, budgets, and schedules assists in propagating consistent behavior across subunits (Kanter et al. 1992). Levels of retention that are too high reflect the reinforcement of past behavior. With repetition, each retained variation becomes more routine to a firm and the chances that it will be used again in the future increase (Stinchcombe 1965; Nelson and Winter 1982; Levitt and March 1988). The current wisdom of the firm stems from past events, but is only useful if the environment remains stable (Campbell 1969; Chakravarthy 1982). Opportunities and risks are often blurred by familiarity (Andrews 1980) and when environments change, prior firm practices and procedures may no longer be functional (Nadler and Tushman 1988). Consequently, firms that fail to look beyond present behavior are vulnerable to surprise (Andrews 1980). Retention activities that are too rigorous act as mechanisms of inertia, which constrain internal variation and selection, and become obstacles to adaptive outcomes and sustained firm performance. The absence of internal retention also promotes low firm performance. Low firm retention levels may indicate: (1) the firm is not drawing from its experience based knowledge; (2) a lack of feedback on performance outcomes of previously implemented variations; and/or (3) a lack of diffusion of selected variations. First, Levinthal (1991) argues that building on existing knowledge enhances firm survival chances. Not utilizing current know-how also results in a resetting of the firm’s liability of newness clock thus, exposing firms to the kinds of risks associated with young firms (Stinchcombe 1965; Carroll and Delacroix 1982; Freeman et al. 1983; Amburgey et al. 1993). Second, retention serves as a database from which to compare future courses of action. Without feedback on the performance outcomes of previously retained variations, trial and error learning breaks down as managers fail to learn which variations result in effective or ineffective outcomes and possibly even why they do so (Levinthal and March 1993). Associations to past actions and the performance of those actions are necessary for learning to occur (Fiol and Lyles 1985; Lounamaa and March 1987; Argote and Hora 2017; Starbuck 2017; Zuzul and Edmondson 2017). Third, a lack of diffusion of retained variations throughout the firm may reflect an inability to integrate knowledge across the firm and/or a lack of leadership support for efforts to change. The absence of diffusion might indicate that the firm is not transforming experience into routines and recording routines in the firm’s memory (Levitt and March 1988). As a result, knowledge of past experience may not be available for integration across the firm. When selected variations are not diffused, parts of a firm continue to base behavior on previous actions which may not satisfy current environmental conditions. In addition, effective diffusion of selected variations requires leadership support for change efforts (Kanter et al. 1992; Bennis 1993; Todnem 2005; Al-Haddad and Kotnour 2015) and consistency between preferred actions

Lamarck vs. Darwin for explaining change  121

resulting from internal selection and the actual behavior of individuals. A lack of leadership support presents a negative influence for employees attempting to implement a variation and limits the ability of a firm to accomplish change effectively. If variations are implemented that are inconsistent with a selected course of action, they may result in outcomes incommensurate with the originally selected change. This jeopardizes the organization’s ability to satisfy environmental pressures and sustain firm performance. Learning in companies relevant to Darwin’s balanced continuity in competitive ecosystems

Internal natural selection is a continuous chain of events (Campbell 1969; Weick 1979). This process allows firms to develop and adopt changes that meet environmental pressures (Burgelman 1994). Each element of the VSR process is necessary for the adoption of variations. Dysfunctionally high or low levels of VSR and/or new learning activities constrain a firm’s ability to adopt changes and could limit the firm’s responsiveness to internal and external pressures. While Campbell (1969) and Weick (1979) emphasized that internal natural selection is a continuous process, they do not address what level of variation, selection or retention is necessary for adaptive outcomes to occur. McKelvey and Aldrich (1983: 125) say, “Managers should attempt a balanced emphasis on all four principles as the best way of increasing the chance of the survival of their organization.” Tushman and Romanelli (1985) argue that successful organizations are those that develop a balance between change and stability, while March (1991) calls for balancing exploration and exploitation. The concept of balanced continuity and balanced learning (about all aspects of organizational change) builds on these arguments. Firms differ on how they structure the internal natural selection process. Two sets of pressures exist: (1) internal pressures for VSR and new learning; and (2) environmental pressures for VSR and new learning. An example of internal pressures involves top management pressuring lower-level employees in an organization to pursue a high level of variation and a low level of retention or much new learning about change but little new learning about what a company is already doing. Depending on how firms structure the process, different distributions of the level of VSR processes will occur across firms, which in turn gives rise to varying patterns of behavior or different VSRs and relevant new learning profiles. Internal VSR processes jointly form an intertwined process that becomes the basis for idiosyncratic dynamic capabilities—companies can’t just focus on new learning about only one possible change; learning has to focus on the entire complex mix. How effectively the links among the VSR processes and relevant new learning are managed influences the firm’s ability to adopt changes, learn from past experience and better control the types of changes adopted. Firms compete using different patterns of change or different VSR profiles. Balanced continuity suggests that, in a competitive environment,

122  Tammy L. Madsen and Bill McKelvey

all three internal VSR pressures must exist in a balanced relationship—with appropriate balanced learning—for firms to achieve adaptive outcomes and thus sustain performance. By finding the appropriate balance among VSR, firms gain an advantage relative to rivals. A match between VSR balance and the sources of competitive advantage (idiosyncratic competencies and resources), in a changing environment, allows a firm to achieve a dynamic capability and thereby sustain its performance. In the absence of balanced continuity, firms lack the ability to adopt changes necessary to sustain performance under changing competitive conditions and become subject to external selection forces. Thus, internal natural selection characterized by balanced continuity contributes to adaptive outcomes, sustained firm performance, and favourable external selection. All of this learning and change can happen at a vastly increased amount and speed of change in the current Digital Age, as noted next.

Organizational learning in digital ecosystems In Chapter 1, we define the complexity dynamics and elements of the Digital Age. The Digital Age consists of articles and books about the Internet of Things (e.g. deCosta 2013; Kellmereit and Obodovski 2013; McEwen and Cassimally 2013; McQuivey 2013; Schmidt and Cohen 2013; Greengard 2015; Miller 2015; Sawicki 2016) and additional articles about the digital-business ecosystems that companies now have to compete within and against other firms (e.g. Tapscott 1995, 2015; Day et al. 2003; Dourmas et al. 2005; Seigneur 2005; Brousseau and Penard 2007; Corallo et al. 2007; Muntaner-Perich and de la Rosa Esteva 2007; Nachira et al. 2007; Pappas et al. 2007; Razavi et al. 2007; Wang and Ahmed 2007; Malecki and Moriset 2008; Tan et al. 2009; Stanley and Briscoe 2010; Tan and Macaulay 2011; Herdon et al. 2012; Li et al. 2012; El Sawy and Pereira 2013; Attour and Della Peruta 2014; Cojocaru et al. 2014; Jensen et al. 2014; Rong and Shi 2014; Liu and Rong 2015; Tafti et al. 2015; Wang 2015; Coupey 2016; Rogers 2016; Choi 2017: Keerthana and Ashika Parveen 2017; Kshetri 2017; Ponce-Jara et al. 2017; Remane et al. 2017; Schallmo et al. 2017; Seo 2017; Singh and Hess 2017; Yager and Espada 2017; Schallmo and Williams 2018; Qiu et al. 2018; Vey et al. 2017; Weill and Woerner 2015, 2018). There are many more relevant available references, but almost none in “management”-oriented journals. Some of the primary differences between managing companies in the Digital Age vs. before, are that: (1) there are many more sources of information in digital form, and readily available anywhere and at any time from mobile devices, sensors, and the like; (2) information flows are vastly much faster; (3) network formations among digitally connected actors can also occur much faster, with many more actors involved; (4) complexity dynamics (detailed in Chapter 1) occur at much faster speeds. But these days, it is not just about learning, but about the effects of the Digital Age on: (1) learning; (2) access to new ideas via growing digital networks;

Lamarck vs. Darwin for explaining change  123

(3) the speed of digital information flows; (4) the vastly increased speed of digital-related complexity dynamics; and (5) the design of the organization. In organizations and other institutions (governments, non-business organizations, universities and schools, armies, etc.) coevolutionary and management theories need to recognize and take into account for the changing realities and dynamics of the Digital Age. Even in regions of the world where resources are scarce, digital realities have enabled individuals to do things such as shop online or use digital payment systems (described in more detail in Chapter 1).7

Discussion and conclusion As discussed above, interactions may exist between Baldwin’s notion of learnings, Lamarck’s theory of evolution, and Darwin’s variation, selection and retention (VSR), given managers’ and employees’ behaviors in organizations. Managers’ learnings and consequent changes (variations) may influence the type of future changes (variations) pursued by employees. Further, extensive control mechanisms may overwhelm new learnings. These results could reflect characteristics of firms that are large old firms. Research shows that large, old firms are more prone to bureaucratic rigidity effects (Haveman 1993). Lamarck’s (1908) adaptation processes—especially his focus on learning—are much more relevant to organizations than Darwin’s VSR processes. This has been indicated in prior literature, which identifies adaptive processes that determine, in part, why some firms are more likely to achieve higher levels of performance than others. First, research in organization science emphasizes that changes occur in organizational populations via two conflicting perspectives: Darwinian evolution (Hannan and Freeman 1977, 1989; McKelvey 1982) vs. Lamarckian evolution (Cyert and March 1963; Lawrence and Lorsch 1967; Pfeffer and Salancik 1978; Nelson and Winter 1982). This chapter directly addresses the foregoing debate by identifying the existing organizational reality that indicates that Lamarck’s learning-based evolutionary theory is much more relevant to human organizations and their coevolution with other competing firms and external environmental changes than Darwin’s (1859) DNA-based evolutionary theory—that was created to explain the evolution of biological species like birds, nuts, and trees. Although Weick’s (1979) internal natural selection process explains both forms of population change, nowadays and especially pertaining to organizations, Lamarck’s theory is more relevant than Darwin’s. And also, in the Digital Age, when learning, change, information flows, and networks all develop at vastly faster rates than biological evolutionary changes, learnings in human-managed organizations obviously dominate organizational coevolution. Needless to say, we emphasize the relevance to human organizations of Lamarck’s theory of evolution instead of Darwin’s. Second, managerial learnings and actions in organizations influence: (1) internal change and evolutionary processes; (2) the external competitive ecosystem and broader environmental coevolutionary system in which they and their organizations are embedded; (3) the coevolution of organizations and their

124  Tammy L. Madsen and Bill McKelvey

contexts (Baum and Singh 1994a: McKelvey 1996; Abatecola et al. 2016; Kashan and Mohannak 2017). This conflicts with the early organizational ecology and environmental determinism views, which emphasize that the nature and the distribution of resources in the environment play a larger role in organizational evolution than the internal learnings and operations of organizations (Hannan and Freeman 1977, 1989; Astley 1985; Baum and Singh 1994a,b; Madsen and McKelvey 2005). Third, the mechanisms that facilitate internal natural selection reaffirm links between different bodies of research. For example, in stable environments, theories about organizing, controlling, planning, and reinforcing behavior— absent learnings—may be appropriate for intra-organizational processes whereas, in changing environments, theories of organizational learning, organizational change, or entrepreneurship may play a more important role. Fourth, developing a portfolio of learnings and consequent variations as well as selection and retention capabilities might better position a firm to respond to shifting environmental conditions than firms lacking these capabilities. Fifth, managerial and employee responses to intra-organizational and broader environmental ecosystem tensions leading to new learnings and changes pertaining to internal natural selection processes are central to achieving adaptive outcomes, dynamic capabilities, and higher levels of performance. In conclusion, the theory presented in this chapter shows that learnings, adaptation, and selection are interrelated processes of change and that evolution at the intrafirm level partially drives performance outcomes at the firm level of analysis. The main contribution of this chapter—based on the discussion of the Baldwin Effect in Chapter 2—is the use of human brains and learnings to explain why Lamarck’s theory about learning and consequent adaptation processes should dominate Darwin’s theory when organizational evolution is the subject of interest. In general, adopting only a firm-level selection or adaptation view misses insights that may be gained by considering these theories of evolution as interrelated intrafirm processes of change. Additionally, limiting research to one kind of analysis (i.e. Darwin’s VSR) severely constrains our understanding of the forces underlying learning processes and dynamic capabilities within and among organizational populations. By identifying the conditions that give rise to learnings in organizations in the Digital Age, this chapter highlights potentially rewarding lines of inquiry in the study of organizational evolution, dynamic capability, and competitive strategy.

Notes 1 Variations are defined as alterations in the state, form, or function of a particular firm attribute (Weick 1979; McKelvey and Aldrich 1983). 2 Idiosyncratic resources are those that are unique, inimitable, and non-substitutable (Barney 1991). 3 See Chapter 2 for more details about Baldwin and his contribution—now called the Baldwin Effect or Baldwin Principle—and about the other two authors introducing the same idea in 1896.

Lamarck vs. Darwin for explaining change  125 4 Conceptually, performance is the achievement of stakeholder satisfaction relative to competitors in the environment and is an outcome of adaptation. Firms sustain performance by learning how to make above average profits, relative to competitors, over the long run (Porter 1985). 5 The community level ‘focuses on the rise and fall of populations as basic units of evolutionary change’ (Astley 1985). 6 High disagreement within units implies potential misinformation among informants, references to different units of analysis or misleading selection of informants, which violates the sampling of key informants. 7 Sad to say, and ironically, authors writing about organizational evolution before the 1990s paid more attention to applying Darwin’s evolutionary theory to organizations than Lamarck’s theory and were unaware of Baldwin’s 1896 focus on learning, which motivated only Waddington (1941, 1942, 1952a,b,c) etc. before Simpson (1953) labeled it the Baldwin Effect, and before all the biologists started doing experiments (after 1980) that proved that parental-learning (mostly mothers’) and their teaching of their offspring enhanced survival and eventually even altered the genetic structure of their species (see all of the studies cited in the Appendices of Chapter 2).

References Aarons, G.A., Ehrhart, M.G., Farahnak, L.R. and Hurlburt, M.S. (2015) “Leadership and organizational change for implementation (LOCI): a randomized mixed method pilot study of a leadership and organization development intervention for evidence-based practice implementation,” Implementation Science, 10 (1): 1–12. Abatecola, G. (2014) “Research in organizational evolution. What comes next?” European Management Journal, 32 (3): 434–443. Abatecola, G., Belussi, F., Breslin, D. and Filatotchev, I. (2016) “Darwinism, organizational evolution and survival: key challenges for future research,” Journal of Management & Governance, 20 (1): 1–17. Aguinis, H., Edwards, J.R. and Bradley, K.J. (2017) “Improving our understanding of moderation and mediation in strategic management research,” Organizational Research Methods, 20 (4): 665–685. Al-Haddad, S. and Kotnour, T. (2015) “Integrating the organizational change literature: a model for successful change,” Journal of Organizational Change Management, 28 (2): 234–262. Aldrich, H. (1979) Organizations and Environments, Englewood Cliffs, NJ: Prentice Hall. Aldrich, H. and Pfeffer, J. (1976) “Environments of organizations,” Annual Review of Sociology, 2 (1): 79–105. Amburgey, T.L., Kelly, D. and Barnett, W.P. (1993) “Resetting the clock: the dynamics of organizational change and failure,” Administrative Science Quarterly, 38 (1): 51–73. Andrews, K.R. (1980) The Concept of Corporate Strategy, Homewood, IL: Richard D. Irwin. Annosi, M.C. and Brunetta, F. (2017) New Organizational Forms, Controls, and Institutions: Understanding the Tensions in ‘Post-Bureaucratic’ Organizations, London: Springer. Argote, L. and Hora, M. (2017) “Organizational learning and management of technology,” Production and Operations Management, 26 (4): 579–590. Astley, W.G. (1985) “The two ecologies: population and community perspectives on organizational evolution,” Administrative Science Quarterly, 30 (2): 224–241. Attour, A. and Della Peruta, M. (2014) “Architectural knowledge: key flows and processes in designing an inter-organisational technological platform,” Knowledge Management Research & Practice, 14 (1): 27–34.

126  Tammy L. Madsen and Bill McKelvey Bachmann, R., Gillespie, N. and Priem, R. (2015) “Repairing trust in organizations and institutions: toward a conceptual framework,” Organization Studies, 36 (9): 1123–1142. Backman, C.A., Verbeke, A. and Schulz, R.A. (2017) “The drivers of corporate climate change strategies and public policy: a new resource-based view perspective,” Business & Society, 56 (4): 545–575. Badcock, P.B., Davey, C.G., Whittle, S., Allen, N.B. and Friston, K.J. (2017) “The depressed brain: an evolutionary systems theory,” Trends in Cognitive Sciences, 21 (3): 182–194. Baldwin, J.M. (1896) “A new factor in evolution,” The American Naturalist, 30 (354): 441–451. Barnett, W. (1994) “The liability of collective action: growth and change among early American telephone companies,” in J.A.C. Baum and J.V. Singh (eds) Evolutionary Dynamics of Organizations, New York: Oxford University Press, 337–354. Barney, J.B. (1986) “Strategic factor markets: expectations, luck, and business strategy,” Management Science, 32 (10): 1231–1241. Barney, J.B. (1989) “Asset stock accumulation and sustained competitive advantage: a comment,” Management Science, 35 (4): 1511–1513. Barney, J.B. (1991) “Firm resources and sustained competitive advantage,” Journal of Management, 17 (1): 99–120. Baum, J.A.C. and Singh, J.V. (eds) (1994a) Evolutionary Dynamics of Organizations, New York: Oxford University Press. Baum, J.A.C. and Singh, J.V. (1994b) “Organization-environment coevolution,” in J.A.C. Baum and J.V. Singh (eds) Evolutionary Dynamics of Organizations, New York: Oxford University Press, 379–402. Bennis, W. (1993) Beyond Bureaucracy, New York: Jossey Bass. Bennis, W.G., Benne, K.D., Chin, R. and Corey, K.E. (1976) The Planning of Change, New York: Holt, Rinehart and Winston. Blau, P.M. and Scott, W.R. (1962) Formal Organizations, San Francisco: Chandler. Bogner, W.C., Mahoney, J. and Thomas, H. (1993) “Paradigm shift: parallels in the origin, evolution, and function of the strategic group concept with the resource-based theory of the firm,” Working Paper #93-0172, Urbana-Champaign, IL: University of Illinois. Boukhedouma, S., Alimazighi, Z. and Oussalah, M. (2017) “Adaptation and evolution frameworks for service based inter-organizational workflows,” International Journal of E-Business Research, 13 (2): 28–57. Brittain, J.W. (1994) “Density-independent selection and community evolution,” in J.A.C. Baum and J.V. Singh (eds.), Evolutionary Dynamics of Organizations, New York: Oxford University Press, 355–378. Brittain, J.W. and Freeman, J.H. (1980) “Organizational proliferation and density dependent selection,” in J. Kimberly and R.H. Miles (eds) The Organizational Life Cycle, San Francisco: Jossey-Bass, 291–338. Brousseau, E. and Penard, T. (2007) “The economics of digital business models: a framework for analyzing the economics of platforms,” Review of Network Economics, 6 (2): 81–114. Brown, T.E. (2004) “Skunk works: a sign of failure, a sign of hope?” in T.E. Brown and J. Ulijn (eds) Innovation, Entrepreneurship and Culture, A Matter of Interaction Between Technology, Progress and Economic Growth? An Introduction, Cheltenham: Edward Elgar, 1–128. Burgelman, R.A. (1991) “Intraorganizational ecology of strategy-making and organizational adaptation: theory and field research,” Organization Science, 2 (3): 239–262.

Lamarck vs. Darwin for explaining change  127 Burgelman, R.A. (1994) “Fading memories: a process theory of strategic business exit in dynamic environments,” Administrative Science Quarterly, 39 (1): 24–56. Burns, T. and Stalker, G.M. (1961) The Management of Innovation, London: Tavistock. Caldart, A., Vassolo, R. and Silvestri, L. (2014) “Induced variation in administrative systems: experimenting with contexts for innovation,” Management Research: The Journal of the Iberoamerican Academy of Management, 12 (2): 123–151. Campbell, D.T. (1969) “Variation and selective retention in socio-cultural evolution,” General Systems, 16 (1): 69–85. Carroll, G.R. (1984) “Organizational ecology,” Annual Review of Sociology, 10 (1): 71–93. Carroll, G.R. and Delacroix, J. (1982) “Organizational mortality in the newspaper industries of Argentina and Ireland: an ecological approach,” Administrative Science Quarterly, 27 (2): 169–198. Carroll, G.R. and Hannan, M. (1989) “Density delay in the evolution of organizational populations: a model and five empirical tests,” Administrative Science Quarterly, 34 (3): 411–430. Chakrabarti, A. (2015) “Organizational adaptation in an economic shock: the role of growth reconfiguration,” Strategic Management Journal, 36 (11) 1717–1738. Chakravarthy, B.S. (1982) “Adaptation: a promising metaphor for strategic management,” Academy of Management Review, 7 (1): 35–44. Chandler, A.D., Jr. (1962) Strategy and Structure: Chapters in the History of the American Industrial Enterprise, Cambridge, MA: MIT Press. Cheng, H., Niu, M.S. and Niu, K.H. (2014) “Industrial cluster involvement, organizational learning, and organizational adaptation: an exploratory study in high technology industrial districts,” Journal of Knowledge Management, 18 (5): 971–990. Child, J. (1972) “Organizational structure, environment, and performance: the role of strategic choice,” Sociology, 6 (1): 1–22. Choi, Y. (2017) Digital Business and Sustainable Development: Asian Perspectives, London: Routledge. Clark, C.S. (2017) Our Origins: Discovering Physical Anthropology, New York: W.W. Norton & Company, Inc. Cojocaru, L.E., Sarraipa, J., Jardim-Golcalves, R. and Stanescu, A.M. (2014) “Digital business ecosystem framework for the agro-food industry,” in Enterprise Interoperability VI, Springer International Publishing, 285–296. Corallo, A., Passiante, G. and Prencipe, A. (eds) (2007) The Digital Business Ecosystem, Camberley: Edward Elgar Publishing. Coupet, J. and McWilliams, A. (2017) “Integrating organizational economics and resource dependence theory to explain the persistence of quasi markets,” Administrative Sciences, 7 (3): 29–42. Coupey, E. (2016) Digital Business: Concepts and Strategies, New York: Routledge (1st ed in 2001). Cyert, R.M. and March, J.G. (1963) A Behavioral Theory of the Firm, Englewood Cliffs, NJ: Prentice-Hall. Darbonnens, C.A. and Zurawska, M. (2017) “Effective crisis and emergency responses in the multinational corporation,” in T.J. Andersen (ed) The Responsive Global Organization: New Insights From Global Strategy and International Business, Bingley: Emerald Publishing Limited, 169–204. Dattée, B. and Barlow, J. (2017) “Multilevel organizational adaptation: scale invariance in the Scottish healthcare system,” Organization Science, 28 (2): 301–319.

128  Tammy L. Madsen and Bill McKelvey Day, G.S., Fein, A.J. and Ruppersberger, G. (2003) “Shakeouts in digital markets: lessons from B2B exchanges,” California Management Review, 45 (2): 131–150. deCosta, F. (2013) Rethinking the Internet of Things, New York: Apress. Dobzhansky, T., Ayala, F.J., Stebbins, G.L. and Valentine, J.W. (1977) Evolution, San Francisco: W.H. Freeman. Dourmas, G., Nikitakos, N. and Lambrou, M. (2005) “The concept of digital business ecosystems applied to the shipping industry,” in Proceedings of the International Association of Maritime Economists’ 2005 Conference. El Sawy, O.A. and Pereira, F. (2013) “Anticipating game changers for ‘enterprise 2020’ in a digitally-intensive world,” in El Sawy, O. A. and Pereira, F. (eds.) Business Modelling in the Dynamic Digital Space, Heidelberg: Springer, 1–12. Fiol, C.M. and Lyles, M.A. (1985) “Organizational learning,” Academy of Management Review, 10 (4): 803–810. Flamholtz, E.G. and Brzezinski, D. (2016) “Strategic organizational development and growing pains: empirical evidence from Europe,” International Review of Entrepreneurship, 14 (1): 51–67. Fombrun, C.J. (1988) “Crafting an institutionally informed ecology of organizations,” in G.R. Carroll (ed) Ecological Models of Organizations, Cambridge, MA: Ballinger, 223–239. Freeman, J., Carroll, G.R. and Hannan, M.T. (1983) “The liability of newness: age dependence in organizational death rates,” American Sociological Review, 48 (5): 692–710. French, W.L., Bell, C.H., Jr. and Zawacki, R.A. (1994) Organization Development (4th ed), Burr Ridge, IL: Irwin. Galbraith, J.R. (1982) “Designing the innovating organization,” Organizational Dynamics, 10 (3): 5–25. Gerdenitsch, C., Kubicek, B. and Korunka, C. (2015) “Control in flexible working arrangements: when freedom becomes duty,” Journal of Personnel Psychology, 14 (2): 61–69. Goldschmidt, W. (1976) “Biological versus social evolution,” American Psychologist, 31 (5): 355–357. Gopalakrishnan, S. and Dugal, M. (1998) “Strategic choice versus environmental determinism: a debate revisited,” The International Journal of Organizational Analysis, 6 (2): 146–164. Greengard, S. (2015) The Internet of Things, Cambridge, MA: MIT Press. Greenwood, R., Hinings, C.R. and Whetten, D. (2014) “Rethinking institutions and organizations,” Journal of Management Studies, 51 (7): 1206–1220. Gwynne, P. (1997) “Skunk works, 1990s-style,” Research-Technology Management, 40 (4): 18–23. Hambrick, D.C. and D’Aveni, R.A. (1988) “Large corporate families as downward spirals,” Administrative Science Quarterly, 33 (1): 1–23. Hannan, M. and Freeman, J. (1977) “The population ecology of organizations,” American Journal of Sociology, 82 (5): 929–984. Hannan, M. and Freeman, J. (1984) “Structural inertia and organizational change,” American Sociological Review, 49 (2): 149–164. Hannan, M. and Freeman, J. (1989) Organizational Ecology, Cambridge, MA: Harvard University Press. Havas, J., Matsui, T., Shaw, R.N. and Machimura, T. (2014) “Ecosystem services management tool development guidelines and framework revision for industries, industry policy makers and industry groups,” Ecosystem Services, 7: 187–200.

Lamarck vs. Darwin for explaining change  129 Haveman, H. (1993) “Organizational size and change: diversification in the savings and loan industry after deregulation,” Administrative Science Quarterly, 38 (1):. 20–50. Helfat, C.E., Finkelstein, S., Mitchell, W., Peteraf, M., Singh, H., Teece, D. and Winter, S.G. (2009). Dynamic Capabilities: Understanding Strategic Change in Organizations, Hoboken, NJ: John Wiley & Sons. Henderson, R. and Cockburn, I. (1994) “Measuring core competence? Evidence from the pharmaceutical industry,” Working paper, Sloan School of Management, MIT, Cambridge, MA. Herdon, M., Várallyai, L. and Péntek, Á. (2012) “Digital business ecosystem prototyping for SMEs,” Journal of Systems and Information Technology, 14 (4): 286–301. Hoobler, J.M., Lemmon, G. and Wayne, S.J. (2014) “Women’s managerial aspirations: an organizational development perspective,” Journal of Management, 40 (3): 703–730. Hornstein, H.A. (2015) “The integration of project management and organizational change management is now a necessity,” International Journal of Project Management, 33 (2): 291–298. Hughes, M. (2017) “Reflections: studying organizational change leadership as a subfield,” Journal of Change Management, 18 (1): 1–13. Jauch, L.R. and Glueck, W.F. (1988) Business Policy and Strategic Management, New York: McGraw-Hill. Jensen, T., Tan, Y.H. and Bjørn-Andersen, N. 2014. “Unleashing the IT potential in the complex digital business ecosystem of international trade: the case of fresh fruit import to European Union,” in the Proceedings the 27th eConference: eEcosystems, Bled, Slovenia, June 1–5. Kanter, R. (1983) The Change Masters, New York: Simon and Schuster. Kanter, R., Stein, B. and Jick, T. (1992) The Challenge of Organizational Change, New York: Free Press. Kashan, A.J. and Mohannak, K. (2017) “The role of knowledge integration in capability development and emergence of innovation ecosystem,” International Journal of Innovation and Technology Management, 14 (05): 1–18. Kauffman, S.A. (1993) The Origins of Order: Self-organization and Selection in Evolution, New York: Oxford University Press. Keerthana, M. and Ashika Parveen, S. (2017) “Internet of Things,” International Journal of Advanced Research Methodology in Engineering and Technology, 1 (2): 105–108. Kellmereit, D. and Obodovski, D. (2013) The Silent Intelligence: The Internet of Things, Warwick, NY: DND Ventures LLC. Klüppel, L.M., Pierce, L. and Snyder, J.A. (2017) “The deep historical roots of organization and strategy: traumatic shocks, culture, and institutions,” Organization Science, 29(4): 702–721. Król, Ł. (2016) “Digital foreign policy: how digital tools can further Europe’s foreign policy goals,” European View, 15 (1): 133–144. Kshetri, N. (2017) “The economics of the Internet of Things in the global south,” Third World Quarterly, 38 (2): 311–339. Kuusela, P., Keil, T. and Maula, M. (2017) “Driven by aspirations, but in what direction? Performance shortfalls, slack resources, and resource-consuming vs. resource-freeing organizational change,” Strategic Management Journal, 38 (5): 1101–1120. Lamarck, J.B. (1908) Zoological Philosophy (translated by H. Elliot), New York: Hafner. Lawler, E.E. III (1991) “Strategic choices for changing organizations,” in A.M. Mohrman, S.A. Mohrman, G.E. Ledford, T.G. Cummings, E.E. Lawler III and Associates (eds) Large Scale Organizational Change, San Francisco: Jossey Bass.

130  Tammy L. Madsen and Bill McKelvey Lawrence, P.R. and Lorsch, J.W. (1967) Organization and Environment: Managing Differentiation and Integration, Cambridge, MA: Harvard Business School Classics. Levinthal, D. (1991) “Organizational adaptation and environmental selection—interrelated processes of change,” Organization Science, 2 (1): 140–145. Levinthal, D.A. and March, J. (1993) “The myopia of learning,” Strategic Management Journal, 14 (S2): 95–112. Levinthal, D.A. and Marino, A. (2015) “Three facets of organizational adaptation: selection, variety, and plasticity,” Organization Science, 26 (3): 743–755. Levinthal, D.A. and Marino, A. (2016) “Organizational evolution and dynamic capabilities,” in D. Teece and S. Leih (eds) The Oxford Handbook of Dynamic Capabilities, New York: Oxford University Press, 1–20. Levitt, B. and March, J. (1988) “Organizational learning,” Annual Review of Sociology, 14 (1): 319–338. Lewontin, R.C. (1978) “Adaptation,” Scientific American, 239 (3): 212–230. Li, W., Badr, Y. and Biennier, F. (2012) “Digital ecosystems: challenges and prospects,” in Proceedings of the International Conference on Management of Emergent Digital EcoSystems, AC Med Digital Library, Association for Computing Machinery, 117–122. Liao, S.H., Chen, C.C., Hu, D.C., Chung, Y.C. and Liu, C.L. (2017) “Assessing the influence of leadership style, organizational learning and organizational innovation,” Leadership & Organization Development Journal, 38 (5): 590–609. Lin, Y. and Wu, L.Y. (2014) “Exploring the role of dynamic capabilities in firm performance under the resource-based view framework,” Journal of Business Research, 67 (3): 407–413. Liu, G. and Rong, K. (2015) “The nature of the co-evolutionary process: complex product development in the mobile computing industry’s business ecosystem,” Group & Organization Management, Online 1059601115593830. Lord, R.G., Dinh, J.E. and Hoffman, E.L. (2015) “A quantum approach to time and organizational change,” Academy of Management Review, 40 (2): 263–290. Lounamaa, P. and March, J.G. (1987) “Adaptive coordination of a learning team,” Management Science, 33 (1): 107–123. Madsen, T. and McKelvey, B. (2005) “Dynamic capabilities and knowledge-driven microevolution: performance effects of intrafirm variation, selection and retention processes,” Research in Competence-based Management, 2: 3–38. Malecki, E.J. and Moriset, B. (2008) The Digital Economy: Business Organization, Production Processes and Regional Developments, New York: Routledge. March, J. (1991) “Exploration and exploitation in organizational learning,” Organization Science, 2 (1): 71–87. Martin, R. L. (2009) The Design of Business: Why Design Thinking is the Next Competitive Advantage. Boston, MA: Harvard Business Press. Mayr, E. (1982) The Growth of Biological Thought, Cambridge, MA: Belknap/Harvard. McEwen, A. and Cassimally, H. (2013) Designing the Internet of Things, Chichester: Wiley. McKelvey, B. (1982) Organizational Systematics: Taxonomy, Evolution and Classification, Berkeley, CA: University of California Press. McKelvey, B. (1994) “Evolution and organization science,” in J.A.C. Baum and J.V. Singh (eds) Evolutionary Dynamics of Organizations, New York: Oxford University Press, 314–326. McKelvey, B. (1996) “Complexity vs. selection among coevolutionary firms: a complexity theory of strategic organizing,” unpublished manuscript, Anderson School, UCLA, Los Angeles.

Lamarck vs. Darwin for explaining change  131 McKelvey, B. and Aldrich, H. (1983) “Populations, natural selection and applied organizational science,” Administrative Science Quarterly, 28 (1): 101–128. Mcquivey, J. (2013) Digital Disruption: Unleashing the Next Wave of Innovation, Las Vegas: Amazon Publishing. Meyer, M.W. (1994) “Turning evolution inside the organization,” in J.A.C. Baum and J.V. Singh (eds) Evolutionary Dynamics of Organizations, New York: Oxford University Press, pp. 109–116. Meyer, M.W. and Zucker, L.G. (1989) Permanently Failing Organizations, Newbury Park, CA: SAGE. Miles, R.E. and Snow, C.C. (1978) Organizational Strategy, Structure and Process, San Francisco: McGraw Hill. Miller, J. (1993) Lockheed’s Skunk Works: The First Fifty Years: 1943–1993, Tulsa, OK: Aerofax. Miller, M. (2015) The Internet of Things, 800 E. 96th Street, Indianapolis, IN: QUE. Miner, A.S. (1994) “Seeking adaptive advantage: evolutionary theory and managerial action,” in J.A.C. Baum and J.V. Singh (eds) Evolutionary Dynamics of Organizations, New York: Oxford University Press, pp. 76–89. Mosakowski, E. (1998) “Entrepreneurial resource, organizational choice and competitive outcomes,” Organization Science, 9 (6): 625–643. Mosakowski, E. and McKelvey, B. (1997) “Predicting rent generation in competence-based competition,” in A. Heene and R. Sanchez (eds) Competence-Based Competition, New York: Wiley, 65–85. Muntaner-Perich, E. and de la Rosa Esteva, J.L. (2007) “Using dynamic electronic institutions to enable digital business ecosystems,” in P. Noriega, J. Vázquez-Salceda, G. Boella, O. Boissier, V. Dignum, N. Fornara and E. Matson (eds) Coordination, Organizations, Institutions, and Norms in Agent Systems II, Berlin: Springer, 259–273. Nachira, F., Dini, P., Nicolai, A., Le Louarn, M. and Rivera Lèon, L. 2007. Digital Business Ecosystems: The Results and the Perspectives of the Digital Business Ecosystem Research and Development Activities in FP6, Luxembourg: Office for Official Publications of the European Community. Nadler, D.A. and Tushman, M.L. (1988) Strategic Organization Design: Concepts, Tools & Process, Chicago, IL: Scott, Foresman & Co. Nelson, R.R. and Winter, S.G. (1982) An Evolutionary Theory of Economic Change, Cambridge, MA: Belknap/Harvard. Palczewski, K. (2017) “Skunkworks project for big pharma,” Pharmacological Research, 124: 167–168. Pappas, N., Kazasis, F.G., Anestis, G., Gioldasis, N. and Christodoulakis, S. (2007) “A knowledge management platform for supporting digital business ecosystems based on P2P and SOA technologies” in Digital EcoSystems and Technologies Conference Proceedings, DEST'07. Inaugural IEEE-IES, 196–202. Penrose, E.T. (1952) “Biological analogies in the theory of the firm,” American Economic Review, 42 (5): 804–819. Peters, T.J. and Waterman, R.H. (1982) In Search of Excellence: Lessons From America’s Bestrun Companies, New York: Harper and Row. Pfeffer, J. and Salancik, G.R. (1978) The External Control of Organizations, New York: Harper and Row. Pianka, E.R. (1994) Evolutionary Ecology (5th ed), New York: HarperCollins. Plotkin, H. (1993) Darwin Machines and the Nature of Knowledge, Cambridge, MA: Harvard University Press.

132  Tammy L. Madsen and Bill McKelvey Ponce-Jara, M.A., Ruiz, E., Gil, R., Sancristóbal, E., Pérez-Molina, C. and Castro, M. (2017) “Smart grid: assessment of the past and present in developed and developing countries,” Energy Strategy Reviews, 18: 38–52. Porter, M.E. (1985) Competitive Advantage: Creating and Sustaining Superior Performance, New York: Free Press. Prahalad, C.K. and Hamel, G. (1990) “The core competence of the corporation,” Harvard Business Review, 68 (3): 79–91. Pratono, A.H. (2016) “Strategic orientation and information technological turbulence: contingency perspective in SMEs,” Business Process Management Journal, 22 (2): 368–382. Qiu, T., Qiao, R. and Wu, D.O. (2018) “EABS: an event-aware backpressure scheduling scheme for emergency Internet of Things,” IEEE Transactions on Mobile Computing, 17 (1): 72–84. Razavi, A.R., Moschoyiannis, S.K. and Krause, P.J. (2007) “A coordination model for distributed transactions in digital business ecosystems,” in Digital EcoSystems and Technologies Conference, DEST'07. Inaugural IEEE-IES, 159–164. Reevis-Conner, K. (1991) “A historical comparison of resource-based theory and five schools of thought within industrial organizational economics: do we have a new theory of the firm?” Journal of Management, 17 (1): 121–154. Remane, G., Hanelt, A., Nickerson, R.C. and Kolbe, L.M. (2017) “Discovering digital business models in traditional industries,” Journal of Business Strategy, 38 (2): 41–51. Rogers, D.L. (2016) The Digital Transformation Playbook, New York: Columbia University Press. Rong, K. and Shi, Y. (2014) Business Ecosystems: Constructs, Configurations, and the Nurturing Process, London: Palgrave Macmillan. Rosenkopf, L. and Tushman, M.L. (1994) “The coevolution of technology and organization,” in J.A.C. Baum and J.V. Singh (eds) Evolutionary Dynamics of Organizations, New York: Oxford University Press, 403–424. Rumelt, R.P. (1987) “Theory, strategy, and entrepreneurship,” in D.J. Teece (ed) The Competitive Challenge, Cambridge, MA: Ballinger, 137–158. Sanders, M. (2016) “Leadership, partnerships, and organizational development: exploring components of effectiveness in three full-service community schools,” School Effectiveness and School Improvement, 27 (2): 157–177. Sawicki, A. (2016) “The Internet of Things,” World Scientific News, 48: 89–96 Schallmo, D., Williams, C.A. and Boardman, L. (2017) “Digital transformation of business models—best practice, enablers, and roadmap,” International Journal of Innovation Management, 21 (8): 1740014-1–17. Schallmo, D.R. and Williams, C.A. (2018) “Roadmap for the digital transformation of business models,” in Digital Transformation Now! Cham: Springer, 41–68. Schmidt, E. and Cohen, J. (2013) The New Digital Age: Transforming Nations, Businesses, and our Lives, New York: Vintage Books. Scott, W.R. (2014) Organizations and Institutions: Ideas, Interest, and Identities, Thousand Oaks, CA: SAGE. Seigneur, J.M. 2005. “Demonstration of security through collaboration in the digital business ecosystem,” in Security and Privacy for Emerging Areas in Communication Networks, Workshop of the 1st International Conference, IEEE, 108–109. Seo, D. (2017) “Digital business convergence and emerging contested fields: a conceptual framework,” Journal of the Association for Information Systems, 18 (10): 687–702. Shirokova, G., Bogatyreva, K., Beliaeva, T. and Puffer, S. (2016) “Entrepreneurial orientation and firm performance in different environmental settings: contingency and configurational approaches,” Journal of Small Business and Enterprise Development, 23 (3): 703–727.

Lamarck vs. Darwin for explaining change  133 Simon, H.A. (1976) Administrative Behavior (3rd ed), New York: Macmillan. Simpson, G.G. (1953) “The Baldwin Effect,” Evolution, 7 (2): 110–117. [Since it is referred to in the literature as the “Baldwin Effect” I am leaving the “E” capitalized.] Singh, A. and Hess, T. (2017) “How Chief Digital Officers promote the digital transformation of their companies,” MIS Quarterly Executive, 16 (1): 1–17. Singh, J.V. (ed.) (1990) Organizational Evolution, Menlo Park, CA: SAGE. Singh, J.V. and Lumsden, C.J. (1990) “Theory and research in organizational ecology,” Annual Review of Sociology, 16 (1): 161–195. Singh, J.V., House, R.J. and Tucker, D.J. (1986) “Organizational change and organizational mortality,” Administrative Science Quarterly, 31 (4): 587–611. Stanley, J. and Briscoe, G. (2010) “The ABC of digital business ecosystems,” arXiv:1005.1899. Starbuck, W.H. (2017) “Organizational learning and unlearning,” The Learning Organization, 24 (1): 30–38. Stinchcombe, A.L. (1965) “Social structure and organizations,” in J.G. March (ed) Handbook of Organizations, Chicago, IL: Rand McNally, 153–193. Tafti, S.F., Kordnaeij, A., Hoseini, S.H.K. and Jamali, M. (2015) “Business ecosystem as a new approach in strategy,” Management and Administrative Sciences Review, 4 (1): 198–205. Tan, B., Pan, S.L., Lu, X. and Huang, L. (2009) “Leveraging digital business ecosystems for enterprise agility: the tri-logic development strategy of Alibaba​.com​,” presented at the International Conference on Information Systems. ICIS 2009 Proceedings, Paper 171. http://aisel​.aisnet​.org​/icis2009​/171 (Accessed March 18, 2018). Tan, Y.L. and Macaulay, L.A. (2011) “Factors affecting regional SMEs progression to digital business ecosystems,” presented at the 17th Americas Conference on Information Systems. Tapscott, D. (1995) The Digital Economy, New York: McGraw-Hill Education. Tapscott, D. (2015) The Digital Economy: Rethinking Promise and Peril in the age of Networks Intelligence (20th Anniversary Edition), New York: McGraw-Hill Education. Teece, D.J. (1982) “Towards an economic theory of the multiproduct firm,” Journal of Economic Behavior and Organization, 3 (1): 39–63. Teece, D.J. (1984) “Economic analysis and strategic management,” California Management Journal, 26 (3): 87–110. Teece, D.J., Pisano, G. and Shuen, A. (1994) “Dynamic capabilities and strategic management,” CCC working paper #94-9, Center for Research in Management, UC Berkeley. Thompson, J.D. (1967) Organizations in Action, New York: McGraw-Hill. Todnem, R. (2005) “Organisational change management: a critical review,” Journal of Change Management, 5 (4): 369–380. Trigeorgis, L. and Reuer, J.J. (2016) “Real options theory in strategic management,” Strategic Management Journal, 1 (38): 42–63. Tsujimoto, M., Kajikawa, Y., Tomita, J. and Matsumoto, Y. (2017) “A review of the ecosystem concept—Towards coherent ecosystem design,” Technological Forecasting and Social Change. Tushman, M.L. and Anderson, P. (1986) “Technological discontinuities and organizational environments,” Administrative Science Quarterly, 31 (3): 439–465. Tushman, M.L. and Romanelli, E. (1985) “Organizational evolution: a metamorphosis model of convergence and reorientation,” Research in Organizational Behavior, 7: 171–222.

134  Tammy L. Madsen and Bill McKelvey Tushman, M.L., Newman, W.H. and Romanelli, E. (1986) “Convergence and upheaval: managing the unsteady pace of organizational evolution,” California Management Review, 29 (1): 29–44. Ulrich, D., Von Glinow, M., Jick, T., Yeung, A. and Nason, S. (1993) “Learning organization, culture change, and competitiveness: how managers can build learning capability,” Research Prospectus, School of Business Administration, University of Michigan, Ann Arbor, MI. Van de Ven, A.H. and Garud, R. (1994) “The coevolution of technical and institutional events in the development of an innovation,” in J.A.C. Baum and J.V. Singh (eds) Evolutionary Dynamics of Organizations, New York: Oxford University Press, 425–443. Vey, K., Fandel-Meyer, T., Zipp, J.S. and Schneider, C. (2017) “Learning & development in times of digital transformation: facilitating a culture of change and innovation,” International Journal of Advanced Corporate Learning, 10 (1): 22–32. Waddington, C.H. (1941) “Evolution of developmental systems,” Nature, 147 (3717): 108–110. Waddington, C.H. (1942) “Canalization of development and the inheritance of acquired characters,” Nature, 150 (3811): 563–565. Waddington, C.H. (1952a) “Selection of the genetic basis for an acquired character,” Nature, 169 (4294): 278–278. Waddington, C.H. (1952b) “Selection of the genetic basis for an acquired character: reply to Begg,” Nature, 169 (4302): 625–626. Waddington, C.H. (1952c) “The evolution of developmental systems,” in D.A. Herbert and A.H. Tucker (eds) Twenty-Eighth Meeting of the Australian and New Zealand Association for the Advancement of Science, Government Printer Brisbane, Brisbane, Australia, 155–159. Waddington, C.H. (1953a) “Epigenetics and evolution,” Symposia of the Society for Experimental Biology, 7: 186–199. Waddington, C.H. (1953b) “Genetic assimilation of an acquired character,” Evolution, 7 (2): 118–126. Waddington, C.H. (1953c) “The ‘Baldwin Effect’, ‘genetic assimilation’, and ‘homeostasis’,” Evolution, 7 (4): 386–387. Waddington, C.H. (1953d) “The evolution of adaptations,” Endeavour, 12 (47): 134–139. Wadongo, B. and Abdel-Kader, M. (2014) “Contingency theory, performance management and organisational effectiveness in the third sector: a theoretical framework,” International Journal of Productivity and Performance Management, 63 (6): 680–703. Walsh, J.P. and Ungson, G.R. (1991) “Organizational memory,” Academy of Management Review, 16 (1): 57–91. Wang, C.L. and Ahmed, P.K. (2007) “Dynamic capabilities: a review and research agenda,” International Journal of Management Reviews, 9 (1): 31–51. Wang, R. (2015) Disrupting Digital Business, Boston, MA: Harvard Business Review Press. Weick, K. (1979) The Social Psychology of Organizing, Reading, MA: Addison-Wesley. Weill, P. and Woerner, S.L. (2015) “Thriving in an increasingly digital ecosystem,” MIT Sloan Management Review, 56 (4): 27–34. Weill, P. and Woerner, S.L. (2018) “Is your company ready for a digital future?” MIT Sloan Management Review, 59 (2): 21–25. Wernerfelt, B. (1984) “A resource–based view of the firm,” Strategic Management Journal, 5 (2): 171–180. Winter, S.G. (2003) “Understanding dynamic capabilities,” Strategic Management Journal, 24 (10): 991–995.

Lamarck vs. Darwin for explaining change  135 Wu, L. and Chiu, M.L. (2015) “Organizational applications of IT innovation and firm's competitive performance: a resource-based view and the innovation diffusion approach,” Journal of Engineering and Technology Management, 35 (C): 25–44. Yager, R.R. and Espada, J.P. (2017) New Advances in the Internet of Things, London: Springer. Yousef, D.A. (2017) “Organizational commitment, job satisfaction and attitudes toward organizational change: a study in the local government,” International Journal of Public Administration, 40 (1): 77–88. Zucker, L.G. (1977) “The role of institutionalization in cultural persistence,” American Sociological Review, 42 (5): 726–743. Zucker, L.G. (1987) “Normal change or risky business: institutional effects on the ‘hazard’ of change in hospital organizations,” Journal of Management Studies, 24 (6): 671–700. Zuzul, T. and Edmondson, A.C. (2017) “The advocacy trap: when legitimacy building inhibits organizational learning,” Academy of Management Discoveries, 3 (3): 302–321.

4

“Simple rules” for improving digital corporate IQ Basic lessons from complexity science Bill McKelvey

Introduction When share prices fall, CEOs often lose their jobs. The best way to keep share prices high is to produce economic rents—defined as above industry average profits (Besanko et al. 2000). Porter (1996) says strategy is about finding new niches and then protecting rents by forcing would-be competitors into disadvantageous trade-offs. Prusak (1996: 6) says: The only thing that gives an organization a competitive edge—the only thing that is sustainable—is what it knows, how it uses what it knows, and how fast it can know something new! In the modern Digital Age, learning something new from the Internet via computers, smartphones, iPads, etc., vastly speeds up learning, compared to learning before 1996. As noted in Chapter 1, the following are some of the most relevant writings about the “Internet of Things”: deCosta 2013; Kellmereit and Obodovski 2013; McEwen and Cassimally 2013; McQuivey 2013; Schmidt and Cohen 2013; Greengard 2015; Miller 2015; Sawicki 2016; Kshetri 2017; Majeed and Rupasinghe 2017; Ponce-Jara et al. 2017. In addition there are now numerous books and articles about digital business (DB), some of which are: Tapscott 1995, 2015; Day et al. 2003; Coupey 2001, 2016; Dourmas et al. 2005; Seigneur 2005; Brousseau and Penard 2007; Corallo et al. 2007; Muntaner-Perich and de la Rosa Esteva 2007; Nachira et al. 2007; Pappas et al. 2007; Razavi et al. 2007; Malecki and Moriset 2008; Tan et al. 2009; Stanley and Briscoe 2010; Tan and Macaulay 2011; Herdon et al. 2012; Li et al. 2012; El Sawy and Pereira 2013; Attour and Della Peruta 2016; Cojocaru et al. 2014; Jensen et al. 2014; Rong and Shi 2014; Liu and Rong 2015; Tafti et al. 2015; Choi 2017; Kshetri 2017; Ponce-Jara et al. 2017; Remane et al. 2017; Schallmo et al. 2017; Seo 2017; Schallmo and Williams 2018; Weill and Woerner 2018. Recent writing about competitive strategy and sustained rent generation, as the citations above indicate, vastly speeds up Prusak’s (1996) emphasis on how fast a firm can develop new knowledge. Rents have traditionally been 

Simple rules for improving digital corporate IQ  137

seen to stem from keeping pace with high-velocity environments (Brown and Eisenhardt 1998) and value migration (Slywotzky 1996); seeing industry trends (Hamel and Prahalad 1994); and staying ahead of the efficiency curve (Porter 1996). However, in the last 20+ years, information flows and learning opportunities connected to the Internet and coupled with all employees owning or having access to computers, smartphones and iPads, etc., have made knowledge based on digital information flows almost instantly accessible, and have actually made remembering pretty much irrelevant, since most (except very personal) information is quickly available by readily-available connections to Internet-based information. For a visual representation of the development of digital technologies since 1960, see Figure 1 in Malecki and Moriset (2008: 5). Dynamic ill-structured environments and learning opportunities become the basis of competitive advantage if firms can be early in their industry to unravel the evolving conditions (Stacey 1995); in the current Digital Age, needless to say, learning opportunities have become available at almost the speed of light— assuming one has a high-speed Internet connection—or at least at the speed of electronic information flows, email messages, etc. Zohar titles her 1997 book Rewiring the Corporate Brain. “Rewiring” places emphasis on the alteration of the connections among people—substituting for neurons—in the corporate brain. I will refer to this—the corporate brain—as distributed intelligence (DI) in firms. DI is a function of strategically relevant human and social capital assets—the networked intellectual capabilities of human agents (Masuch and Warglien 1992; Argote 1999). High corporate IQ, defined as knowledge capacity and creativity coupled with learning speed, is a direct function of DI. What should CEOs do to foster emergent DI in their firms, speed up its appreciation rate, and steer it in strategically important directions in the Digital Age? To answer this question, I begin by making the link between DI and Ashby’s (1962) definition of emergent order. Then I discuss DI in firms. Next I translate the complexity scientists’ concept of “energy-differentials” into the notion of “adaptive tension.” Very simply, if a firm is strategically “here” and it needs to be strategically “there” to generate income, this is adaptive tension. This is followed with an introduction to basic complexity theory, specifically the Bénard (1901) cell effect and the Region of Emergent Complexity between the first and second critical values. I then present twelve “simple rules” that CEOs can follow so as to improve their firm’s corporate IQ. I also discuss the impact of the Digital Age here and there.

Intelligence as constrained order According to Merriam-Webster’s dictionary (1996: 818) “order” and its synonyms means “put persons or things into their proper places in relation to each other.”1 Disorder, to natural scientists, means the Second Law of Thermodynamics, namely, inexorable dissipation toward randomness (entropy). Kauffman (1993) and Holland (1995) use the term “order” in the

138  Bill McKelvey

titles of their books, respectively The Origins of Order and Hidden Order. They focus on emergent order, equating it to spontaneous self-organization. What causes emergent order and self-organization? Darwin’s theory of natural selection (Darwin 1859) explained speciation in the biological world, that is: why are there different kinds of organisms? Durkheim (1893) and Spencer (1898) also defined order as the emergence of different kinds of new order, but more specifically, social entities. Half a century later, however, Sommerhoff (1950), Ashby (1956, 1962), and Rothstein (1958) defined order not in terms of entities but rather in terms of the connections among them. Nowadays social connections are mostly digital, especially if the people involved are not able to talk to each other face-to-face. Ashby (1962) adds two critical observations. Order (organization), he says, exists between two entities, A and B, only if the link is “conditioned” by a third entity, C (1962: 255). If C symbolizes the “environment,” which is external to the relation between A and B, then tensions imposed by environmental constraints are what cause new order (Ashby 1956). This observation leads to his “Law of Requisite Variety” (1956). It holds that for a biological or social entity to be efficaciously adaptive, the variety of its internal order must equal the variety of the environmental constraints. Furthermore, he also observed that order does not emerge when the environmental constraints are chaotic. However, in the current Digital Age, the order of social entities such as organizations and economies is much more based on the digital information flows characteristic of the modern Digital Age, but sometimes there are so many forces pressuring for new orders all at once that chaos results—further explained later. Zohar (1997: xv) quotes Andrew Stone, a director of the global retailing giant, Marks and Spencer, in 1997: “My work is in a building that houses three thousand people who are essentially the individual ‘particles’ of the ‘brain’ of an organization that consists of sixty thousand people worldwide.” Each “particle” presumably has some intellectual capability—what Becker (1975) terms human capital, H. And some humans talk to each other—via what Burt (1992) calls social capital networks, S. Together, H and S comprise what I refer to as “distributed intelligence” (DI). Human capital is a property of individual employees. Taken to the extreme, even geniuses offer a firm only minimal adaptive capability if they are isolated from everyone else. A firm’s core competencies, dynamic capabilities, and knowledge, required for competitive advantage increasingly appear as networks of human-capital holders. These knowledge networks also increasingly appear throughout firms rather than being narrowly confined to upper management (Norling 1996). Employees have become responsible for adaptive capability rather than just being bodies to carry out orders. Here is where networks become critical. Especially in the last two decades, much of the effectiveness and economic value of human capital held by individuals has been shown to be subject to the nature of the social networks in which the human agents are embedded (Granovetter 1985; Nohria and Eccles 1992; Burt 1997). Burt

Simple rules for improving digital corporate IQ  139

(1992: 2) goes so far as to say that competitive advantage is a function of network relations, not individual knowledge attributes. But human networks are even more likely everywhere, and more digital, in the Digital Age.

Distributed intelligence I draw on both modern brain and distributed computer systems’ research to demonstrate that Becker and Burt each are half-right. Respectively, they naïvely could be interpreted to imply that “isolated geniuses” or “networked idiots” can generate rents. More likely, they would agree that H and S are jointly important. If so, the theory of the firm most relevant to rent generation appears as a revised Cobb-Douglas function: Y = f (K, L, D); where D stands for a configuration of H and S likely to produce optimal DI for a particular firm. DI—in brains and in parallel processing computer systems—is a function of both the knowledge in the nodes (nodes have minimal knowledge in brains) and in the emergent connections among nodes (primitive in computer systems). Leaving aside nodes for the moment, intelligence is a function of links among nodes. DI in a brain is entirely a function of its capability for producing emergent networks among neurons, which behave as simple “threshold gates” that have one behavioral option—fire or not fire (Fuster 1995: 29). As intelligence increases, it is represented in the brain as emergent connections (synaptic links) among neurons. Human intelligence is “distributed” across really dumb agents, i.e. single isolated neurons! DI in parallel processing computer systems is mostly a function of the built-in intelligence capability of computers-as-agents, with minimal DI improvement stemming from emergent networks among the computer/agents. In early computer DI systems, computers played the role of neurons. They were more “node-based” than “network-based.” Artificial intelligence resides in the intelligence capability of the computers as agents, with emergent network-based intelligence rather primitive (Garzon 1995). Garzon’s analysis notwithstanding, the distributed computer literature shows only marginal progress toward emergent DI. But, of course, many modern computers are networked with other computers and, thus, can show collective, networked DI.

Complexity theory How should CEOs accelerate the rate of DI increase? Complexity theory points the way. Complexity theorists define systems in the emergent complexity category as being in a state “far from equilibrium” (Prigogine and Stengers 1984) and “at the edge of chaos” (Kauffman 1993). Prigogine and colleagues observed that energy-importing, self-organizing, open systems create structures that in the first instance increase negentropy,2 but nevertheless ever after become sites of energy and new order creation. Prigogine (1955, 1962) labeled them “dissipative structures” because they create new order by focusing on the reduction of the imposed

140  Bill McKelvey

tensions. Self-organized—and self-contained—dissipative structures, once formed, exhibit persistence and predictable qualities. Complexity caused self-organizing structures are now seen as ubiquitous natural phenomena (Mainzer 2007) and broadly applicable to firms (Maguire and McKelvey 1999; Marion 1999). The region of emergent complexity is defined by the first and second “critical values” (Mainzer 2007). Nothing is so basic to the definition of complexity science as the Bénard (1901) cell—two metal plates with a fluid in between. An energy (heat) differential between the plates—defined here as “adaptive tension,” T—creates a molecular motion of some velocity, R, as hotter molecules move toward the colder plate. The energy-differential in the Bénard cell parallels that between the hot surface of the earth and its cold upper atmosphere—hotter air molecules move upward and if they move fast enough, create storm cells that take on predictable structure and create tornadoes every once in a while. The role T plays in defining the region of complexity “at the edge of chaos” is fundamental to complexity science. If T remains below the first critical value, new structure does not emerge. If T increases beyond the second critical value, a system moves into the region of chaotic complexity. Here the system is likely to oscillate between different point attractors (basins of attraction)—thereby creating chaotic behavior. Attractors are defined in Box 4.1. Suppose a large firm acquires another firm needing a turnaround and the acquiring firm allows T to stay below the first critical value—existing management stays in place with little incentive to make changes. There is, thus, little reason for people in the acquired firm to create new structures. If T goes above the second critical value, complexity theory predicts chaos. Suppose the acquiring firm changes several of the acquired firm’s top managers and sends in what could be called “MBA terrorists” to change the management systems “overnight”—new budgeting and information systems; new personnel procedures, new promotion approaches, new benefits packages; and new production and marketing systems. In this circumstance, two basins of attraction could emerge: one basin defined around demands of the MBA terrorists and the other centered around the comfortable pre-acquisition ways of doing business and resistance to change. The activities of the system could oscillate between these two basins, seemingly exhibiting the characteristics of a strange attractor. Between the first and second critical values lies the Region of Emergence (i.e. Emergent Complexity). Here, network structures emerge to solve the T problems. Using the storm cell metaphor, in this region the equivalent of “heat conduction,” i.e. emergent interpersonal dynamics among sporadically communicating individuals is insufficient to reduce the observed T. To pick up the adaptive pace, the equivalent of organizational storm cells consisting of “bulk” adaptive workflows have to start. Formal and/or informal structures emerge, such as new network formations, informal or formal group activities, departments, entrepreneurial ventures, and so on. Also, as described in Chapter 1 referring to the digital effects on basic complexity agility dynamics, there are three phases in the development of complexity theory, each of which can show changes that are very much speeded up by digital reality.

Simple rules for improving digital corporate IQ  141

BOX 4.1 DEFINITIONS OF ATTRACTORS BY GLEICK (1987) “Point attractors” act as equilibrium points. A system, even though oscillating or perturbed, eventually returns to repetitious behavior centered around the point attractor—traditional control style management decision structures may act in this manner (appearing as Newtonian complexity); “Periodic attractors” or “limit cycles” (pendulum behavior) foster oscillation predictably from one extreme to another—recurrent shifts in the centralization and decentralization of decision-making, or functional specialization vs. cross-functional integration fit here (also appearing as Newtonian complexity); If adaptive tension is raised beyond some critical value, systems may be subject to “strange attractors” in that, if plotted, they show never intersecting, stable, low-dimensional, nonperiodic spirals and loops, that are not attracted by some central equilibrium point, but nevertheless appear constrained not to breach the confines of what might appear as an imaginary bottle. If they intersected, the system would be in equilibrium (Gleick 1987: 140) following a point attractor. The attractor is “strange” because it “looks” like the system is oscillating around a central equilibrium point, but it isn’t. As a metaphor, think of a point attractor as a rabbit on an elastic tether—the rabbit moves in all directions but as it tires it is drawn toward the middle where it lies down to rest.Think of a strange attractor as a rabbit in a pen with a dog on the outside—the rabbit keeps running to the side of the pen opposite from the dog but as it tires it comes to rest in the middle of the pen.The rabbit ends up in the “middle” in either case.With the tether the cause is the pull of the elastic. In the pen the cause is repulsion from the dog unsystematically attacking from all sides.

“Simple rules” for managing networks The rules (guidelines) required for improving network effectiveness in firms are as follows: 1. Assemble heterogeneous agents: If all the agents (employees) are the same, there is no advantage to networking (Holland 1995). Nevertheless, biological diversity, or as Campbell (1974) called it “blind variation,” was much more relevant for social innovation than “rational” variations. Furthermore, Johnson (2000), LeBaron (2000), and Allen (2001) all show that novelty, innovation, and learning all collapse as the attributes of agents change from heterogeneity to homogeneity. The definition of creativity favored by psychologists—remote associates”—essentially holds that creativity emerges when agents having different ideas or concepts interact and, consequently, ideas previously separated are combined to produce something new.

142  Bill McKelvey

Heterogeneity loses its effect if agents become too similar or have no “mutual” absorptive capacity (Cohen and Levinthal 1990). Agents need to be able to absorb and understand to some extent ideas from agents they interact with. Nor can the continuing availability of heterogeneous agents be taken for granted. The control systems that are so prevalent in organizations (Morgan 1997; Jones 2000) invariably diminish or negate heterogeneity. In the Digital Age, as noted previously, however, it seems more likely that strong ties and homogeneity could dominate, since all agents have easy digital access to the same information. However as also noted before, agents’ digital access to the many and more different ideas from the Internet could prolong heterogeneity. 2. Assure human capital formation: Human capital is the basis of agent heterogeneity. As Becker (1975) argued, the economists’ Cobb-Douglas production function needs a component to reflect the knowledge people hold, as well as capital and labor. This is especially true in today’s knowledge economy—the economic advantage of the US, today, is much more a function of digital human capital, resulting from digital information flows and digital networks among people and the Internet than financial capital or labor. However, Zucker and Darby (1996) find that one genius appropriately networked is superior to larger networks comprised of less talented agents. The “absorptive capacity” literature (Cohen and Levinthal 1990) implies that if agents don’t have some pre-existing level of knowledge relevant to understanding impinging “variety” (Ashby 1956) or complexity scientists’ “degrees of freedom” (Mainzer 2007), they won’t be very good at collecting additional information pertaining to the impinging contextual adaptive tensions. Also, absorptive capacity is a positive feedback process—the more absorptive capacity an agent has the more new, technical information he/she absorbs; the more information absorbed, the higher his/her absorptive capacity and the more she or he benefits from access to digital information flows. There is also considerable training involved that is really of the human and social capital kind—training in what the agents do “technically” over and above how they network. Specifically, special training may be needed so employees can: a. Develop all the human capital skills, including absorptive capacity, required for observing and understanding impinging digital contextual adaptive tensions; b. Develop all of the social capital skills implicit in appropriate digital—or nondigital—networking; c. Learn about “who knows what about what” across the network (transactive memory; Argote 1999); and d. Learn how to do it at a fast enough rate. e. And of course, all agents can easily connect with each other digitally. Only further research can tell us whether the Digital Age improves the spread of networked geniuses’ knowledge and creativity if, in fact, geniuses are actually creative.

Simple rules for improving digital corporate IQ  143

3. Aim for a “moderate” number of connections: The main argument throughout Kauffman’s book (1993): is that some connections—not very many, actually—among agents improves system fitness, but that fitness deteriorates as the number of connections between each agent and various other agents increases toward the maximum. Kauffman calls this effect “complexity catastrophe,” arguing that it thwarts Darwin’s selectionist evolution-toward-improved-fitness theory (1993: 36). Using his NK[C] computational model, Kauffman also finds that the upper bound at which “catastrophe” sets in is raised if intrasystem agents are connected to a moderate extent with agents outside the system. Barabási and Bonabeau (2003) find that number of connections per node follows a power law, so it should be expected that one individual in a network will have many links and some will have almost none; it is not necessary that all agents have the same number of connections. CEOs should remember that connections are like fertilizer: Just because some is good doesn’t mean that a lot is better! The concept of what “connections” are has obviously changed in the Digital Age, since most connections these days are digital. Presumably, some agents will avoid digitally connecting with all other agents by not becoming connected to the various “digital medias,” But of course, many other agents will choose to join digital social medias like Facebook and Twitter, etc. 4. Create appropriate physical conditions: People who see each other all the time usually develop strong ties. People who never see each other tend not to interact. This is to say, networking is usually a function of physical adjacency. But, of course, the Internet, electronic email, telephones, and so forth, overcome many limitations of physical adjacency, but many remain. Therefore, it is important to create physical “mixing” events that bring heterogeneous agents into person-to-person contact. Combining these mixing events with increased awareness of newly appearing adaptive tensions meets some of the basic conditions of new order creation, as outlined in McKelvey (2003a)— and especially the action of the 0th law of thermodynamics (McKelvey 2004b). CEOs can also create “tags”—events that instigate coevolutionary dynamics (Holland 1995). Job-related “new” mixing is also possible. Moving people who have succeeded at one job into another—that is, changing their job position and physical location is a way of creating new weak ties, as GE found out (Kerr 2000), before it started shrinking. In short, various ways of creating conditions that minimize “strong-tie” effects have already been developed. However, ways of avoiding digital-caused “strong-tie” effects in the current Digital Age call for new kinds of creativity in the new digital world we now live in. 5. Arrange coaching: The organization development (OD) literature (French and Bell 1984) and people applying complexity theory both realize that coaching is needed to help many employees form network connections appropriately (Goldstein 1994; Kelly and Allison 1999). One can’t assume that all relevant employees arrive with networking skills. Given the possibility of both personal conflict and task conflict, there is every reason to expect that coaches need to act as catalysts to help networking progress.

144  Bill McKelvey

In the Digital Age, needless to say, coaching—i.e. “digital coaching”—is also required. It is one thing to coach employees to form personal network connections. But another kind of coaching is called for so as to reduce the dominance of too many digital connections. 6. Aim for near decomposability: How to create the “cells” in what Miles et al. (1999) term “cellular networks?” Simon (1962) argued that systems (i.e. cellular networks) evolve toward fitness fastest when the cells (modules) are nearly, but not totally, disconnected from higher levels in biological or social system hierarchies. Sanchez (1993) confirms this empirically in his extensive research on the effectiveness of modularly designed firms; also corroborated by Schilling (2000). Needless to say, two of the above articles were written before the Digital Age started growing. Digital reality means that it is much more difficult to disconnect digital connections resulting in digital networks. “Modular-designed” organizations are much more difficult to create in the Digital Age, simply because agents can easily digitally connect, often whether they really want to or not. While face-to-face networks may be made more modular, digital networks are much more difficult to modularize, especially if the agents who have digital connections don’t want to lose them. 7. Take advantage of adaptive tension:3 An externally imposing, and internally recognized adaptive tension activating the agents (McKelvey 2004a) is required. Tensions are not point attractors, but they serve as energizing devices for CEOs to take advantage of. I discuss how CEOs can use adaptive tension and distinguish between point and strange attractor management elsewhere (McKelvey 2004a). As noted above, in the Digital Age, many digital-based tensions may impose on an agent, all at the same time, causing chaos, and especially if multiple agents are networked. 8. Modify the critical values: Adaptive tensions cause phase transitions (i.e. new order types of nonlinearities) if the tension, T, is above the first critical value (Prigogine 1955; 1962). Use of the first and second critical values to define the region of emergence is critically important—below the first critical value, bureaucratic behavior prevails; above the second critical value, chaos prevails (Brown and Eisenhardt 1998; McKelvey 2004a). Employee training and experience works to lower threshold gates so that adaptive tensions may take effect at lower values. Employees can also be trained so as to work in hightension conditions without becoming dysfunctional. In the Digital Age, however, tensions are more apt to be imposed on agents. But chaos is also more likely, since digital reality and digital connections can impose too many tensions on agents—all at the same time—thereby leading to chaos. Research has yet to focus on whether or not digital connections and digital networks easily lead to increased chaos among agents in a firm—or not. 9. Set up strange attractors: Steering the network by “strange attractor limit setting” rather than by point attractors created by top-down goal setting (McKelvey 2004a)—which is reminiscent of Bennis’s (1996) “herding cats” phrase, and Morgan’s (1997) “avoidance of noxiants.” This is not just an opportunity available to CEOs; it is a necessity. 10. Create periodic weak-tie flooding: Granovetter’s classic research finding (1973; 1982) is that novelty and innovation happen more frequently in networks

Simple rules for improving digital corporate IQ  145

consisting of “weak ties” as opposed to “strong ties.” The latter tend to produce groupthink (Janis 1972). This weak-tie effect is reconfirmed by Burt’s (1992) discovery of the entrepreneurial power of “weak-tie bridges.” And, of course, weak-tie effects go hand in hand with my first rule’s emphasis of agent heterogeneity. Given an existing system, which tends toward strong-tie formations as agents get to know each other better and experience the build-up of what I previously (McKelvey 2003a) labeled “entanglement ties,” path dependencies result from ties that build up over repeated interactions, with the effect that the behaviors of entangled-tie agents become increasingly similar and predictable (see March 1991 for confirmation). While modularization speeds up adaptive response rates, modules (cells) are also prone to become strong-tie cliques. Therefore: a. New agents must be moved here and there among a firm’s cellular networks so as to avoid the effect of path dependencies and thereby weaken strong-tie cliques (March 1991; McKelvey 2003a); b. Weak-tie bridges need to be encouraged so as to avoid non-communicating strong-tie cliques or modules (Burt 1992); c. Besides “flooding” a network with new entrants, moving people around an organization into new positions has the same effect—i.e. creating and taking advantage of weak-tie effects. d. Needless to say, digital connections make all of the above movements easier, but also can more easily keep digital-based strong ties more prevalent. 11. Manage coevolution: Coevolution is a fundamental dynamic in complexity science. In biology, coevolution is kept under control by damping mechanisms and organisms have no control over the speed of their progression toward new order. But, as I discuss elsewhere (McKelvey 2002): coevolving systems are always liable to coevolve in unwanted directions, not coevolve fast enough in the right directions, or start at a good rate and then suffer the effects of damping processes, etc. As a result, coevolution has to be managed. In my 2002 article, I discuss twelve ways in which this may be pursued but space precludes repeating them here. And, of course, digital realities in the Digital Age make managing coevolution much more difficult, simply because the networks are more likely digital; with digital coevolution a dynamic that is more difficult to control, if control is needed, can be more likely. 12. Set up appropriate incentive structures: My discussion here is mostly patterned after network incentives developed at GE, under the leadership of Jack Welch (Kerr 2000; McKelvey 2010). Some of the things Kerr mentions are: Strong incentives: Defeat barriers to information sharing ••

Hoarding vs. “not invented here”—people have to get new best practices they have discovered into the network;

146  Bill McKelvey

•• •• •• •• ••

The “core” of most ideas mostly is generalizable; Reject only after trying to make it work; Make expertise readily available; Put people in positions where they might fail—which is to say, keep moving them around; Create constant new learning opportunities.

Though not explicitly drawn from complexity science, or explicitly aimed at what Miles et al. (1999) call cellular networks, the foregoing GE “rules” had the effect of greatly improving GE’s network functioning. This, coupled with Welch’s reliance on the broad adaptive tension rule (“Be #1 or 2 or you will be divested”), does indeed amount to an unknowing application of basic ideas from complexity science (McKelvey 2004a; McKelvey 2010). Here are some rules generalized from the foregoing GE-explicit rules: a. Agents are incentivized to get information out on the network in a form abstract enough for all users to quickly try out; b. Agents gaining success in one part of the network, or with one kind of human capital, are moved around—given additional “opportunities to fail”—which is a way of building competence, diversity, and weak ties; c. Agents are incentivized to produce novelties, with the most critical (top priority) novelties expected at a consistent rate each year (say, five “most critical” novelties per year)—i.e. novelties created in response to the prevailing contextual tensions and rates of change in the external environment; d. Agents “above” the cellular networks, such as CEOs, are incentivized to expect and review some specific number of “most critical” novelties, and some novelties of lesser criticality without reservation—but remember the “near decomposability” rule. Whether GE’s (Welch’s) method of improving network functions, and thereby incentive structures, applies to digital networks in the Digital Age remains to be proved. Most likely, digitization could make defeating “barriers” more difficult. While it is possible that barriers could be more readily discovered in digital networks, because the sharing information via digital connections is easier, if not actually unavoidable, it could also be, however, that digital networking leads to increased “strong-tie” connections among agents, and consequently reduces barriers to information sharing, thereby causing agents to be more alike, i.e. increases agents’ homogeneity.

Conclusion CEOs wishing to generate sustainable rents in a changing world would be more successful if they focused on human and social capital appreciation, that is, distributed intelligence. I use complexity theory and adaptive tension to show how CEOs may speed up the rate of improvement of distributed intelligence in the Digital Age, while at the same time suppressing the emergence of bureaucracy—a point

Simple rules for improving digital corporate IQ  147

elaborated in McKelvey (2004a). Complexity science recognizes that the many kinds of complexity are not immutable; they are the result of adaptive tension and additional complexity dynamics, most of which vastly speed up in the Digital Age. Knowing this, if leaders alter the adaptive tension imposed on a system, its kind of complexity and emergent order can change very quickly nowadays. Specifically, tuning adaptive tension to between the first and second critical values can produce emergent network structures; but, because of the increased digital accessibility, chaos may more often be the result. Theories of bureaucracy and organization (Scott 1998) put intelligence in the job positions and in the people holding them, and emphasized human capital appreciation as the basis of competitive advantage. In the Digital Age, intelligence can result more from digital-based knowledge flows, which may quickly be from other human agents, e.g. via digital email, or directly from the Internet. While past theories of the brain and human intelligence held that intelligence “is the network” (Fuster 1995: 11)—a view previously used by Burt (1992) with his emphasis of social capital appreciation as the basis of competitive advantage—it is still true that intelligence “is the network” but modern digital networks offer much quicker information flows and, therefore, can be even more dominant causes of human brains’ intelligence. In fact, however, none of these views is correct only by itself. Combined brain and digital-based distributed systems place intelligence both in the agents and in their networks. This chapter emphasizes that the use of knowledge in rapidly changing competitive contexts depends on high levels of network functioning within firms. Just as intelligence in people is a function of neurons and synaptic links, I argue that humans plus social capital in firms are the basic building blocks of distributed intelligence. Since people are spatially distributed throughout a firm, corporate intelligence can be quickly distributed by digital information flows. Given this, digital networks are critical. I also draw on a classic article by Ashby (1962) to argue that emergent distributed intelligence is subject to his proviso that “order” and self-organization result only in the context of environmental constraints, which now are much more digital. Using a strict constructionist interpretation of complexity theory, I develop several activities that CEOs can set in motion to improve corporate IQ by using adaptive tension and incentives to foster emergent order. I also show how the reality of the Digital Age can speed up complexity dynamics, but also increase the probability of strong-tie dominance rather than the dominance of more innovative weak ties. Analysis elsewhere argues that strong, visionary, but narcissistic CEO-level leadership may produce levels of group cohesion inhibiting the improvement of corporate IQ (Marion and Uhl-Bien 2001; McKelvey 2004a; Uhl-Bien et al. 2007). Many of the “complexity-theoryapplied-to-management” books reviewed in Maguire and McKelvey (1999)— see also Allen et al. 2011—argue that strong command-and-control structures, often created by strong visionary narcissistic CEO leaders, also inhibit emergent order/intelligence via self-organization. In this chapter I argue that complexity theory offers guidelines for designing aggressive CEO activities aimed

148  Bill McKelvey

at improving corporate IQ while at the same time reducing these well-known downside effects of strong leadership at the top.

Notes 1 Merriam-Webster’s current online definition of the meaning of “order” is: “to put things in a particular order or position.” https​:/​/ww​​w​.mer​​r iam-​​webst​​er​.co​​m​/dic​​tiona​​​ry​/ ag​​ility​ (Accessed April 6th, 2018). 2 Schrödinger (1944) created the term “negentropy” to refer to energy importation. 3 Rules 7, 8, 9, and 10 are elaborated in McKelvey (2004a).

References Allen, P.M. (2001) “A complex systems approach to learning, adaptive networks,” International Journal of Innovation Management, 5: 149–180. Allen, P.M., Maguire, S. and McKelvey, B. (2011) The SAGE Handbook of Complexity and Management, London: SAGE Publications. Andriani, P. and McKelvey, B. (2009) “From Gaussian to Paretian thinking: causes and implications of power laws in organizations,” Organization Science, 20 (6): 1053–1071. Argote, L. (1999) Organizational Learning: Creating, Retaining and Transferring Knowledge, Norwell, MA: Kluwer. Ashby, W.R. (1956) An Introduction to Cybernetics, London: Chapman and Hall. Ashby, W.R. (1962) “Principles of the self-organizing system,” in H. von Foerster and G.W. Zopf (eds) Principles of Self-Organization, New York: Pergamon, 255–278. Attour, A. and Della Peruta, M. (2016) “Architectural knowledge: key flows and processes in designing an inter-organisational technological platform,” Knowledge Management Research & Practice., 14 (1): 27–34. Auerbach, F. (1913) “Das gesetz der bevolkerungskoncentration,” Petermann Geographhische Mitteilungen, 59: 74–76. Barabási, A.-L. and Bonabeau, E. (2003) “Scale-free networks,” Scientific American, 288 (5): 60–69. Becker, G.S. (1975) Human Capital (2nd ed), Chicago, IL: University of Chicago Press. Bénard, H. (1901) “Les tourbillons cellulaires dans une nappe liquide transportant de la chaleurpar convection en régime permanent,” Annales de Chiemie et Physique, 23: 62–144. Bennis, W.G. (1996) “Becoming a leader of leaders,” in R. Gibson (ed) Rethinking the Future, London: Brealey, 147–163. Besanko, D., Dranove, D. and Shanley, M. (2000) The Economics of Strategy (2nd ed), New York: Wiley. Brousseau, E. and Penard, T. (2007) “The economics of digital business models: a framework for analyzing the economics of platforms,” Review of Network Economics, 6 (2): 81–114. Brown, S.L. and Eisenhardt, K.M. (1998) Competing on the Edge, Boston, MA: Harvard Business School Press. Burt, R.S. (1992) Structural Holes, Cambridge, MA: Harvard University Press. Burt, R.S. (1997) “The contingent value of social capital,” Administrative Science Quarterly, 42 (2): 339–365. Campbell, D.T. (1974) “Unjustified variation and selective retention in scientific discovery,” in F.J. Ayala and T. Dobzhansky (eds) Studies in the Philosophy of Biology, London: Macmillan, 139–161.

Simple rules for improving digital corporate IQ  149 Choi, Y. (2017) Digital Business and Sustainable Development: Asian Perspectives, London: Routledge. Cohen, W.M. and Levinthal, D.A. (1990) “Absorptive capacity: a new perspective on learning and innovation,” Administrative Science Quarterly, 35 (1): 128–152. Cojocaru, L.E., Sarraipa, J., Jardim-Golcalves, R. and Stanescu, A.M. (2014) “Digital business ecosystem framework for the agro-food industry,” in K. Mertins, F. Bénaben, R. Poler and J.P. Bourrières (eds) Enterprise Interoperability VI: Interoperability for Agility, Resilience and Plasticity of Collaborations, Cheltenham: Springer International Publishing, 285–296. Coupey, E. (2001) Marketing and the Internet Conceptual Foundations. Upper Saddle River, NJ: Prentice-Hall. Darwin, C. (1859) On the Origin of Species, London: John Murray. Day, G.S., Fein, A.J. and Ruppersberger, G. (2003) “Shakeouts in digital markets: lessons from B2B exchanges,” California Management Review, 45 (2): 131–150. deCosta, F. (2013) Rethinking the Internet of Things: A Scalable Approach to Connecting Everything, New York: Apress. Dourmas, G., Nikitakos, N. and Lambrou, M. (2005) “The concept of digital business ecosystems applied to the shipping industry,” in the Conference Proceedings of the International Association of Maritime Economists. Durkheim, É. (1893) De la Division du Travail Social, Paris: F. Alcan. El Sawy, O.A. and Pereira, F. (2013) “Anticipating game changers for ‘enterprise 2020’ in a digitally-intensive world,” in O.A. El Sawy and F. Pereira (eds) Business Modelling in the Dynamic Digital Space, Heidelberg: Springer, 1–12. French, W.L. and Bell, C.H. (1984) Organization Development: Behavioral Science Interventions for Organizational Improvement, Englewood Cliffs, NJ: Prentice-Hall. Fuster, J.M. (1995) Memory in the Cerebral Cortex, Boston, MA: MIT Press. Garzon, M. (1995) Models of Massive Parallelism, Berlin: Springer-Verlag. Gleick, J. (1987) Chaos: Making a new Science, New York: Penguin. Goldstein, J. (1994) Unshackled Organization, Portland, OR: Productivity Press. Granovetter, M. (1973) “The strength of weak ties,” American Journal of Sociology 78 (6): 1360–1380. Granovetter, M. (1982) “The strength of weak ties: a network theory revisited,” in P.V. Marsden and N. Lin (eds) Social Structures and Network Analysis, Beverly Hills, CA: SAGE, 105–130. Granovetter, M. (1985) “Economic action and social structure: the problem of embeddedness,” American Journal of Sociology, 91 (3): 481–510. Greengard, S. (2015) The Internet of Things, Cambridge, MA: MIT Press. Haken, H. (1983) Synergetics, An Introduction: Non-Equilibrium Phase Transitions and Selforganization in Physics, Chemistry, and Biology (3rd ed), Berlin: Springer-Verlag. Hamel, G. and Prahalad, C.K. (1994) Competing for the Future, Boston, MA: Harvard Business School Press. Herdon, M., Várallyai, L. and Péntek, Á. (2012) “Digital business ecosystem prototyping for SMEs,” Journal of Systems and Information Technology, 14 (4): 286–301. Holland, J.H. (1988) “The global economy as an adaptive system,” in P.W. Anderson, K.J. Arrow and D. Pines (eds): The Economy as an Evolving Complex System, Reading, MA: Addison-Wesley, 117–124. Holland, J.H. (1995) Hidden Order, Reading, MA: Addison-Wesley. Janis, I.L. (1972) Victims of Group Think, Boston, MA: Houghton Mifflin. Jensen, T., Tan, Y.H. and Bjørn-Andersen, N. (2014) “Unleashing the IT potential in the complex digital business ecosystem of international trade: the case of fresh fruit import

150  Bill McKelvey to European Union,” in the Proceedings the 27th eConference: eEcosystems, Bled, Slovenia, June 1–5, 14–27. Johnson, N.L. (2000) “The development of collective structure and its response to environmental change,” Report #LA-UR-02-3125, Los Alamos, NM: Los Alamos National Laboratory. Jones, G.R. (2000) Organizational Theory (3rd ed): Reading, MA: Addison-Wesley. Kauffman, S.A. (1993) The Origins of Order, New York: Oxford University Press. Kellmereit, D. and Obodovski, D. (2013) The Silent Intelligence: The Internet of Things, Warwick, NY: DND Ventures LLC. Kelly, S. and Allison, M.A. (1999) The Complexity Advantage: How the Science of Complexity can Help Your Business Achieve Peak Performance, New York: McGraw-Hill. Kerr, S. (2000) “The development and diffusion of knowledge at GE,” Presentation at the Organization Science Winter Conference, Keystone, CO, February. Kshetri, N. (2017) “The economics of the Internet of Things in the global south,” Third World Quarterly, 38 (2): 311–339. LeBaron, B. (2000) “Financial market efficiency in a coevolutionary environment,” in Proceedings of the Workshop on Simulations of Social Agents, Chicago, IL: Argonne National Laboratory and University of Chicago, 33–51. Li, W., Badr, Y. and Biennier, F. (2012) “Digital ecosystems: challenges and prospects,” in Proceedings of the International Conference on Management of Emergent Digital EcoSystems, ACM Digital Library, Association for Computing Machinery, 117–122. Liu, G. and Rong, K. (2015) “The nature of the co-evolutionary process: complex product development in the mobile computing industry’s business ecosystem,” Group & Organization Management, 1059601115593830. Maguire, S. and McKelvey, B. (1999) “Complexity and management: moving from fad to firm foundations,” Emergence, 1 (2): 19–61. Mainzer, K. (2007) Thinking in Complexity: The Complex Dynamics of Matter, Mind, and Mankind (5th ed): New York: Springer-Verlag. [His 1st edition was published in 1994.] Majeed, A.A. and Rupasinghe, T.D. (2017) “Internet of Things (IoT) embedded future supply chains for industry 4.0: an assessment from an ERP-based fashion apparel and footwear industry,” International Journal of Supply Chain Management, 6 (1): 25–40. Malecki, E.J. and Moriset, B. (2008) The Digital Economy: Business Organization, Production Processes and Regional Developments, New York: Routledge. March, J.G. (1991) “Exploration and exploitation in organization learning,” Organization Science, 2 (1): 71–87. Marion, R. (1999) The Edge of Organization: Chaos and Complexity Theories of Formal Social Systems, Thousand Oaks, CA: SAGE. Marion, R. and Uhl-Bien, M. (2001) “Leadership in complex organizations,” Leadership Quarterly, 12: 389–418. Masuch, M. and Warglien, M. (1992) Artificial Intelligence in Organization and Management Theory, Amsterdam: Elsevier Science. McEwen, A. and Cassimally, H. (2013) Designing the Internet of Things, Chichester: Wiley. McKelvey, B. (2002) “Managing coevolutionary dynamics: some leverage points,” Presented at the 18th EGOS Conference, Barcelona, Spain, July. McKelvey, B. (2003a) “Emergent order in firms: complexity science vs. the entanglement trap,” in E. Mitleton-Kelly (ed) Complex Systems and Evolutionary Perspectives on Organizations, Amsterdam, NL: Elsevier Science, 99–125. McKelvey, B. (2004a) “Microstrategy from macroleadership: distributed intelligence via new science,” in A.Y. Lewin and H. Volberda (eds) Mobilizing the Self-Renewing Organization, Armonk, NY: M. E. Sharp.

Simple rules for improving digital corporate IQ  151 McKelvey, B. (2004b) “Toward a 0th law of thermodynamics: order-creation complexity dynamics from physics & biology to bioeconomics,” Journal of Bioeconomics, 6 (1): 65–96. McKelvey, B. (2010) “Complexity leadership: the secret of Jack Welch’s success,” Internation Journal of Leadership and Management, 1 (1): 4–36. McQuivey, J. (2013) Digital Disruption: Unleashing the Next Wave of Innovation, Las Vegas: Amazon Publishing. Miles, R., Snow, C.C., Matthews, J.A. and Miles, G. (1999) “Cellular-network organizations,” in W.E. Halal and K.B. Taylor (eds) Twenty-First Century Economics: Perspectives of Socioeconomics for a Changing World, New York: Macmillan, 155–173. Miller, M. (2015) The Internet of Things, 800 E. 96th Street, Indianapolis, IN: QUE. Morgan, G. (1997) Images of Organization (2nd ed): Thousand Oaks, CA: SAGE. Muntaner-Perich, E. and de la Rosa Esteva, J.L. (2007) “Using dynamic electronic institutions to enable digital business ecosystems,” in P. Noriega, J. Vázquex-Salceda, G. Beolla, O. Boissier, V. Dignum, N. Formara and E. Matson (eds) Coordination, Organizations, Institutions, and Norms in Agent Systems II, Berlin: Springer, 259–273. Nachira, F., Dini, P., Nicolai, A., Le Louarn, M. and Rivera Lèon, L. (2007) Digital Business Ecosystems: The Results and the Perspectives of the Digital Business Ecosystem Research and Development Activities in FP6, Luxembourg: Office for Official Publications of the European Community. Nohria, N. and Eccles, R.G. (eds) (1992) Networks and Organizations, Boston, MA: Harvard Business School Press. Norling, P.M. (1996) “Network or not work: harnessing technology networks in DuPont,” Research Technology Management, 39 (1): 42–48. Pappas, N., Kazasis, F.G., Anestis, G., Gioldasis, N. and Christodoulakis, S. (2007) “A knowledge management platform for supporting digital business ecosystems based on P2P and SOA technologies,” in Digital EcoSystems and Technologies Conference Proceedings, DEST'07. Inaugural IEEE-IES, 196–202. Ponce-Jara, M.A., Ruiz, E., Gil, R., Sancristóbal, E., Pérez-Molina, C. and Castro, M. (2017) “Smart grid: assessment of the past and present in developed and developing countries,” Energy Strategy Reviews, 18: 38–52. Porter, M.E. (1996) “What is strategy?,” Harvard Business Review, 74 (6): 61–78. Prigogine, I. (1955) An Introduction to Thermodynamics of Irreversible Processes, Springfield, IL: Thomas. Prigogine, I. (1962) Non-Equilibrium Statistical Mechanics, New York: Wiley Interscience. Prigogine, I. and Stengers, I. (1984) Order Out of Chaos, New York: Bantam. Prusak, L. (1996) “The knowledge advantage,” Planning Review, 24 (2): 6–8. Razavi, A.R., Moschoyiannis, S.K. and Krause, P.J. (2007) “A coordination model for distributed transactions in digital business ecosystems,” in Digital EcoSystems and Technologies Conference Proceedings, DEST'07. Inaugural IEEE-IES, 159–164. Remane, G., Hanelt, A., Nickerson, R.C. and Kolbe, L.M. (2017) “Discovering digital business models in traditional industries,” Journal of Business Strategy, 38 (2): 41–51. Rong, K. and Shi, Y. (2014) Business Ecosystems: Constructs, Configurations, and the Nurturing Process, London: Palgrave Macmillan. Rothstein, J. (1958) Communication, Organization and Science, Indian Hills, CO: Falcon’s Wing Press. Sanchez, R. (1993) “Strategic flexibility, firm organization, and managerial work in dynamic markets,” Advances in Strategic Management, 16 (S1): 135–159. Sawicki, A. (2016) “The Internet of Things,” World Scientific News, 48: 89–96 Schallmo, D., Williams, C.A. and Boardman, L. (2017) “Digital transformation of business models—best practice, enablers, and roadmap,” International Journal of Innovation Management, 21 (08): 1740014-1–17.

152  Bill McKelvey Schallmo, D.R. and Williams, C.A. (2018) “Roadmap for the digital transformation of business models,” in Digital Transformation Now! Cham: Springer, 9–13. Schilling, M.A. (2000) “Toward a general modular systems theory and its application to interfirm product modularity,” Academy of Management Review, 25 (2): 312–334. Schmidt, E. and Cohen, J. (2013) The New Digital Age: Transforming Nations, Businesses, and our Lives, New York: Vintage Books. Scott, W.R. (1998) Organizations: Rational, Natural, and Open Systems (4th ed), Upper Saddle River, NJ: Prentice-Hall. Seigneur, J.M. (2005) “Demonstration of security through collaboration in the digital business ecosystem,” in Workshop of the 1st International IEEE Conference on Security and Privacy for Emerging Areas in Communication Networks, 108–109. Seo, D. (2017) “Digital business convergence and emerging contested fields: a conceptual framework,” Journal of the Association for Information Systems, 18 (10): 687–702. Simon, H.A. (1962) “The architecture of complexity,” Proceedings of the American Philosophical Society, 106 (6): 467–482. Slywotzky, A. (1996) Value Migration, Boston, MA: Harvard Business School Press. Sommerhoff, G. (1950) Analytical Biology, London: Oxford University Press. [Chapter 2 reprinted as “Purpose, Adaptation and ‘Directive Correlation,’” in W. Buckley (ed) 1968, Modern Systems Research for the Behavioral Scientist. Chicago: Aldine, 281–295.] Spencer, H. (1898) The Principles of Sociology, New York: D. Appleton & Co. Stacey, R.D. (1995) “The science of complexity: an alternative perspective for strategic change processes,” Strategic Management Journal, 16 (6): 477–495. Stanley, J. and Briscoe, G. (2010) “The ABC of digital business ecosystems,” arXiv preprint arXiv:1005.1899. Tafti, S.F., Kordnaeij, A., Hoseini, S.H.K. and Jamali, M. (2015) “Business ecosystem as a new approach in strategy,” Management and Administrative Sciences Review, 4 (1): 198–205. Tan, B., Pan, S.L., Lu, X. and Huang, L. (2009) “Leveraging digital business ecosystems for enterprise agility: the tri-logic development strategy of Alibaba.Com,” 30th ICIS Proceedings, paper 171. Tan, Y.L. and Macaulay, L.A. (2011) “Factors affecting regional SMEs progression to digital business ecosystems,” presented at the 17th Americas Conference on Information Systems (AMCIS). Tapscott, D. (1995) The Digital Economy, New York: McGraw-Hill Education. Tapscott, D. (2015) The Digital Economy: Rethinking Promise and Peril in the age of Networks Intelligence [2nd ed (20th Anniversary ed)], New York: McGraw-Hill Education. Uhl-Bien, M., Marion, R. and McKelvey, B. (2007) “Complexity leadership: shifting leadership from the industrial age to the knowledge era,” The Leadership Quarterly, 18 (4): 298–318. Weill, P. and Woerner, S.L. (2018) “Is your company ready for a digital future?” MIT Sloan Management Review, 59 (2): 21–25. Zohar, D. (1997) Rewiring the Corporate Brain, San Francisco, CA: Berrett-Koehler. Zucker, L.G. and Darby, M.R. (1996) “Star scientists and institutional transformation: patterns of invention and innovation in the formation of the biotechnology industry,” Proceedings of the National Academy of Sciences of the United States of America, 93 (23): 12, 709–712, 716.

5

Digital dynamic capabilities María Paz Salmador, Renata Kaminska, and Bill McKelvey

Introduction In the modern age and going forward, companies and customers are increasingly embedded in rapidly coevolving digital business (DB) ecosystems (Tapscott 1995, 2015; Coupey 2001, 2016; Day et al. 2003; Dourmas et al. 2005; Seigneur 2005; Brousseau and Penard 2007; Corallo et al. 2007; Muntaner-Perich and de la Rosa Esteva 2007; Nachira et al. 2007; Pappas et al. 2007; Razavi et al. 2007; Wang and Ahmed 2007; Malecki and Moriset 2008; Tan et al. 2009; Stanley and Briscoe 2010; Tan and Macaulay 2011; Herdon et al. 2012; Li et al. 2012; El Sawy and Pereira 2013; Attour and Della Peruta 2016; Cojocaru et al. 2014; Jensen et al. 2014; Rong and Shi 2014; Liu and Rong 2015; Tafti et al. 2015; Wang 2015; Choi 2017; Kshetri 2017; Ponce-Jara et al. 2017; Remane et al. 2017; Schallmo et al. 2017; Seo 2017; Schallmo and Williams 2018; Weill and Woerner 2018). Information flows are digitally connected and transfer at high speed (Bharadwaj et al. 2013; McGrath 2013a; Gupta 2014; McLay 2014). Everyone can easily and quickly learn about almost anything they choose to look for via the Internet of Things (Weber and Weber 2010; Atzori et al. 2014; Rifkin 2014; Sterling 2014: Turber et al. 2014; Fleisch et al. 2015; Greengard 2015; Miller 2015), which includes high-speed digital information connections from, to, or about: customers, competitors, products, supply chains, plans to innovate, and even secrets about future product designs via hacking (Sahito et al. 2011; deCosta 2013; Kellmereit and Obodovski 2013; Reddy and Reddy 2014; The Economist 2015; Want et al. 2015; Huang 2017; Reena and Gulia 2017; Seigfried-Spellar et al. 2017; Gunkel 2018). How to manage maintain and/or improve a firm’s existence in a rapidly coevolving DB ecosystem? But not just managing a company, but also managing the kind of digital platform (Muffatto and Roveda 2000; Koufteros et al. 2002, 2005; Sköld and Karlsson 2007; Magnusson and Pasche 2014; Zhang 2015; de Reuver et al. 2017; Langley and Leyshon 2017; Nambisan 2017) and the ecosystem (Sage et al. 2005; Krishnan et al. 2007; Pappas et al. 2007; Tan et al. 2009; Selander et al. 2010; Mäkinen and Dedehayir 2012; Weill and Woerner 2013, 2015; The Economist 2015, 2018; Kache and Seuring 2017; Sussan and Acs 2017; Teece and Linden 2017) the firm is using. Iansiti and Levien (2004) 

154  María Paz Salmador et al.

argue that Microsoft should also focus its strategy on maintaining the 38,233 other firms in its ecosystem (before the article was published), since the success of Microsoft depends on the success of its ecosystem. In the Digital Age most companies are digitally connected to customers and other firms in their ecosystem in so many different ways that their survival depends on how successfully they coevolve (Kauffman 1993) with the other firms in their ecosystem. And, since these connections are primarily via digital flows of information in the Digital Age, this coevolution process requires firms to change and improve their competitive capabilities very rapidly. Porter and Heppelmann (2014: 67) focus on what they call “smart, connected products” and end their article with a focus on ten “implications for strategy” (pp. 78–86). None of their implications, however, focus on just how managers are supposed to update their company—i.e. create new order in the form of new organizational and managerial abilities—so as to give it an advantage in surviving and even growing in the Digital Age, which means creating a company that can stay even with or take the lead in competing in its rapidly coevolving digital ecosystem. Since new-order creation in complex systems results from agent1 interactions (Holland 1975, 1995; Anderson 1999; Cowan et al. 1994; Johnson and Burton 1994; Mainzer 2007; Stacey 1995; Arthur et al. 1997; McKelvey 2001, 2004a,b, 2008, 2013a,b,c), digital connections of agents to the Internet, or via electronic email systems, vastly speeds up their interaction effects, which produces accelerated complexity dynamics, i.e. changes in agents’ attributes, connectivities, new social orders in the form of networking, networks and groupings, departments and other groupings within firms (Newman 2001; Barabási 2002; Dodds et al. 2003; Watts 2003; Csányi and Szendrői 2004; Powell et al. 2005; Tavlaki 2005; Briscoe and De Wilde 2006; Souma et al. 2006; Chmiel et al. 2007; Hamilton et al. 2007; Krishnan et al. 2007; Mislove et al. 2007; Santiago and Benito 2008; Hu et al. 2013; Zhai et al. 2013; Gatautis and Medziausiene 2014; Kutsikos et al. 2014; Mizuno et al. 2014; Crawford et al. 2015; Jianqiu and Mengke 2015; van Mierlo et al. 2015; Gabaix 2016; Srinivas and Mitra 2016; Patriarca et al. 2017; Tomasi et al. 2017). We begin the process of updating management theory so it becomes relevant to the Digital Age and what are now called DBs and digital platforms so that managers and their firms can effectively compete in the Digital Age of increasingly dominant, rapidly coevolving, digital ecosystems. We focus primarily on information systems, strategy, leadership, and organizational change. Porter and Heppelmann’s (2014: 67) “smart, connected products” exist in a rapidly coevolving digital world. Most—if not almost all—smart, connected products cannot coevolve with other digital products by updating themselves; they depend on employees in the “owning” firm to do this. Given this, how should firms best make sure the updating and coevolving processes are progressing as rapidly and appropriately as possible? This is what “digital management” has to focus on in the Digital Age of rapidly coevolving DBs. We begin with a short review of literature pertaining to the progression from Porter’s (1979) original description of his “five forces” to his recent

Digital dynamic capabilities  155

connection of the five forces with smart, connected products (Porter and Heppelmann 2014). Next we offer brief descriptions of platform strategies and digital ecosystems. Then we review key elements of new-order creation in complex systems, all of which occur much more rapidly in DBs. Finally, we suggest a new theory about how managers can best manage and stay ahead of the rapid digital coevolution of a DB, strategic platforms, and the digital ecosystem within which their firm is embedded.

Dynamic capabilities: literature review The resource-based view (RBV) conceptualizes firms as systems of tangible and intangible resources (Wernerfelt 1984; Barney 1991). Resources are heterogeneously distributed across firms and this difference persists over time (Grant 1991, 1996). Competence is defined as “an ability to sustain the coordinated deployment of assets in a way that helps a firm achieve its goals” (Sanchez et al. 1996: 8). Many authors argue that competencies evolve slowly and arise through collective learning, especially through coordinating diverse production skills and integrating multiple streams of technologies (Dierickx and Cool 1989; Rumelt 1994). The early 1990s were marked by a rapid acceleration of change in markets that challenged the original propositions of the RBV as being static and neglecting the influence of market dynamism (Wang and Ahmed 2007). This led to the development of the dynamic capabilities (DC) framework that extended the RBV to dynamic markets (Teece et al. 1997; Teece 2012). The term “capability” denotes a capacity to integrate, reconfigure and renew internal and external skills and resources in order to leverage and build core competences and the term “dynamic” refers to the capacity to renew competences so as to achieve congruence with a changing world (Teece et al. 1997). Since their seminal article, the literature on DCs has proliferated (Helfat et al. 2007; Argote and Ren 2012; Winter 2012; Peteraf et al. 2013). Nowadays, as the Digital Age has emerged to dominate the 21st century, dramatic redesigns of corporate structures and strategies can occur within months instead of the slow multi-year process characterizing the 1980s–1990s. The dominant message in the Digital Age is that firms holding to traditional change rates will go out of business. Many firms have already paid the price of new competition effects by Amazon​.co​m, which does online (digital) sales—and now ranks #1 in its S&P Sector, based on market capitalization. Teece (2007) provides a more comprehensive framework for understanding the different elements and processes comprising DCs. He disaggregates DCs into three elements that unfold sequentially. They are: 1. “Sensing” threats and opportunities. It involves identification, creation, and calibration of technological, market, and competitive opportunities via internal and external information scanning and interpretation.

156  María Paz Salmador et al.

2. “Seizing” emerging opportunities, i.e. changing organizational routines, decision rules, strategies, and leadership so as to “strategize around investment decisions, getting the timing right, building on increasing returns advantages, and leveraging products and services from one application to another” (Teece 2007: 1329). 3. “Reconfiguring” its tangible and intangible resources so as to escape the negative effects as markets and technologies change and competitors change their business model designs, asset-alignments and their existing routines. Teece also highlights the importance of individuals, middle, and top management and lists a number of factors necessary for effective “sensing,” “seizing,” and “reconfiguring”. However, not much is said about the micro-processes of building these collective capabilities from employees’ individual actions. Until now, authors tend to disaggregate macro phenomena into micro-processes rather than explaining how micro- and macro- processes coevolve. The “micro-processes” area of research is now emerging, however. Some authors try to address the problem through the cognitive-science perspective, more precisely, by focusing on search routines (Fredette and Branzei 2010). Others look at the problem through the prism of organization theory to fully capture and understand the processes of organizational change and strategic renewal (Güttel and Konlechner 2010). In the latter perspective, previous literature has already analyzed certain organizational aspects of renewing capabilities in high velocity markets. In this context, according to Eisenhardt and Martin (2000: 1106): DCs are: “simple, experiential, unstable processes that rely on quickly created new knowledge and iterative execution to produce adaptive, but unpredictable outcomes.” In a high velocity market, “semistructures” (Brown and Eisenhardt 1998) or “incompleteness” (Garud et al. 2006) support the emergence of DCs. Overall, DCs appear to be a mix of intentional and emergent actions (Rothaermel and Hess 2007). Needless to say, all of the above now occur much faster in the Digital Age. Finally, some authors focus on the emergence of DCs from an organizational change perspective. For example, Ambrosini et al. (2009) suggest that there are three levels of DCs—incremental, renewing, and regenerative—and that these levels are related to the way managers perceive the dynamics of their competitive environment. In a relatively stable environment, incremental change is OK. In an environment perceived to be dynamic, more dramatic changes to DCs are required for a firm’s survival. When an environment is perceived as being turbulent and unpredictable (D’Aveni 1994), regenerative DCs can be designed to modify all the rest of the DCs. In the Digital Age, all three levels need to progress faster, but especially the latter two. However, we don’t really know much about how the high-speed dynamics of effectively conducting digital business affect social interactions, the interplay of individuals and groups, and the mix of intentionality and emergence-processes that enable the emergence of DCs, as described by Abell et al. (2008). In

Digital dynamic capabilities  157

order to address this research gap, we turn to complexity theory and make special note of how complexity-change dynamics have speeded up in the Digital Age. This helps us to better understand how unplanned social interactions in response to digital effects can progressively and more quickly renew capabilities and competences. Especially, it focuses on how micro events can sometimes spiral up into macro (even extreme) outcomes. Existing DC, micro-processes, and Teece’s theory are all well and good, but they focus on “normal” (now old-fashioned) change rates by firms that were facing slowly developing pressures for change from a slowly changing competitive context. They describe what needs to be done, yes, but how to do it quickly under the current conditions of digital business that require changes at digital speeds? This is the problem that 21st century firms face. For theoretical help on this, we shift our focus to how complexity-change dynamics speed up in the Digital Age.

Complexity and coevolution effects Complexity science

Complexity science is “order-creation science” (Mainzer 2007; Holland 1995; McKelvey 2004a,b). Instead of focusing on trends toward equilibrium, which is the underlying assumption in physics and economics (Holland 1988; Mirowski 1989; Greene 2017), complexity science focuses on why and how we see emergent new order in situations ranging from bee and ant colonies to living systems comprised of humans that now face constant change toward emerging new order at digital change rates that give rise to new groupings and hierarchies, or predator/prey and M&A behaviors, in competitive ecologies of modern DB ecosystems (Tapscott 1995, 2015; Corallo et al. 2007; Bones and Cohen 2013; Westerman et al. 2014; Liu and Rong 2015; Tafti et al. 2015; Coupey 2016; Kache and Seuring 2017; Sussan and Acs 2017; Teece and Linden 2017). To highlight the importance of three basic complexity concepts—tension, connectivity, and what Bak (1996) called “self-organized criticality” (SOC),— we start with his sandpile example. Bak explains sand grain movements as follows: falling grains of sand slowly accumulate in a pile. Eventually the sandpile becomes high enough and its slope steep enough to trigger sand avalanches of varying sizes. These restore stability to the slope. The steepness of the slope depends on two elements: (1) gravity and (2) the sharp irregular shape of the individual sand grains. Take away gravity and there is no force causing the grains to slide down past each other—call the influence of this force the tension effect. Take away the irregular shape of the individual grains, on the other hand, and they become frictionless, unable to resist the downward force exerted by gravity—somewhat like smooth M&M candy, they will then scatter, unable to cohere enough to build up a pile. Call the influence of friction the connectivity effect. Bak et al. (1987) discovered that sand-grain movements vary from the frequent but barely perceptible movement of a few isolated grains to the rare but large avalanches in which thousands of sand grains move in unison.

158  María Paz Salmador et al.

To move SOC from sand to social systems we draw on a more recent article by Kron and Grund (2009). They also emphasize tension and connectivity, and the social equivalent of small and large avalanches. By way of example, they offer a detailed analysis about how a series of seemingly meaningless (tiny) behaviors by Serbia, Austria, Russia, Germany, France, and Great Britain triggered “an apocalyptic ‘avalanche of warlike actions’” in 1914 (2009: 78). They then detail how these initiating events scaled up into World War I. They conclude as follows: “The concept of self-organized criticality can contribute to find answers to open or disputed historical questions. This is exemplified in the case of the outbreak of World War I” (p. 79). As applied more specifically to firms, Holland (1988) offers some details about how SOC is achieved, as follows:2 ••

••

••

•• •• ••

Tension dynamics: Because new competitors, niches, and strategic possibilities are continually created, tensions requiring change are continually imposed on employees, managers, different levels of a hierarchy, or the entire firm; Self-organizing heterogeneous agents: The actions of any given agent (employee) may follow from actions of a few neighboring agents, i.e. their micro-dynamics. The coevolving self-organizing behaviors of many of these agents—whether managers or other employees—can have a profound effect on aggregate organizational and strategic change; SOC and change: The entangled contexts continually create new niches, which then call for new adaptive behaviors amongst the entangled entities, whether inside or outside a given firm. New niches are continually created by interacting new technologies, new markets, new institutions, new predator/prey (M&A): and various other behaviors; Adaptation: Because of all of the foregoing influence streams, human agents continually revise what they hope are efficaciously adaptive behaviors; Hierarchy and influence: Firms consist of multiple levels and many kinds of influence streams going up, down, sideways, and diagonally among the various agents; No global controller or cause: No global controller or single cause or “top manager” totally controls a firm [although there are some (narcissistic) CEOs who try and do so].

From all of the above we focus on three key elements that are crucial to understand the emergent self-organization and new order-creation aspects of dynamic capabilities as firms respond to drastic contextual change: (1) imposing tension; (2) positive feedback; and (3) scalability. Imposing tension

Starting in physics, complexity scientists dealt with physical phenomena and emphasized phase transitions resulting from externally imposing tensions

Digital dynamic capabilities  159

(Prigogine 1955, 1962; Nicolis and Prigogine 1989). They focused on the definition of two “critical values,” RC1 and RC2, which correspond to the energy differential between the system and its surroundings. These values define the upper and the lower bounds of the “Region of Emergent Complexity” (i.e. when self-organization and emergent new order may occur), which is situated between RC1, the “edge of order” (below which we see few variables and few degrees of freedom, low tension, low energy, high order, and slow, if any, change), and RC2, the “edge of chaos” (above which we see very high and/or many different imposed tensions, many degrees of freedom, and so many different changes needed all at the same time that the result is chaos and dysfunctionality). Maguire and McKelvey (1999) describe this region of emergent complexity as a state that allows the emergence of self-organization and creative destruction (Schumpeter 1942; see also Maguire et al. 2006; Allen et al. 2011). Applied to a firm, the energy differential is defined as imposing tension (T) measuring the difference between the current situation and future desired (or required) situation in response to imposing conditions, whether internal or external (McKelvey 2001). RC1 and RC2 are the two critical values of T, which define the Region of Emergent Complexity. Emergent change appears as new structures, processes, networks, and adaptive responses (i.e. the search for DCs) that emerge from interactions among the different micro- and macro-level people or outcomes in a firm (Jo 2007) that coevolve so as to re-create a firm. If employees in an existing firm that is failing because it can’t cope with new competitors or a changing competitive environment have enough freedom to network with each other and learn from each other and then experiment with new ways of doing things, a micro-foundational self-organization process may begin and perhaps spiral up so as to lead to a radical macro-level change of DCs (i.e. new order) (Balogun 2007; Fenton 2007; Pena e Cunha and Rego 2010; Argote and Ren 2012; Teece 2012; Devinney 2013; Greve 2013). Positive feedback

Complexity scientists associated with the Santa Fe Institute emphasize emergent “complex adaptive system” behavior; specifically, how new order arises in (living) biological and social systems. They studied heterogeneous agents interacting in between the edges of order and chaos, which is the Region of Emergent Complexity—the “melting zone” (Kauffman 1993). Spontaneous order creation begins when three elements are present: (1) heterogeneous agents (employees); (2) motives to connect—improving fitness, performance, learning, and so forth; and (3) connections among the agents—their ability to connect by talking face-to-face, by traditional postal methods, or by modern digital methods. Remove any one element and nothing happens. The signature elements of the melting zone are self-organization, emergence, and nonlinearity. Kauffman (1993) studied all three of these using agent-based computational models. Self-organization results in emergence (i.e. new order of some kind). Following Holland (2002),

160  María Paz Salmador et al.

we recognize emergent phenomena as multi-level hierarchies, intra- and interlevel causal processes, and nonlinearities. Positive feedback is one of the several causes of complex system adaptation and nonlinear outcomes, i.e. tiny initiating events that spiral into significant changes in DCs. For empirical substantiation that recursive interactions among employees at different company levels foster innovation, see Rothaermel and Hess (2007). Recursive interactions, with positive feedback, define the mutual causal processes driving self-organizing behavior (Maruyama 1963). Positive feedback amplifies the tiny initiating micro events such that they spread through an entire organization (Plowman et al. 2007; Nonaka et al. 2014). Positive feedback, however, is just one of many scale-free causes that lead to nonlinear (often extreme) good (or bad) outcomes (for definitions of additional causes of nonlinear outcomes, see West and Deering 1995; Sornette 2000; Newman 2005; Andriani and McKelvey 2009; Asmussen 2015; Chen et al. 2017; Fujiki et al. 2017; Guszejnov et al. 2017; Jäger et al. 2017; Yang and Liu 2018). Scalability and fractal structures

Researchers have increasingly found that many physical, living, social, and financial phenomena appear as rank/frequency Pareto (power-law [PL]) distributions rather than normal distributions (West and Deering 1995; Stanley et al. 1996; Mantegna and Stanley 2000; Gell-Mann 2002; Newman 2005; Andriani and McKelvey 2009; McKelvey and Salmador Sanchez 2011; Crawford and McKelvey 2012, 2018; Crawford et al. 2014, 2015; Prieto and Sarabia 2017; Touboul and Destexhe 2017; Zhang et al. 2017; Conyon 2018; Zhang et al. 2018). Holland’s (2002) “tags,” i.e. tiny initiating events, often initiate causal dynamics and self-organizing processes that scale up in impact; that is, the same causal dynamic grows from an initial small micro-impact to an eventual large-scale (even extreme) outcome that has a macro impact. McKelvey and Lichenstein (2007) propose a scale-free theory of emergence suggesting that emergence occurs in the same pattern across stages and/or levels in an organization. These mirror (1) Mandelbrot’s (1983) fractal geometry of mathematical creations, and (2) natural creations such as cauliflowers: from the smallest floret to the whole, each part looks and functions essentially the same. Also, we see roughly similar patterns in social phenomena and firms (Newman 2005, 2017; Plowman et al., 2007; Andriani and McKelvey 2009, 2011; Crawford et al. 2015; Muchnik et al. 2015; Ronda-Pupo and Katz 2017, 2018; Tarasova and Tarasov 2018; Zang et al. 2018). McKelvey et al. (2012) point to the similarity between biological predator/prey fractals and the fractal nature of firms’ competitive contexts. According to McKelvey (2001, 2004a) none of the three foregoing perspectives are sufficient by themselves, especially in social settings. External force/tension effects, internal positive feedback processes, and scalable interactions among agents are co-producers of an emergent new order (Maguire et al. 2006). As focus on complexity phenomena relevant to organizations

Digital dynamic capabilities  161

and management increases, the tendency of complexity dynamics to produce non-Gaussian distributions and extreme outcomes has become a central factor of attention (Pina e Cunha and Rego 2010; Allen et al. 2011; McKelvey 2013a,b,c,e; Nonaka et al. 2014; Frey et al. 2017; Saberi et al. 2017). Coevolution

Evolution in biology is now seen as coevolution (Ehrlich and Raven 1964). Kauffman (1993: 237) observes that “The true and stunning success of biology reflects the fact that organisms do not merely evolve, they coevolve both with other organisms and with a changing abiotic environment” (our italics). Nowhere is this point made more wonderfully than in Maruyama’s classic paper of 1963, where he studies the interaction of coevolution and mutation rates, drawing on the earlier work of Wright (1932). The “coevolution” term has since seeped into organization science (Boulding 1968; McKelvey 1997; Lewin and Volberda 1999; Pacheco et al. 2014; Grodal et al. 2015; Cantner et al. 2017; Weber 2017). While not using the term, social psychologists have long noted what is really the “coevolution” of member attitudes and group norms (Homans 1950); sociologists have observed the interaction of formal and informal systems in organizations—coevolution in effect, though they did not use the term (Scott 1998; Lazaric 2000). And certainly, economists have long noted the interaction between firms’ behaviors in creating industries and industry effects on firms (Nelson and Winter 1982; Carroll and Hannan 1995), as well as coevolution of economic agents inside firms (Lazaric and Denis 2001). Founders of the Santa Fe Institute (for the study of complexity sciences), such as Arthur (1988, 2000) and Kauffman (1993), argue that coevolution is at the root of self-organizing behavior, constant change in systems, the production of novel macro-structures, and associated nonlinearities. Many writers applying complexity theory to improve the management of firms argue that complexity theory is a tool that can help managers manage better in a rapidly changing nonlinear competitive context (McKelvey 1997, 2001, 2004a; Brown and Eisenhardt 1998; Anderson 1999; Kelly and Allison 1999; Maguire et al. 2006; Allen et al. 2011; Frey et al. 2017; Saberi et al. 2017). A problem generally ignored in organizational applications of coevolution is that it approximates a mutual-causal, deviation-amplifying, positive feedback process (Maruyama 1963). Thus, A reacts to B; B reacts to A; A reacts to B’s change, and so on; the deviation-amplifying cycle repeats indefinitely until some damping mechanism halts it. In the Galapagos Islands, birds’ beaks strengthen as nuts harden and the reverse. But if beaks get too heavy the bird can’t fly and gets eaten; if nuts get too hard they don’t germinate and reproduce; and so coevolution is damped out. In biology, damping mechanisms ultimately prevail over all coevolutionary processes. But in the econosphere such is not the case. Maruyama anticipates, and Krugman (1996) shows, that coevolutionary processes give rise to cities and more specifically to the inverse PL relation between rank and population. McCain (2000) uses coevolution to

162  María Paz Salmador et al.

explain the increasing dichotomous relation between rich and poor, strong and weak economies, between the G7 and the third world. Absent antitrust efforts, economists have long observed industries tending toward monopolists— Rockefeller’s Standard Oil Trust, and Gate’s Microsoft—the larger the firm, the more of its industry it controls; the more industry it controls, the larger the firm. The Lewin/Volberda Special Issue (1999) focuses on firm-industry coevolutions. Recent organization studies also identify many instances of coevolution inside firms (Burgelman 1991; Baum and Singh 1994; Baum and McKelvey 1999; Lazaric and Denis 2001; Kaminska-Labbé and Thomas 2004; Bryant and Nguyen 2017; McKelvey and Saemundsson 2017; Almudi and Fatas-Villafranca 2018). Needless to say, given that “coevolutionary” theory dates back to Kauffman (1993) and even before, it is rather surprising that none of the articles in the recent Special Issues of the Journal of Management (2011) and Academy of Management Perspectives (2013) that focus on the interaction of micro- and macro-organizational entities mention the term “coevolution” even though “evolution” is mentioned frequently and most of the articles focus on the “microfoundations” (i.e. individual employees’ cognitions, behaviors and interactions) of the more “macro” conceptualizations of “business policy and strategy,” which lead to new organizational macro-structures (e.g. Aguinis et al. 2011; Barney and Felin 2013; Devinney 2013; Foss and Lindenberg 2013; Greve 2013; Van de Ven and Lifschitz 2013; Winter 2013; Weyland 2014, 2016; Noorderhaven et al. 2015; Mykkänen 2017). Some other articles that also focus on the interactions of microfoundations and macro-structures created to improve DCs, but do not mention “coevolution” are: Gavetti 2005; Argote and Ren 2012; Foss et al. 2012; Teece 2012 and Weyland 2014, 2016.

Digital Age realities There are now many articles and books describing digital business (DB) ecosystems, and how they are different from prior business ecosystems, some of which we cited earlier. Some additional ones are: Day et al. (2003); Brousseau and Penard (2007); Muntaner-Perich and de la Rosa Esteva (2007); Razavi et al. (2007); Tan et al. (2009); Strader (2010); Li et al. (2012); El Sawy and Pereira (2013); Brynjolfsson and Mcafee (2014); Phillips (2014); Rong and Shi (2014); Graça and Camarinha-Matos (2017); Järvi and Kortelainen (2017); Sussan and Acs (2017). The “Internet of Things” and other Digital World realities

The “Internet of Things” (deCosta 2013; Kellmereit and Obodovski 2013; McEwen and Cassimally 2013; McQuivey 2013; Schmidt and Cohen 2013; Greengard 2015; Miller 2015; Atzori et al. 2017; Dhillon et al. 2017; Thapliyal 2018) consists of miniature computers in everyday objects that are wirelessly connected. Miniature computers range from desktops to laptops

Digital dynamic capabilities  163

to iPods and iPads to smart phones to the cloud created and made available by Microsoft. One of the best articles about what companies need to do so as to achieve and/or stay at a competitive position in a rapidly coevolving DB ecosystem was written in 2014 by Brian Leavy. It is titled “Strategy, organization and leadership in a new ‘transient-advantage’ world.” He begins with what firms need to do to achieve McGrath’s (2013b) “transient advantage.” This includes: •• •• ••

“Making innovation an everyday proficiency; Practicing healthy disengagement as a normal, regular activity; Continuously reconfiguring resources and activities to achieve a dynamic balance between stability [efficiency] and agility [innovation and change]” (p. 5).

To keep up with customer preferences and new competing products in the Digital Age of rapid coevolution among entities in a firm’s ecosystem, continuous change toward novelty and letting go of what already exists because the Internet of Things lets customers and firms to continuously look and compare the products and aspirations of competing companies. Leavy then discusses key aspects from Kotter’s (2014) book. Kotter notes that managers currently focus primarily on reliability and efficiency, which calls for focusing their actions mainly on: “plans/budgets,” “job descriptions,” “compensation,” “metrics,” and “problem solving”—all of which are elements of “management-driven hierarchies” (shown in Leavy’s [2014] Figure 2.3). Instead of these actions, Kotter emphasizes what he calls the “strategy acceleration network,” which includes “agility and speed,” “constant innovation and leadership development,” and then eight of what Kotter terms “accelerators” (shown in Leavy’s Table listing Kotter’s eight accelerators) (paraphrased here, as opposed to direct quotes): 1. 2. 3. 4. 5.

Create a sense of urgency; Build a guiding coalition of volunteers; Form a vision of what needs to be changed and create strategic initiatives; Enlist an army of volunteers to create the vanguard leading change; Enable action by removing barriers; change the hierarchy to foster change rather than stability; 6. Create and focus on short-term wins; continuous small changes are better than occasional big ones; 7. Sustain acceleration; keep innovation and change activities in involved networks in continuous motion; 8. Institute changes to institutionalize wins; the changes have to be infused into an organization’s culture. Kotter’s (2014) focus on what he calls “strategy acceleration networks” lines up with Kumar et al.’s (2015) transformation of Porter’s (1997) “five forces”

164  María Paz Salmador et al.

concept of viable industry structures into what they call “the new five forces and nodal advantage” that characterize firms and networks in digital ecosystem structures. The fact is that digital connectivities are much more likely to result in networks in which one or a few firms appear as the dominant nodes and then show up as outliers out toward, or at the end, of demonstrably skewed distributions—which Salmador Sánchez and I briefly discuss later—read Chapter 8. The current reality is that until an industry consists almost entirely of firms that are characterized by Kotter’s “strategy acceleration networks” showing his “eight accelerators” the use of “normal” Gaussian distributions and statistics characterizes failing firms in what are increasingly digital ecosystems and therefore research findings based on Gaussian statistics offer little, if any, strategically helpful information (as illustrated by Crawford et al. 2015). DB ecosystems

The concept of an ecosystem comes from biology (Moore 1993). Levin (1998) points out that biological ecosystems emerge as members of species and then species compete for survival; sometimes a few dominant species survive (e.g. elephants, lions, killer whales, and great white sharks) but smaller members (e.g. bacteria, mosquitoes, flies, mice, small birds) show much larger populations. He explains the dynamics of biological ecosystems by using complexity-science concepts such as self-organization, self-management, sustainability, and scalability (see also: Lewin 1992; Kauffman 1993; Mainzer 2007; Barabási 2002; Brisco et al. 2007; Stanley and Briscoe 2010; McKelvey 2013a,b,c). As of now, the business ecosystem literature is entirely about DB ecosystems (e.g. Razavi et al. 2010; Selander et al. 2010; Tsatsou et al. 2010; Mäkinen and Dedehayir 2012; Battistella et al. 2013; Pilinkienė and Mačiulis 2014; Kumar et al. 2015; Graça and Camarinha-Matos 2017; Järvi and Kortelainen 2017; Kache and Seuring 2017; Lavassani and Movahedi 2017; Sussan and Acs 2017). In their process-model of DB ecosystem transformative coevolution Selander et al. (2010: 12) focus on the “tensions between competing values among participants of the new relationship” (their italics). Though they don’t cite Prigogine and Stengers (1984) or Prigogine (1997), it is obvious that they focus on “tensions” to explain the coevolution of DB ecosystems, in which the subsequent changes in the network structure of the ecosystem are emergent “dissipative structures” (Prigogine’s label). In their review of the digital ecosystem literature, Mäkinen and Dedehayir (2012: 9) focus on the coevolution among ecosystem members, noting that “changes in the ecosystem’s social, economic, technological, and competitive environment serve as exogenous factors in governing evolution of the ecosystem.” They also note that “bottlenecks which constrain value creation act as focusing devices and motivate innovation within the ecosystem.” Krishnan et al. (2007) study how peer-to-peer (P2P) relations can create networks that distribute digital content and which can also allow users to discover specialized and niche products and can also help an industry deal with

Digital dynamic capabilities  165

software piracy. Battistella et al. (2013) focus on how DB network structures interact with each other so as to influence business systems. Morisse et al. (2014) emphasize the development of information technology (IT) and trust as firms in DB ecosystems coevolve. Weill and Woerner (2013) emphasize three trends that improve DB ecosystem effectiveness: (1) increasing digitization aids learning about customers and conducting business; (2) the number of digital experts (they call them “digital natives”) who increasingly expect digital interactions among customers and businesses and within a company; and (3) online digital communications from customers are giving them much more impact on ratings of companies, their products, and their reputations. It’s the Twitter and other social media effects. Vollmer and Egol (2014) have a blog post, in which they identify “five rules for strategic partnerships in a digital world” (p. 1): (1) “Never innovate alone.” Partnerships with other firms are now essential and digitally available; (2) “Understand that no single company has a lock on user preferences.” Digital partnerships with other firms are essential; consumers and other companies keep changing; (3) “Focus first on a great user experience, not the value exchange.” Partnerships with other firms need to solve user problems and put customer/user experiences first; (4) “Strike the right balance between scale and customization.” Apps and software can be used to maximize reach (scalability) while at the same time reducing costs stemming from customization; and (5) “Treat your partnership like your business…Run the partnership more like a true business and less like a deal.” Partners have to trust each other, not compete or cheat. Traditional businesses used methods akin to Porter’s (1979) five forces to become dominant firms in their traditional business ecosystems by focusing on economies of scope—i.e. internal efficiency—by producing better products faster, better, and cheaper, as pointed out by Razavi et al. (2010). Razavi et al. continue by describing the emergence of DB ecosystems with P2P interactions, many digital connectivities, and rapid communication via the Internet that serve to create many more opportunities for agile SMEs (small and medium-sized enterprises) to survive, multiply, and coevolve in what are now rapidly coevolving digital ecosystems (see also: Krishnan et al. 2007; Pappas et al. 2007; Battistella et al. 2013; Dereń et al. 2017; Tanriverdi and Lim 2017). While Microsoft’s ecosystem of SMEs consisted of some 38,000+ firms (Iansiti and Levien 2004) there is always the danger that firms like Apple, Amazon, or Samsung could dominate even a DB ecosystem—as they obviously do now, in 2018. Digital platforms (discussed next) also offer digital methods by which some of the many smaller SMEs might be able to dominate their DB ecosystem, though it is true that networks tend to be skew (PL)distributed (Auerbach 1913; Zipf 1949; Barabási et al. 2000). We offer additional studies showing power-law distribution networks of all kinds. We also argue that complexity-theory concepts such as tension effects, heterogeneous agent behavior, self-organization, emergent new order, scalability, also foster coevolution; and that, in the Digital Age, this coevolution can be very rapid.

166  María Paz Salmador et al.

Key digital effects on business ecosystems: a summary In this section we summarize some of the specific digital effects on the main complexity components that impact companies. Digital Age effects on complexity dynamics

Some of the digital-speed effects broadly defined are: •• •• •• •• •• •• •• •• ••

••

Peoples’ high-speed digital equipment: laptops, desktops, iPods, iPhones, iPads, etc. has increased; Almost everyone is connected to the Internet of Things; A large number of digital connections are readily available and constantly impact many organizational employees; Many employees are connected to various networks, which can result in faster learning about new knowledge, but also can create skew distribution effects; Communication and knowledge sharing occur at digital speed; IC in companies can grow at digital speed; Speedy digital knowledge about customers’ preferences and competitors’ products increases; Competitors’ can change their preferences and knowledge about different products at digital speed; Companies can learn more quickly about competitors’ new product ideas and innovations (by hacking): but they can also be hacked by competitors so the latter can learn about their new strategies and product concepts. By hacking, they can accomplish this learning before the products actually appear in a market; Coevolution can occur at digital speed.

Digital tension effects:

•• •• •• •• •• ••

Tensions can arise and/or dissipate very rapidly; The amount of any given tension can quickly change because of digital connections; The number of tensions impacting a particular employee or department can change rapidly because of digital connections; The size, impact, and nature of any given network to which an employee is connected can change rapidly, depending on who remains digitally connected; Chaos can easily result because employees are so frequently digitally connected to multiple sources of tension; Chaos may appear or disappear rapidly since tension-involved digital information flows can rapidly change such that only one or a very few tensions are imposed or many can seem to be imposed all at the same time.

Digital dynamic capabilities  167 Digital effects in the region of emergence:

•• •• •• •• •• •• ••

Digital connections among agents can change quickly, thereby increasing the chances of heterogeneous agents connecting and learning new ideas from each other; Or: the many digital connections a person has could result in “strong-tie” effects, such that the person behaves very much like the people she/he is digitally connected to; i.e., the creation of homogeneous agents; Motives to connect, survive, and grow can change quickly because of the digital basis of information and available knowledge about the nature, aspirations, and motives of other employees; Agent interactions can be instantaneous and with many rapidly changing agents involved; Upward and downward influences in organizations are more likely since digital information can flow quickly and easily up or down the hierarchy; Information seen as “negative” in some way by management or other people can’t easily or readily suppressed in the Digital Age; Self-organized change in social systems may be much more likely because of digital information flows.

Digital effects on nonlinearities

•• •• ••

•• •• ••

Emergent networks and hierarchies are likely to happen more often and more quickly because of digital information and digital connectivities; They can also disappear more quickly because of digital disconnections; As already indicated by the many photos and comments going viral on Facebook or Twitter, we already know that some digital connectivities and interactions among connected individuals can readily result in very pronounced skew distributions in some instances, i.e., they go viral via the Web; Because of networking and digitization, some seemingly tiny messages to begin with can have incredible impact on thousands of individuals or all members of a company; It is frequently impossible to prevent in advance or stop messages from going viral after they get out onto the Web; Emergent nonlinearities are much more likely because networks are more readily created and enlarged and tension effects can spread much more quickly with the result that positive feedback and/or other scale-free causes are likely to cause random tiny initiating events to scale up into larger positive (or negative) outcomes.

For all of the foregoing reasons, emergent new order is much more likely to become more rapidly and frequently possible, but also be more easily evaluated, such that it grows or may be stopped more quickly (the latter is, however, also more difficult because of digitization).

168  María Paz Salmador et al.

For a graphic summary of how key elements of DB strategy influence business performance, see Figure 5.1. It shows that various causes influence key aspects of DB strategy, which then affect business performance.

Conclusions As noted earlier, although there is some interest in how individual agent (employee) behaviors influence top management—called “microfoundations” by strategy authors (e.g. Aguinis et al. 2011; Barney and Felin 2013; Devinney 2013; Foss and Lindenberg 2013; Greve 2013; Van de Ven and Lifschitz 2013; Winter 2013)—and although some strategy authors discuss the interaction of microfoundations vs. macro phenomena such as DCs (dynamic capabilities) (e.g. Gavetti 2005; Argote and Ren 2012; Teece 2012), they don’t explicitly mention “coevolution” and how the micro and macro entities interact with each other over time so as to coevolve toward an improved—even optimal— behavioral and/or structural outcome. Given that “coevolution” in the academic literature dates back to Kauffman’s famous statement in 1993 that: “The true and stunning success of biology reflects the fact that organisms do not merely evolve, they coevolve both with other organisms and with a changing abiotic environment” (p. 237)—and even earlier—we are somewhat surprised that strategy authors haven’t already focused more explicitly on the coevolution among entities (e.g. employees, managers, CEOs, and/or other organization-structural elements, as well as competing companies in the broader ecosystem a company is in) that eventually create competitively effective DCs. It is not enough to just write about microfoundations here and there that somehow lead to more effective DCs; strategy theorists and researchers need to focus more explicitly on the coevolution process that eventually leads to more effective DCs. It doesn’t happen by accident; it is a coevolutionary process that needs to be more explicitly defined, unraveled, and described, so that practicing managers can manage the coevolutionary process more effectively in their company. In complex companies nothing comes to fruition by itself; it is always a coevolutionary process of some kind. And, nowadays, coevolution can be very digital.

Notes 1 “Agents” may be people, attributes of people that make them different or influence their interactions with other agents, groups, departments, firms, economies, countries, etc. 2 Holland outlines the basic essentials of the self-organization that leads to SOC in living systems, including economies (1988: 117–118). Our bullets are paraphrased from his.

References Abell, P., Felin, T. and Foss, N.J. (2008) “Building microfoundations for the routines, capabilities, and performance links,” Managerial and Decision Economics, 29 (6): 489–502.

Digital dynamic capabilities  169 Aguinis, H., Boyd, B.K., Pierce, C.A. and Short, J.C. (2011) “Walking new avenues in management research methods and theories: bridging micro and macro domains,” Journal of Management, 37 (2): 395–403. Allen, P., Maguire, S. and McKelvey, B. (eds) (2011) The SAGE Handbook of Complexity and Management, London: SAGE. Almudi, I. and Fatas-Villafranca, F. (2018) “Promotion and coevolutionary dynamics in contemporary capitalism,” Journal of Economic Issues, 52: 80–102. Ambrosini, V. Bowman, C. and Collier, N. (2009) “Dynamic capabilities: an exploration of how firms renew their resource base,” British Journal of Management, 20 (S1): S9–S24. Anderson, P. (1999) “Complexity theory and organization science,” Organization Science, 10 (3): 216–232. Andriani, P. and McKelvey, B. (2011) “From skew distributions to power-law science,” in P. Allen, S. Maguire and B. McKelvey (eds) The SAGE Handbook of Complexity and Management, London: SAGE, 254–273. Andriani, P. and McKelvey, B. (2009) “From Gaussian to Paretian thinking: causes and implications of power laws in organizations,” Organization Science, 20 (6): 1053–1071. Argote, L. and Ren, Y. (2012) “Transactive memory systems: a microfoundation of dynamic capabilities,” Journal of Management Studies, 49 (8): 1375–1382. Arthur, W.B. (1988) “Self-reinforcing mechanisms in economics,” in P. Anderson, K.J. Arrow and D. Pines (eds): The Economy as an Evolving Complex System, Reading, MA: Addison-Wesley, 9–31. Arthur, W.B. (2000) “Complexity and the economy,” in D. Colander (ed) The Complexity Vision and the Teaching of Economics, Cheltenham: Edward Elgar, 19–28. Arthur, W.B., Durlauf, S.N. and Lane, D.A. (eds) (1997) The Economy as an Evolving Complex System, Proceedings of the Santa Fe Institute, Vol. XXVII. Reading, MA: AddisonWesley, 1–14. Asmussen, C.G. (2015) “Strategic factor markets, scale free resources, and economic performance: the impact of product market rivalry,” Strategic Management Journal, 36 (12): 1826–1844. Attour, A. and Della Peruta, M. (2016) “Architectural knowledge: key flows and processes in designing an inter-organisational technological platform,” Knowledge Management Research & Practice, 14 (1): 24–34. Atzori, L., Iera, A. and Morabito, G. (2014) “From ‘smart objects’ to ‘social objects’: the next evolutionary step of the Internet of Things,” Communications Magazine, IEEE, 52 (1): 97–105. Atzori, L., Iera, A. and Morabito, G. (2017) “Understanding the Internet of Things: definition, potentials, and societal role of a fast evolving paradigm,” Ad Hoc Networks, 56: 122–140. Auerbach, F. (1913) “Das Gesetz der Bevolkerungskoncentration,” Petermanns Geographische Mitteilungen, 59: 74–76. Bak, P, Tang, C. and Wiesenfeld K (1987) “Self-organized criticality: an explanation of the 1/f noise,” Physical Review Letters, 59 (4): 381–384. Bak, P. (1996) How Nature Works: The Science of Self-organized Criticality, New York: Copernicus. Balogun, J. (2007) “The practice of organizational restructuring: from design to reality,” European Management Journal, 25 (2): 81–91. Barabási, A. (2002) Linked: The new Science of Networks, Cambridge, MA: Perseus. Barney, J. (1991) “Firm resources and sustained competitive advantage,” Journal of Management, 17 (1): 99–120.

170  María Paz Salmador et al. Barney, J. and Felin, T. (2013) “What are microfoundations?” Academy of Management Perspectives, 27 (2): 138–155. Battistella, C., Colucci, K., De Toni, A.F. and Nonino, F. (2013) “Methodology of business ecosystems network analysis: a case study in Telecom Italia future centre,” Technological Forecasting & Social Change, 80 (6): 1194–1210. Baum, J.A.C. and McKelvey, B. (eds) (1999) Variations in Organization Science: In Honor of Donald T. Campbell, Thousand Oaks, CA: SAGE. Baum, J.A.C. and Singh, J.V. (eds) (1994) Evolutionary Dynamics of Organizations, New York: Oxford University Press. Bharadwaj, A., El Sawy, O.A., Pavlou, P.A. and Venkatraman, N. (2013) “Digital business strategy: toward a next generation of insights,” MIS Quarterly, 37 (2): 471–482. Bones, C. and Cohen, J. (2013) Leading Digital Strategy, London: Kogan Page Limited. Briscoe, G. and De Wilde, P. (2006) “Digital ecosystems: evolving service-orientated architectures,” in Proceedings of the 1st International Conference on Bio Inspired Models of Network, Information and Computing Systems, ACM, Madonna di Campigilo, Italy, December 11–13, 1–6. Brousseau, E. and Penard, T. (2007) “The economics of digital business models: a framework for analyzing the economics of platforms,” Review of Network Economics, 6 (2): 81–114. Brown, S.L. and Eisenhardt, K.M. (1998) Competing on the Edge: Strategy as Structured Chaos, Boston, MA: Harvard Business School Press. Bryant, S. and Nguyen, T. (2017) “Major factors affecting knowledge sharing in emerging economies: a theoretical study,” International Journal of Management & Human Resources, 5 (1): 37–48. Brynjolfsson, E. and Mcafee, A. (2014) The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies, New York: W. W. Norton & Co. Chen, Y., Shu, T., Chen, S., Wang, S., Lai, K.K. and Gan, L. (2017) “Strong–weak collaborative management in coping supply chain disruption risk transmission based on scale-free networks,” Applied Economics, 49 (39): 3943–3958. Chmiel, A., Sienkiewicz, J., Suchecki, K. and Hołyst, J. (2007) “Networks of companies and branches in Poland,” Physica A, 383 (1): 134–138. Choi, Y. (2017) Digital Business and Sustainable Development: Asian Perspectives, London: Routledge. Cojocaru, L.E., Sarraipa, J., Jardim-Golcalves, R. and Stanescu, A.M. (2014) “Digital business ecosystem framework for the agro-food industry,” in Enterprise Interoperability VI, Cham: Springer International Publishing, 285–296. Conyon, M. (2018) “Comments on two sides of CEO pay injustice: a power law conceptualization of CEO over and underpayment,” Management Research: Journal of the Iberoamerican Academy of Management. Corallo, A., Passiante, G. and Prencipe, A. (eds) (2007) The Digital Business Ecosystem, Camberley: Edward Elgar Publishing. Coupey, E. (2001) Digital Business: Concepts and Strategy, Upper Saddle River, NJ: Pearson/ Prentice Hall. Coupey, E. (2016) Digital Business: Concepts and Strategies (3rd ed), New York: Routledge. Cowan, G.A., Pines, D. and Meltzer, D. (eds) (1994) Complexity: Metaphors, Models, and Reality. Proceedings of the Santa Fe Institute, Vol. XIX. Reading, MA: Addison-Wesley. Crawford, G.C. and McKelvey, B. (2012) “Strategic implications of power-law distributions in the creation and emergence of new ventures: power-law analyses in three panel studies, Chapter XII. Strategy,” Frontiers of Entrepreneurship Research, 32 (1): 1–15.

Digital dynamic capabilities  171 Crawford, G.C. and McKelvey, B. (2018) “Using maximum likelihood estimation methods and complexity science concepts to research power-law distributed phenomena,” in E. Mitleton-Kelly, A. Paraskevas and C. Day (eds): Handbook of Research Methods in Complexity Science, Cheltenham: Edward Elgar. Crawford, G.C., McKelvey, B. and Lichtenstein, B.B. (2014) “The empirical reality of entrepreneurship: how power-law distributed outcomes call for new theory and method,” JBV Insights, 1–2 (1): 3–7. Crawford, G.C., Aguinis, H., Lichtenstein, B.B., Davidsson, P. and McKelvey, B. (2015) “Power law distributions in entrepreneurship: implications for theory and research,” Journal of Business Venturing, 30 (5): 696–713. Csányi, G. and Szendrői, B. (2004) “Structure of a large social network,” Physical Review. Part E, 69 (3): 036131-1–5. D’Aveni, R. (1994) Hypercompetition: Managing the Dynamics of Strategic Maneuvering, New York: Free Press. Day, G.S., Fein, A.J. and Ruppersberger, G. 2003. “Shakeouts in digital markets: lessons from B2B exchanges,” California Management Review, 45 (2): 131–150. de Reuver, M., Sørensen, C. and Basole, R.C. (2017) “The digital platform: a research agenda,” Journal of Information Technology, 1–12. https​:/​/do​​i​.org​​/10​.1​​057​/s​​41265​​-01​6-​​0033-​3 deCosta, F. (2013) Rethinking the Internet of Things, New York: Apress. Dereń, A.M., Górski, A., Parkitna, A. and Skonieczny, J. (2017) “Dimensions of business ecosystem efficiency,” in International Conference on Information Systems Architecture and Technology Proceedings, Cham: Springer, 128–140. Devinney, T.M. (2013) “Is microfoundational thinking critical to management thought and practice?” Academy of Management Perspectives, 27 (2): 81–84. Dhillon, H.S., Huang, H. and Viswanathan, H. (2017) “Wide-area wireless communication challenges for the Internet of Things,” IEEE Communications Magazine, 55 (2): 168–174. Dierickx, I. and Cool, K. (1989) “Asset stock accumulation and sustainability of competitive advantage,” Management Science, 35 (12): 1504–1511. Dodds, P., Watts, D. and Sabel, C. 2003. “Information exchange and the robustness of organizational networks,” Proceedings of the National Academy of Sciences of the United States of America, 100 (21): 12516–12521. Dourmas, G., Nikitakos, N. and Lambrou, M. (2005) “The concept of digital business ecosystems applied to the shipping industry,” in Proceedings of the International Association of Maritime Economists’ 2005 Conference. Ehrlich, P.R. and Raven, P.H. (1964) “Butterflies and plants: a study in coevolution,” Evolution, 18 (4): 586–608. Eisenhardt, K.M. and Martin, J.A. (2000) “Dynamic capabilities: what are they?,” Strategic Management Journal, 21 (10–11): 1105–1121. El Sawy, O.A. and Pereira, F. (2013) “Anticipating game changers for ‘enterprise 2020’ in a digitally-intensive world,” in Business Modelling in the Dynamic Digital Space, Heidelberg: Springer, 1–12. Fenton, E.M. (2007) “Visualising strategic change: the role and impact of process maps as boundary objects in reorganization,” European Management Journal, 25 (2): 104–117. Fleisch, E., Weinberger, M. and Wortmann, F. (2015) “Business models and the Internet of Things,” in I.P. Zarko, K. Pripuzic and M. Serrano (eds): Interoperability and Open-source Solutions for the Internet of Things, Cham: Springer International Publishing, 6–10. Foss, N.J. and Lindenberg, S. (2013) “Microfoundations for strategy: a goal-framing perspective on the drivers of value creation,” Academy of Management Perspectives, 27 (2): 85–102.

172  María Paz Salmador et al. Fredette, C. and Branzei, O. (2010): ‘A practice-based view of capability discovery in group settings,” in Fourth International Conference on Organizational Routines: ‘Organizational Routines Between Change and Stability: Linking Empirical Research to Recent Theoretical Debates’, Nice, France, June, 11–12. Frey, N.C., Matin, S., Stanley, H.E. and Salinger, M. (2017) “Universal fluctuations in growth dynamics of economic systems,” arXiv preprint arXiv:1712.02003. Fujiki, Y., Mizutaka, S. and Yakubo, K. (2017) “Fractality and degree correlations in scalefree networks,” The European Physical Journal B, 90 (7): 126. Gabaix, X. (2016) “Power laws in economics: an introduction,” Journal of Economic Perspectives, 30 (1): 185–205. Garud, R., Kumaraswamy, A. and Sambamurthy, V. (2006) “Emergent by design: performance and transformation at infosys technologies,” Organization Science, 17 (2): 277–286. Gatautis, R. and Medziausiene, A. (2014) “Digital business ecosystems for regional development: evidences from EU countries’ pilots,” in the Proceedings of the 13th Wuhan International Conference on e-Business—Innovation Management and IT Business Value, Paper #32. Gavetti, G. (2005) “Cognition and hierarchy: rethinking the microfoundations of capabilities’ development,” Organization Science, 16 (6): 599–617. Gell-Mann, M. (2002) “What is complexity?” in A.Q. Curzio and M. Fortis (eds): Complexity and Industrial Clusters, Heidelberg: Physica-Verlag, 13–24. Graça, P. and Camarinha-Matos, L.M. (2017) “Performance indicators for collaborative business ecosystems—literature review and trends,” Technological Forecasting and Social Change, 116: 237–255. Grant, R.M. (1991) “The resource-based theory of competitive advantage: implications for strategy formulation, California Management Review, 33 (3): 114–135. Grant, R.M. (1996) “Prospering in dynamically-competitive environments: organizational capability as knowledge integration,” Organization Science, 7 (4): 375–387. Greene, W.H. (2017) Econometric Analysis (8th ed), New York: Pearson. Greengard, S. (2015) The Internet of Things, Cambridge, MA: MIT Press. Greve, H.R. (2013) “Microfoundations of management: behavioral strategies and levels of rationality in organizational action,” Academy of Management Perspectives, 27 (2): 103–119. Grodal, S., Gotsopoulos, A. and Suarez, F.F. (2015) “The coevolution of technologies and categories during industry emergence,” Academy of Management Review, 40 (3): 423–445. Gunkel, D.J. (2018) Hacking Cyberspace, London: Routledge. Gupta, A. (2014) “Global business environment and international challenges,” Innovative Journal of Business and Management, 3 (2): 22–26. Guszejnov, D., Hopkins, P.F. and Grudić, M.Y. (2017) “Universal scaling relations in scalefree structure formation,” arXiv preprint arXiv:1707.05799. Güttel, W.H. and Konlechner, S.W. (2010) “Dynamic capacity: on the interplay between routinized and ad-hoc change,” in Fourth International Conference on Organizational Routines: ‘Organizational Routines Between Change and Stability: Linking Empirical Research to Recent Theoretical Debates’, Nice, France, June, 11–12. Hamilton, M., Milne, B., Walker, R., Burger, O. and Brown, J. (2007) “The complex structure of hunter-gatherer social networks,” Proceedings of the Royal Society of London, Series B, 274: 2195–2203. Helfat, C.E., Finkelstein, S., Mitchell, W., Peteraf, M., Singh, H., Teece, D. and Winter, S. (2007) Dynamic Capabilities: Understanding Strategic Change in Organizations, Blackwell, London. Herdon, M., Várallyai, L. and Péntek, Á. 2012. “Digital business ecosystem prototyping for SMEs,” Journal of Systems and Information Technology, 14 (4): 286–301.

Digital dynamic capabilities  173 Holland, J.H. (1975) Adaptation in Natural and Artificial Systems, Ann Arbor, MI: University of Michigan Press. Holland, J.H. (1988) “The global economy as an adaptive system,” in P.W. Anderson, K.J. Arrow and D. Pines (eds) The Economy as an Evolving Complex System, Reading, MA: Addison-Wesley, 117–124. Holland, J.H. (1995) Hidden Order, Reading, MA: Addison-Wesley. Holland, J.H. (2002) “Complex adaptive systems and spontaneous emergence,” in A.Q. Curzio and M. Fortis (eds) Complexity and Industrial Clusters, Heidelberg: Physica-Verlag, 24–34. Homans, G.C. (1950) Human Group, New York: Harcourt. Hu, S., Yang, H., Cai, B. and Yang, C. (2013) “Research on spatial economic structure for different economic sectors from a perspective of a complex network,” Physica A, 392 (17): 3682–3697. Huang, X. (2017) “Technology law in China: computer hacking, cloud computing and robotics as examples,” in E. Hilgendorf and U. Seidel (eds) Robotics, Autonomics, and the Law, Baden-Baden, Germany: Nomos, 109–128. Iansiti, M. and Levien, R. (2004) “Strategy as ecology,” Harvard Business Review, 82 (3): 68–78. Jäger, G., Hofer, C., Kapeller, M. and Füllsack, M. (2017) “Hidden early-warning signals in scale-free networks,” PloS one, 12 (12): e0189853. Järvi, K. and Kortelainen, S. (2017) “Taking stock of empirical research on business ecosystems: a literature review,” International Journal of Business and Systems Research, 11 (3): 215–228. Jensen, T., Tan, Y.H. and Bjørn-Andersen, N. 2014. “Unleashing the IT potential in the complex digital business ecosystem of international trade: the case of fresh fruit import to European Union,” in the Proceedings the 27th eConference: eEcosystems, Bled, Slovenia, June 1–5. Jianqiu, Z. and Mengke, Y. (2015) “Internet plus and networks convergence,” Communications, China, 12 (4): 42–49. Jo, T.-H. (2007) “Heterodox microfoundations: a methodological appraisal,” Journal of Interdisciplinary Research, 1 (1): 93–111. Johnson, J.L. and Burton, B.K. (1994) “Chaos and complexity theory for management,” Journal of Management Inquiry, 3 (4): 320–328. Kache, F. and Seuring, S. (2017) “Challenges and opportunities of digital information at the intersection of big data analytics and supply chain management,” International Journal of Operations & Production Management, 37 (1): 10–36. Kaminska-Labbé, R. and Thomas, C. (2004) “Unravelling entangled organizational dynamics: coevolutionary causalities,” Annual Organization Science Winter Conference, Steamboat, CO, USA. Kauffman, S.A. (1993) The Origins of Order: Self-organization and Selection in Evolution, New York: Oxford University Press. Kellmereit, D. and Obodovski, D. (2013) The Silent Intelligence: The Internet of Things, Warwick, NY: DND Ventures LLC. Kelly, S. and Allison, M.A. (1999) The Complexity Advantage, New York: McGraw-Hill. Kotter, J.P. (2014) Accelerate: Building Strategic Agility for a Faster-moving World, Boston, MA: Harvard Business Review Press. Koufteros, X.A., Vonderembse, M.A. and Doll, W.J. (2002) “Integrated product development practices and competitive capabilities: the effects of uncertainty, equivocality, and platform strategy,” Journal of Operations Management, 20 (4): 331–355.

174  María Paz Salmador et al. Koufteros, X., Vonderembse, M.A. and Jayaram, J. (2005) “Internal and external integration for product development: the effects of uncertainty, equivocality, and platform strategy,” Decision Sciences, 36 (1): 97–133. Krishnan, R., Smith, M.D., Tang, Z. and Telang, R. (2007) “Digital business models for peer-to-peer networks: analysis and economic issues,” Review of Network Economics, 6 (2): 194–213. Kron, T. and Grund, T. (2009) “Society as a self-organized critical system,” Cybernetics and Human Knowledge, 16 (1–2): 65–82. Krugman, P. (1996) The Self-Organizing Economy, Oxford: Blackwell. Kshetri, N. (2017) “The economics of the Internet of Things in the global south,” Third World Quarterly, 38 (2): 311–339. Kumar, P., Dass, M. and Kumar, S. (2015) “From competitive advantage to nodal advantage: ecosystem structure and the new five forces that affect prosperity,” Business Horizons, 58 (4): 469–481. Kutsikos, K., Konstantopoulos, N., Sakas, D. and Verginadis, Y. (2014) “Developing and managing digital service ecosystems: a service science viewpoint,” Journal of Systems and Information Technology, 16 (3): 233–248. Langley, P. and Leyshon, A. (2017) “Platform capitalism: the intermediation and capitalisation of digital economic circulation,” Finance and Society, 3 (1): 11–31. Lavassani, K.M. and Movahedi, B. (2017) “Applications driven information systems: beyond networks toward business ecosystems,” International Journal of Innovation in the Digital Economy, 8 (1): 61–75. Lazaric, N. (2000) “The role of routines, rules and habits in collective learning: some epistemological and ontological considerations,” European Journal of Economic and Social Systems, 14 (2): 157–171. Lazaric, N. and Denis, B. (2001) “How and why routines change: some lessons from the articulation of knowledge with ISO 9002 implementation in the food industry,” Economies et Sociétés, 6 (2): 585–612. Leavy, B. (2014) “Strategy, organization and leadership in a new ‘transient-advantage’ world,” Strategy & Leadership, 42 (4): 3–13. Levin, S.A. (1998) “Ecosystems and the biosphere as complex adaptive systems,” Ecosystems, 1 (5): 431–436. Lewin, A.Y. and Volberda, H.W. (1999) “Prolegomena on coevolution: a framework for research on strategy and new organizational forms,” Organization Science, 10 (5): 519–534. Lewin, R. (1992) Complexity: Life at the Edge of Chaos, Chicago, IL: University of Chicago Press. [2nd ed. published in 1999.] Li, W., Badr, Y. and Biennier, F. (2012) “Digital ecosystems: challenges and prospects,” in Proceedings of the International Conference on Management of Emergent Digital EcoSystems, AC Med Digital Library, Association for Computing Machinery, 117–122. Liu, G. and Rong, K. (2015) “The nature of the co-evolutionary process: complex product development in the mobile computing industry’s business ecosystem,” Group & Organization Management, Online 1059601115593830. Magnusson, M. and Pasche, M. (2014) “A contingency-based approach to the use of product platforms and modules in new product development,” Journal of Product Innovation Management, 31 (3): 434–450. Maguire, S. and McKelvey, B. (1999) “Complexity and management: moving from fad to firm foundations,” Emergence, 1 (2): 19–61. Maguire, S., McKelvey, B., Mirabeau, L. and Öztas, N. (2006) “Complexity science and organization studies,” in S. Clegg, C. Hardy, T. Lawrence, and W. Nord (eds) Handbook of Organization Studies, Thousand Oaks, CA: SAGE, 165–214.

Digital dynamic capabilities  175 Mainzer, K. 2007. Thinking in Complexity: The Complex Dynamics of Matter, Mind, and Mankind (7th ed), New York: Springer-Verlag. [1st ed in 1994]. Mäkinen, S.J. and Dedehayir, O. (2012) “Business ecosystem evolution and strategic considerations: a literature review,” in B. Katzy, T. Holzmann, K. Sailer and K.D. Thoben (eds) Proceedings of the 2012 18th International Conference on Engineering, Technology and Innovation. Malecki, E.J. and Moriset, B. (2008) The Digital Economy: Business Organization, Production Processes and Regional Developments, New York: Routledge. Mandelbrot, B.B. (1983) The Fractal Geometry of Nature (2nd ed), New York: Freeman. Mantegna, R.N. and Stanley, H.E. (2000) An Introduction to Econophysics, Cambridge: Cambridge University Press. Maruyama, M. (1963) “The second cybernetics,” American Scientist, 51 (2): 164–179. McCain, R.A. (2000) Agent-Based Computer Simulation of Dichotomous Economic Growth, Boston, MA: Kluwer. McEwen, A. and Cassimally, H. (2013) Designing the Internet of Things, Chichester: Wiley. McGrath, R.G. (2013a) “Transient advantage,” Harvard Business Review, 91 (6): 62–70. McGrath, R.G. (2013b) The End of Competitive Advantage: How to Keep Your Strategy Moving as Fast as Your Business, Boston, MA: Harvard Business Review Press. McKelvey, B. (1997) “Quasi-natural organization science,” Organization Science, 8 (4): 351–380. McKelvey, B. (2001) “What is complexity science? It’s really order-creation science,” Emergence, 3: 137–157. McKelvey, B. (2004a) “Complexity science as order-creation science: new theory, new method,” Emergence: Complexity and Organization, 6 (4): 2–27. McKelvey, B. (2004b) “Toward a 0th law of thermodynamics: order-creation complexity dynamics from physics & biology to bioeconomics,” Journal of Bioeconomics, 6 (1): 65–96. McKelvey, B. (2008) “Emergent strategy via complexity leadership: using complexity science & adaptive tension to build distributed intelligence,” in M. Uhl-Bien and R. Marion (eds): Complexity and Leadership, Vol. I: Conceptual Foundations, Charlotte, NC: Information Age Publishing, 225–268. McKelvey, B. (ed) (2013a) Routledge Great Works Series: Complexity: Critical Concepts: Origins of Order-creation Science: Complexity Science From Basic Disciplines, Oxford: Routledge. McKelvey, B. (ed) (2013b) Self-organization, Emergence & Self-organized Criticality, Oxford: Routledge. McKelvey, B. (ed) (2013c) Organization & Management Complexity Dynamics, Oxford: Routledge. McKelvey, B. and Lichtenstein, B.B. (2007) “Leadership in four stages of emergence,” in J.K. Hazy, J. Goldstein and B.B. Lichtenstein (eds): Complex Systems Leadership Theory, Mansfield, MA: ISCE Publishing Company, 93–107. McKelvey, B. and Salmador Sanchez, M.P. (2011) “Explaining the 2007 bank liquidity crisis: lessons from complexity science and econophysics,” Working paper, UCLAAnderson School of Management, Los Angeles, CA. McKelvey, B., Lichtenstein, B.B. and Andriani, P. (2012) “When organizations and ecosystems interact: toward a law of requisite fractality in firms,” International Journal of Complexity in Leadership & Management, 2 (1): 104–136. McKelvey, M. and Saemundsson, R.J. (2017) “An evolutionary model of science policy: routines and the growth of knowledge in policy-making organisations,” International Journal of Economics and Business Research, 14 (3–4): 298–311. McLay, A. (2014) “Re-reengineering the dream: agility as competitive adaptability,” International Journal of Agile Systems and Management, 7 (2): 101–225.

176  María Paz Salmador et al. Mcquivey, J. (2013) Digital Disruption: Unleashing the Next Wave of Innovation, Las Vegas: Amazon Publishing. Miller, M. (2015) The Internet of Things, 800 E. 96th Street, Indianapolis, IN: QUE. Mirowski, P. (1989) More Heat Than Light, Cambridge: Cambridge University Press. Mislove, A., Marcon, M., Gummadi, K., Druschel, P. and Bhattacharjee, B. (2007) “Measurement and analysis of online networks,” in 7th ACM SIGCIMM Conference on Internet Measurement, IMC’07, San Diego, CA, USA. Mizuno, T., Souma, W. and Watanabe, T. (2014) “The structure and evolution of buyersupplier networks,” Working paper. Faculty of Economics, University of Tokyo. Moore, J.F. (1993) “Predators and prey: the new ecology of competition,” Harvard Business Review, 71 (3): 75–83. Morisse, M., Horlach, B., Kappenberg, W., Petrikina, J., Robel, F. and Steffens, F. (2014) “Trust in network organizations—a literature review on emergent and evolving behavior in network organizations,” in Proceedings of the 47th Hawaii International Conference on System Sciences, IEEE Computer Society, 4578–4587. Muchnik, L., Pei, S., Parra, L.C., Reis, S.D., Andrade Jr, J.S., Havlin, S. and Makse, H.A. (2015) “Corrigendum: origins of power-law degree distribution in the heterogeneity of human activity in social networks,” Scientific Reports, 5: 15932–15939. Muffatto, M. and Roveda, M. (2000) “Developing product platforms: analysis of the development process,” Technovation, 20 (11): 617–630. Muntaner-Perich, E. and de la Rosa Esteva, J.L. (2007) “Using dynamic electronic institutions to enable digital business ecosystems,” in P. Noriega, J. Vázquez-Salceda, G. Boella, O. Boissier, V. Dignum, N. Fornara and E. Matson (eds) Coordination, Organizations, Institutions, and Norms in Agent Systems II, Berlin: Springer, 259–273. Mykkänen, M. (2017) “Clarifying communication professionals’ tasks in contributing to organizational decision making,” International Journal of Social Sciences and Humanities Invention, 4 (5): 3460–3468. Nachira, F., Dini, P., Nicolai, A., Le Louarn, M. and Rivera Lèon, L. 2007. Digital Business Ecosystems: The Results and the Perspectives of the Digital Business Ecosystem Research and Development Activities in FP6, Luxembourg: Office for Official Publications of the European Community. Nambisan, S. (2017) “Digital entrepreneurship: toward a digital technology perspective of entrepreneurship,” Entrepreneurship Theory and Practice, 41 (6): 1029–1055. Nelson, R.R. and Winter, S. (1982) An Evolutionary Theory of Economic Change, Cambridge, MA: Belknap. Newman, M.E.J. (2001) “The structure of scientific collaboration networks,” Proceedings of the National Academy of Sciences of the United States of America, 98 (2): 404–409. Newman, M.E.J. (2005) “Power laws, Pareto distributions and Zipf’s law,” Contemporary Physics, 46 (5): 323–351. Newman, M.E.J. (2017) “Power-law distribution,” Significance, 14 (4): 10–11. Nicolis, G. and Prigogine, I. (1989) Exploring Complexity: An Introduction, New York: Freeman. Nonaka, I., Kodama, M., Hirose, A. and Kohlbacher, F. (2014) “Dynamic fractal organizations for promoting knowledge-based transformation – A new paradigm for organizational theory,” European Management Journal, 32 (1): 137–146. Noorderhaven, N., Sorge, A. and Koen, C. (2015) Comparative International Management, London: Routledge. Pacheco, D.F., York, J.G. and Hargrave, T.J. (2014) “The coevolution of industries, social movements, and institutions: wind power in the United States,” Organization Science, 25 (6): 1609–1632.

Digital dynamic capabilities  177 Pappas, N., Kazasis, F.G., Anestis, G., Gioldasis, N. and Christodoulakis, S. (2007) “A knowledge management platform for supporting digital business ecosystems based on P2P and SOA technologiesm,” in Digital EcoSystems and Technologies Conference Proceedings, DEST’07. Inaugural IEEE-IES, 196–202. Patriarca, M., Heinsalu, E., Chakraborti, A. and Kaski, K. (2017) “The microscopic origin of the Pareto law and other power-law distributions,” in F. Abergel, H. Aoyama, B. Charkrabarti, A. Chakraborti, N. Deo, D. Raina and I. Vodenska, Econophysics and Sociophysics: Recent Progress and Future Directions, New York: Springer International Publishing: 159–176. Pena e Cunha, M. and Rego, A. (2010) “Complexity, simplicity, simplexity,” European Management Journal, 28 (2): 85–94. Peteraf, M., Stefano, G. and Verona, G. (2013) “The elephant in the room of dynamic capabilities: bringing two diverging conversations together,” Strategic Management Journal, 34 (12): 1389–1410. Phillips, J. (2014) Building a Digital Analytics Organization, Upper Saddle River, NJ: Pearson. Pilinkienė, V. and Mačiulis, P. (2014) “Comparison of different ecosystem analogies: the main economic determinants and levels of impact,” Procedia: Social and Behavioral Sciences, 156: 365–370 Pina e Cunha, M. and Rego, A. (2010) “Complexity, simplicity, simplexity,” European Management Journal, 28 (2): 85–94. Plowman, D.A., Baker, L.T., Beck, T.E., Kulkarni, M., Solansky, S.T. and Travis, D.V. (2007) “Radical change accidently: the emergence and amplification of small change,” Academy of Management Journal, 50 (3): 515–543. Ponce-Jara, M.A., Ruiz, E., Gil, R., Sancristóbal, E., Pérez-Molina, C. and Castro, M. (2017) “Smart grid: assessment of the past and present in developed and developing countries,” Energy Strategy Reviews, 18: 38–52. Porter, M.E. 1979. “How competitive forces shape strategy,” Harvard Business Review, 57 (2): 137–145. Porter, M.E. and Heppelmann, J.E. (2014) “How smart, connected products are transforming competition,” Harvard Business Review, 92 (11): 64–88. Powell, W., White, D., Koput, K. and Owen-Smith, J. (2005) “Network dynamics and field evolution,” American Journal of Sociology, 110 (4): 1132–1205. Prieto, F. and Sarabia, J.M. (2017) “A generalization of the power law distribution with nonlinear exponent,” Communications in Nonlinear Science and Numerical Simulation, 42: 215–228. Prigogine, I. (1955) An Introduction to Thermodynamics of Irreversible Processes, Springfield, IL: Thomas. Prigogine, I. (1962) Non-Equilibrium Statistical Mechanics, New York: Wiley Interscience. Prigogine, I. (with I. Stengers) (1997) The End of Certainty, New York: Free Press. Prigogine, I. and Stengers, I. (1984) Order Out of Chaos, New York: Bantam. Razavi, A.R., Moschoyiannis, S.K. and Krause, P.J. (2007) “A coordination model for distributed transactions in digital business ecosystems,” in Digital EcoSystems and Technologies Conference, DEST’07. Inaugural IEEE-IES, 159–164. Razavi, A.R., Krause, P.J. and Stgrømmen-Bakhtiar, A. (2010) “From business ecosystems towards digital business ecosystems,” in 4th IEEE International Conference on Digital Ecosystems and Technologies, 290–295. Reddy, G.N. and Reddy, G.J. (2014) “A study of cyber security challenges and its emerging trends on latest technologies,” arXiv preprint arXiv:1402.1842. Reena, M. and Gulia, P. (2017) “Review of security in AD-HOC networks using FTP,” Advances in Computational Sciences and Technology, 10 (5): 1417–1426.

178  María Paz Salmador et al. Remane, G., Hanelt, A., Nickerson, R.C. and Kolbe, L.M. (2017) “Discovering digital business models in traditional industries,” Journal of Business Strategy, 38 (2): 41–51. Rifkin, J. (2014) The Zero Marginal Cost Society: The Internet of Things, the Collaborative Commons, and the Eclipse of Capitalism, London: Palgrave Macmillan. Ronda-Pupo, G.A. and Katz, J.S. (2017) “The scaling relationship between citation-based performance and coauthorship patterns in natural sciences,” Journal of the Association for Information Science and Technology, 68 (5): 1257–1265. Ronda-Pupo, G.A. and Katz, J.S. (2018) “The power law relationship between citation impact and multi-authorship patterns in articles in information science & library science journals,” Scientometrics, 114 (3): 919–932. Rong, K. and Shi, Y. (2014) Business Ecosystems: Constructs, Configurations, and the Nurturing Process, London: Palgrave Macmillan. Rosenbloom, R.S. (2000) “Leadership, capabilities, and technological change: the transformation of NCR in the electronic era,” Strategic Management Journal, 21 (10–11): 1083–1103. Rothaermel, F.T. and Hess, A.M. (2007) “Building dynamic capabilities: innovation driven by individual-, firm-, and network-level effects,” Organization Science, 18 (6): 898–921. Rumelt, R.P. (1994) “Foreword to competence-based competition,” in G. Hamel and A. Heene (eds) Competence Based Competition, New York: Wiley, 14–19. Saberi, M., Mahmassani, H.S., Brockmann, D. and Hosseini, A. (2017) “A complex network perspective for characterizing urban travel demand patterns: graph theoretical analysis of large-scale origin–destination demand networks,” Transportation, 44 (6): 1383–1402. Sage, J., Husmann, E. and Dewever, F. (2005) “Nurturing digital business ecosystems – European policy implications and experiences,” Presented at the Advanced International Summer School, ISUFI, University of Lecce, Ostuni, Italy, July. Sahito, F.H., Slany, W. and Shahzad, S.K. (2011) “Search engines: the invader to our privacy—a survey,” in Computer Sciences and Convergence Information Technology (ICCIT): 2011 6th International Conference, 640–646. Sanchez, R., Heene, A. and Thomas, H. (eds) (1996) Dynamics of Competence-based Competition: Theory and Practice in the new Strategic Management, Oxford: Elsevier Science & Technology. Santiago, A. and Benito, R. (2008) “Connectivity degrees in the threshold preferential attachment model,” Physica A, 387 (10): 2365–2376. Schallmo, D., Williams, C.A. and Boardman, L. (2017) “Digital transformation of business models—best practice, enablers, and roadmap,” International Journal of Innovation Management, 21 (8): 1740014-1–17. Schallmo, D.R. and Williams, C.A. (2018) “Roadmap for the digital transformation of business models,” in Digital Transformation Now! Cham: Springer, 41–68. Schmidt, E. and Cohen, J. (2013) The New Digital Age: Transforming Nations, Businesses, and our Lives, New York: Vintage Books. Schumpeter, J. (1942) Capitalism, Socialism and Democracy, New York: Harper & Row. Scott, W.R. (1998) Organizations: Rational, Natural, and Open Systems (4th ed.): Upper Saddle River, NJ: Prentice-Hall. Seigfried-Spellar, K.C., Villacís-Vukadinović, N. and Lynam, D.R. (2017) “Computer criminal behavior is related to psychopathy and other antisocial behavior,” Journal of Criminal Justice, 51: 67–73. Seigneur, J.M. 2005. “Demonstration of security through collaboration in the digital business ecosystem,” in Security and Privacy for Emerging Areas in Communication Networks, Workshop of the 1st International Conference, IEEE, 108–109.

Digital dynamic capabilities  179 Selander, L., Henfridsson, O. and Svahn, F. (2010) “Transforming ecosystem relationships in digital innovation,” in ICIS 2010 Proceedings. Paper 138, AIS Electronic Library (AISeL), 1–15. http:​/​/ais​​el​.ai​​snet.​​org​/i​​cis20​​10​_su​​bmis​s​​ions/​​138 (Accessed March 18, 2018). Seo, D. (2017) “Digital business convergence and emerging contested fields: a conceptual framework,” Journal of the Association for Information Systems, 18 (10): 687–702. Shaw, M.J. and Mahoney, J.T. (2000) “Information rules: a strategic guide to the network economy,” Academy of Management Review, 25 (2): 441–443. Sköld, M. and Karlsson, C. (2007) “Multibranded platform development: a corporate strategy with multimanagerial challenges,” Journal of Product Innovation Management, 24 (6): 554–566. Sornette, D. (2000) Critical Phenomena in Natural Sciences, Heidelberg: Springer-Verlag. Souma, W., Aoyama, H., Fujiwara, Y., Ikeda, Y., Iyetomi, H. and Kaizoji, T. (2006) “Correlation in business networks,” Physica A, 370 (10): 151–155. Srinivas, V. and Mitra, P. (2016) Link Prediction in Social Networks: Role of Power law Distribution, New York: Springer International Publishing. Stacey, R.D. (1995) “The science of complexity: an alternative perspective for strategic change processes,” Strategic Management Journal, 16 (6): 477–495. Stanley, J. and Briscoe, G. (2010) “The ABC of digital business ecosystems,” arXiv:1005.1899. Stanley, M.H.R., Amaral, L.A.N., Buldyrev, S.V., Havlin, S., Leschhorn, H., Maass, P., Salinger, M.A. and Stanley, H.E. (1996) Scaling behaviour in the growth of companies,” Nature, 379 (6568): 804–806. Sterling, B. (2014) The Epic Struggle of the Internet of Things, Moscow: Strelka Press. Strader, T.J. (2010) Digital Product Management, Technology and Practice: Interdisciplinary Perspectives, Hershey, PA: IGI Global. Sussan, F. and Acs, Z.J. (2017) “The digital entrepreneurial ecosystem,” Small Business Economics, 49 (1): 55–73. Tafti, S.F., Kordnaeij, A., Hoseini, S.H.K. and Jamali, M. (2015) “Business ecosystem as a new approach in strategy,” Management and Administrative Sciences Review, 4 (1): 198–205. Tan, B., Pan, S.L., Lu, X. and Huang, L. (2009) “Leveraging digital business ecosystems for enterprise agility: the tri-logic development strategy of Alibaba​.com​,” Presented at the International Conference on Information Systems. ICIS 2009 Proceedings, Paper 171. http://aisel​.aisnet​.org​/icis2009​/171 (Accessed March 18, 2018). Tan, Y.L. and Macaulay, L.A. (2011) “Factors affecting regional SMEs progression to digital business ecosystems,” presented at the 17th Americas Conference on Information Systems (AMCIS). Tanriverdi, H. and Lim, S.Y. (2017) “How to survive and thrive in complex, hypercompetitive, and disruptive ecosystems? The roles of IS-enabled capabilities,” AIS Electronic Library. Tapscott, D. (1995) The Digital Economy, New York: McGraw-Hill Education. Tapscott, D. (2015) The Digital Economy: Rethinking Promise and Peril in the age of Networks Intelligence (20th Anniversary Edition), New York: McGraw-Hill Education. Tarasova, V.V. and Tarasov, V.E. (2018) “Dynamic intersectoral models with power-law memory,” Communications in Nonlinear Science and Numerical Simulation, 54: 100–117. Tavlaki, E. (2005) “Business model: a prerequisite for success in the network economy,” in the Proceedings of the 18th Bled eConference: eIntegration in Action, Bled, Slovenia, June 6–8. Teece, D.J. (2007) “Explicating dynamic capabilities: the nature and microfoundations of (sustainable) enterprise performance,” Strategic Management Journal, 28 (13): 1319–1350. Teece, D.J. (2012) “Dynamic capabilities: routines versus entrepreneurial action,” Journal of Management Studies, 49 (8): 1395–1401.

180  María Paz Salmador et al. Teece, D.J. and Linden, G. (2017) “Business models, value capture, and the digital enterprise,” Journal of Organization Design, 6 (1): 8–22. Teece, D.J., Pisano, G. and Shuen, A. (1997) “Dynamic capabilities and strategic management,” Strategic Management Journal, 18 (7): 509–533. Thapliyal, H. (2018) “Internet of things-based consumer electronics: reviewing existing consumer electronic devices, systems, and platforms and exploring new research paradigms,” IEEE Consumer Electronics Magazine, 7 (1): 66–67. The Economist (2015) “Cyber-crime and business: think of a number and double it,” January 15, 1–3. The Economist (2018) “The battle for digital supremacy,” March 17, 11. Tomasi, D.G., Shokri-Kojori, E. and Volkow, N.D. (2017) “Brain network dynamics adhere to a power law,” Frontiers in Neuroscience, 11: 1–9. Touboul, J. and Destexhe, A. (2017) “Power-law statistics and universal scaling in the absence of criticality,” Physical Review. Part E, 95 (1): 012413. Tsatsou, P., Elaluf-Calderwood, S. and Liebenau, J. (2010) “Towards a taxonomy for regulatory issues in a digital business ecosystem in the EU,” Journal of Information Technology, 25 (3): 288–307. Turber, S., vom Brocke, J., Gassmann, O. and Fleisch, E. (2014) “Designing business models in the era of Internet of Things,” in M.C. Tremblay, D. VanderMeer, M. Rothenberger, A. Gupta, and V. Yoon (eds) Advancing the Impact of Design Science: Moving From Theory to Practice, Cham: Springer International Publishing, 17–31. Van de Ven, A.H. and Lifschitz, A. (2013) “Rational and reasonable microfoundations of markets and institutions,” Academy of Management Perspectives, 27 (2): 156–172. van Mierlo, T., Hyatt, D. and Ching, A.T. (2015) “Mapping power law distributions in digital health social networks: methods, interpretations, and practical implications,” Journal of Medical Internet Research, 17 (6): e160. Vollmer, C.A.H. and Egol, M. (2014) “Five rules for strategic partnerships in a digital world,” Strategy+business; b+b BLOGS, December 22, 1–3. http:​/​/www​​.stra​​tegy-​​busin​​ ess​.c​​om​/bl​​og​/Fi​​ve​-Ru​​les​-f​​or​-St​​rateg​​ic​-Pa​​rtner​​ships​​-in​-a​​-Di​gi​​tal​-W​​orld?​​gko​=e​​bbc1 (Accessed March 18, 2018). Wang, C.L. and Ahmed, P.K. (2007) “Dynamic capabilities: a review and research agenda,” International Journal of Management Reviews, 9 (1): 31–51. Wang, R. (2015) Disrupting Digital Business, Boston, MA: Harvard Business Review Press. Want, R., Schilit, B.N. and Jenson, S. (2015) “Enabling the Internet of Things,” Computer, 48 (1): 28–35. Watts, D. (2003) Six Degrees: The Science of a Connected age, New York: Norton. Weber, L. (2017) “A sociocognitive view of repeated interfirm exchanges: how the coevolution of trust and learning impacts subsequent contracts,” Organization Science, 28 (4): 744–759. Weber, R.H. and Weber, R. (2010) Internet of Things, New York: Springer. Weill, P. and Woerner, S.L. (2013) “Optimizing your digital business model,” MIT Sloan Management Review, 54 (3): 71–83. Weill, P. and Woerner, S.L. (2015) “Thriving in an increasingly digital ecosystem,” MIT Sloan Management Review, 56 (4): 27–34. Weill, P. and Woerner, S.L. (2018) “Is your company ready for a digital future?,” MIT Sloan Management Review, 59 (2): 21–25. Wernerfelt, B. (1984) “A resourced-based view of the firm,” Strategic Management Journal, 5 (2): 171–180.

Digital dynamic capabilities  181 West, B.J. and Deering, B. (1995) The Lure of Modern Science: Fractal Thinking, Singapore; World Scientific. Westerman, G., Bonnet, D. and Mcafee, A. (2014) Leading Digital: Turning Technology Into Business Transformation, Boston, MA: Harvard Business Review Press. Weyland, K. (2014) Making Waves: Democratic Contention in Europe and Latin America Since the Revolutions of 1848, Cambridge: Cambridge University Press. Weyland, K. (2016) “Patterns of diffusion: comparing democratic and autocratic waves,” Global Policy, 7 (4): 557–562. Winter, S.G. (2012) “Capabilities: their origins and ancestry,” Journal of Management Studies, 49 (8): 1402–1406. Winter, S.G. (2013) “Habit, deliberation, and action: strengthening the microfoundations of routines and capabilities,” Academy of Management Perspectives, 27 (2): 120–137. Wright, S. (1932) “The roles of mutation, inbreeding, crossbreeding and selection in evolution,” Proceedings of the Sixth International Congress on Genetics, 1: 356–366. Yang, Z. and Liu, J. (2018) “Robustness of scale-free networks with various parameters against cascading failures,” Physica A, 492: 628–638. Zang, C., Cui, P., Faloutsos, C. and Zhu, W. (2018) “On power law growth of social networks,” IEEE Transactions on Knowledge and Data Engineering, 99. Zhai, L., Yan, S. and Zhang, G. (2013) “A centrality measure for communication agility in weighted network,” Physica A, 392 (23): 6107–6117. Zhang, H.T., Zhu, T., Fu, D., Xu, B., Han, X.P. and Chen, D. (2018) “Spatiotemporal property and predictability of large-scale human mobility,” Physica A, 495: 40–48. Zhang, L.L. (2015) “A literature review on multitype platforming and framework for future research,” International Journal of Production Economics, 168: 1–12. Zhang, T., Gu, G.F., Xu, H.C., Xiong, X., Chen, W. and Zhou, W.X. (2017) “Power-law tails in the distribution of order imbalance,” Physica A, 483: 201–208. Zipf, G.K. (1949) Human Behavior and the Principle of Least Effort, New York: Hafner.

6

Understanding value conflict between business and society: a new perspective from neuro and complexity sciences Elena Goryunova and Bill McKelvey

Introduction Business and society tensions typically arise out of ethical conflicts when corporate decisions and actions violate a society’s expectations about ethical or socially responsible business behavior (Carroll and Bucholtz 2012). The irresponsible corporate behavior driven by economists’ excessive focus on shortterm profits at the expense of others’ interests and an improved environmental sustainability is highly costly both for business and society. For business companies, management thinking with “ridiculously short time horizons” (Porter in Driver’s interview 2012: 426) can lead not only to moral transgressions, a loss of business legitimacy, and stakeholders’ negative reactions (e.g. consumers’ boycotts, NGOs’ attacks, lawsuits) but also to overlooking upside opportunities and broader influences that are vital for the firm’s long-term growth and success (Kramer and Porter 2011). For our modern global society, unethical short-sighted behavior at individual and organizational levels, when accumulated at the macro-societal level, gives rise to diverse sustainability challenges, e.g. the fragility of the financial and trade markets, inefficient allocation of resources, a growing income inequality, unemployment, poverty, environmental pollution, a loss of biodiversity, climate change, etc. (Stiglitz 2012; MacKenzie and Millo 2003). Despite the persistence of moral conflicts in businesses and their wideranging negative impact on the global society, management research has provided little consensus on what constitutes socially responsible business behavior.1 According to Wicks and Freeman (1998), the main reason for the marginalized role of ethics within the mainstream management research is epistemology—the long-lasting debate between positivism (modernism) and anti-positivism (post-modernism) known also as the incommensurability problem (Scherer and Steinmann 1999). While the modernist school of thought—built on the Cartesian split between descriptive and normative science—advocates an objectivist value-free research program that excludes ethics from scientific inquiry,2 postmodernists, even though concerned with human values, also “embrace a moral relativism that precludes [prevents] the integration of ethics” (Wicks and Freeman 1998: 124). This paradigmatic tension is not only harmful to the progress of management 

Understanding value conflict  183

research (Pfeffer 1993) by making scientific debate problematic without paradigm integration (Donaldson 1998), but most importantly prevents organizational scholars from providing a coherent rational basis to assess legitimacy of managerial actions (Boisot and McKelvey 2010; Scherer and Steinmann 1999). Previous attempts to reconcile these polarized paradigmatic positions at a meta-meta level were unsuccessful in offering a theoretical solution to the incommensurability problem (see Scherer and Steinmann 1999 for a detailed discussion). Boisot and McKelvey (2010), however, proposed a new theoretical perspective—complexity science—to integrate modernist and postmodernist research agendas. The authors, in particular, argue that two antagonistic ontologies—atomistic and connectionist3—underlying respectively, modernism and postmodernism, can be accommodated “in a single overarching ontology that makes the appropriateness of either modernist or postmodernist perspectives contingent on the degree of tension and connectivity present in the system” (Boisot and McKelvey 2010: 426). Although we sympathize with Boisot and McKelvey’s idea of complexity science as the overarching theoretical framework, we also agree with Scherer and Steinmann’s argument that “incommensurability is a situation of conflict between different value positions” whose “mixing does not necessarily lead to more ‘comprehensive’ and better explanations.” Because modernist and postmodernist approaches have their own “deficiencies,” their pure combination even under conditions of contingency is not sufficient (Scherer and Steinmann 1999: 523). In this chapter we use complexity science as a new epistemological framework in order to better understand the nature of these value inconsistencies. Based on the interdisciplinary approach of complexity science, we aim to demonstrate that ethical conflicts underlying business and society tensions and the incommensurability problem in organizational research come from the latent long-lasting tension between classical science and humanities. Basically, the conflicts are between (a) self-oriented materialistic values, propagated via business education (neoclassical economics and other derived management theories); vs. (b) self-transcendent ethical values imbedded in human cultural heritage. To overcome this value conflict it is critically important to go beyond the epistemological dualism in management research and find an alternative new approach that reconciles good science with human needs and purposes and provides room for ethics within mainstream management research (Wicks and Freeman 1998). Following this logic, we argue that complexity science has the potential: (1) to unify management science by selecting the best from two research agendas: (a) the modernist’s criterion for empirical validity of truth claims with law-like generalizations; and (b) the postmodernist’s assumption about the connectionist nature of reality, while putting aside their respective drawbacks—the former’s deterministic value-free approach and the latter’s theoretical/moral relativism; and thus (2) minimize value conflict in management research. To develop these arguments, we first present an epistemological approach based on complexity science to replace classical reductionist science. By shifting emphasis from material objects to human relationships and interactions—and,

184  Elena Goryunova and Bill McKelvey

as you will read later, to value-conflicts—complexity science recognizes the connectionist nature of human reality but at the same time continues the scientific tradition of empirical hypothesis testing thanks to significant progress in research technology experiments via agent-based computational simulations.4 We also define key concepts of complexity science in order to understand the conditions for a healthy functioning of complex adaptive systems (human brains, organizations, economies, and societies)—a modern view of science. We then apply complexity lenses to human society (a meta-perspective) and the human brain (a micro-perspective). Finally, we note that complexity dynamics occur at much higher rates of speed in the Digital Age. Managers need to be aware that Digital Age information flows and consequent changes, or needs to change, occur much more rapidly than they did 30+ years ago. We also present neuroscience findings about how and when parts of human brains can change, so as to explain how ethical self-transcendent values are critical, both for healthy brain development and the sustainability of global society. This is in opposition to the kind of value competition introduced by social-reductionist science and neoclassical economics, both of which have adverse effects on both brain function and broader effects, such as value conflicts among people and environmental sustainability. Finally, we focus on implications for theory and research about business, society, and management.

Complexity science instead of reductionism The traditional approach to science—reductionism—embedded in the modernist outlook, is based on the assumption that a complex real-world phenomenon can be understood by decomposing the whole into its parts and by detaching the object of study from its context. This discrete and discontinuous understanding of reality presumes that reality can be manipulated and controlled through personal agency (Nisbett et al. 2001). The ignorance of the dynamic changing relationships (interactions and networks) and focus on material (observable) properties of the physical world (i.e. “matter”) gave rise to a mechanical/materialistic representation of the world, a stable clockwork universe with linear and deterministic cause-effect relationships (Louth 2011: 63). This is expressed in neoclassical economics (Wisman 1979: Ghoshal 2005; Kirman 2009). While the purpose of classical reductionist science is to describe a deterministic and reversible order, which can be found only in “limiting, simple cases” (Prigogine and Stengers 1984: 8), complexity scholars emphasize the evolutionary, irreversible nature of our human reality (temporality) in which more complex higher-order phenomena (e.g. human consciousness, social groupings, societies) continually emerge from lower-level interactions among individual parts (e.g. atoms, neurons, cells, individuals, groups, companies, economies, societies, etc.). Thus, complexity is the science of emergence and

Understanding value conflict  185

self-organization where the simultaneous non-linear interactions of many relatively simple components produce “stable configurations,” which is to say that molecules self-organize into cells, neurons into brains, human individuals into social organizations, species into ecosystems, with consumers and companies self-organizing into economies (Waldrop 1992: 88). These complex phenomena (cells, brains/individuals, social groups, organizations, societies, etc.)—generally called complex adaptive systems (CASs)—are irreducible entities where the collective properties of the whole cannot be deduced from the knowledge of individual parts (like reductionist science assumes) but even so, they are subject to the same dynamical forces (Colander 2008). Because complexity arises from the simultaneous interaction of many components, traditional deductive logic is not helpful to understand these “messy” non-linear dynamics “as Newton discovered in attempting to solve the threebody problem” (Lansing 2003: 185). To compensate for this loss, complexity scholars suggest that different complex adaptive systems (biological, cognitive, social, organizational, ecological, etc.) are “self-similar” (McKelvey 2009: 11) because they exhibit some common underlying principles, mechanisms, or forces across different levels of their structures. For this reason, complexity researchers adopt “cross-disciplinary comparisons” or comparisons between natural and artificial systems (e.g. computer simulations) that allow them to “distil general properties and processes” (Brownlee 2007: 1) inherent to the functioning of all complex adaptive systems and explain how these systems co-adapt to changes in their environment and evolve over time while maintaining their own “coherence and persistence” (Holland 1995: 5). In other words, complexity science is built on an interdisciplinary approach that draws its theoretical conceptualizations and law-like generalizations from a wide range of scientific disciplines. Accordingly, complexity science seeks to understand and explain properties of collective behavior, i.e. of “mass phenomena” where “the hope of understanding the workings of causation at the level of individual elements” should be abandoned (Lansing 2003: 185). Complexity laws are “statistical probabilistic laws” that “refer to large groups of actors and are not reducible to laws of individual actors” (Colander 2008: 4). While the behavior of individual parts can be chaotic and unpredictable, the behavior of the whole system may follow some “ordered pattern” and thus may be deterministic (ibid: 4). The global pattern of systemic behavior depends upon context or initial conditions, “a few crucial parameters” that determine which path a system will follow. These “parameters cannot always be characterized in advance, however; they must emerge from a careful study of the whole problem” (Gell-Mann 1994: 365). In this regard, complexity science does not reject the orthodox criterion for empirical verification because: the prediction that a pattern of a certain kind will appear in defined circumstances is a falsifiable (and therefore empirical) statement. Knowledge

186  Elena Goryunova and Bill McKelvey

of the conditions in which a pattern of a certain kind will appear, and of what depends on its preservation, may be of great practical importance. (Hayek 1964: 58) With the development of computational technology, however, it is possible to create hypothetical conditions and observe whether a system will follow the predicted pattern of behavior without real-life experiments (Waldrop 1992), i.e. use agent-based computational modeling to conduct laboratorystyle experiments, since we can’t realistically insert companies, societies, or economies into lab experiments. It is precisely the enormous progress in agent-based computational simulation technologies that has made complexity science possible. “Computers and simulations are the foundation of the complexity approach” that allow scientists to deal with more complicated research models involving non-linear dynamics (Colander 2008: 6). As Waldrop (1992: 65) explains, “nonlinear equations are notoriously difficult to solve by hand, which is why scientists tried to avoid them for so long.” In fact, it is the scientific standard for empirical verification that forced social science in general (Eve et al. 1997) and economic science in particular (Colander 2008) to be formed into limited, simplistic analytical frameworks based on empirically testable linear models. From this point of view, the complexity approach to science and computer experiments offers new possibilities for social sciences; it makes it possible to embrace important aspects of human reality that were neglected until recently due to unsophisticated research technology. Just as the telescope and microscope revolutionized the way people constructed reality, [i.e. astro- and nano-realities] the computer is having a similar effect today. These tools of intervention are our new sensory organs. Our reality changes as our ability to detect phenomena changes. (Dent 1999: 16) This is even more so in the current Digital Age. Therefore, the complexity approach to science does not undermine the scientific enterprise and its scientific method—empirical verification of truth claims—that gives science its superiority over merely metaphysical speculation (Popper 1959). Because cause and effect are separated in time by dynamic nonlinear agents’ interactions in complex biological, cognitive, social, and ecological systems, complexity scientists challenge (1) humans’ limited capacities for empirical observation and (2) scientists’ inability to put large social systems (e.g. organizations, companies, governments, and societies) into laboratories so as to conduct experiments to test what philosophers of science call “counterfactual conditionals,”5 and that is why they adopt computer modeling and simulation (see Casti 1997; McKelvey 2004). To sum up, reductionist and complexity approaches to science differ in what they focus on and their logic of causation: the former focuses on an

Understanding value conflict  187

individual object at the expense of its context (a static picture of reality) and makes predictions based on individual properties of objects, whereas complexity science puts emphasis on the relationships/interactions among multiple agents6 and their context (usually requiring a dynamic worldview) and tries to predict future events based on the dynamic patterns of these interactions. Thus, if traditional science was “a quest for the ultimate particles”—i.e. decomposing a whole into its smallest parts—complexity science is “about flux, change, and the forming and dissolving of patterns” (Arthur cited in Waldrop 1992: 17). While the classical reductionist approach led to a science of differentiation and specialization, Gell-Mann saw complexity science as a way of tackling “the great, emerging syntheses in science—ones that involve many, many disciplines” (quoted by Waldrop 1992: 75). Despite the opposite direction that complexity science pursues, however, it should be considered as significant scientific progress built on the accumulated knowledge of classical science in different domains and more sophisticated research technology (Colander 2008). As we note later, since individual human agents can learn new ideas from each other at digital speed, the “flux, change, and the forming and dissolving of patterns” (Arthur cited in Waldrop 1992: 17) now occurs much more rapidly and among thousands or millions more individuals than before the Digital Age began—circa 1982.

Complex adaptive systems: key concepts Complex phenomena have long been pervasive in our world (Hayek 1964). Unlike closed physical systems composed of homogeneous static components (atoms), complex adaptive systems (biological, cognitive, social, organizational, ecological, social, etc.) can be represented as networks of heterogeneous, simultaneously interacting agents (biomolecules, cells/neurons, brains, social groups, species, economies, societies, etc.) such that each can be regarded as complex adaptive systems themselves (Gell-Mann 1994; Holland 1995). Thus, the essential feature of complex adaptive systems is their nested hierarchic7 structure with “cross-cutting connections” (Simon 1962/2005: 138); that is, “at any level of analysis, order is an emergent property of individual interactions at a lower level of aggregation” (Anderson 1999: 219). In other words, each complex adaptive system constitutes a “building block” for a higher-order system, which is more complex (contains more regularities) than its constituent parts (Gell-Mann 1994; Holland 1995). Being composed of the same building material, “most complex structures found in the world are enormously redundant, and we can use this redundancy to simplify their description” (Simon 1962: 481) and “to describe, and even to ‘see’ such systems and their parts” (Simon 1962: 477). The adaptive agents/systems possess computational and/or digital information-processing capabilities that allow them to quickly learn from each other, to exchange and store information about their external environment in order to better fit with this environment (Holland 1995), and even more so now, in

188  Elena Goryunova and Bill McKelvey

2021. These capacities for learning and memory make agents’ behavior interor context-dependent (information from their environment affects agents’ behavior). They are able to coordinate their actions and self-organize into a more complex higher-order structure (i.e. behave as a CAS), which possesses a greater adaptive (and computational) capacity with a higher chance of survival. For example, a highly networked human brain can process much more information about its environment than its individual neurons and thus provide a more adaptive fine-tuned response to changes or challenges in the brain’s external environment. The same is valid for a social group or society, which have more brains and are usually more adaptive (if they are healthy) than a single individual brain. And, as we discuss in Chapter 2, organizational evolution and the evolution of other human-based systems can coevolve much more quickly because humans’ brains are much larger and more competent than the biological species’ brains that Darwin (1859) based his evolutionary theory on. Nowadays many humans can learn and change even at digital speed. By contrast to simple inorganic matter with an unchangeable structure, a complex living phenomenon is capable of reproducing itself and maintaining its dynamic order (identity) thanks to its capacity to store [in its brain(s)] “information about its environment and its own interaction with that environment” in a highly condensed form, i.e. schema (Gell-Mann 1994: 35) or an internal model (Holland 1995). A system-level schema contains not only lessons from past experience but also prescriptions for local agents’ behaviors (Gell-Mann 1994). For example, genetic material (DNA) stores all necessary information about an organism’s ontogeny, thereby guiding the reproduction of its stable biological subsystems (cells, tissues, organs) and their adequate functioning for survival (Simon 1962/2005). In social systems, social norms and values imbedded in cultural DNA (traditions, customs, laws, social practices, i.e. memes instead of genes [Dawkins 1976; Durham 1991; Dennett 2006; Shepard and McKelvey 2009]) guide human individuals in reproducing their stable social subsystems (e.g. diverse social institutions like family, schools, universities, hospitals, business organizations, legal systems, etc.) and adopting appropriate social behavior that makes a society possible (Waldrop 1992; Gell-Mann 1994). And, more and more in current times, all of the above human responses can occur at digital speed. Because CASs usually operate in an ever-changing environment, a system’s order should not be too rigid or static as excessive stability can be harmful or even fatal for its survival—entities (like CASs) need to keep evolving (changing) so as to survive in the same, though changing, environment (Kauffman 1995)—which now can occur at digital speed—so as to quickly become better adapted to a changing environment. In order to better fit with their external environment (Holland 1995), adaptive agents need to constantly challenge their system’s order (accumulated knowledge) by “always exploring, seeking out opportunities, experimenting with novelty” (Gell-Mann 1994: 257). Locally successful rules of an agent’s behavior (agent-level schema) with “favorable outcomes” or highest payoffs are imitated and propagated by other

Understanding value conflict  189

adaptive agents via positive self-reinforcing feedback loops thus altering the system-level behavior/schema. For example, gene mutations are the driving force for biological evolution, while new ideas and knowledge (memes) are critical for the successful evolution of social systems (Durham 1991; Shepard and McKelvey 2009). New information introduced by local agents is not always beneficial, however, for their system’s survival and sometimes can lead to maladaptive systemic behavior over the long run (Gell-Mann 1994). A system’s ability to evolve, so as to improve its own fitness, depends on the capacities of its local agents, not only to transmit new information, but also to store previously accumulated systemic experience in a “highly compressed form” (Gell-Mann 1994: 69). This ability of adaptive agents to “both store and transmit information is optimized at the edge of chaos” when a system enters the “transition zone” between order and chaos, “between the periodic and chaotic regimes” (Lansing 2003: 191). Hence, changes in what we call the Region of Emergence between the first and second critical values (i.e. what used to be called the “edge of chaos” (Lewin 1992) or the “transition zone” [Lansing 2003: 191]) can be understood as a kind of balance between a previously established order maintained by system-level schema (long-term memory) and constantly created chaotic disorder resulting from too many variations in agent-level schemata occurring at the same time. As Prigogine and Stengers (1984) note, a system crosses the first critical value (the “edge of order”) when it creates a phase transition in response to an imposed tension. Complexity scientists now focus on what they call the “edge of chaos”—because, when too many tensions are imposed at the same time and a system can’t figure out how to effectively respond to so many tensions simultaneously, chaos results (perhaps because each agent suggests a different response). For example, biological complexity or evolution takes place when new more sophisticated genes are added to regulate or coordinate the previous ones. As Herbert Simon explained, biological evolution proceeds not by totally reconstructing a previous genetic program but by “adding new processes that would modify a simpler form into a more complex one—to construct gastrula, take a blastula and alter it!” (Simon 1962/2005: 152). Similarly, although humans and chimpanzees share 96% of their genetic material (genome), only 4% accounts for their striking differences, especially in information processing—human brains are much more competent than those of chimps. Therefore, a system can evolve to improve its own fitness in its external environment when new information provided by local agents does not compromise a previously created kind of order or universal guiding rules for agents’ interaction. These system-level rules should be universal and non-contradictory, so their adoption by adaptive agents provides a minimum order (e.g. universal driving code for traffic or universal DNA code in a biological organism) that assure the “coherence and persistence” of this system (Holland 1995). When system-level rules are compromised by contradictory information, the overall pattern of system behavior shifts toward a chaotic or explosive state

190  Elena Goryunova and Bill McKelvey

and thus triggers the degeneration of the whole system. A powerful illustration of a system’s disintegration can be found in biological systems in the case of tumor development. As Davies and Lineweaver (2011) explain, a cell tumor arises when damage in the DNA (a loss of accumulated information in system-level schema) deactivates “more-recently evolved genes” responsible for cellular cooperation/communication and provokes the proliferation of “ancestral” selfish cells (normally active only during embryonic growth). This DNA deregulation disrupts the complex coordination between differentiated cells and thus destroys the host’s multicellular organism. The final point that deserves special attention for understanding the dynamic changes in CASs concerns the question of time scale. In general, the speed of change in complex systems depends on the extent of coupling in a system, that is “intra-component linkages are generally stronger than intercomponent linkages” (Simon 1962/2005: 149). To put it simply, changes at the low level of agents’ interaction are more rapid than those at the higher level. For example, the time required for the firing of neurons is “extremely small on the order of tens of milliseconds” (Damasio 1994: 259) so new information may instantly affect the structure or patterns of neuronal connectivity in the human brain. However, brain-to-brain interactions among people generally take much more time and changes in social/business organizations take months to years to occur (Holland 1995). However, in the modern Digital Age, more and more human interactions are based on high-speed digital information flows via the Internet (Corallo et al. 2007; Bharadwaj et al. 2013; deCosta 2013; Kellmereit and Obodovski 2013; McEwen and Cassimally 2013; McQuivey 2013; Schmidt and Cohen 2013; Woodard et al. 2013; Greengard 2015; Miller 2015; Nathan and Rosso 2015; Tapscott 2015; Coupey 2016; Lobo and Whyte 2017; Scott et al. 2017). In addition there are now numerous books and articles about digital business (DB): e.g. Tapscott 1995, 2015; Day et al. 2003; Coupey 2016; Dourmas et al. 2005; Seigneur 2005; Brousseau and Penard 2007; Corallo et al. 2007; Muntaner-Perich and de la Rosa Esteva 2007; Nachira et al. 2007; Pappas et al. 2007; Razavi et al. 2007; Malecki and Moriset 2008; Tan et al. 2009; Stanley and Briscoe 2010; Laudon and Laudon 2013; Porter and Kramer 2011; Tan and Macaulay 2011; Herdon et al. 2012; Li et al. 2012; El Sawy and Pereira 2013; Cojocaru et al. 2014; Jensen et al. 2014; Rong and Shi 2014; Ziyae et al. 2014; Liu and Rong 2015; Tafti et al. 2015; Attour and Della Peruta 2016; Choi 2017; Kshetri 2017; Ponce-Jara et al. 2017; Remane et al. 2017; Schallmo et al. 2017; Scott et al. 2017: Seo 2017; Swaminathan and Meffert 2017; Schallmo and Williams 2018; Weill and Woerner 2015, 2018, to mention just a few of the many recent articles and books. Everyone is learning about all sorts of things rapidly and all the time via the Internet and the use of their smartphones, iPads, and computers. Needless to say, the evolution of human-involved phenomena is occurring much more rapidly than ever before. While, at the level of a nation’s culture, important changes were triggered over spans of years and decades, and though the rise and fall of civilizations may last

Understanding value conflict  191

from centuries to millenniums of cultural interaction (Gell-Mann 1994), in the Digital Age even these kinds of changes may happen much more rapidly—for example social events and behaviors can go viral via the Internet and affect thousands to millions of people within hours, minutes, and even seconds—as recent (i.e. in 2018) false information flows in Facebook, etc. demonstrate. Therefore, we now need to keep these time-scale differences in mind while discussing changes (evolution or degeneration) in social systems. In Chapter 1 the Digital Age effects on various complexity dynamics are explained in more detail. Complexity science, therefore, suggests that society may first be understood as a CAS with human beings (and their brains) at a basic agent-level of analysis (whether “agents” are things, individuals, or larger social systems like organizations, economies, societies, etc,). The overall pattern of a society’s behavior depends on the aggregate behavior of its adaptive agents—especially its human beings. Being a part of a larger environment (ecosystem), societies should continuously adapt to their external changes (scarce resources, climate changes, etc.) so as to achieve better fits with their environmental constraints. This global capacity of societies to adapt or evolve as a whole is critical if they are to maintain their identity and existence over time (sustainability). For this reason, “healthy societies…have to keep order and chaos in balance,” to “reach that elusive, ever-changing balance between freedom and control” (Farmer cited in Waldrop 1992: 294, 320). In Western societies built on “a belief in personal agency” and science (Nisbett et al. 2001: 296), individual freedom is a natural right so chaos spontaneously arises from their self-expression and searches for local fitness by human agents. But how might a society provide an optimal control or order that is not detrimental to human creativity and exploration of new ideas but still guide human behavior in a socially preferable way? In this chapter we argue that ethical self-transcendent values imbedded in human cultural heritage support this critical regulatory function but an accidental breakdown of society-level schemata or various cultural traditions might severely compromise their effective functioning.

Understanding value conflicts: a society-level perspective From the complexity meta-perspective, human society can be seen as a CAS with human beings (as individuals or as groups) as its interactive agents. Over many centuries human society has been evolving by replicating its “cultural DNA including specific traditions, customs, laws, and myths” (i.e. memes) that “contains not only lessons but also, at least by implication, prescriptions for behavior” (Gell-Mann 1994: 278). Like biological evolution that operates through transmission of viable genes that contain useful information for an organism’s survival, the evolution of human society has been possible via its continuous transmission of human values; “an enduring belief(s) that a specific mode of conduct or end-state of existence is personally and socially preferable

192  Elena Goryunova and Bill McKelvey

to alternative modes of conduct or end-states of existence” (Rokeach 1980: 160)—imbedded in cultural artifacts; see also Rokeach (2008). Since human agents can have multiple personal value conflicts (which we describe further in a later section describing neuroscience findings about brain damage) and also value conflicts with other people, our review of complexity science indicates that chaos results when individuals suffering from too many value conflicts all at the same time can’t decide which of the many conflicts they are facing to try to resolve first. As we describe later, neuroscience findings about the “thinking part of the emotional brain” (Schore 2001: 39) show that brain damage amplifies the number of value conflicts, thereby making a person’s life much more chaotic, complicated, emotionally damaged, and consequently dysfunctional. According to Damasio (1994: 124), social conventions and rules were developed as an “additional layer of control” to biological mechanisms in order to “ensure survival for the individual and for others (especially if they belong to the same species) in circumstances in which a preset response from the natural repertoire would be immediately or eventually counterproductive.” Unlike biological genes, human values (memes) are not just “transmitted only through education and socialization, from generation to generation,” but also are “inextricably linked to the neural representation of innate regulatory biological processes” (Damasio 1994: 125). Being deeply encoded in non-conscious emotional processes, human values provide intuitive adaptive responses to complex, uncertain, rapidly changing real-life situations (Damasio 1994: 124, 179). In the pre-scientific era, however, human values were transmitted in a dogmatic fashion “by writing them on stone tablets” (Waldrop 1992: 319) thus restraining selection of progressive ideas/beliefs and slowing down the development of a whole society. Major changes began in the Age of Enlightenment when human curiosity and striving for a better life (search for a better fitness) gave rise to the modern scientific enterprise. Science began to liberate humanity; to “separate the wheat [truth] from the chaff [metaphysics]” (Wisman 1979: 21); “painstaking observation of nature, and analysis of the empirical findings, came to be seen as a truer source of knowledge than pure philosophical reflection” (Stapp 2009: 182). Needless to say in the modern Digital Age, information flows at digital speed among people’s smartphones and computers via the Internet, as we have noted previously, thereby causing chaos to occur more readily, more broadly, and also much faster. The objective, value-free stance underlying modern science that was successful in the study of inanimate physical phenomena, in a world of simplicity— triggering an impressive progress in technology and the material conditions of human life, the extension of objectivist scientific method into a world of social complexity, where the objects of study (human beings) are able to perceive external/scientific information and modify their behavior according to newly discovered social “laws” (Allen 2009) (and much faster in the Digital Age)—had far-reaching consequences for traditional systems of human values.

Understanding value conflict  193

But, with the accelerating disintegration of the established cultural traditions, brought on by increased fluxes of people and ideas,” human beliefs and values have become “increasingly determined by science” shaping not only our immediate actions, but also, over the course of time, the form of our society…It is at least conceivable that what science has been telling us for three hundred years about man and his place in nature could be playing by now an important role in our lives. (Stapp 2009: 181) By reducing the complexity of social reality to the observable material world, reductionist social science, epitomized by neoclassical economics, which had to make many false assumptions about human behavior (described and listed by Read 1990) so it could gain traditional scientific credibility by using mathematics (like physics) to enhance its scientific legitimacy, shifted emphasis from the rich complexity of human nature to an average “rational” man guided by his own self-interest; and of course reduced the chaos emerging from too many diverging thoughts, values, and conflicts. “This kind of morality may seem to be immoral but it appears to be the rational outcome of accepting completely the mechanical or materialistic view of man” (Stapp 2009: 183). The selfinterested “economic man” without “noble, heroic, or altruistic aim” became the social norm, the universally accepted standard that neoclassical economics promoted and that became a reality in the self-fulfilling social environment (Ghoshal 2005; Miller 1999). The self-fulfilling power of self-interest can be (1) direct: through teaching of economics (Gregorowicz and Hegji 1998; Offer 2012; Shaub et al. 2005; and Zhong et al. 2006) and Hühn 20148 where individuals implicitly learn abstract rules for reasoning (Larrick et al. 1990); and (2) indirect: implicitly learn: (a) abstract rules for self-interest-oriented reasoning (Larrick et al. 1990); and (b) about political messages that direct people’s attention to their own self-interest (Miller 1999). The belief in self-interest is so deeply imbedded in Western cultures as a natural law that it motivates people to follow their self-interest rather than other motivations (Miller 1999). They need “rational” material justification to engage in a socially oriented action even though they are often willing to do so for other non-vested motives (Ratner and Miller 2001). The ad hoc assumption of self-interest has been so powerful that it created a broadly shared cultural belief (Miller 1999) that continues to be sustained and reinforced through positive feedback loops between widely accepted social/management theories and social/business practices (Schwartz 1997; Ferraro et al. 2005). The continuous proliferations of materialistic values into complex social systems have far-reaching, dramatic effects on these systems over time. The financial sector is one of the most illustrative examples where human greed coupled with ingenious human creativity—used for developing highly sophisticated financial products (Shaub et al. 2005)—resulted in the recent economic and financial crisis9 (Kirman 2010; i.e. the Great Recession starting December

194  Elena Goryunova and Bill McKelvey

2007): an unprecedented loss of economic wealth and employment for millions of individuals (Stiglitz 2012). More generally, the widely promoted norm of self-interest in Western cultures (Miller 1999) creates ever-increasing focus on self-interest and greed that in conjunction with an exponentially growing population has led to the over-consumption of resources and “a plundering of future generations, both economically and ecologically…This materialist binge cannot be sustained” (Stapp 2009: 185). Therefore, the dominant position of materialistic values threatens the sustainability of human society and compromises its healthy functioning. But of course, not just in Western cultures; most of the dictators of African countries selling commodities like oil, diamonds, gold, copper, coffee, tea, etc. have become billionaires even though most of their governments’ citizens still live in abject poverty.10 Using a biological-system analogy, we suggest that materialistic self-oriented values (the norm of self-interest) in a society act as primitive “ancestral” genes in a biological organism. They both result from a damage of long-term memory (cultural traditions and DNA respectively) and incite shortsighted agent behavior that turns out to be irrational and harmful from a long-term systemlevel perspective. Indeed, from the evolutionary perspective of complexity science, materialistic self-centered values can be viewed as more primitive and less complex than universal ethical values.11 As the most complex emergent structure, human society requires its agents—human beings—to accommodate and transcend themselves, to develop “devices which can cope with greater complexity” (Damasio 1994: 190). More sophisticated ethical values can be regarded as the product of evolution, “the neuronal representation of the wisdom” (Damasio 1994: 125) that “provides a way of structuring human behavior in a way that allows a functioning society” (Farmer cited in Waldrop 1992: 319). Our analogy between social and biological systems is further supported with findings in computer experiments. In their agent-based computational modeling, Bazzan et al. (1998) demonstrate how shortsighted self-interested egotistic agents perform well in the short run but compromise their gains in the long run due to their lack of cooperation. In contrast, the presence of altruistic agents with moral sentiments continuously improve the performance of a whole group “without compromising too much their individual performance.” The authors conclude that the proportion of egoists in a group is critical for its adaptive capacity and its survival over long term, which is why a social group dominated by self-interested individuals cannot be sustained. The norm of material self-interest poses not only a serious challenge to the sustainability of human society but it also adversely affects human psychological and even physical health. The growing body of empirical studies indicates that strong materialistic value orientations are associated with low personal well-being (Belk 1985; Kasser and Ryan 1993); dissatisfaction with one’s life (Richins and Dawson 1992; Ryan and Dziurawiec 2001); neuroticism (Flouri 1999); higher substance use like tobacco, alcohol, and drugs (Vansteenkiste et al. 2006): low levels of happiness, vitality, self-actualization but higher levels of depression, anxiety, and

Understanding value conflict  195

distress (Kasser and Ahuvia 2002; Kasser and Ryan 1996): and mental disorders like narcissism, paranoia, obsessive and anti-social behavior (see Kasser 2002 for a meta-review and extensive discussion). The foregoing research clearly indicates that materialistic self-oriented values introduce small-scale inconsistencies in human minds that are amplified over time by non-linear human agents’ interactions that can eventually grow into large-scale problems for societies. To change the dynamic patterns of global society or to change “human actions globally one must change human beliefs globally” (Stapp 2009: 186). To change human beliefs at a global scale, it is important to introduce “cross-cultural ferment…discoveries about how [our own culture can] restrain the appetite for material goods and substitute more spiritual appetites” (Gell-Mann cited in Waldrop 1992: 352). In the following section, we provide some recent evidence from moral neuroscience and social psychology in order to better understand the proliferation of value conflicts at an individual level and among some number of individuals, and the role of education in creating them.

Understanding value conflicts from a VMPC perspective The pervasive role of emotion in normal decision-making was discovered after the study of neurological patients with local damage to the ventromedial prefrontal cortex (VMPC): known as “the thinking part of the emotional brain” (Schore 2001: 39). As we mention in a previous paper (Goryunova and McKelvey 2018: 7), The potentially alienating effects of [neoclassical-economics] training on human cognition and behavior have been explained by: (1) learning effects (Larrick et al. 1990; Cipriani et al. 2009); (2) self-selection and indoctrination (Carter and Irons 1991; Frank et al. 1993; Bauman and Rose 2011; Etzioni 2015; Miller and Xu 2016); (3) lack of ethical emotionladen learning (Ahmed 2008; Manner 2010); (4) focus on what’s good for shareholders (Rose 2007; Heracleous and Lan 2010); or (5) moral deprivation (Stiglitz 2012). It is also micro-economists’ focus on self-interest that diminishes individuals’ sense of free will and personal responsibility thus increasing the likelihood of cheating and unethical behavior (Vohs and Schooler 2008). Damage to the VMPC “leads to a loss of self-directed behavior in favor of more automatic sensory-driven behavior” (Bechara 2005: 1458). In order to understand why and how value differences/conflicts appear among human individuals, it is important to gain insight into the VMPC’s functioning—i.e. the complexity of a human brain’s healthy functioning. The somatic marker hypothesis (Damasio 1994) is the neurobiological framework that helps us to explain the complex differences in social values based on the biological process of human emotion. Bechara (2005: 1458) also finds that two kinds of

196  Elena Goryunova and Bill McKelvey

changes in brain behavior are especially important: (1) managers’ brains are programmed into a non-conscious “automatic sensory-driven behavior” that they can’t readily or easily change; and (2) the VMPC effect explicitly shows up as managers’ persistent driving objective of self-interest, even though changing employee, broader social, and environmental conditions call for changing corporate behaviors—this is especially true of narcissistic CEOs. According to Damasio (1994), emotion plays a crucial role in making fast and advantageous decisions in complex social environments characterized by conditions of high uncertainty and ambiguity. Despite their demonstrated intellectual abilities, as measured by IQ and other tests, VMPC patients systematically exhibit inappropriate social behavior in real-life situations (Bechara et al. 1998; Greene 2009). Based on his clinical evidence, Damasio concludes that healthy decision-making in real-life and real-time situations requires not only conscious processing of factual information, but also the simultaneous assessment of different, often conflicting, options for actions by non-conscious emotion-processing brain regions. These non-conscious emotional regions of the brain embody the tacit knowledge that humans continuously learn from their personal and social experiences. Individuals—via interactions with their social environment, via pleasant and unpleasant personal experience or reward/ punishment mechanism—implicitly make associations between different types of stimuli (persons, situations, behaviors) and their own emotional or somatic (bodily) states. As personal and social experiences accumulate, human minds develop the ability to “generate estimates of probabilities” for anticipating “a certain degree of badness or goodness” for future outcomes of their decisions and actions (Damasio 1994: 219). This tacit emotionally laden somatic knowledge mediates the decision-making process covertly “by enhancing attention and working memory related to options for action and future consequences of choices, as well as to bias the process overtly, by qualifying options for actions or outcomes of actions in emotional terms” (Anderson et al. 1999: 1035). For additional, and more recent research, see: Gregorowicz and Hegji 1998; Bechara et al. 1998, 2000; Bechara, et al. 2000; Finucane et al. 2000; Stuss and Alexander 2000; Carter and Smith Pasqualini 2004; Shaub et al. 2005; Shiv et al. 2005; Ylisaker et al. 2005; Verdejo-García et al. 2006; Zhong et al. 2006; Koenigs et al. 2007; Raine 2008; Critchley 2009; Verdejo-García and Bechara 2009; Reimann and Bechara 2010; Damasio et al. 2012; Hühn 2014; and Goryunova and McKelvey 2018. The Damasio et al. (2012) book contains 12 chapters, many of which are also descriptions of research studies. Individuals with impaired emotional brains, like VMPC patients—whose frontal cortexes have been damaged—lose their capacity to perceive and process social cues that normally activate a person’s internal system of values and preferences that are necessary for adaptive decision-making in uncertain, changing environments (Damasio 1994). Without these somatic markers to effectively guide behavioral options (Greene and Haidt 2002), a person can only rely on conscious, effortful cognitive mechanisms such as cost-benefit

Understanding value conflict  197

analyses (Damasio 1994). The inability to feel (or cold-bloodedness) not only “degrades the speed of deliberation,” but most importantly “degrades the adequacy of the choice, i.e. patients may choose disadvantageously” (Bechara and Damasio 2005: 339). As Bechara and Damasio (2005: 338) explain, VMPC patients “are unable to learn from previous mistakes,” their actions “often lead to losses of diverse order, e.g. financial losses, losses in social standing, losses of family and friends…in spite of maintaining a normal intellect.” Despite being “fully aware [of] what is right and wrong,” their behavior is rather impulsive, involves risk-taking, and is directed by short-term outcomes (ibid: 348). From a complexity perspective, the VMPC can be understood as holding a system-level schema—i.e. the long-term memory of the human brain. According to Bechara (2005: 1459) “VMPC is a critical neural structure involved in triggering the affective/emotional signals of long-term outcomes” from memory, knowledge, and cognition. While the low-level impulsive brain system— amygdala—provides emotional responses (pain or pleasure) to immediate stimuli in the environment, the higher-order reflective VMPC system integrates this affective information in form of somatic (bodily) states (Damasio 1994). During the process of decision-making, the VMPC exerts top-down control over impulsive automatic emotional responses (immediate gratification) and even decides whether or not to integrate emotion into cognition by “computing the contextual relevance of emotional information for decision-making” (Beer et al. 2006: 452). For this reason, damage to VMPC functioning causes more sensory-driven behavior instead of self-directed behavior (Bechara 2005). Without this critical control function, the human brain as a system does not evolve but degenerates; the “rationality that makes us distinctively human and allows us to decide in consonance with a sense of personal future, social convention, and moral principle” is severely compromised (Damasio 1994: xii). This VMPC-dominated emotional mechanism is especially relevant for an individual’s moral development—i.e. learning and applying ethical knowledge that covertly regulates interpersonal or social behavior by intuitively rejecting disadvantageous options for action associated with negative future outcomes like punishment, social disapproval or negative moral emotions like guilt, shame, or remorse (Damasio 1994). As Anderson et al.’s (1999) study demonstrates, the same VMPC structure—i.e. the “Senior Executive of the social-emotional brain” (Schore 2001: 40)—is involved in the process of a person’s moral development. In their study of neurological patients with focal brain lesions (e.g. tumor resections, damage caused by accidents, etc.) to the VMPC, Anderson and colleagues (1999) compare patients with recent adult-onset VMPC damage to patients with brain damage occurring during childhood. While the lateonset patients exhibited poor decision-making in their life since their accident, their moral reasoning or abstract ethical knowledge remained intact as they were able to “solve social problems when presented in a laboratory setting, that is, in a verbal format, outside of real time” (1999: 1035). The early-onset patients, however—i.e. people whose damage to their VMPC occured before

198  Elena Goryunova and Bill McKelvey

age 20–23, after which is when a brain changes very slowly or freezes (see Hart 2009, quoted later in this chapter)—in addition to their antisocial behavior, had never acquired the basic rules of human moral behavior as they showed “deficiencies in moral reasoning relative to age-matched controls” (ibid: 1035). These findings validate the crucial role of the emotional brain regions not only for learning ethical knowledge, but also for applying social conventions and ethical rules in real-life and real-time situations where anticipation and pattern recognition of social stimuli are important (Anderson et al. 1999). Consistent with Damasio’s somatic-marker hypothesis, Haidt (2001) offers a social-intuitionist model that emphasizes the driving force of moral emotions or intuitions for moral judgment. In contrast to the economists’ rationalist view of moral judgment as a product of controllable moral reasoning (Kohlberg 1969), Haidt argues that moral evaluations suddenly and effortlessly come into consciousness with “an affective valence (good or bad), but without any conscious awareness of having gone through steps of searching, weighing evidence, or inferring a conclusion” (Haidt 2001: 818). This intuitive moral judgment may be followed, however, by slow and intentional moral reasoning, particularly when an individual’s personal intuitions conflict (private moral reasoning) or when moral agents should justify their decisions by influencing “the intuitions (and hence judgments) of other people’s” interpersonal moral reasoning (Haidt 2001: 814). Otherwise, moral intuition is “the default process, handling everyday moral judgments in a rapid, easy, and holistic way” (Haidt 2001: 820). Even though individuals’ moral emotions/intuitions might be culture-specific (Haidt et al. 1993), the systematic differences in moral/social behavior in the context of social dilemmas have been observed within the same cultural groups (see Bogaert et al. 2008 for a meta-review). While individuals with “prosocial” value orientations exhibit strong preferences for collective benefits (Declerck and Bogaert 2008), “proselfs” are driven more by short-term selfish gains (Emonds et al. 2011). This result can be explained by the fact that proselfs have not internalized the norms of social responsibility and reciprocity (De Cremer and Van Lange 2001) during the development period of the human brain that continues “until the age of 20–23 years” (Hart 2009). Social norms and conventions are transmitted “during the process of education and socialization” (Damasio 1994: 177) when “repeated exposure to moral exemplars” develops beneficial moral emotions (Vianello et al. 2010: 391) or a challenging socio-emotional environment makes “unconscious internal working models… more complex” (Schore 2001: 46). Without emotionally salient learning experiences, individuals lack the background or somatic knowledge to assess the appropriateness of a given action (Damasio 1994). It is also probable that proselfs have internalized contradictory norms of social behavior (self-interest vs. ethical norms) from their previous experiences or education. According to Rokeach (1980: 167–168), feelings of inconsistency can arise when individuals uncritically or unconsciously internalize

Understanding value conflict  199

contradictory values from their reference groups. This value incongruence might produce an internal conflict of moral intuitions in a person’s emotional brain thus activating an effortful moral reasoning (Haidt 2001; Greene 2009). As O’Reilly (2010: 5) explains, “if a given action is associated with high levels of conflict or error, then more cognitive effort will be required, or it [the action] will not be as likely to be taken.” Such cognitive effort signifies activation of the dorsolateral prefrontal cortex (DLPFC) responsible for abstract reasoning and cognitive control (Miller and Cohen 2001; Greene et al. 2004). Emonds et al.’s (2011) fMRI12 study measuring brain activity during socialdilemma games confirms that proselfs activate more DLPFC, the brain region responsible for conscious effortful reasoning, whereas prosocials tend to use more of their emotional brain (VMPC). Therefore, individuals with prosocial value orientations are more prone to adhere to universal ethical values (e.g. protecting the environment, a world of beauty, social justice, equality, a world at peace, etc.) than proselfs (Gärling 1999). Thanks to their superior social skills, prosocials are able to detect subtle trust signals in potential partners (Boone et al. 2010), so as to delay immediate gratification and instead engage in mutually beneficial social exchanges with other prosocials (Rilling et al. 2002). By contrast, proselfs, due to some deficiencies or inconsistencies in their education, heavily rely on rational effortful reasoning (Emonds et al. 2011) and are only sensitive to external material rewards (Boone et al. 2010). Since Damasio’s originating 1994 book, many empirical and/or experimental research studies have been published, some of which are: Bechara et al. 2000; Finucane et al. 2000; Stuss and Alexander 2000; Carter and Smith Pasqualini 2004; Shiv et al. 2005; Ylisaker et al. 2005; Koenigs et al. 2007; Raine 2008; Critchley 2009; Verdejo-García and Bechara 2009; Verdejo-García et al. 2009; Reimann and Bechara 2010; and Damasio et al. 2012. Damasio et al.’s book (2012) contains 12 chapters, many of which are also descriptions of research studies.

Implications for business and society relationships Our chapter provides a new way of looking at the business and society interface. From a broad complexity perspective, the roots of the current tensions underlying business and society relationships can be found at the level of human culture, which is “a rich complex of myths and symbols that implicitly define a people’s beliefs about their world and their rules for correct behavior” (Waldrop 1992: 179). A healthy human culture should provide human agents both with an individual freedom and some sort of social control in order to create conditions for the evolution of human society while maintaining its identity and functional complexity over time. As Nisbett et al. (2001) argue, freedom or belief in personal agency—that led to the Greeks’ curiosity and their creation of science—is naturally present in Western societies. The main challenge consists in achieving a new kind of social influence—i.e. prosocial

200  Elena Goryunova and Bill McKelvey

rather than proself motivations that foster human freedom, innovation, and creativity. In the modern Digital Age—as the previously cited articles and books indicate—all of the underlying complexity dynamics progress at a much more rapid rate, which results in prosocial effects occurring much more rapidly. In this chapter, we argue that the recent advances in moral neuroscience help us to gain a deeper and more useful insight into this problem. According to Damasio (1994: 125), a healthy brain developed in a healthy culture where ethical norms and conventions are implicitly transmitted “through education and socialization, from generation to generation” develops all the necessary neurobiological brain mechanisms for managing value conflicts while also avoiding the chaos of trying to manage too many conflicts all at the same time. Without “a sophisticated, high quality cultural environment in which to develop there will be vast reaches of our somatic, especially neural, organizational space that we cannot use because it is not accessible to us” (Hooker 2009: 34). Why? Because human culture contains important socio-emotional information necessary for the full maturation of human brains. It is the ethical emotion-laden values imbedded in human cultural artifacts (myths, storytelling, great literature) that: appeal to moral emotions, develop human sensitivity to moral beauty and excellence, and thus implicitly transmit the patterns of moral behavior to be emulated (Algoe and Haidt 2009; Vianello et al. 2010). While human culture is critical for realizing the full potential of the human brain, complexity scholars argue that the emergence and “astonishing success” of classical science since the Age of Enlightenment created a “cultural polarization” or value conflict between science and the humanities (Prigogine and Stengers 1984: 11). This value competition became more apparent and more harmful for human society with the growth of the scientific legitimation of the norm of material self-interest (proself) promoted by neoclassical economics (Wisman 1979; Callon 1998) and other economics-derived management theories. Thus, our modern business education—i.e. MBA Programs in modern business schools—is dominated by a proself, self-interest dominated microeconomics view of human nature that implicitly transmits models of immoral/ opportunistic human behavior (Ghoshal 2005). It is no wonder, then, that a recent report released by the Economic Policy Institute indicates that from 1978 to 2014 self-interested CEOs increased their salaries by nearly 1000% whereas the pay to typical workers rose by only 11% (Mishel and Davis 2015); see also Miller and Xu (2016). This deviation of social reductionist science from humanistic cultural heritage “gained through centuries and millennia of interaction with nature and with human culture” (Gell-Mann 1994: 278) offers an explanation of “moral deprivation” in the business domain (Stiglitz 2012) and the consequent global societal challenges (e.g. increasing social inequality, economic crises, radical movements, pollution, climate change, etc.) and increasing threats to the sustainability/identity of human societies seen today. To remedy this business and society problem, complexity science suggests that society as a CAS should learn from its mistakes, so as to: (1) better understand existing social problems as signals of misfit or an “informational gap”

Understanding value conflict  201

between internal proself representations of reality (mainstream self-interest social/economic theories); (2) better understand the prosocial external implications of an ever-changing world—much more rapid in the Digital Age— and thus the constant need to adjust its society-level schema (values imbedded in knowledge) for a better fitness; and (3) learn how to avoid the chaos of too many value conflicts occurring at the same time. Needless to say, chaos can occur much more rapidly in the Digital Age. We suggest that management theories built on the ethical view of human nature might rehabilitate the legitimacy of humanistic ethical values and provide practitioners (managers, political, and business leaders) with clear guidance in their everyday decisionmaking and actions. In addition to abstract management theories, business curricula should be enriched with practical real-life examples of moral and business excellence (see the “Shared Value” approach of Porter and Kramer (2011), with inspirational models of moral behavior in organizational settings that could be replicated and become a reality via the self-fulfilling mechanism between management theories and business practices (Ferraro et al. 2005).

Implications for management research Complexity science draws a completely different picture of reality by shifting emphasis from a static material view of our universe where an existing order is given or deterministic (resulting from equilibrium) towards a more dynamic idiosyncratic agent-based human computational-model-tested understanding of reality that is continuously constructed and reconstructed through informational flows between different interconnected hierarchical levels (biomolecules, cells/neurons, human brains, social/business organizations, species, societies, ecosystems, civilizations, etc.)—digital or not—with different time scales of interaction. At each level of analysis (e.g. biological, cognitive, social), a dynamic order of systems is maintained thanks to their system-level schemata or “informational units” (DNA, VMPC, human culture, respectively) that store all critical information for a system’s or society’s healthy functioning and survival in its open-ended ever-changing environment. According to complexity scholars, the development of classical science since the Age of Enlightenment has achieved “a level of populism…whereby the apparent simplicity of its scientific laws transcended to the point that, culturally, they became a form of folklore” (Louth: 71). For this reason, modern science has played an active role in shaping the proself cultural patterns of human behavior (human values)—especially in microeconomics (Greene 2017). This indivisible, self-fulfilling link between social theories and social reality signifies that the traditional distinction between descriptive and normative science should be abolished (Sabine 1912). Therefore complexity science, by introducing time scales in its conceptualizations, has resolved the great paradox of classical science where “the temporality was looked down upon as an illusion” (Prigogine and Stengers 1984: 9).

202  Elena Goryunova and Bill McKelvey

The fact that the causes explained by all social theories are normative by nature and only time (from years to decades) separates their wide-ranging impact on social and business realities has important implications for management. New opportunities and responsibilities are unveiled for social science. New opportunities lie in the idea that ethical management theories can have a positive impact on organizations and social systems, regenerate their adaptive capacities, and make them more resilient when confronted by wide-ranging challenges. If management scholars better recognize that ethical values in mainstream social theories are the critical parameters for maintaining complexity and integrity of human society, they will be able to introduce various small changes or tiny initiating events (Holland 1995) into social systems (systemlevel schemata) such that they can scale up via dynamic non-linear human interactions to have far-reaching significant impacts on the global patterns of an organization’s or society’s behavior. This idea has been already advocated by Ghoshal (2005: 87): If we are to have an influence in building a better world for the future, adapting the pessimistic, deterministic theories will not get us there. If we really wish to reinstitute ethical or moral concerns in the practice of management, we have to first reinstitute them in our mainstream theory. By accepting the need for a more “new order-creation” focus by management researchers (McKelvey 2004), these scholars can also take more responsibility for providing universal, non-contradictory information (management theories) that would guide managers’ decisions and actions in a socially consistent way. We argue that this internal contradiction in management theories underlying the long-lasting debate between modernism (classical science) and post-modernism (humanities) can be resolved by using the new approach of complexity science as a bridge between modernists and postmodernists (Boisot and McKelvey 2010). More specifically, a middle ground can be found if modernist scholars accept the “connectionist” nature of social reality where complexity-based scientific theories actively shape human values and thus exert enormous influence on human society and postmodernists recognize the scientific need for empirical verification (i.e. computer simulations, since companies, economies, societies, and countries can’t be put into lab experiments like small animals—e.g. insects, mice, toads, tadpoles, birds, etc.) and the danger of moral relativism (see Figure 6.1). We also believe that there is no reason for the Incommensurability Problem to continue in management research since the most influential modernist proself school of thought—i.e. neoclassical economics—has been officially declared “dead” by historians of economic thought (Ormerod 1997; Blaug 1998; Colander 2000). As “good economists…are open to new approaches and ideas,” (our italicization) the new mainstream economics “is moving away from a strict adherence to the holy trinity—rationality, selfishness, and equilibrium—to a more eclectic position of purposeful behavior, enlightened self-interest, and

Understanding value conflict  203

Figure 6.1 Complexity science as paradigmatic synthesis between modernism and postmodernism.

sustainability” (Colander et al. 2004: 485). For the time being, these changes can be characterized as evolutionary, “being introduced ‘data set by data set’ and ‘new technique by new technique’ as well as ‘funeral by funeral’ as mainstream economists gradually accept new methods and approaches of complexity science” (Colander et al. 2004: 488). Nevertheless, Colander and his colleagues are confident that “as the work at the edge progresses and accumulates, it shifts the center of economist’s approach [illustrated in Greene’s (2017) book about econometrics], and…eventually will create a new orthodoxy centered on a broader complexity vision” (Colander et al. 2004: 497). Because the traditional field of management research was invaded by “economic reasoning, values, and methods” (Barney 1990: 383): introducing a “narrow model of human behavior” and “negative moral evaluations” (Donaldson 1990: 371–372), it is an opportunity for organizational scholars “to lead, not follow, this transformation” of economic science (Maguire et al. 2006: 203). We agree with other scholars that it is time for management researchers to revisit the intrusion of economic thinking, so as to “separate the wheat [truth] from the chaff [metaphysics]” (Wisman 1979: 21) and “to start afresh on the more positive agenda” (Ghoshal 2005: 87). This alternative “positive” research agenda may reasonably include the rehabilitation of traditional management research such as stewardship theory (Davis et al. 1997) based on an ethical view of human nature that is now perceived as an “anachronism” (Hambrick 2005). Organizational scholars should also pay more research attention to the emergent phenomenon of social entrepreneurship “that should be aimed at

204  Elena Goryunova and Bill McKelvey

mainstream business rather than treated as a peripheral activity or specialization” (Porter interviewed by Driver 2012: 422). The alternative “positive” research program for management science also consists in accepting and embracing the complexity of social and business reality. As we argue in this chapter, a complexity approach to science provides necessary theoretical and methodological tools for this new, more correct way of pursuing management researchers’ scientific endeavors. Given the self-fulfilling link between researcher and the object of study—network and socialoriented phenomena—the purpose of complexity science is not to describe an existing reality that might be proself and pathological, but rather to describe hypothetic conditions or the ideal environment that is necessary to maintain the complexity of cognitive and social systems (human brains, social/business organizations, and societies) and enable their organic growth and coevolution. The empirical validity of such hypothetical conditions may be tested via agent-based computational simulations that make it possible to model complex social systems and observe their development over time as well as to “explore the connection between the micro-level behavior of individuals and the macro-level patterns that emerge from their interaction,” which is the objective of agent-based computational modeling (NetLogo User Manual 2012).

Conclusion In this chapter, we argue that value conflict between business and society can only be resolved if management scholars adopt the new vision and methods of complexity science, which are obviously much more rapidly occurring complexity dynamics in the Digital Age. While classical reductionist science positioned itself as an independent, value-free observation and description of an existing reality out there (Wicks and Freeman 1998), the complexity science approach to science redefines the scientific enterprise as a “supreme cognitive cultural creation” (Hooker 2009: 54) that possesses enormous distributed power to affect the course (dynamic pattern) of human history. If this power is used unwisely or blindly, science can become “the destroyer of a civilization that no brain has designed but which has grown from the free efforts of millions of individuals” (Hayek 1975: 442). But if social scholars recognize the full potential of scientific knowledge—and the increasingly rapid digital information flows and consequent changes and growth in learning from the digital Internet—and understand (thanks to advances in cognitive neurosciences) the invisible mechanisms by which social theories exert a subtle (extended in time) influence on human cognition, they will be able to overcome their internal disagreements and rewrite mainstream management theories so as to create a blueprint for a sustainable and progressive human society. But of course, the above-mentioned mechanisms—often stemming from the faster occurring complexity dynamics in the Digital Age—now change at much faster rates (McKelvey 2018).

Understanding value conflict  205

Notes 1 Despite the well-established status within academic literature and its growing popularity among practitioners, Corporate Social Responsibility (CSR) is still regarded as an embryonic concept in its “continuing state of emergence” (Lockett et al. 2006: 133) or as “a tortured concept” (Godfrey and Hatch 2007: 87): a contested construct (Moon 2002) or “largely ahistorical, empirically weak, theoretically thin, and politically naïve” thinking (Utting and Marques 2010: 3). A lack of a dominant paradigm within CSR research generates diverse (mis)interpretations of the CSR meaning and compromises an academic consensus on the CSR definition (Carroll 1999). 2 According to the bibliometrical analysis of CSR research (Lockett et al. 2006): CSR is mostly approached from instrumental economic perspective by asking if ethics pays. More precisely, ethical imperatives become subordinated to economic materialistic goals. 3 Atomistic ontology is based on the assumption that “the world as a collection of discrete objects” while the connectionist ontology holds that "the world is a collection of overlapping and interprenetrating stuffs or substances" (Nisbett et al. 2001: 293). 4 Since organizations, economies, and societies can’t be put into lab experiments like rats, birds, and students, management researchers need to use agent-based computational experiments to authentically test what philosophers of science call “counterfactual conditionals,” i.e. truly test the following: If A exists, B will exist; if A is removed, B will disappear (Fillenbaum 1974; Pearl 2000). 5 Defined in note 4. Needless to say, experiments are the ultimate tests of truth claims. 6 “Agent” refers to semi-autonomous entities such as parts of a system such as biomolecules, cells, organs, species, people groups, firms, governments, societies, etc.—depending on the size of the system being labeled as an “agent.” 7 However, while from the traditional management perspective hierarchy is viewed as a static top-down structure with “central control,” complexity scientists understand hierarchy as an emergent, frequently changing bottom-up structure, as an evolution from simple to more complex forms (Simon 1962/2005; Gell-Mann 1994). 8 Additional empirical studies that document the potentially alienating effects of neoclassical economics training on human cognition and behavior such as a higher level of selfish and cheating behavior, violation of ethical norms, and free riding among business/ MBA students or students taking courses in economics include: Hegarty and Sims 1978; Marwell and Ames 1981; Carter and Irons 1991; Frank et al. 1993; Li-Ping et al. 2008; Tang and Chen 2008; Irlenbusch and Villeval 2015; Long et al. 2016; Belle and Cantarelli 2017. 9 As Kirman (2010: 504) explains “the rules of the game were gently modified in the banking sector. It became acceptable to lend to people who had little chance of being able to repay their loans, it became acceptable to use more and more leveraged positions, and it became standard practice to hive off dubious loans in the form of derivatives with the argument that the risk was being ‘diversified’. All of this happened because the actors saw others acting in a certain way and being successful and therefore imitated their behavior, not because they had reappraised their own portfolio and changed their estimate of its riskiness..​.th​is slow and almost unconscious shift was at the root of the crisis.” 10 Ask yourself, "Do I know of any truly noncorrupt politicians?" 11 Empirical evidence from moral neuroscience suggests that ethical values (manifested through ethical decision-making and behavior) require a complex interaction between emotional (VMPFC) and rational (DLFPC) parts of human brain, (defined later) while materialistic self-oriented values are only supported by the rational calculative cortex (Baumgartner et al. 2011). 12 Functional magnetic resonance imaging or functional MRI (i.e. fMRI) procedure detects areas of brain activity by using contrast between oxygen-rich and oxygen-poor blood flows.

206  Elena Goryunova and Bill McKelvey

References Ahmed, A. (2008) “Can education affect pro-social behavior? Cops, economists and humanists in social dilemmas,” International Journal of Social Economics, 35 (4): 298–307. Algoe, S. and Haidt, J. (2009) “Witnessing excellence in action: the other-praising emotions of elevation, admiration, and gratitude,” Journal of Positive Psychology, 4 (2): 105–127. Allen, P.M. (2009) ‘Complexity and management,” in C. Hooker (ed) Philosophy of Complex Systems, Amsterdam: Elsevier, 783–808. Anderson, P. (1999) “Complexity theory and organization science,” Organization Science, 10 (3): 216–232. Anderson, S.W., Bechara, A., Damasio, H., Tranel, D. and Damasio, A.R. (1999) “Impairment of social and moral behavior related to early damage in human prefrontal cortex,” Nature Neuroscience, 2 (11): 1032–1037. Attour, A. and Della Peruta, M. (2016) “Architectural knowledge: key flows and processes in designing an inter-organisational technological platform,” Knowledge Management Research & Practice, 14 (1): 27–34. Barney, J.B., (1990) “The debate between traditional management theory and organizational economics: substantive differences or intergroup conflict?,” Academy of Management Review, 15 (3): 382–393. Bauman, Y. and Rose, E. (2011) “Selection or indoctrination: why do economics students donate less that the rest?’ Journal of Economic Behavior & Organization, 79 (3): 318–327. Baumgartner, T., Knoch, D., Hotz, P., Eisenegger, C. and Fehr, E. (2011) “Dorsolateral and ventromedial prefrontal cortex orchestrate normative choice,” Nature Neuroscience, 14 (11): 1468–1474. Bazzan, A.L.C., Bordini, R.H. and Campbell, J.A. (1998) “Moral sentiments in multi-agent systems,” in J.P. Műller, M.P. Singh and S. Rao (eds) Intelligent Agents V—Proceedings of the Fifth International Workshop on Agent Theories, Architectures, and Languages (ATAL-98): Held as Part of the Agents’ World, Paris, 4–7 July, (1998, #1555 in Lecture Notes in Artificial Intelligence, 1999. Heidelberg: Springer-Verlag, 113–131. Bechara, A. (2005) “Decision making, impulse control and loss of willpower to resist drugs: a neurocognitive perspective,” Nature Neuroscience, 8 (11): 1458–1463. Bechara, A. and Damasio, A.R. (2005) “The somatic marker hypothesis: a neural theory of economic decision,” Games and Economic Behavior, 52 (2): 336–372. Bechara, A., Damasio, H., Tranel, D. and Anderson, S.W. (1998) “Dissociation of working memory from decision making within the human prefrontal cortex,” Journal of Neuroscience, 18 (1): 428–437. Bechara, A., Damasio, H. and Damasio, A.R. (2000) “Emotion, decision making and the orbitofrontal cortex,” Cerebral Cortex, 10 (3): 295–307. Beer, J.S., Knight, R.T. and D’Esposito, M. (2006) “Controlling the integration of emotion and cognition: the role of frontal cortex in distinguishing helpful from hurtful emotional information,” Psychology Science, 17 (5): 448–453. Belk, R.W. (1985) “Materialism: trait aspects of living in the material world,” Journal of Consumer Research, 12 (3): 265–280. Belle, N. and Cantarelli, P. (2017) “What causes unethical behavior? A meta-analysis to set an agenda for public administration research,” Public Administration Review, 77 (3): 327–339. Bharadwaj, A., El Sawy, O.A., Pavlou, A. and Venkatraman, N. (2013) “Digital business strategy: toward a next generation of insights,” MIS Quarterly, 37: 471–482. Blaug, M. (1998) “Disturbing currents in modern economics,” Challenge, 41 (3): 11–34.

Understanding value conflict  207 Bogaert, S., Boone, C. and Declerck, C. (2008) “Social value orientation and cooperation in social dilemmas: a review and conceptual model,” British Journal of Social Psychology, 47 (3): 453–480. Boisot, M. and McKelvey, B. (2010) “Integrating modernist and postmodernist perspectives on organizations: a complexity science bridge,” Academy of Management Review, 35 (3): 415–433. Boone, C., Declerck, C. and Kiyonari, T. (2010) “Inducing cooperative behavior among proselfs versus prosocials: the moderating role of incentives and trust,” Journal of Conflict Resolution, 54 (5): 799–824. Brousseau, E. and Penard, T. (2007) “The economics of digital business models: a framework for analyzing the economics of platforms,” Review of Network Economics, 6 (2): 81–114. Brownlee, J. (2007) “Complex adaptive systems,” CIS Technical Report 070302A. Callon, M. (1998) The Laws of the Markets, Oxford: Blackwell. Carroll, A.B. and Bucholtz, A.K. (2012) Business and Society: Ethics, Sustainability and Stakeholder Management (8th ed), Boston: South-Western Cengage Learning. Carter, J.R. and Irons, M.D. (1991) “Are economists different, and if so, why?,” Journal of Economic Perspectives, 5 (2): 171–177. Carter, S. and Smith Pasqualini, M. (2004) “Stronger autonomic response accompanies better learning: a test of Damasio’s somatic marker hypothesis,” Cognition and Emotion, 18 (7): 901–911. Casti, J.L. (1997) Would-be Worlds: How Simulation is Changing the Frontiers of Science, Hoboken, NJ: John Wiley. Choi, Y. (2017) Digital Business and Sustainable Development: Asian Perspectives, London: Routledge. Cipriani, G.P., Lubian, D. and Zago, A. (2009) “Natural born economists?,” Journal of Economic Psychology, 30 (3): 455–468. Cojocaru, L.E., Sarraipa, J., Jardim-Golcalves, R. and Stanescu, A.M. (2014) “Digital business ecosystem framework for the agro-food industry,” in K. Mertins, F. Bénaben, R. Poler and J.P. Bourrières (eds) Enterprise Interoperability VI: Interoperability for Agility, Resilience and Plasticity of Collaborations, Cheltenham: Springer International Publishing, 285–296. Colander, D. (2000) “The death of neoclassical economics,” Journal of the History of Economic Thought, 22 (2): 127–143. Colander, D. (2008) “Complexity and the history of economic thought,” Middlebury College economics discussion paper # 08–04 Colander, D., Holt, R.P.F. and Rosser, J.B. (2004) “The changing face of mainstream economics,” Review of Political Economy, 16 (4): 485–499. Corallo, A., Passiante, G. and Prencipe, A. (eds) (2007) The Digital Business Ecosystem, Cheltenham: Edward Elgar Publishing. Coupey, E. (2016) Digital Business: Concepts and Strategies, New York: Routledge. [1st ed published in 2001.] Critchley, H.D. (2009) “Psychophysiology of neural, cognitive and affective integration: fMRI and autonomic indicants,” International Journal of Psychophysiology, 73 (2): 88–94. Damasio, A.R. (1994) Descartes’ Error: Emotion, Reason, and the Human Brain, New York: Grosset/Putnam. Damasio, A.R. Damasio, H. and Christen, Y. (eds) (2012) Neurobiology of Decision-making, Berlin: Springer-Verlag. Darwin, C. (1859) On the Origin of Species, London: John Murray.

208  Elena Goryunova and Bill McKelvey Davies, P.C.W. and Lineweaver, C.H. (2011) “Cancer tumors as Metazoa 1.0: tapping genes of ancient ancestors,” Physical Biology, 8 (1): 1–7. Davis, J.H., Schoorman, F.D. and Donaldson, L. (1997) “Toward a stewardship theory of management,” Academy of Management Review, 22 (1): 20–47. Dawkins, R. (1976) The Selfish Gene, Oxford: Oxford University Press. Day, G.S., Fein, A.J. and Ruppersberger, G. (2003) “Shakeouts in digital markets: lessons from B2B exchanges,” California Management Review, 45 (2): 131–150. De Cremer, D. and Van Lange, P.A.M. (2001) “Why prosocials exhibit greater cooperation than proselfs: the roles of social responsibility and reciprocity,” European Journal of Personality, 15 (S1): 5–18. Declerck, C.H. and Bogaert, S. (2008) “Social value orientation: related to empathy and the ability to read the mind in the eyes,” Journal of Social Psychology, 148 (6): 711–726. deCosta, F. (2013) Rethinking the Internet of Things, New York: Apress. Dennett, D.C. (2006) “From typo to thinko: when evolution graduated to semantic norms,” in S.C. Levinson and P. Jaisson (eds) Evolution and Culture, Cambridge, MA: Bradford/ MIT Press, 133–145. Dent, E.B. (1999) “Complexity science: a worldview shift,” Emergence, 1 (4): 5–19. Donaldson, L. (1990) The ethereal hand: organizational economics and management theory,” Academy of Management Review, 15 (3): 369–381. Donaldson, L. (1998) “The myth of paradigm incommensurability in management studies: comments by an integrationist,” Organization, 5 (2): 267–272. Dourmas, G., Nikitakos, N. and Lambrou, M. (2005) “The concept of digital business ecosystems applied to the shipping industry,” in Proceedings of the International Association of Maritime Economists’ 2005 Conference, 22–25. Driver, M. (2012) “An interview with Michael Porter: social entrepreneurship and the transformation of capitalism,” Management Learning & Education, 11 (3): 421–431. Durham, W.H. (1991) Coevolution: Genes, Culture, and Human Diversity, Stanford, CA: Stanford University press. El Sawy, O.A. and Pereira, F. (2013) “Chapter 1: Anticipating game changers for ‘enterprise 2020’ in a digitally-intensive world,” in Business Modelling in the Dynamic Digital Space an ecosystem approach, Berlin Heidelberg: Springer, 1–12, https​:/​/ww​​w​.wor​​ldcat​​.org/​​title​​ /busi​​ness-​​model​​ling-​​in​-th​​e​-dyn​​amic-​​digit​​al​-sp​​ace​-a​​n​-eco​​syste​​m​-app​​roach​​/oclc​​/8205​​7​ 1499​​&referer​=brief​_results. Emonds, G., Declerck, C.H., Boone, C., Vandervliet, E.J.M. and Parizel, M. (2011) “Comparing the neural basis of decision making in social dilemmas of people with different social value orientations, a fMRI study,” Journal of Neuroscience, Psychology, and Economics, 4 (1): 11–24. Etzioni, A. (2015) “The moral effects of economic teaching,” Sociological Forum, 30 (1): 228–233). Eve, R.A, Horsfall, S. and Lee, M.E. (1997) Chaos, Complexity, and Sociology: Myths, Models, and Theories, London: SAGE. Ferraro, F., Pfeffer, J. and Sutton, R.I. (2005) “Economics language and assumptions: how theories can become self-fulfilling,” Academy of Management Review, 30 (1): 8–24. Fillenbaum, S. (1974) “Information amplified: memory for counterfactual conditionals,” Journal of Experimental Psychology, 102 (1): 44–49. Finucane, M.L., Alhakami, A., Slovic, P. and Johnson, S.M. (2000) “The affect heuristic in judgments of risks and benefits,” Journal of Behavioral Decision Making, 13 (1): 1–17. Flouri, E. (1999) “An integrated model of consumer materialism: can economic socialization and maternal values predict materialistic attitudes in adolescents?,” Journal of SocioEconomics, 28 (6): 707–724.

Understanding value conflict  209 Frank, R.H., Gilovich, T. and Regan, D.T. (1993) “Does studying economics inhibit cooperation?’ Journal of Economic Perspectives, 7 (2): 159–171. Gärling, T. (1999) “Value priorities, social value orientations and cooperation in social dilemmas,” British Journal of Social Psychology, 38 (4): 397–408. Gell-Mann, M. (1994) The Quark and the Jaguar: Adventures in the Simple and the Complex, New York: Freeman. Ghoshal, S. (2005) “Bad management theories are destroying good management practices,” Academy of Management Learning and Education, 4 (1): 75–91. Godfrey, C. and Hatch, N.W. (2007) “Researching corporate social responsibility: an agenda for the 21st century,” Journal of Business Ethics, 70 (1): 87–98 Goryunova, E. and McKelvey, B. (2018) “How modern nuroscience undermines neoclassical economists’ emphasis of self-interest: its relevance to management,” working paper, UCLAAnderson School of Management, Los Angeles, CA. Greene, J. and Haidt, J. (2002) “How (and where) does moral judgment work?,” Trends in Cognitive Sciences, 6 (12): 517–523. Greene, J.D. (2009) “The cognitive neuroscience of moral judgment,” in M.S. Gazzaniga (ed) The Cognitive Neuroscieneces (4th ed), Cambridge, MA: MIT Press, 987–999. Greene, J.D., Nystrom, L.E., Engell, A.D., Darley, J.M. and Cohen, J.D. (2004) “The neural bases of cognitive conflict and control in moral judgment,” Neuron, 44 (2): 389–400. Greene, W.H. (2017) Econometric Analysis (8th ed), New York: Pearson. Greengard, S. (2015) The Internet of Things, Cambridge, MA: MIT Press. Gregorowicz, P. and Hegji, C.E. (1998) “Economics in the MBA curriculum: some preliminary survey results,” Journal of Economic Education, 29 (1): 81–87. Haidt, J. (2001. “The emotional dog and its rational tail: a social intuitionist approach to moral judgment,” Psychological Review, 108 (4): 814–834. Haidt, J., Koller, S. and Dias, M. (1993) “Affect, culture, and morality, or is it wrong to eat your dog?,” Journal of Personality and Social Psychology, 65 (4): 613–628. Hambrick, D.C. (2005) “Just how bad are our theories? A response to Ghoshal,” Academy of Management Learning and Education, 4 (1): 104–107. Hart, S. (2009) Brain, Attachment, Personality: An Introduction to Neuro-affective Development, Kindle DX version. Retrieved from Amazon​.co​m. Hayek, F.A. (1964) “The theory of complex phenomena,” in M. Martin and L.C. McIntyre (eds) Readings in the Philosophy of Social Science (1994), chapter 4. Cambridge, MA: Bradford Book/MIT Press, 55–70. Hayek, F.A. (1975) “The pretense of knowledge,” Swedish Journal of Economics, 77: 433–442. Hegarty, W.H. and Sims, H.P. (1978) “Some determinants of unethical decision behavior: an experiment,” Journal of Applied Psychology, 63 (4): 451–457. Heracleous, L. and Lan, L.L. (2010) “The myth of shareholder capitalism,” Harvard Business Review, 88 (4): 24–24. Herdon, M., Várallyai, L. and Péntek, Á. (2012) “Digital business ecosystem prototyping for SMEs,” Journal of Systems and Information Technology, 14 (4): 286–301. Holland, J. (1995) Hidden Order: How Adaptation Builds Complexity, New York: Basic Books. Hooker, C. (2009) Philosophy of Complex Systems: Handbook of the Philosophy of Science, Vol. 10, Amsterdam: Elsevier. Hühn, M.P. (2014) “You reap what you sow: how MBA programs undermine ethics,” Journal of Business Ethics, 121 (4): 527–541. Irlenbusch, B. and Villeval, M.C. (2015) “Behavioral ethics: how psychology influenced economics and how economics might inform psychology?,” Current Opinion in Psychology, 6: 87–92.

210  Elena Goryunova and Bill McKelvey Jensen, T., Tan, Y.H. and Bjørn-Andersen, N. (2014) “Unleashing the IT potential in the complex digital business ecosystem of international trade: the case of fresh fruit import to European Union,” in the Proceedings the 27th eConference: eEcosystems, Bled, Slovenia, June 1–5, 14–27. Kasser, T. (2002) The High Price of Materialism, Kindle DX version. Retrieved from Amazon​ .co​m. Kasser, T. and Ahuvia, A.C. (2002) “Materialistic values and well-being in business students,” European Journal of Social Psychology, 32 (1): 137–146. Kasser, T. and Ryan, R.M. (1993) “A dark side of the American dream: correlates of financial success as a central life aspiration,” Journal of Personality and Social Psychology, 65 (2): 410–422. Kasser, T. and Ryan, R.M. (1996) “Further examining the American dream: differential correlates of intrinsic and extrinsic goals,” Personality and Social Psychology Bulletin, 22 (2): 280–287. Kauffman, S. (1995) “Technology and evolution: escaping the Red Queen effect,” The McKinsey Quarterly, 1: 118–129. Kellmereit, D. and Obodovski, D. (2013) The Silent Intelligence: The Internet of Things, Warwick, NY: DND Ventures LLC. Kirman, A. (2009) “Economic theory and the crisis,” Real-world Economics Review, 51: 80–83. Kirman, A. (2010) “The economic crisis is a crisis for economic theory,” CESifo Economic Studies, 56 (4): 498–535. Koenigs, M., Young, L., Adolphs, R., Tranel, D., Cushman, F., Hauser, M. and Damasio, A. (2007) “Damage to the prefrontal cortex increases utilitarian moral judgements,” Nature, 446 (7138): 908–911. Kramer, M.R. and Porter, M. (2011) “Creating shared value,” Harvard Business Review, 89: 62–77. Kshetri, N. (2017) “The economics of the internet of things in the global south,” Third World Quarterly, 38 (2): 311–339. Lansing, J.S. (2003) “Complex adaptive systems,” Annual Review of Anthropology, 32 (1): 183–204. Larrick, R.P., Morgan, J.N. and Nisbett, R.E. (1990) “Teaching the use of cost-benefit reasoning in everyday life,” American Psychological Society, 1 (6): 362–370. Laudon, K.C. and Laudon, J.P. (2013) Management Information Systems: Managing the Digital Firm, (13th ed), Upper Saddle River, NJ: Pearson/Prentice Hall. Lewin, R. (1992) Complexity: Life at the Edge of Chaos, Chicago, IL: University of Chicago Press. [2nd ed published in 1999]. Li, W., Badr, Y. and Biennier, F. (2012) “Digital ecosystems: challenges and prospects,” in Proceedings of the International Conference on Management of Emergent Digital EcoSystems, ACM Digital Library, Association for Computing Machinery, 117–122. Li-Ping Tang, T., Chen, Y.J. and Sutarso, T. (2008) “Bad apples in bad (business) barrels: the love of money, Machiavellianism, risk tolerance, and unethical behavior,” Management Decision, 46 (2): 243–263. Liu, G. and Rong, K. (2015) “The nature of the co-evolutionary process: complex product development in the mobile computing industry’s business ecosystem,” Group & Organization Management, July 6, 1059601115593830. Lobo, S. and Whyte, J. (2017) “Aligning and reconciling: building project capabilities for digital delivery,” Research Policy 46 (1): 93–107.

Understanding value conflict  211 Lockett, A., Moon, J. and Vesser, W. (2006) “Corporate social responsibility in management research: focus, nature, salience and sources of influence,” Journal of Management Studies, 43 (1): 115–136. Long, D.M., Wann, C. and Brockman, C. (2016) “Unethical business behavior and stock performance,” Academy of Accounting and Financial Studies Journal, 20 (3): 115–122. Louth, J. (2011) “From Newton to Newtonianism: reductionism and the development of the social sciences,” Emergence: Complexity & Organization, 13 (4): 63–83. MacKenzie, D. and Millo, Y. (2003) “Constructing a market, performing theory: the historical sociology of a financial derivatives exchange,” American Journal of Sociology, 109 (1): 107–145. Maguire, S., McKelvey, B., Mirabeau, L. and Ôztas, N. (2006) “Complexity science and organization studies,” in S.R. Clegg, C. Hardy, T. Lawrence and W.R. Nord (eds) The Sage Handbook of Organization Studies, London: SAGE Publications, 165–214. Malecki, E.J. and Moriset, B. (2008) The Digital Economy: Business Organization, Production Processes and Regional Developments, New York: Routledge. Manner, M.H. (2010) “The impact of CEO characteristics on corporate social performance,” Journal of Business Ethics, 93 (1): 53–72. Marwell, G. and Ames, R.E. (1981) “Economists free ride, does anyone else?,” Journal of Public Economics, 15 (3): 295–310. McEwen, A. and Cassimally, H. (2013) Designing the Internet of Things, Chichester: Wiley. McKelvey, B. (2004) “Complexity science as order-creation science: new theory, new method,” Emergence: Complexity & Organization, 6 (4): 2–27. McKelvey, B. (2009) “Murray Gell-Mann: celebrating connectivity dynamics among agents in organizations—from deep simplicity to requisite fractality,” in Olivier Germain (ed) Les Grands Inspirateurs de la Theorie des Organisations, Caen, Fr: Editions Management et Societe, (EMS). McKelvey, B. (2018) Management in the Age of Digital Business & Complexity, London: Routledge. McQuivey, J. (2013) Digital Disruption: Unleashing the Next Wave of Innovation, Las Vegas: Amazon Publishing. Miller, D. and Xu, X. (2016) “A fleeting glory: self-serving behavior among celebrated MBA CEOs,” Journal of Management Inquiry, 25 (3): 286–300. Miller, D.T. (1999) “The norm of self-interest,” American Psychologist, 54 (12): 1053–1060. Miller, E.K. and Cohen, J.D. (2001) “An integrative theory of prefrontal cortex function,” Annual Review of Neuroscience, 24 (1): 167–202. Miller, M. (2015) The Internet of Things, 800 E. 96th Street, Indianapolis, IN: QUE. Mishel, L. and Davis, A. (2015) “Top CEOs make 300 times more than typical workers,” Issue Brief #399, Economic Policy Institute, Washington, DC. Moon, J. (2002) “Business social responsibility and new governance,” Government and Opposition, 37 (3): 385–408. Muntaner-Perich, E. and de la Rosa Esteva, J.L. (2007) “Using dynamic electronic institutions to enable digital business ecosystems,” in J. Vázquez-Salceda, P. Noriega, G. Boella, O. Boissier, V. Dignum, N. Fornara and E. Matson (eds) Coordination, Organizations, Institutions, and Norms in Agent Systems II, Berlin: Springer, 259–273. Nachira, F., Dini, P., Nicolai, A., Le Louarn, M. and Rivera Lèon, L. (2007) Digital Business Ecosystems: The Results and the Perspectives of the Digital Business Ecosystem Research and Development Activities in FP6, Luxembourg: Office for Official Publications of the European Community.

212  Elena Goryunova and Bill McKelvey Nathan, M. and Rosso, A. (2015) “Mapping digital businesses with big data: some early findings from the UK,” Research Policy, 44 (9): 1714–1733. Nisbett, R.E., Peng, K., Choi, I. and Norenzayan, A. (2001) “Culture and systems of thought: holistic versus analytic cognition,” Psychological Review, 108 (2): 291–310. O’Reilly, R.C. (2010) “The what and how of prefrontal cortical organization,” Trends in Neurosciences, 33 (8): 355–361. Offer, A. (2012) “A warrant for pain,” Real-world Economics Review 61: 85–147. Ormerod, P. (1997) The Death of Economics, Hoboken, NJ: John Wiley. Pappas, N., Kazasis, F.G., Anestis, G., Gioldasis, N. and Christodoulakis, S. (2007) “A knowledge management platform for supporting digital business ecosystems based on P2P and SOA technologies,” in Digital EcoSystems and Technologies Conference Proceedings, DEST’07, Inaugural IEEE-IES, 196–202. Pearl, J. (2000) Causality: Models, Reasoning, and Inference, New York: Cambridge University Press. Pfeffer, J. (1993) “Barriers to the advance of organization science: paradigm development as a dependent variable,” Academy of Management Review, 18 (4): 599–620. Ponce-Jara, M.A., Ruiz, E., Gil, R., Sancristóbal, E., Pérez-Molina, C. and Castro, M. (2017) “Smart grid: assessment of the past and present in developed and developing countries,” Energy Strategy Reviews, 18: 38–52. Popper, K. (1959) The Logic of Scientific Discovery, Kindle DX version. Retrieved from Amazon​.co​m. Porter, M. and Kramer, M. (2011) “Creating shared value: how to reinvent capitalism – and unleash a wave of innovation and growth,” Harvard Business Review, January-February, 1–17. Prigogine, I. and Stengers, I. (1984) Order Out of Chaos: Man’s New Dialogue With Nature, London: Fontana Paperbacks. Raine, A. (2008) “From genes to brain to antisocial behavior,” Current Directions in Psychological Science, 17 (5): 323–328. Ratner, R.K. and Miller, D.T. (2001) “The norm of self-interest and its effects on social action,” Journal of Personality and Social Psychology, 81 (1): 5–16. Razavi, A.R., Moschoyiannis, S.K. and Krause, P.J. (2007) “A coordination model for distributed transactions in digital business ecosystems,” in Digital EcoSystems and Technologies Conference Proceedings. DEST’07. Inaugural IEEE-IES, 159–164. Read, D.W. (1990) “The utility of mathematical constructs in building archaeological theory,” in A. Voorrips (ed) Mathematics and Information Science in Archaeology: A Flexible Framework, Vol. 3, Helos: Bonn, 29–60. Reimann, M. and Bechara, A. (2010) “The somatic marker framework as a neurological theory of decision-making: review, conceptual comparisons, and future neuroeconomics research,” Journal of Economic Psychology, 31 (5): 767–776. Remane, G., Hanelt, A., Nickerson, R.C. and Kolbe, L.M. (2017) “Discovering digital business models in traditional industries,” Journal of Business Strategy, 38 (2): 41–51. Richins, M.L. and Dawson, S. (1992) “A consumer values orientation for materialism and its measurement: scale development and validation,” Journal of Consumer Research, 19 (3): 303–316. Rilling, J.K., Gutman, D.A., Zeh, T.R., Pagnoni, G., Berns, G.S. and Kilts, C.D. (2002) “A neural basis for social cooperation,” Neuron, 35 (2): 395–405. Rokeach, M. (1980) Beliefs, Attitudes and Values: A Theory of Organization and Change, San Francisco: Jossey-Bass Publishers.

Understanding value conflict  213 Rokeach, M. (2008) Understanding Human Values: Individual and Societal, Kindle DX version. Retrieved from Amazon​.co​m. Rong, K. and Shi, Y. (2014) Business Ecosystems: Constructs, Configurations, and the Nurturing Process, London: Palgrave Macmillan. Rose, J.M. (2007) “Corporate directors and social responsibility: ethics versus shareholder value,” Journal of Business Ethics, 73 (3): 319–331. Ryan, L. and Dziurawiec, S. (2001) “Materialism and its relationship to life satisfaction,” Social Indicators Research, 55 (2): 185–197. Sabine, G.H. (1912) “Descriptive and normative sciences,” The Philosophical Review, 21 (4): 433–450. Schallmo, D., Williams, C.A. and Boardman, L. (2017) “Digital transformation of business models—best practice, enablers, and roadmap,” International Journal of Innovation Management, 21 (08): 1740014-1–17. Schallmo, D.R. and Williams, C.A. (2018) “Roadmap for the digital transformation of business models,” in Digital Transformation Now!: Guiding the Successful Digitalization of Your Business Model, Cham: Springer, 41–68. Scherer, A.G. and Steinmann, H. (1999) “Some remarks on the problem of incommensurability in organization studies,” Organization Studies, 20 (3): 519–544. Schmidt, E. and Cohen, J. (2013) The New Digital Age: Transforming Nations, Businesses, and our Lives, New York: Vintage Books. Schore, A.N. (2001) “Effects of a secure attachment relationship on right brain development, affect regulation, and infant mental health,” Infant Mental Health Journal, 22 (1–2): 7–66. Schwartz, B. (1997) “Psychology, idea technology, and ideology,” Psychological Science, 8 (1): 21–27. Scott, S.V., Reenen, J.V. and Zachariadis, M. (2017) “The long-term effect of digital innovation on bank performance: an empirical study of SWIFT adoption in financial services,” Research Policy 46 (4): 984–1004. Seigneur, J.M. (2005) “Demonstration of security through collaboration in the digital business ecosystem,” in Workshop of the 1st International IEEE Conference on Security and Privacy for Emerging Areas in Communication Networks, 108–109. Seo, D. (2017) “Digital business convergence and emerging contested fields: a conceptual framework,” Journal of the Association for Information Systems, 18 (10): 687–702. Shaub, M.K., Collins, F, Holzmann, O. and Lowensohn, S.H. (2005) “Self-interest vs. concern for others,” Strategic Finance, 86 (9): 40–45. Shepard, J. and McKelvey, B. (2009) “An empirical investigation of organizational memetic variation,” Journal of Bioeconomics, 11 (2): 135–164. Shiv, B., Loewenstein, G., Bechara, A., Damasio, H. and Damasio, A.R. (2005) “Investment behavior and the negative side of emotion,” Psychological Science, 16 (6): 435–439. Simon, H. (1962) “The architecture of complexity,” Proceedings of the American Philosophical Society, 106 (1965): 467–482. [Republished in 2005 in Emergence: Complexity & Organization, 7: 138–154] Stanley, J. and Briscoe, G. (2010) “The ABC of digital business ecosystems,” arXiv preprint arXiv:1005.1899. Stapp, H.P. (2009) Mind, Matter and Quantum Mechanics, Berlin Heidelberg: Springer-Verlag. Stiglitz, J.E. (2012) The Price of Inequality: How Today’s Divided Society Endangers our Future, Kindle DX version. Retrieved from Amazon​.co​m. Stuss, D.T. and Alexander, M.P. (2000) “Executive functions and the frontal lobes: a conceptual view,” Psychological Research, 63 (3–4): 289–298.

214  Elena Goryunova and Bill McKelvey Swaminathan, A. and Meffert, J. (2017) Digital @ Scale: How you can Lead Your Business to the Future With Digital@scale, Hoboken, NJ: Wiley. Tafti, S.F., Kordnaeij, A., Hoseini, S.H.K. and Jamali, M. (2015) “Business ecosystem as a new approach in strategy,” Management and Administrative Sciences Review, 4 (1): 198–205. Tan, B., Pan, S.L., Lu, X. and Huang, L. (2009) “Leveraging digital business ecosystems for enterprise agility: the tri-logic development strategy of Alibaba. Com,” 30th ICIS Proceedings. Paper 171. Tan, Y.L. and Macaulay, L.A. (2011) “Factors affecting regional SMEs progression to digital business ecosystems,” presented at the 17th Americas Conference on Information Systems (AMCIS). Tang, T. and Chen, Y.-J. (2008) “Intelligence vs. Wisdom: the love of money, Machiavellianism, and unethical behavior across college major and gender,” Journal of Business Ethics, 82 (1): 1–26. Tapscott, D. (2015) The Digital Economy, New York: McGraw-Hill Education. [1st ed. published in 1995.] Utting, P. and Marques, J.C. (2010) “Introduction: the intellectual crisis of CSR,” in P. Utting and J.C. Marques, (eds) Corporate Social Responsibility and Regulatory Governance, New York: Palgrave Macmillan and United Nations Research Institute for Social Development, 1–25. Vansteenkiste, M., Duriez, B., Simons, J. and Soenens, B. (2006) “Materialistic values and well-being among business students: further evidence of their detrimental effect,” Journal of Applied Social Psychology, 36, 12: 2892–2908. Verdejo-García, A. and Bechara, A. (2009) “A somatic marker theory of addiction,” Neuropharmacology, 56 (1): 48–62. Verdejo-García, A., Bechara, A., Recknor, E.C. and Perez-Garcia, M. (2006) “Executive dysfunction in substance dependent individuals during drug use and abstinence: an examination of the behavioral, cognitive and emotional correlates of addiction,” Journal of the International Neuropsychological Society, 12 (3): 405–415. Verdejo-García, A., López-Torrecillas, F., Calandre, E.P., Delgado-Rodríguez, A. and Bechara, A. (2009) “Executive function and decision-making in women with fibromyalgia,” Archives of Clinical Neuropsychology, 24 (1): 113–122. Vianello, M., Galliani, E.M. and Haidt, J. (2010) “Elevation at work: the effects of leaders’ moral excellence,” The Journal of Positive Psychology, 5 (5): 390–411. Vohs, K.D. and Schooler, J.W. (2008) “The value of believing in free will encouraging a belief in determinism increases cheating,” Psychology Science, 19 (1): 49–54. Waldrop, M.M. (1992) Complexity: The Emerging Science at the Edge of Order and Chaos, New York: Simon & Schuster. Weill, P. and Woerner, S.L. (2015) “Thriving in an increasingly digital ecosystem,” MIT Sloan Management Review, 56 (4): 27–34. Weill, P. and Woerner, S.L. (2018) “Is your company ready for a digital future?,” MIT Sloan Management Review, 59 (2): 21–25. Wicks, A.C. and Freeman, R.E. (1998) “Organization studies and the new pragmatism: positivism, anti-positivism, and the search for ethics,” Organization Science, 9 (2): 123–140. Wisman, J.D. (1979) “Toward humanistic reconstruction of economic science,” Journal of Economic Issues, 13 (1): 19–48. Woodard, C.J., Ramasubbu, N., Tschang, F.T. and Sambamurthy, V. (2013) “Design capital and design moves: the logic of digital business strategy,” MIS Quarterly, 37 (2): 537–564.

Understanding value conflict  215 Zhong, C.B., Ku, G., Lount, R.B. and Murnighan, J.K. (2006) “Group context, social identity, and ethical decision-making: a preliminary test,” in Tenbrunsel, A.E. (Ed.) Ethics in Groups (Research on Managing Groups and Teams, Vol. 8), Emerald Group Publishing Limited, Bingley, pp. 149–175. https://doi​.org​/10​.1016​/S1534​-0856(06)08008-X. Ziyae, B., Sajadi, S. and Mobaraki, M.H. (2014) The deployment and internationalization speed of e-business in the digital entrepreneurship era. Journal of Global Entrepreneurship Research, 4 (1): 1–15.

7

A digital perspective about how complexity science pushes firms into the stochastic frontier María Paz Salmador and Bill McKelvey

Introduction Since the beginning of the Digital Age before 1995 (Tapscott 1995, 2015), firms involved in “digital business,” e.g. Apple, Google, Amazon, and Facebook, have relatively quickly moved out toward the end of their industry’s stochastic frontier (SF) (defined later), as indicated by our Appendix 1 showing data from the S&P Stock Market Index dated March 31st, 2018. In this chapter we use these data to show how quickly modern firms, using Digital Age technology, have begun to dominate their industry, i.e., by moving out to the top end of their industry-sector’s SF. In the Information Technology Sector of the S&P, Apple, Google (Alphabet), Microsoft, and Facebook are now far out on the SF; Amazon now has the highest market-capitalization in the Consumer Discretionary Sector. The difference between Walmart vs. Apple and Google is that Walmart follows the conventional definition of a SF: a focus on efficiency (Kumbhakar and Knox Lovell 2000; Hollingsworth and Street 2006), whereas Apple and Google achieved their SF positions because of their use of digital skills and technology. Amazon has moved to the top of the Consumer Discretionary Sector (as shown in our Appendix 1) because of its modern digital skills and online shopping. Since emergent behavior is a function of tiny initiating events (Holland 1988, 1995), networking (Albert and Barabási 2002; Barabási 2002, 2005; Caldarelli 2007) and other complexity-science recognized scale-free causes (Newman 2005; Andriani and McKelvey 2009), emergent industry rank/frequencies should appear as the long-tailed, Pareto and/or power-law (PL)1 distributions studied by econophysicists (Mantegna and Stanley 2000; Chakrabarti et al. 2006; Sinha et al. 2011). In this chapter we also argue (as in various previous chapters) that, in the Digital Age, complexity dynamics are vastly speeded up. Moving out into toward the SF in the modern Digital Age is a function of how well the digitalcomplexity dynamics of both the firm and its industry are operating. We draw on aspects of econophysics to create a 21st-century perspective about how best to analyse and describe the path firms take, if and when they move out toward their industry’s SF frontier, as noted above. Nowadays, this is often more 

Complexity science and the stochastic frontier  217

because of their digital skills than traditional production and sales—as currently indicated by Microsoft, Apple, Google, Amazon, and Facebook. Using a sample of “digital content” companies that we found on the Web, our data analysis shows that 10 out of 11 of these companies that are included in the S&P Stock Market Index have market capitalizations that get them out into the SF. We begin with a short review of the SF literature. This is followed by a review of key elements of complexity theory, focusing especially on digitalcaused high-speed complexity dynamics. Next we discuss rank/frequency distributions, emergent fractals, and PL distributions, which can result from scale-free digital causes. PLs are defined as indicators of effective—i.e. digital— performance in modern industries in the Digital Age. The data we use show that digital expertise is most likely to cause companies to move out toward the end of an SF distribution. Conclusions follow.

The stochastic frontier: a brief review Firms in a given industry, as comparable economic agents, can be assumed to operate according to a common technology. The SF for such firms is defined as the maximum technically feasible output given inputs (Aigner et al. 1977; Battese and Coelli 1995; Lee 2014; Lensink and Meesters 2014; Oh and Hildreth 2014; Power and Cacho 2014; Ravishankar and Stack 2014; Anaya and Pollitt 2017; Na et al. 2017). Accordingly, firms can be thought of as: (1) operating either at or moving toward the SF; or (2) showing market capitalization below the SF, which therefore, indicates their inefficiency (Aigner et al. 1977; Kumbhakar and Knox Lovell 2000; Mutz et al. 2017; Simar et al. 2017; Titus et al. 2017; Kwietniewski and Schreyögg 2018). Over time, output growth can be defined with respect to three different components: (1) efficiency change, meaning that a firm can potentially become less efficient and needs to “catch up” and, ideally, move out into the SF; (2) technical change, implying that the SF itself can shift over time, implying technical progress—Digital Age effects have caused many change and improvements in companies; and (3) input change, indicating that a firm can move toward the SF by changing its inputs (Koop et al. 1999). These definitions provide a framework for addressing a number of questions, including issues about which firms are making the most efficient use of their inputs, and whether an industry’s growth is driven by changes in technology or input changes. Changes because of digital whatever, are changes in technology, not changes in inputs. The identification of reliable and scientifically valid efficiency-measurement strategies is what is typically focused on for growth into the SF (Hollingsworth and Street 2006). Most of the research related to SF analyses focuses on measures of tangible elements in firms (Coelli et al. 2005; Adamu et al. 2017). However, other researchers use measures of intellectual capital (IC) elements of firms to test what causes firms to move out into the SF (e.g. Hollingsworth and Wildman 2002; Gravelle et al. 2003, 2004; Mutz et al. 2017; Parshakov

218  María Paz Salmador and Bill McKelvey

2017). Nowadays, of course, Digital Age effects are dominating because they can also improve efficiencies in firms. PL-distributed networks

As shown in Chapter 1, Digital Age complexity dynamics indicate how digital effects speed up various complexity dynamics. Why are social and organizational phenomena PL distributed? As agents self-organize, there is some probability that their interactions will lead to one or more networks—especially in the Digital Age. True, in some instances networks may never appear. Usually, however, when self-organization occurs, one or more networks among agents will emerge. For example in a company, self-organization could lead to just one network, but more likely there could be separate networks of employees within production, marketing, finance and accounting, personnel, and management groupings. Research indicates that such networks are often fractal: collaboration networks (Newman 2001); moviegoer networks (De Vany 2003); interfirm relationships (Saito et al. 2007); degrees of connectivities (Santiago and Benito 2008); organizational networks (Dodds et al. 2003; Watts, 2003); director networks (Battiston and Catanzaro 2004); social networks (Csányi and Szendrői 2004; Hamilton et al. 2007); shocks (Andersend and Sornette 2004); alliance networks (Gay and Dousset 2005); network dynamics (Powell et al. 2005); company networks (Souma et al. 2006; Chmiel et al. 2007); online networks (Mislove et al. 2007); interfirm relationships (Saito, Watanabe, and Iwamura 2007); degrees of connectivities (Santiago and Benito 2008); and investment networks (Song, Jiang, and Zhou 2009); financial commentaries in newspapers (Gerow and Keane 2011); financial markets (Nobi et al. 2013); communication ability (Zhai et al. 2013); stock-based and economic sector networks (Hu et al. 2013); buyer-supplier networks (Mizuno et al. 2014); social networks (Pentland 2015); finance and social networks (Fracassi 2016); and advice networks (Horng et al. 2017), just to mention some. In short, employees, when put in self-organization situations, often create social networks that many studies (some of which are mentioned above) indicate result in PL distributed links among the people involved. The various connections and integrated tacit knowledge builds up the number and quality of networks, which increases the chance that a company may move out into a SF. The foregoing is greatly enhanced in the Digital Age and vastly speeded up via digital complexity dynamics. Adaptive R/F distributions

While we have briefly defined rank/frequency (R/F) and PL distributions in endnote #1, we now offer more detailed descriptions of R/F distributions (i.e. fractals) and PLs. They are distributions resulting if firms have small-to-large movements out into the SF.

Complexity science and the stochastic frontier  219 Fractals and PLs

Fractals refer to the feature of a total structure in which, despite the increasingly small size, each lower-level component performs the same function and has the same design as the structure above and below it in size. Fractals can result from mathematical formulas—as shown in Mandelbrot’s “Fractal Geometry” (1983); but they also occur in real biological and human lives. In biological fractal structures—like cauliflowers—the same adaptation dynamics appear at multiple levels. And McKelvey et al. (2012) cite 19 studies showing adaptation-based predator/prey fractal dynamics. Among companies, Zanini (2008) shows that the same effects hold for merger and acquisition activities in business niches. Fractal structures are often indicated by PLs. A well-formed Pareto R/F distribution plotted by using double-log scales appears as a PL—an inverse sloping line. We illustrate the difference in Figure 7.1. PLs often take the form of rank/

Figure 7.1 Pareto and power-law distributions compared.

220  María Paz Salmador and Bill McKelvey

size expressions such as F ~ N –α, where F is frequency, N is rank (the variable) and α, the exponent, is constant. The now famous PL “signature” dates back to Auerbach (1913) and Zipf (1929, 1949). Early on, PLs were presumed to always result in straight distribution lines, but more recent research shows that the “lines” are not always straight (West and Deering 1995; Clementi and Gallegati 2004; Perline 2005; Newman 2005; Clauset et al. 2009). Andriani and McKelvey (2007, 2009) list ~140 kinds of PLs in physical, biological, social, and organizational phenomena. When measured using unconstrained continuous and discrete scales over time, it is likely that all inputs and outcomes of human and corporate activity are PL-distributed (Crawford and Kreiser 2015). Which is consistent with similar PL distributions occurring, for example, in world economies (Buldyrev et al. 2003); among all firms in the United States (Axtell 2001); industry dynamics (Zanini 2008); and among entrepreneurial firms in many industries (Crawford et al. 2015, 2018). In the modern Digital Age, fractal structures can occur simply because companies have more digital skills than their competitors, as is obvious in the Information Technology and Consumer Discretionary Sectors—shown in Appendix 1—because digital-oriented companies like Amazon, Google, Microsoft, Facebook, and other digital-oriented firms are out at the end of the SFs, which results from PL distributions. Although Amazon was not yet at the top of a sector in the S&P of November, 2014, now, in 2018, it is at the top of the Consumer Discretionary Sector and its CEO, Jeff Bezos, is the richest billionaire in the world— as indicated by the 2018 Fortune Magazine ranking—worth $130 billion as of April 2018. Why? Amazon has grown mainly because it sells products (made by other companies) online, i.e. by electronic word-of-mouth (Brynjolfsson et al. 2003, 2006; Hennig-Thurau et al. 2004; Jansen et al. 2009; Khammash and Griffiths 2011; Lee et al. 2011; Arenas Márquez et al. 2014; Ghosh et al. 2017; Higgins 2017; Huo and Palmer 2017): i.e., its selling is digital and the machines it uses in its warehouses (instead of people) also depend on modern digital technology. Rank/frequency (R/F) distributions

Since PLs mostly appear to be the result of self-organization, they often, if not always, signify active self-organization processes at work maintaining some kind of complexity dynamics. Thus, Ishikawa (2006) shows PLs in adaptive and changing industries (as opposed to static ones). Podobnik et al. (2006) show PLs in the stock markets of transition economies. The Dow Jones market capitalizations of the 30 largest U.S. publicly traded firms show a PL—again, evidence of fractals when traders are free to buy and sell as they wish (Glaser 2013).2 Iansiti and Levien (2004) show that the software industry is the most resilient across the 2002 dotcom crash. As compared to the machinery and chemical industries, Zanini (2008) shows the software industry to be much more Pareto distributed. Self-organization within a species helps the species adapt to predators and prey. The animals within a species are normally distributed in size and weight and capabilities—as are people. But the size of different species—e.g. from flies (millions of

Complexity science and the stochastic frontier  221

very small insects) to elephants (a few thousands of very large animals)—are R/F distributed. Companies also don’t follow the same size rule. Companies in the same industry are also not normally distributed but, rather, can be R/F-distributed. In industries (and even within organizations) the outcomes of self-organization and emergent new order often produce R/Fs. They date back to Pareto (1897). We present a stylized depiction of a R/F distribution in Figure 7.2—one extreme outcome (firm) out at the end of the X-axis and thousands to millions of companies (e.g. in the retail industry) up at the top of the Y-axis. Following Holland (2002), we recognize that emergent R/F distributed phenomena in multi-level hierarchies, in intra- and inter-level causal processes appear nonlinear. These nonlinearities incorporate two key ideas: butterfly events and scalability. Tiny (usually random) butterfly-events3 and scalability produce nonlinearities that may extend across multiple levels within organizations or across multiple firms within a business ecosystem. Extreme outcomes, longtailed Pareto distributions, and PLs (Zipf 1929, 1949; Newman 2005): scalability (Brock 2000): and scale-free causes (Zipf 1949; West and Deering 1995; Andriani and McKelvey 2007, 2009) often result. From a conceptual perspective, complexity science scholars use the concepts of phase transition, threshold, bifurcation point, and critical value to identify the point where a system changes from an additive, linear state into a multiplicative, nonlinear state, i.e. this state is qualitatively different than the previous state (Prigogine 1955; West and Deering 1995; Dooley and Van de Ven 1999; Clementi and Gallegati 2004; McKelvey 2004a,b; Perline 2005; Newman 2005; Clauset et al. 2009). We elucidate the location of this point, as shown by the dashed vertical line in Figure 7.2. Firms beyond (to the right of) this point are operating in an interdependent, highly scalable, nonlinear environment, and have the potential to influence outcomes at a higher level. Firms below this point have a much more constrained ability to cause nonlinear outcomes within their

Figure 7.2 Stylized rank/frequency distribution.

222  María Paz Salmador and Bill McKelvey

existing environment. This point—later labeled the xmin point in this paper—is the beginning of the SF distribution in an industry, or in an S&P sector. As traditionally portrayed, the Gaussian region of a distribution represents normal outcomes. The Paretian region of a distribution represents nonlinear outcomes, i.e. extreme events. Thus, the xmin is the point beyond which the nonlinearity (i.e. non-Gaussian distribution) of events can be assumed. Within the SF, the environment is poised at a critical state, where the actions of one firm can cascade across and between multiple levels.

Method Our method for producing relevant data and creating the PL and SF distributions is described in more detail in Crawford et al. (2015). While our main interest is still about what existing firms do so as to produce the kind of success that gets them out into the SF, our focus in this chapter is to find out whether—in the Digital Age—there is any evidence that shows that firms now doing digital business are more likely to move out into the SF? Our interest is in the SF—which contains the largest and most successful firms in an industry. Google, Apple and now Amazon are at the top of their S&P Sector’s SF region. We have found a list of the top 100 “digital content” business companies, which of course includes Apple, Amazon and Google. In this chapter we test the following hypothesis: H1: Does a focus on “digital content” help a company move out into the SF? Data collection

Our data come from a list of “The Top 100 Companies in the Digital Content Industry” (Cramer 2016) and the S&P500 Stock Market Index.4 We basically test whether a focus on digital content gets a company’s market capitalization out into the SF of one of the ten sectors that comprise the S&P Index. For our test in this chapter, we use the most recent reported month-end S&P 500 Index data from the stock market’s close on March 31st, 2018.5 We then subdivide the Index according to its ten sectors. In our data analysis, we test whether a focus on digital content helps a company move out into the SF. Data analysis

In the empirical research that Crawford et al. wrote about in their 2015 paper (Crawford et al. 2015), they used MATLAB (2018a) software and the plfit.m and parplpva2.m scripts6, following the protocol and techniques for calculating PL-model fit, as described by Clauset et al. (2009). We follow this approach in our current analysis. The first script estimates the parameters for the scaling exponent of a PL probability-density function, p(x) ~ x–α, which calculates the

Complexity science and the stochastic frontier  223

maximum likelihood estimation (MLE). The scaling parameter α represents the overall dynamics of the distribution: the closer the number to 1.0, the heavier the tail, and the greater proportion of the total distribution is in the tail (i.e. greater proportion of extreme scores). Thus, a distribution with α of 1.6 has a greater proportion of extreme scores than a distribution with α of 2.6. The formula for calculating α of discrete data is shown as (1): where xi, i = 1…n are the observed values of x such that xi ≥ xmin. é aˆ = 1 + n ê ê êë

n

å i =1

ln

x min

-1

ù ú (1) 1 - ú 2 úû

xi

Results Our results pertaining to all of the market sectors in the S&P 500 Index are shown in Appendix 1, which contains a complete list of the S&P 500 firms, categorized by market sector and ranked by market capitalization. This Table also identifies the firms currently out in the SF, i.e. those having a market-capitalization value above the xmin point—which is indicated by a line in Appendix 1. Next, we identify the results of our hypothesis test. General findings

In Appendix 1, readers can see that Amazon has moved to the top of the SF distribution in the Consumer Discretionary Sector. Of the eleven companies included in the list of the “The Top 100 Companies in the Digital Content Industry” nine of the ten remaining firms (i.e. not including Amazon) are in the SF region of the Information Technology Sector. And only one of the eleven “Top 100 companies” shown in the S&P Index does not appear in its Sector’s SF. Although the S&P Index only includes 11 of the Top 100 digital firms, it is important to note that 10 of these 11 are out in their Sector’s SF—10 out of 11 firms. Indeed, we would have preferred to see more of the Top 100 “digital content” companies listed in the S&P Index, but the fact that 10 of the 11 listed (i.e. 91%) are out in the SF portion of the S&P’s market-capitalizations offers recent useful empirical evidence that “digital business” is improving companies’ ability to increase their market capitalization value in the Digital Age. This finding suggests that the market sectors within the S&P are good indicators of value based on well-functioning industries, i.e. (1) little if any collusion among powerful or large firms—as used to be the case before the 1990s (Porter 1980); (2) a definitely free market exists in which firms can pursue M&A activities without government control; and (3) it makes the S&P Index as a whole a much less valid measure of the modern (Digital Age) well-functioning industry component of the US economy.

224  María Paz Salmador and Bill McKelvey

The message is clear: if companies (1) wish to quickly increase their value and/or compete successfully against their competitors; or (2) learn via data—or information-hacking—into their competitors’ internal communications about new products they (the competitors) plan to bring to market, i.e. before the products actually go on the market,they need to increase their digital business skills and/or abilities.

Conclusion Our empirical findings and complexity-based recognition of all the dynamics that are speeded up because of imposed digital effects can guide future research to discover how these digital effects are created and how better to manage them. In addition, our complexity-science perspective can direct top-level executives and managers to leverage these distributions and push their firms out into the stochastic frontier (SF). This perspective can also guide the search for the factors that are constraining firms in the S&P 500. The Digital Age’s effects on complexity dynamics suggest that the “simple rules,” from both top-down and bottom-up origins, that drive agent actions and interactions, can take effect much more rapidly, broadly and profoundly now. The relative novelty of digital-based knowledge (whether, for example, to create a new product, a new firm, or a new industry, or hacking a competitor’s digital communications so as to better compete against an aggressive competitor) needs to become part of the past, such that all effective modern managers now learn how to take advantage of it. From the top-down, future research could collect mission and/or vision statements from S&P 500 firms’ annual reports (or from those listed in NASDAQ and perform digital-content analyses to identify the relative digital capabilities and skills of each firm compared against its industry competitors. The results could then be analysed to show—or not show—whether digital capabilities of modern firms increase their market capitalization and thereby move them out into the SF. Complexity science suggests that “simple rules” from both top-down and bottom-up origins drive agent actions and interactions—these rules are an agent’s schemata, a mental framework where expectations of future outcomes drive behavior (Drazin and Sandelands 1992; Anderson 1999). In the Digital Age, more and more of these “rules” are digital by design and in effect. The relative novelty of the expectations (whether, for example, to create a new product, a new firm, or a new industry) drive entrepreneurial activity and firm growth (Crawford and Kreiser 2015)—these digital expectations could be measured within each firm. From the top-down, future research could collect mission and/or vision statements from S&P 500 and/or NASDAQ firms’ annual reports and perform content analyses to identify the digital ability of company compared to its industry competitors; the results could then be correlated with market capitalization data. From the bottom-up, future research could collect data on digitally-oriented innovative activities within each firm, using various digitally-oriented measures, and then assess how these activities

Complexity science and the stochastic frontier  225

influence macro-level outcomes like market capitalization ranking within each market sector over time. Managers now need to recognize the fact that digital abilities got 10 of the 11 firms we got market-capitalization data about out to the top of their S&P Sector’s rankings.

Acknowledgement We thank Christopher Crawford for his help with our data analysis, which was supported by the National Science Foundation’s Science of Organizations Program, grant #1734567, titled “Modeling the Emergence of Outliers in Entrepreneurship.”

Appendix 1:  Standard & Poor 500 Index Firm Market Capitalization on 03/31/2018 by Sector (with the SF thresholds marked with a line) Consumer Discretionary Sector Amazon​.c​om Inc. Home Depot Comcast Corp. The Walt Disney Company McDonald’s Corp. Nike Booking Holdings Inc Charter Communications Starbucks Corp. Time Warner Inc. Lowe’s Cos. 21st Century Fox Class A & B TJX Companies Inc. General Motors Marriott Int’l Carnival Corp. Ford Motor Target Corp. V.F. Corp. Ross Stores Yum! Brands Inc. Dollar General Royal Caribbean Cruises Ltd Hilton Worldwide Holdings Inc. Stanley Black & Decker Dollar Tree Aptiv Plc Best Buy Co. Inc. O’Reilly Automotive

Mkt Cap ($B) 664.190 199.305 155.684 148.353 125.308 104.319 98.067 80.100 79.051 73.930 70.189 66.573 50.938 50.083 47.136 46.155 43.366 37.166 29.007 28.955 27.845 25.109 24.850 24.422 23.158 22.405 21.619 20.147 19.666

226  María Paz Salmador and Bill McKelvey Wynn Resorts Ltd CBS Corp. MGM Resorts International Lennar Corp. Dish Network Mohawk Industries AutoZone Inc. Omnicom Group Expedia Inc. Discovery Inc. Class A & C D. R. Horton Tapestry, Inc. Genuine Parts Viacom Inc. Ulta Beauty Newell Brands Gap Inc. Tiffany & Co. Norwegian Cruise Line PVH Corp. LKQ Corporation Wyndham Worldwide Garmin Ltd Carmax Inc. Whirlpool Corp. Kohl’s Corp. Hasbro Inc. Darden Restaurants L Brands Inc. BorgWarner Michael Kors Holdings News Corp. Class A & B Macy’s Inc. Polo Ralph Lauren Corp. Interpublic Group Chipotle Mexican Grill

19.631 19.410 19.198 18.054 17.364 16.940 16.494 16.411 16.214 15.963 15.905 14.666 12.731 12.699 12.389 11.999 11.972 11.802 11.638 11.620 11.502 11.078 10.926 10.836 10.636 10.545 10.473 10.385 10.334 10.293 9.426 9.028 8.861 8.783 8.716 8.661

Advance Auto Parts Snap-On Inc. Pulte Homes Inc. Nordstrom Tractor Supply Company Harley-Davidson Under Armour Class A & C Hanesbrands Inc. Goodyear Tire & Rubber Leggett & Platt TripAdvisor Foot Locker Inc. Mattel Inc.

8.280 8.253 8.176 7.975 7.481 7.059 6.619 6.551 6.281 5.702 5.437 5.176 4.495

Complexity science and the stochastic frontier  227 Consumer Staples Sector Wal-Mart Stores Procter & Gamble Coca-Cola Company (The) PepsiCo Inc. Philip Morris International Altria Group Inc. Costco Wholesale Corp. Kraft Heinz Co. Walgreens Boots Alliance CVS Health Colgate-Palmolive Mondelez International Estee Lauder Cos. Constellation Brands Kimberly-Clark Monster Beverage Sysco Corp. Tyson Foods General Mills Brown-Forman Corp. Archer-Daniels-Midland Co. Kellogg Co. Dr Pepper Snapple Group Kroger Co. The Hershey Company Hormel Foods Corp. The Clorox Company Molson Coors Brewing Company Conagra Brands McCormick & Co. JM Smucker Coty, Inc. Campbell Soup Church & Dwight

Mkt Cap ($B) 252.432 195.125 181.966 151.952 151.623 114.996 80.194 73.202 62.574 61.927 61.271 60.382 54.310 44.460 37.488 31.503 30.459 27.416 26.141 25.412 23.624 21.877 21.228 20.518 20.371 17.734 16.653 15.836 14.380 13.630 13.606 13.258 12.702 11.945

Energy Sector Exxon Mobil Corp. Chevron Corp. Schlumberger Ltd ConocoPhillips EOG Resources Occidental Petroleum Phillips 66 Halliburton Co. Valero Energy Marathon Petroleum Kinder Morgan Baker Hughes, a GE Company Anadarko Petroleum Corp.

Mkt Cap ($B) 310.267 214.407 87.981 68.031 59.559 49.516 44.278 40.325 39.455 34.061 32.870 31.280 29.977

228  María Paz Salmador and Bill McKelvey Pioneer Natural Resources

28.425

ONEOK Concho Resources Williams Cos. Devon Energy Corp. Andeavor Hess Corporation Apache Corporation Noble Energy Inc. National Oilwell Varco Inc. TechnipFMC Marathon Oil Corp. EQT Corporation Cabot Oil & Gas Cimarex Energy Helmerich & Payne Newfield Exploration Co. Range Resources Corp.

23.053 22.386 20.294 15.967 15.377 15.280 14.457 14.328 13.701 13.325 13.231 12.176 10.675 8.571 7.032 4.710 3.415

Financials Sector Berkshire Hathaway JPMorgan Chase & Co. Bank of America Corp. Wells Fargo Citigroup Inc. Goldman Sachs Group Morgan Stanley BlackRock US Bancorp American Express Co. PNC Financial Services Charles Schwab Corporation Chubb Limited CME Group Inc. The Bank of New York Mellon Corp. American International Group, Inc. MetLife Inc. S&P Global, Inc. Capital One Financial Prudential Financial Intercontinental Exchange Marsh & McLennan BB&T Corporation The Travelers Companies Inc. State Street Corp. Progressive Corp. Aon plc AFLAC Inc. Allstate Corp.

Mkt Cap ($) 481.842 367.813 299.666 250.387 173.360 97.365 94.466 84.237 82.013 78.273 69.946 67.962 61.863 53.702 50.528 47.971 46.670 46.538 45.599 42.589 41.307 41.158 39.591 36.599 35.765 34.832 34.020 33.489 33.089

Complexity science and the stochastic frontier  229 SunTrust Banks Moody’s Corp.

30.945 30.321

M&T Bank Corp. T. Rowe Price Group Synchrony Financial Discover Financial Services Northern Trust Corp. Fifth Third Bancorp Ameriprise Financial Regions Financial Corp. Citizens Financial Group KeyCorp Willis Towers Watson Franklin Resources Hartford Financial Svc. Gp. Principal Financial Group Comerica Inc. Loews Corp. Huntington Bancshares Lincoln National E*Trade Nasdaq, Inc. XL Capital Cboe Global Markets Invesco Ltd Raymond James Financial Inc. SVB Financial Arthur J. Gallagher & Co. Cincinnati Financial Everest Re Group Ltd. Unum Group Zions Bancorp Affiliated Managers Group Inc. Torchmark Corp. Leucadia National Corp. People’s United Financial Brighthouse Financial Inc. Block H&R Assurant Inc. Navient

26.715 25.602 24.829 24.600 22.876 21.262 20.843 20.389 20.045 20.023 19.699 18.454 18.013 17.211 16.198 15.784 15.747 15.490 14.405 14.246 14.196 12.677 12.670 12.571 12.346 12.215 11.875 10.390 10.324 10.095 9.833 9.359 7.873 6.382 5.980 5.140 4.633 3.342

Health Care Sector Johnson & Johnson United Health Group Inc. Pfizer Inc. AbbVie Inc. Merck & Co. Amgen Inc. Medtronic plc

Mkt Cap ($B) 334.150 210.176 208.648 145.118 143.626 119.649 105.014

230  María Paz Salmador and Bill McKelvey Abbott Laboratories Bristol-Myers Squibb Gilead Sciences Lilly (Eli) & Co. Thermo Fisher Scientific Danaher Corp. Celgene Corp. Stryker Corp. Becton Dickinson Biogen Inc. Anthem Inc. Allergan, Plc Aetna Inc. Intuitive Surgical Inc. CIGNA Corp. Vertex Pharmaceuticals Inc. Zoetis Humana Inc. Express Scripts Boston Scientific Regeneron Baxter International Inc. Illumina Inc. HCA Holdings McKesson Corp. Edwards Lifesciences Alexion Pharmaceuticals Zimmer Biomet Holdings Agilent Technologies Inc. Mylan N.V. IQVIA Holdings Inc. Align Technology Cardinal Health Inc. Cerner Centene Corporation AmerisourceBergen Corp Incyte Laboratory Corp. of America Holding IDEXX Laboratories Nektar Therapeutics Waters Corporation Mettler Toledo ResMed Quest Diagnostics DaVita Inc. Perrigo Dentsply Sirona Universal Health Services, Inc.

101.654 101.372 95.364 82.717 81.618 67.120 65.492 58.299 56.790 56.464 56.292 56.102 54.962 45.325 40.049 39.686 39.396 38.761 38.111 37.026 35.600 34.113 33.800 33.573 28.848 28.515 24.157 21.615 20.777 20.354 19.931 19.448 19.200 18.991 18.595 18.160 17.063 16.354 16.247 15.894 15.344 14.320 13.692 13.281 11.639 11.418 11.148 10.973

Complexity science and the stochastic frontier  231 Varian Medical Systems The Cooper Companies Henry Schein Hologic PerkinElmer Envision Healthcare Industrials Sector Boeing Company 3M Company General Electric Honeywell Int'l Inc. Union Pacific United Technologies Lockheed Martin Corp. United Parcel Service Caterpillar Inc. General Dynamics FedEx Corporation Raytheon Co. Northrop Grumman Corp. Illinois Tool Works Deere & Co. CSX Corp. Emerson Electric Company Norfolk Southern Corp. Delta Air Lines Inc. Waste Management Inc. Eaton Corporation Southwest Airlines Johnson Controls International Roper Technologies Fortive Corp. Cummins Inc. American Airlines Group PACCAR Inc. Parker-Hannifin Rockwell Collins Republic Services Inc. Rockwell Automation Inc. Ingersoll-Rand PLC United Continental Holdings IHS Markit Ltd Cintas Corporation AMETEK Inc. Verisk Analytics L-3 Communications Holdings TransDigm Group Grainger (W.W.) Inc.

10.871 10.867 10.158 10.128 8.186 4.589 Mkt Cap ($B) 189.345 126.447 113.928 106.592 103.230 98.795 95.179 89.466 85.992 65.243 63.731 61.155 60.253 51.853 49.157 48.567 42.077 37.631 36.724 36.138 34.036 31.949 31.423 28.234 26.256 26.203 23.553 22.678 22.163 22.006 21.704 21.592 20.778 19.009 19.000 17.865 17.287 16.764 16.244 15.715 15.508

232  María Paz Salmador and Bill McKelvey Fastenal Co. Textron Inc. Dover Corp. United Rentals, Inc. Equifax Inc. Xylem Inc. C. H. Robinson Worldwide J. B. Hunt Transport Services Masco Corp. Pentair Ltd Huntington Ingalls Industries Kansas City Southern Nielsen Holdings Expeditors International Arconic Inc. A.O. Smith Corp. Fortune Brands Home & Security Jacobs Engineering Group Allegion Fluor Corp. Robert Half International Alaska Air Group Inc. Acuity Brands Inc. Flowserve Corporation Quanta Services Inc. Stericycle Inc. Information Technology Sector Apple Inc. Alphabet Inc. Class A & C Microsoft Corp. Facebook Inc. Visa Inc. Intel Corp. Cisco Systems Oracle Corp. Mastercard Inc. International Business Machines Nvidia Corporation Netflix Inc. Adobe Systems Inc. Accenture plc Texas Instruments Broadcom PayPal Salesforce​.c​om QUALCOMM Inc. Micron Technology Applied Materials Inc. Automatic Data Processing

15.306 14.951 14.819 14.021 13.808 13.512 12.945 12.661 12.142 11.995 11.344 11.153 10.995 10.903 10.660 10.568 8.455 8.177 7.923 7.831 7.143 7.126 5.762 5.564 5.171 4.956 Mkt Cap ($B) 845.736 701.779 681.586 451.408 243.947 228.359 197.566 183.745 180.418 138.240 133.735 121.631 104.542 99.151 98.950 93.520 90.012 84.444 79.658 58.058 55.571 50.222

Complexity science and the stochastic frontier  233 Activision Blizzard Cognizant Technology Solutions Intuit Inc. eBay Inc. Electronic Arts HP Inc. TE Connectivity Ltd Analog Devices, Inc. Lam Research Fidelity National Information Svs Fiserv Inc. DXC Technology Autodesk Inc. Western Digital Hewlett Packard Enterprise Amphenol Corp Red Hat Inc. Corning Inc. Paychex Inc. Microchip Technology Harris Corporation Xilinx Inc. Skyworks Solutions Global Payments Inc. Motorola Solutions Inc. Seagate Technology KLA-Tencor Corp. NetApp Symantec Corp. Total System Services

49.379 46.580 43.508 39.835 36.415 34.616 34.035 32.820 31.592 31.366 29.148 28.420 26.800 26.777 26.591 25.676 25.634 22.955 21.673 20.535 18.953 17.592 17.584 17.418 16.900 16.449 16.418 15.850 15.663 15.399

CA, Inc. ANSYS Citrix Systems IPG Photonics Corp. Synopsys Inc. Akamai Technologies Inc Alliance Data Systems Verisign Inc. Take-Two Interactive Gartner Inc. Cadence Design Systems Advanced Micro Devices Inc. Western Union Co. F5 Networks Qorvo Juniper Networks Xerox Corp. FLIR Systems CSRA Inc.

13.633 12.905 12.435 12.158 12.013 11.775 11.536 11.241 10.973 10.542 10.154 9.236 8.719 8.702 8.669 8.274 7.146 6.881 6.757

234  María Paz Salmador and Bill McKelvey Materials Sector DowDuPont Monsanto Co. LyondellBasell Praxair Inc. Ecolab Inc. Sherwin-Williams Air Products & Chemicals Inc. PPG Industries Freeport-McMoRan Inc. International Paper Newmont Mining Corporation Nucor Corp. WestRock Company Vulcan Materials Eastman Chemical Ball Corp. Martin Marietta Materials Intl Flavors & Fragrances Packaging Corporation of America FMC Corporation Albemarle Corp. Avery Dennison Corp. The Mosaic Company CF Industries Holdings Inc. Sealed Air Real Estate Sector American Tower Corp A Simon Property Group Inc. Crown Castle International Corp. Public Storage Prologis Equinix Weyerhaeuser Corp. Equity Residential AvalonBay Communities Inc. Digital Realty Trust Inc. Welltower Inc. SBA Communications General Growth Properties Inc. Boston Properties Ventas Inc. CBRE Group Essex Property Trust, Inc. Realty Income Corporation Host Hotels & Resorts Vornado Realty Trust

Mkt Cap ($B) 144.302 51.397 40.919 40.802 38.630 35.935 34.275 26.992 24.816 21.114 20.959 19.029 15.805 14.855 14.660 13.633 12.647 10.582 10.372 10.085 9.850 9.151 9.123 8.546 6.996 Mkt Cap ($B) 63.408 47.467 45.116 34.714 32.515 32.369 26.169 22.473 22.463 22.070 20.075 19.704 19.533 18.746 17.598 15.784 15.652 14.551 13.427 12.691

Complexity science and the stochastic frontier  235 Alexandria Real Estate Equities Inc. HCP Inc. Extra Space Storage Mid-America Apartments Regency Centers Corporation UDR Inc. Iron Mountain Incorporated Duke Realty Corp SL Green Realty Federal Realty Investment Trust Macerich Apartment Investment & Mgmt Kimco Realty

12.330 10.886 10.809 10.264 9.802 9.414 9.236 9.189 8.755 8.464 7.802 6.369 5.944

Telecommunication Services Sector AT&T Inc. Verizon Communications CenturyLink Inc.

Mkt Cap ($B) 215.568 194.534 17.428

Utilities Sector NextEra Energy Duke Energy Dominion Energy Southern Co. Exelon Corp. American Electric Power Sempra Energy Public Serv. Enterprise Inc. Consolidated Edison Xcel Energy Inc. PG&E Corp. Edison Int’l Wec Energy Group Inc. PPL Corp. DTE Energy Co. Eversource Energy

Mkt Cap ($B) 76.443 54.017 45.115 44.571 36.920 33.614 29.064 25.173 23.922 22.838 22.354 20.559 19.655 19.457 18.629 18.592

FirstEnergy Corp. American Water Works Company Inc. Entergy Corp. Ameren Corp. CMS Energy CenterPoint Energy NRG Energy Alliant Energy Corp. Pinnacle West Capital NiSource Inc. AES Corp. SCANA Corp.

16.146 14.457 14.318 13.755 12.714 11.641 9.638 9.437 8.836 8.047 7.510 5.426

236  María Paz Salmador and Bill McKelvey

Notes 1 If one plots a well-formed Pareto rank/frequency (R/F) distribution with both X- and Y-axes as log scales, a negatively sloped line will appear; this is the inverse PL signature. PLs usually (if not always) take the form of R/F expressions such as F ~ N –β, where F is frequency, N is rank (the variable) and β, the exponent, is constant. In exponential functions the exponent is the variable and N is constant. 2 For example, correlations between PL distribution and straight-lines range from 0.9477 to 0.9896 for various Dow Jones stock-market prices in 1960 and 1980. Firms include AT&T, GM, IBM, Standard Oil, Du Pont, and GE. For a more recent analysis, see Crawford et al. (2018). 3 The label “butterfly event” comes from a famous paper presented by Edward Lorenz (1972). 4 We found this list of 100 ‘digital content’ companies on the Internet. See: http:​/​/www​​ .econ​​tentm​​ag​.co​​m​/Art​​icles​​/Edit​​orial​​/Feat​​ure​/T​​he​-To​​p​-100​​-Comp​​anies​​-in​-t​​he​-Di​​ gital​​-Cont​​ent​-I​​ndust​​r y​-Th​​e​-201​​6​​-201​​7​-ECo​​ntent​​-100-​​11415​​6​.htm​ (accessed April 14, 2018).   The list also includes short descriptions of the digital “category” the companies do business in. 5 http:​/​/us.​​spind​​ices.​​com​/i​​ndice​​s​/equ​​i​ty​/s​​p​-500​ (accessed April 14, 2018). 6 www​.santafe​.edu/​~aaronc​/powerlaws/ (downloaded December 20, 2012).

References Adamu, M., Soon, J.J. and Ahmad, S.A. (2017) “Factors affecting efficiency of higher education institutions: a theoretical and empirical review,” Social Sciences, 12 (7): 1235–1241. Aigner, D.J., Lovell, C.A.K. and Schmidt, P. (1977) “Formulation and estimation of stochastic frontier production function models,” Journal of Econometrics, 6 (1): 21–37. Albert, R. and Barabási, A.-L. (2002) “Statistical mechanics of complex networks,” Reviews of Modern Physics, 74 (1): 47–97. Anaya, K.L. and Pollitt, M.G. (2017) “Using stochastic frontier analysis to measure the impact of weather on the efficiency of electricity distribution businesses in developing economies,” European Journal of Operational Research, 263 (3): 1078–1094. Anderson, P. (1999) “Complexity theory and organization science,” Organization Science, 10 (3): 216–232. Andersen, J. and Sornette, D. (2004) “Fearless versus fearful speculative financial bubbles,” Physica A: Statistical Mechanics and Its Applications, 337 (3–4): 565–585. Andriani, P. and McKelvey, B. (2007) “Beyond Gaussian averages: extending organization science to extreme events and power laws,” Journal of International Business Studies, 38 (7): 1212–1230. Andriani, P. and McKelvey, B. (2009) “From Gaussian to Paretian thinking: causes and implications of power laws in organizations,” Organization Science, 20 (6): 1053–1071. Arenas Márquez, F.J., Martínez-Torres, M.R. and Toral, S.L. (2014) “Electronic word-ofmouth communities from the perspective of social network analysis,” Technology Analysis & Strategic Management, 26 (8): 927–942. Auerbach, F. (1913) “Das Gesetz Der Bevölkerungskoncentration,” Petermanns Geographische Mitteilungen, 59: 74–76. Axtell, R.L. (2001) “Zipf distribution of U.S. firm sizes,” Science, 293 (5536): 1818–1820. Barabási, A.-L. (2002) Linked: The New Science of Networks, Cambridge, MA: Perseus.

Complexity science and the stochastic frontier  237 Barabási, A.-L. (2005) “The origin of bursts and heavy tails in human dynamics,” Nature, 435 (7039): 207–211. Battese, G.E. and Coelli, T.J. (1995) “A model for technical inefficiency effects in a stochastic frontier production function for panel data,” Empirical Economics, 20 (2): 325–332. Battiston, S., and Catanzaro, M. (2004) “Statistical properties of corporate board and director networks,” European Physical Journal B 38: 345–352. Brock, W.A. (2000) “Some Santa Fe scenery,” in D. Colander (ed) The Complexity Vision and the Teaching of Economics, Cheltenham: Edward Elgar, 29–49. Brynjolfsson Erik, H.Y.J. and Smith, M.D. (2006) “From niches to riches: the anatomy of the long tail,” MIT Sloan Management Review, 47: 67–71. Brynjolfsson, E., Hu, Y. and Smith, M.D. (2003) “Consumer surplus in the digital economy: estimating the value of increased product variety at online booksellers,” Management Science, 49 (11): 1580–1596. Buldyrev, S,V., Dokholyan, N.V., Erramilli, S., Hong, M., Kim, J.Y., Malescio, G. and Stanley, H.E. (2003) “Hierarchy in social organization,” Physica A, 330 (3–4): 653–659. Caldarelli, G. (2007) Scale-Free Networks: Complex Webs in Nature and Technology, Oxford: Oxford University Press. Chakrabarti, B.K., Chakraborti, A. and Chatterjee, A. (eds) (2006) Econophysics and Sociophysics: Trends and Perspectives, Weinheim: WILEY-VCH. Chmiel, A., Sienkiewicz, J., Suchecki, K. and Hołyst, J. (2006). Networks of companies and branches in Poland. Physica A: Statistical Mechanics and Its Applications 383: 134–138. Clauset, A., Shalizi, C.R. and Newman, M.E.J. (2009) “Power-law distributions in empirical data,” SIAM Review, 51 (4): 661–703. Clementi, F. and Gallegati, M. (2005) “Power law tails in the Italian personal income distribution,” Physica A: Statistical Mechanics and Its Applications, 350, 10.1016/j. physa.2004.11.038. Coelli, T.J., Rao, D.S.P., O’Donnell, C.J. and Battese, G.E. (2005) An Introduction to Efficiency and Productivity Analysis, New York: Springer. Cramer, T. (ed) (2016) “The top 100 companies in the digital content industry,” http:​/​ /www​​.econ​​tentm​​ag​.co​​m​/Art​​icles​​/Edit​​orial​​/Feat​​ure​/T​​he​-To​​p​-100​​-Comp​​anies​​-in​-t​​he​ -Di​​gital​​-Cont​​ent​-I​​ndust​​ry​-Th​​e​-201​​6​​-201​​7​-ECo​​ntent​​-100-​​11415​​6​.htm​ Crawford, G.C. and Kreiser, P. (2015) “Corporate entrepreneurship strategy: extending the integrative framework through the lens of complexity science,” Small Business Economics, 45 (2): 403–423. Crawford, G.C., Aguinis, H., Lichtenstein, B., Davidsson, P. and McKelvey, B. (2015) “Power law distributions in entrepreneurship: implications for theory and research,” Journal of Business Venturing, 30 (5): 696–713. Crawford, G.C., Salmador Sanchez, M.P. and McKelvey, B. (2018) “Competitive market dynamics: using complexity science and self-organized criticality to push firms to the stochastic frontier,” working paper, Rutgers University, Newark, NJ. Csányi, G, and Szendroi, B. (2004) “Fractal-small-world dichotomy in real-world networks,” Physical Review E, Statistical, Nonlinear, and Soft Matter Physics, 70. De Vany, A. (2003) Hollywood Economics How Extreme Uncertainty Shapes the Film Industry, Routledge. Dodds, P.S., Muhamad, R., and Watts, D.J. (2003) “An experimental study of search in global social networks,” Science. Aug 8, 301 (5634): 827–829.

238  María Paz Salmador and Bill McKelvey Dooley, K.J. and Van de Ven, A.H. (1999) “Explaining complex organizational dynamics,” Organization Science, 10 (3): 358–372. Drazin, R. and Sandelands, L. (1992) “Autogenesis: a perspective on the process of organizing,” Organization Science, 3 (2): 230–249. Dunbar, R., Arnaboldi, V., Conti, M., and Passarella, A. (2015) “The structure of online social networks mirrors those in the offline world,” Social Networks, 43: 39–47. Fracassi, C. (2016) “Corporate finance policies and social networks,” Management Science, 63 (8): 2420–2438. Gay, B. and Dousset, B. (2005) “Innovation and network structural dynamics: Study of the alliance network of a major sector of the biotechnology industry,” Research Policy, 34 (10): 1457–1475. Gerow, A. and Keane, M.T. (2011) “Mining the web for the ‘voice of the herd’ to track stock market bubbles,” in Proceedings of the 22nd International Joint Conference on Artificial Intelligence, Barcelona, Spain. Ghosh, A., Pfister, J.P. and Cook, M. (2017) “Optimised information gathering in smartphone users,” arXiv preprint arXiv:1701.02796. Glaser, P. (2013) “Inequality in an increasing returns world: applying the tools of complexity economics to study the changing distribution of us stock market capitalizations from 1930 to 2008,” in B. McKelvey (ed) Complexity: Critical Concepts, Vol. 5, London: Routledge, 410–423. Gravelle, H., Jacobs, R., Jones, A. and Street, A. (2003) “Comparing the efficiency of national health systems: a sensitivity analysis of the WHO approach,” Applied Health Economics and Health Policy, 2 (3): 141–148. Gravelle, H., Jacobs, R., Jones, A. and Street, A. (2004) “Distinguishing between heterogeneity and inefficiency: stochastic frontier analysis of the world health organization’s panel data on national health care systems,” Health Economics, 13 (10): 959–980. Haken, H. (1983) Synergetics, An Introduction (3rd ed), Berlin: Springer-Verlag. Hennig-Thurau, T., Gwinner, K.P., Walsh, G. and Gremler, D.D. (2004) “Electronic word-of-mouth via consumer-opinion platforms: what motivates consumers to articulate themselves on the Internet?,” Journal of Interactive Marketing, 18 (1): 38–52. Higgins, D.M. (2017) “Residential property market performance and extreme risk measures,” Pacific Rim Property Research Journal, 23 (1): 1–13. Holland, J.H. (1988) “The global economy as an adaptive system,” in The Economy as an Evolving Complex System, Vol. 5, P.W. Anderson, K.J. Arrow and D. Pines (eds) Reading, MA: Addison-Wesley, 117–124. Holland, J.H. (1995) Hidden Order: How Adaptation Builds Complexity, Reading, MA: Addison-Wesley. Holland, J.H. (2002) “Complex adaptive systems and spontaneous emergence,” in Complexity and Industrial Clusters, A.Q. Curzio and M. Fortis (eds) Heidelberg: PhysicaVerlag, 25–34. Hollingsworth, B. and Street, A. (2006) “The market for efficiency analysis of health care organisations,” Health Economics, 15 (10): 1055–1059. Hollingsworth, B. and Wildman, J. (2002) “The efficiency of health production: re-estimating the who panel data using parametric and on-parametric approaches to provide additional information,” Health Economics, 12 (6): 1–11. Horng, J.J., Liao, K.Y. and Wang, S.Y. (2017) “Using social network theory to analyze the effectiveness of environment, safety and health management for two petrochemical companies,” Process Safety Progress, 36 (2): 121–132.

Complexity science and the stochastic frontier  239 Hu, S., Yang, H., Cai, B. and Yang, C. (2013) “Research on spatial economic structure for different economic sectors from a perspective of a complex network,” Physica A, 392 (17): 3682–3697. Huo, Q. and Palmer, A. (2017) “Structural influences on online network seeding targets,” in Patricia Rossi (Ed.), Marketing at the Confluence between Entertainment and Analytics, Cham: Springer, 1337–1353. Iansiti, M. and Levien, R. (2004) “Strategy as ecology,” Harvard Business Review, 82 (3): 68–81. Ishikawa, A. (2006) “Pareto index induced from the scale of companies,” Physica A, 363 (2): 367–376. Jansen, B.J., Zhang, M., Sobel, K. and Chowdury, A. (2009) “Twitter power: tweets as electronic word of mouth,” Journal of the American Society for Information Science and Technology, 60 (11): 2169–2188. Khammash, M. and Griffiths, G.H. (2011) “Arrivederci CIAO​.co​m, Buongiorno Bing. com’—Electronic word-of-mouth (eWOM): antecedences and consequences,” International Journal of Information Management, 31 (1): 82–87. Koop, G., Osiewalski, J. and Steel, M. (1999) “The components of output growth: a stochastic frontier analysis,” Oxford Bulletin of Economics and Statistics, 6 (1): 455–487. Kumbhakar, S.C. and Knox Lovell, C.A. (2000) Stochastic Frontier Analysis, New York: Cambridge University Press. Kwietniewski, L. and Schreyögg, J. (2018) “Profit efficiency of physician practices: a stochastic frontier approach using panel data,” Health Care Management Science, 21 (1): 76–86. Lee, B.L. (2014) “Efficiency and productivity of Singapore’s manufacturing sector 2001– 2010: an analysis using bootstrapped truncated approach,” The Singapore Economic Review, 59 (05): 1450039-1-17. Lee, J., Lee, J.-N. and Shin, H. (2011) “The long tail or the short tail: the category-specific impact of eWOM on sales distribution,” Decision Support Systems, 51 (3): 466–479. Lensink, R. and Meesters, A. (2014) “Institutions and bank performance: a stochastic frontier analysis,” Oxford Bulletin of Economics and Statistics, 76 (1): 67–92. Lorenz, E. (1972) “Predictability: does the flap of a butterfly’s wings in Brazil set off a tornado in Texas?” Paper presented at the 1972 meeting of the American Association for the Advancement of Science, Washington, DC. Malecki, E.J. and Moriset, B. (2008) The Digital Economy: Business Organization, Production Processes and Regional Developments, New York: Routledge. Mandelbrot, B.B. (1983) The Fractal Geometry of Nature (2nd ed), New York: Freeman. Mantegna, R.N. and Stanley, H.E. (2000) An Introduction to Econophysics: Correlations and Complexity in Finance, Cambridge: Cambridge University Press. McKelvey, B. (2004a) “Complexity science as order-creation science: new theory, new method,” Emergence: Complexity and Organization, 6 (4): 2–27. McKelvey, B., Lichtenstein, B.B. and Andriani, P. (2012) “When systems and ecosystems collide: toward a law of requisite fractality in firms,” International Journal of Complexity In Leadership & Management, 2 (1): 104–136. Mislove, Alan, Marcon, Massimiliano, Gummadi, Krishna P., Druschel, Peter and Bhattacharjee, Bobby (2007) “Measurement and analysis of online social networks,” Proceedings of the ACM SIGCOMM Internet Measurement Conference, IMC. 29–42. Mizuno, T., Souma, W. and Watanabe, T. (2014) “The structure and evolution of buyersupplier networks,” Working paper, Faculty of Economics, University of Tokyo.

240  María Paz Salmador and Bill McKelvey Mutz, R., Bornmann, L. and Daniel, H.D. (2017) “Are there any frontiers of research performance? Efficiency measurement of funded research projects with the Bayesian stochastic frontier analysis for count data,” Journal of Informetrics, 11 (3): 613–628. Na, H.S., Hwang, J., Hong, J.Y.J. and Lee, D. (2017) “Efficiency comparison of digital content providers with different pricing strategies,” Telematics and Informatics, 34 (2): 657–663. Newman, M.E.J. (2001) “The structure of scientific collaboration networks,” Proceedings of the National Academy of Sciences of the United States of America, 98 (2): 404–409. Newman, M.E.J. (2005) “Power laws, Pareto distributions and Zipf’s law,” Contemporary Physics, 46 (5): 323–351. Nobi, A., Maeng, S., Ha, G. and Lee, J. (2013) “Network topologies of financial market during the global financial crisis,” arXiv preprint arXiv: 1307.6974. Oh, S.-C. and Hildreth, A.J. (2014) “Estimating the technical improvement of energy efficiency in the automotive industry—stochastic and deterministic frontier benchmarking approaches,” Energies, 9 (7): 6196–6222. Pareto, V. (1897) Cours d’Economie Politique, Paris: Rouge & Cie. Parshakov, P. (2017) “Observing unobservable: estimating the time-varying efficiency of intellectual capital,” Measuring Business Excellence, 21 (4): 377–387. Pentland, A. (2015) Social Physics: How Social Networks Can Make us Smarter, London: Penguin. Perline, R. (2005) “Strong, weak and false inverse power laws,” Statistical Science, 20 (1): 68–88. Podobnik, B., Fu, D., Jagric, T., Grosse, I. and Stanley, H.E. (2006) “Fractionally integrated process for transition economics,” Physica A, 362 (2): 465–470. Porter, M.E. (1980) Competitive Strategy: Techniques for Analyzing Industries and Competitors, New York: Free Press. Powell, W.W., Koput, K.W., White, D.R., and Owen-Smith, J. (2005) “Network dynamics and field evolution: The growth of interorganizational collaboration in the life sciences,” American Journal of Sociology, 110 (4): 1132–1205. Power, B. and Cacho, O.J. (2014) “Identifying risk-efficient strategies using stochastic frontier analysis and simulation: an application to irrigated cropping in Australia,” Agricultural Systems, 125 (C): 23–32. Prigogine, I. (1955) An Introduction to Thermodynamics of Irreversible Processes, Springfield, IL: Thomas. Ravishankar, G. and Stack, M.M. (2014) “The gravity model and trade efficiency: a stochastic frontier analysis of eastern European countries' potential trade,” World Economy, 37 (5): 690–704. Saito, Y., Watanabe, T. and Iwamura, M. (2007) “Do larger firms have more interfirm relationships?,” Physica A, 383 (1): 158–163. Santiago, A. and Benito, R. (2008) “Connectivity degrees in the threshold preferential attachment model,” Physica A, 387 (10): 2365–2376. Simar, L., Van Keilegom, I. and Zelenyuk, V. (2017) “Nonparametric least squares methods for stochastic frontier models,” Journal of Productivity Analysis, 47 (3): 189–204. Sinha, S., Chatterjee, A., Chakraborti, A. and Chakrabarti, B.K. (2011) Econophysics: An Introduction, Weinheim: WILEY-VCH. Souma, W., Fujiwara, Y. and Aoyama, H. (2003), “Complex networks and economics,” Physica A, 324 (1/2): 396–401. Song, D.M., Jiang, Z.Q. and Zhou, W.X. (2009) “Statistical properties of world investment networks,” Physica A: Statistical Mechanics and Its Applications 388(12): 2450–2460.

Complexity science and the stochastic frontier  241 Swaminathan, A. and Meffert, J. (2017) Digital @ Scale: How You Can Lead Your Business to the Future, Hoboken, NJ: Wiley. Tapscott, D. (1995) The Digital Economy, New York: McGraw-Hill Education. Tapscott, D. (2015) The Digital Economy: Rethinking Promise and Peril in the Age of Networks Intelligence (20th Anniversary Edition), New York: McGraw-Hill Education. Titus, M.A., Vamosiu, A. and McClure, K.R. (2017) “Are public master’s institutions cost efficient? a stochastic frontier and spatial analysis,” Research in Higher Education, 58 (5): 469–496. Watts, D.J. (2003) Six Degrees: The Science of a Connected Age. New York: Norton. West, B.J. and Deering, B. (1995) The Lure of Modern Science: Fractal Thinking, Singapore: World Scientific. Zanini, M. (2008) “Using power curves to assess industry dynamics,” McKinsey Quarterly, 1: 1–6. Zhai, Li, Yan, Xiangbin, and Zhang, Guojing (2013) “A centrality measure for communication ability in weighted network,” Physica A Statistical Mechanics and Its Applications. 392: 6107–6117 Zipf, G.K. (1929) “Relative frequency as a determinant of phonetic change,” Harvard Studies in Classical Philology, 40: 1–95. Zipf, G.K. (1949) Human Behavior and the Principle of Least Effort, New York: Hafner.

8

Using Crowd wisdom via crowdsourcing Proof of concept of an efficient digital strategy in the age of digital business complexity Nadine Escoffier and Bill McKelvey

Introduction New product development is a hit-or-miss bet. Many newly launched products suffer from high failure rates because they simply have no market (Ogawa and Piller 2006). Even one of the most innovative companies nowadays, such as Google (Murray 2015), counts at least 17 of its new products that bombed, died, or disappeared (Hartmans 2016). Ultimately, the consumer decides a product’s fate. An added force behind this fact is that Web 2.0 allows users to interact and collaborate online at digital speed. Indeed, after the first stage of the World Wide Web (retroactively called Web 1.0), came the second stage of the World Wide Web, i.e. Web 2.0. DiNucci explains the transition in his article called “Fragmented Future” (DiNucci 1999: 32). The Web we know now, which loads into a browser window in essentially static screenfuls, is only an embryo of the Web to come. The first glimmerings of Web 2.0 are beginning to appear, and we are just starting to see how that embryo might develop. The Web will be understood not as screenfuls of text and graphics but as a transport mechanism, the ether through which interactivity happens. Indeed, the Web 1.0 (inception 1989/1990) is usually described as just static webpages where the user could simply view and download the content. Inversely, the Web 2.0 is dynamic, allowing interaction and collaboration among users, content providers, and enterprises. Thus, this second stage of the World Wide Web is characterized especially by the change from static web pages to dynamic or user-generated content and the growth of social media. Since then, and not surprisingly, even though managers may have found it difficult to catch the attention of the Generation 2.0, the 2.0 Generation will not hesitate to go viral with negative word-of-web if a new product is not appealing; sometimes they do this even before the product goes to market. Now, since the consumer decides a product’s fate, what if the customer is involved in the development process?



Using Crowd wisdom via crowdsourcing  243

In 1907, Francis Galton was surprised that the crowd at a county fair accurately guessed the weight of an ox when their individual guesses were averaged. In 1976, Eric Von Hippel (Von Hippel 1976) observed that most product innovations do not come out of corporate research and development labs but from the people who use the products. In 1999, the rise of the Web 2.0 allowed users to interact and collaborate online at digital speed. In 2004, Surowiecki presented numerous case studies and anecdotes to illustrate his argument that the Crowd is better than individuals or even experts at solving problems, fostering innovation, coming to wise decisions, even predicting the future. Finally, the term “Crowdsourcing,” coined by Howe (Howe 2006a, Howe 2006b), led to a new wave of literature revolving around this term (e.g., Howe 2008; Leimeister et al. 2009; Brabham 2010; Afuah and Tucci 2012; Estellés-Arolas and González-Ladrón-de-Guevara 2012; Poetz and Schreier 2012; Boudreau and Lakhani 2013; Escoffier and McKelvey 2014; Lüttgens et al. 2014; Escoffier and McKelvey 2015; Faullant et al. 2016; Escoffier et al. 2018). Crowdsourcing is defined as “the act of a company or institution taking a function once performed by employees and outsourcing it to an undefined (and generally large) network of people in the form of an open call” (Howe 2006a). Our chapter focuses on the correctness and relevance of the Wisdom of Crowds (i.e., the results) using Crowdsourcing (i.e., the methodology). Now, more and more companies use “the Wisdom of crowds” (Surowiecki 2004) (i.e., crowd wisdom) via Crowdsourcing (Howe 2006a) to help them evaluate ideas and generate ideas for their new product before risking high investment, market failure, and negative word-of-web. We call it “Crowdwisdom strategy.” Traditional companies such as BMW use this strategy on specific projects. Some startups such as Threadless are organized by using this strategy in a continuous way. According to our analysis, based on numerous real case studies using Crowd wisdom via Crowdsourcing, we construct a Crowd-wisdom strategy business model (i.e., Figure 8.1) as a source of constant market value creation and positive word-of-web. Even though we argue that using Crowd-wisdom strategy seems to be the best option so far to reduce market failure, what is the real value of this strategy? Where is the proof of concept? From our conceptual model, we localize “Crowd wisdom” to a Crowd’s ability to generate ideas and evaluate ideas. We have chosen to study idea evaluation (i.e., studies 1 & 2) and idea generation (i.e., study 3) in one of the most hit-or-miss businesses: the movie industry. We chose the movie industry because a movie is a complex, expensive, and high-risk product (Escoffier and McKelvey 2014, 2015). Our presumption is that if the Crowd can help improve the value of a complex product like a movie, it can surely help improve the value of simpler products. In our first study, where 500 participants evaluated trailers a few weeks before opening weekend, and after correlating these evaluations with opening weekend receipts and also four weeks later, we provide the proof of concept that the Crowd has a high ability to evaluate even a complex new product’s market value (i.e., a movie) before it comes to market even better

244  Nadine Escoffier and Bill McKelvey

Figure 8.1 The Crowd-wisdom strategy’s business model. Note: By “you” we mean the company.

than experts. We note that, in our first study, our self-selected participants independently evaluated trailers (i.e., none of the participants had information about anyone else’s rating), the main reason being that the Crowdwisdom effect has been described as the tendency for the average of the independent estimates of all group members to be more accurate than any group member’s individual estimate (Hall 2011). However, the reality is that we are now all digital-oriented individuals and using the Crowd as a source of information to help us make decisions is part of our everyday life, and the Crowd on review-aggregation websites such as Yelp, Tripadvisor, Amazon, Rotten Tomatoes, etc., is biased by a certain degree of social influence. Moreover, even though in our first study we show that the Crowd is better than experts when it comes to measuring a complex new product’s market value (i.e., a movie), our Crowd was large (500 participants) compared to the number of experts (30).

Using Crowd wisdom via crowdsourcing  245

Therefore, our second study (Escoffier and McKelvey 2015) tests not only the Wisdom of Crowds effect with and without social influence but also the accuracy of a small crowd compared to a small group of experts. The results show that even though the Wisdom of Crowds effect is more accurate without social influence, its accuracy increases over time under conditions of social influence. We also provide proof of concept that the Wisdom of Crowds effect resulting from the independent evaluations of a small crowd is more accurate than the evaluations of a small number of experts. Even though the Crowd is highly accurate when evaluating ideas—even better than experts—what is the Crowd’s ability when it comes to generating ideas? In our third study (Escoffier et al. 2018), we focus on idea generation. By evaluating the market value of a new product (i.e., short movie) before and after Crowdsourcing, we offer proof of concept that Crowd wisdom pertaining to newproduct idea generation improves its market value significantly, both in terms of ratings and comments after just one integration of the Crowd into the new product development process. Why is this important? Nowadays, if a product is not appealing to begin with, spending millions of dollars on ads will not help companies very much. Indeed, more and more consumers base their buying decision process on online reviews which, not surprisingly, has an actual impact on revenues (Luca 2011; Anderson and Han 2016). It is important to note that the improvement of the new product’s market value took place before a large investment was done. More surprisingly, this market value after Crowdsourcing is the same for the Crowd involved in the Crowdsourcing process than for the potential customers of the new product created by using this process. After describing our three experimental studies, we provide a summary of the main results and limitations of our studies, which actually offer clues about opportunities for future research. Our conclusion section ends with a discussion about the limitations of big corporations’ current strategy including the digital ones while facing the reality of the Digital Age. We then offer a real solution with its challenges and our vision of its potential development in a near future.

Study 1: The Crowd has a high ability to evaluate ideas but is not fooled by expensive marketing tools Movie production is an appealing industry for studying the risk of new product development because of its high economic value and high performance risk (Escoffier and McKelvey 2015). Actually, one of the most famous dictums about Hollywood belongs to the screenwriter William Goldman (1983: 39): “Nobody knows anything.” In his book Hollywood Economics, De Vany (2004) supplies reams of data supporting Goldman’s “nobody knows anything” pronouncement and concludes: “Motion pictures are among the most risky of products” (71). Other books and articles about the movie industry— whether from the creative side or the business side —reach the same conclusion: the success of a movie is unpredictable (Lucey 1996; Honthaner 2001; Squire 2004; Furby and Randell 2005; Simonton 2009; Wang, Cai and Huang

246  Nadine Escoffier and Bill McKelvey

2010; Rushton 2011; Simonton 2011; Sinnerbrink 2011; McKenzie 2012; Velikovsky 2012). According to Velikovsky (2012), “7 in 10 films currently lose money” (9). Teti’s (2013) empirical study of risk and return in the movie industry (based on 12 years of box office performance) finds that there is a “random association between costs and rates of return” and that therefore “the success of a new film production is extremely uncertain” (730). This is the reason why studios spend about 50% on average of their production budget to chase the audience. In fact, it seems that the trend among studios is to bet on strong opening weekends supported by mass-media advertising to reduce risk rather than try to make better original movies so as protect against negative word-of-web communications that will hurt sales later on. However, box office watchers say that audiences use Twitter (one of the many latest weapons in the arsenal of smartphones) to critique films quickly, often when they are still sitting in theaters. “If people don’t like the movie now on Friday it can die by Saturday” said Paul Dergarabedian, President of a box office tracking firm, Hollywood​.c​om (Dobuzinskis 2009). This phenomenon is well known as the Twitter effect. Thus, it appears that current studios’ riskmanagement strategy—(1) a similar recipe that worked before is reproduced along with (2) an expensive marketing campaign to sell it and (3) bet on strong opening weekends—is at best a short-term one. In the end, not only does the “sequel strategy” not guarantee a market value and a positive word-of-web but also it increases costs and decreases creativity. Finally, in order to create a sequel, one needs to create an original appealing movie in the first place. Another way to attract the audience is by using new technological trends (i.e., 3D effects, virtual reality, interactive movies). But in the end, what matters most is content. Even if eventually the industry will offer all movies in virtual reality with an interactive experience, audiences will still need attractive content that will make them laugh, cry, and get emotionally immersed in the movie. But how to make sure that a movie will be appealing to audiences in the first place, i.e., before spending millions of dollars, but also before moviegoers start to quickly spread negative word-of-web, and by doing so shatter the chance of the movie’s success before it gets to market? In Hollywood Economics, Arthur De Vany (2004: 72) says: “The audience makes a movie a hit and no amount of star power or marketing hype can alter that.” Since in the end, the audiences decide a movie’s fate, why not integrate audiences in the process of making a movie? Common knowledge supported by some evidence indicates that, in the end, industry experts follow their instincts instead of really listening to the audience. We argue that one of the reasons for this might be that experts have no proof of concept showing that the audience has the ability to correctly evaluate the market value of a movie. Parallel to Arthur De Vany’s book, Surowiecki (2004) begins his book The Wisdom of Crowds by describing the 1907 article in Nature (Galton 1907) where the author was surprised that the crowd at a county fair accurately guessed the weight of an ox when their individual guesses were averaged. The average was closer to the ox’s correct butchered weight than the estimates of most crowd members

Using Crowd wisdom via crowdsourcing  247

and also closer than any of the separate estimates made by cattle experts. He then presents numerous case studies and anecdotes to illustrate his argument that the Crowd is better than individuals, or even experts, at solving problems, fostering innovation, coming to wise decisions, and even predicting the future. Via our theoretical analysis based on real case studies using the Wisdom of Crowds via Crowdsourcing, we localize the Crowd wisdom to a Crowd’s ability to generate ideas and evaluate ideas. Our first study focuses on the latter. In our first study, when our self-selected participants evaluated each trailer a few weeks before opening weekend, and after correlating these evaluations with opening-weekend receipts (0.79 correlation) and four weeks later (0.74 correlation), we provide proof of concept that Crowd wisdom pertaining to new-product ideas’ evaluation is highly valuable—even better than Experts’ evaluations who made their evaluation after seeing the actual movie (respectively 0.58 correlation on opening weekend and 0.47 correlation four weeks later). More surprisingly, we find that the Crowd was not fooled by expensive marketing tools (i.e., trailers). In fact, this marketing tool (i.e., trailers) was used by the Crowd as an informative tool rather than a marketing tool, which confirms Elberse and Anand’s findings (Elberse and Anand 2006). Consequently, the idea of using an expensive marketing tool to attract customers early after the release of a non-attractive new product is not an effective strategy. We note that the highest Crowd evaluation was for the Oscar-nominated movie, The Blind Side. In fact, the success of this movie (production budget only $29 million; worldwide revenue $309 million) was so unexpected that some investors were left puzzling over a question that has not often troubled the movie business: what went right? (Cieply and Schwartz 2010). Moreover, in March 2018, in an email to its customers to celebrate its “20th Anniversary with a walk down memory lane,” Netflix, the big-data movie company, announced that The Blind Side was the most rented DVD of all time, adding “We did not see that one coming!”—but our Crowd did. The highest Experts’ evaluation was for the movie Pirate Radio, which, after its commercial failure at the British box office, was re-edited to trim its running time by twenty minutes, retitled Pirate Radio (the original title was The Boat That Rocked) and its trailer embellished. Despite this, the US box office was still a market failure (production budget only $50 million; worldwide revenue $37 million). We also note that the Crowd and experts agreed on the worst movie Transylmania which was also listed as one of the top five all-time worst opening-weekend gross receipts for a wide release movie (boxofficemojo​.c​ om “Worst Wide Openings: 600+ theaters”). Accordingly, the Crowd seems to be equally capable of correctly evaluating market success and market failure whereas our experts (i.e., movie critics) seem to be better on the latter. Finally, it is important to note that even though we were not able to compare our worldwide box office results with our domestic box office results (in some instances movies did not go to the foreign market), the correlation between the Crowd’s evaluations and worldwide receipts (0.77) is highly significant as well as much better than the evaluations by Experts (0.07).

248  Nadine Escoffier and Bill McKelvey

Even though our first study clearly offers proof of concept that the Crowd is wise when evaluating ideas, David Leonhardt (Washington bureau chief of The New York Times) announced in The New York Times in 2012 that the Crowd wasn’t wise when it predicted on “Intrade,” an online real-world-events prediction market, that the Supreme Court would rule Obama’s healthcare law unconstitutional (Leonhardt 2012). Note, however, that this same online “prediction market”—where each member of the Crowd can trade on real-world events and tap into the Wisdom of Crowds—was a more reliable guide to the 2006 midterm election than cable networks. On election night, its odds showed that the Democrats had become the favorites to retake the Senate, while television commentators were still telling viewers it was unlikely. So what happened? Could it be, as Leonhardt argues, that “if the circle of people who possess information is small enough—as with the selection of a Vice President or a Pope or a decision by the Supreme Court—the Crowd may not have much wisdom to impart?” Or could it be that an open-source prediction market generates a signal about a market to customers telling them what is the right choice but when the final decision is not theirs (e.g., a decision by the Supreme Court) the decisionmaker has no basis and no interest in reflecting the Crowd’s wisdom, thereby making the Crowd appear unwise? The best answer, now, is that the Crowd— randomly created—is really good at reflecting the interests and preferences of a Crowd, i.e., a large randomly selected population. It cannot be expected to guess what a small group of decision-makers are apt to conclude. Crowd wisdom is, thus, inappropriately applied to most auction markets where a Court, a political group, a sports team, or more generally a non-randomly selected set of people, make a decision. One other hypothesis about why the Crowd could become unwise could be explained by a certain bias by others’ decisions such as in the example of the opensource prediction market mentioned above. By what measure can the Crowd be influenced? The latest incident on the last presidential election where Cambridge Analytica tried to influence the outcome of the election is a good example of why this problem matters. Another common hypothesis is that a large Crowd can accurately evaluate ideas even better than a small group of experts but what about the prediction ability of a small Crowd compared to a small group of experts? Therefore, in our second study, we focus on: (1) on what level the influence factor can affect the Wisdom of Crowd effect; and (2) what the ability is of a small Crowd to evaluate ideas compared to a small group of experts.

Study 2: Social influence undermines the Wisdom of Crowd effect but even a small independent Crowd has a high ability to evaluate ideas better than a small group of experts Even though in our Study 1 we offer proof of concept that the Crowd is wise when independently evaluating ideas (Escoffier and McKelvey 2014), and that the Wisdom

Using Crowd wisdom via crowdsourcing  249

of Crowd effect is described as the tendency for the average of the independent estimates of all group members to be more accurate than any group member’s individual estimate (Page 2007), the reality is that our decision-making process is more and more affected by online reviews such as Yelp, Amazon, Tripadvisor, and Netflix. Therefore, studying the accuracy of these review-aggregation websites that help us make decisions in our everyday life is relevant. Some researchers investigated the influence factor within the Crowd (Mannes 2009; Golub and Jackson 2010; Lorenz et al. 2011; Mavrodiev et al. 2012; Baddeley 2013). It has been found that even mild social influence is sufficient to undermine the utility of the Crowd-wisdom effect. One reason for this could be that the effect is not a social phenomenon but a statistical one (Lorenz et al. 2011). The social influence effect undermines the utility of the Crowd-wisdom effect even more when the answer to a question is subjective, as opposed to factual (Salganik et al. 2006; Prechter and Parker 2007). In the movie industry, a number of studies have shown the accuracy of the online prediction market—that is, the Hollywood Stock Exchange (Pennock et al. 2001; Wolfers and Zitzewitz 2004; Karniouchina 2011; McKenzie 2013). These studies test the accuracy of the Crowd-wisdom effect under social influence when the answer is factual (i.e., box office estimates). In our first study, where our self-selected participants independently evaluated trailers a few weeks before opening weekend, we find that the Wisdom of Crowds effect is accurate when the answer to a question is subjective (i.e., measurement of a movie’s quality) (Escoffier and McKelvey 2014). Still, even though the Crowd-wisdom effect is a statistical phenomenon where the independence criterion seems to be essential for an objective measurement of a movie’s quality (i.e., subjective answer), the Crowd-wisdom effect has never been compared with and without social influence. Moreover, Lorenz et al. (2011) (1) show that the Crowd-wisdom effect tends to decline over time under conditions of social influence when questions have a factually correct answer, and (2) assume that this phenomenon is even more pronounced for opinions or attitudes for which no predefined correct answer exists (i.e., a movie’s quality). Furthermore, even though the Wisdom of Crowd effect commonly indicates that large groups of people (i.e., the Crowd) are smarter than an elite few (i.e., the experts), no matter how brilliant those elite few may be (Surowiecki 2004), we cannot help but wonder: what happens when the Wisdom of Crowd effect results from a very small Crowd rather than a large one? Wagner and Vinaimont (2010) empirically show that even a relatively small Crowd (30) can demonstrate expert-like performance. Consequently, three propositions are tested in Study 2: 1. The Crowd-wisdom effect is more accurate for measuring a movie’s quality when the Crowd is not subject to social influence. 2. The Crowd-wisdom effect tends to decline over time under conditions of social influence, especially when the answer is subjective (i.e., a movie’s quality). 3. The accuracy of the Crowd-wisdom effect, pertaining to a movie’s quality, is similar for a small Crowd and a small number of experts.

250  Nadine Escoffier and Bill McKelvey

Our results show that the Wisdom of Crowd effect resulting from the independent evaluation of a small Crowd (i.e., 0.88 for N=40) is more accurate than the evaluations of a small number of experts (i.e., 0.47 for N=30). One explanation for this could be that the social influence factor can affect experts’ evaluation processes as well. But it may also be due to political issues such as peer or industry pressure to conform (Ravid et al. 2006; White 2010; Najar, Brunet and Legoux 2011; Baddeley 2013). Our results also show that even though the Wisdom of Crowd effect is significant when participants are influenced by other people’s responses in their evaluation of movie quality, the effect is greater when participants’ measurement of movie quality is independent. The “social influence” effect appears to require a larger sample size (i.e., 0.87 for N = 200,000) to achieve essentially the same correlation outcome as a small sample of independent evaluators (i.e., 0.88 for N = 40). Moreover, even though we find that the social influence factor undermines Crowd wisdom when the answer to a question is subjective (i.e., measurement of a movie’s quality), in contradiction to Lorenz et al.’s assumption (2011) we also find that Crowd wisdom increases over time under conditions of social influence when the answer to a question is subjective (i.e., 0.63 for N = 500 vs. 0.87 for N = 200,000). One explanation for this could be that, nowadays, decisions are impacted by various information sources (Simonson and Rosen 2014), including the opinions of other users. A study conducted by the Harvard Business School found that a one-star increase in Yelp rating leads to a 5% to 9% increase in revenue (Luca 2011). Therefore, the influence factor affects not only opinions but also, more broadly, customers’ actual buying decisions with an actual impact on revenues (Luca 2011; Anderson and Han 2016). Our findings show that since the Wisdom of Crowds effect is indeed undermined by social influence, we can assume that the Cambridge Analytica strategy might have had an influence on the US Presidential election’s outcome. But instead of trying to influence the outcome of an election by manipulating the Crowd, which of course cannot be efficient in the long run now that the Crowd is aware of it, how about asking the Crowd to submit ideas about issues that matter the most to improve society as whole such as in the example of the EU asking the Crowd to help them shape the future of Europe (European Commission 2018)? Would (1) the Crowd provide useful ideas and (2) would the word-of-mouse following the Crowdsourcing initiative be positive and therefore help on the outcome? Our third study focuses on the ability of the Crowd to generate ideas.

Study 3: The Crowd has a high ability to generate valuable ideas since these ideas increase ratings and positive comments for both the Crowd involved and customers The results of our Studies 1 and 2 clearly demonstrate that the Crowd has a high ability to evaluate new-product ideas (even better than experts) with or without influence. In 2013, Google confirms the Crowd’s ability for idea

Using Crowd wisdom via crowdsourcing  251

evaluation with its “Quantifying Movie Magic” (Chen and Panaligan 2013). Although Google’s advice is to use Crowd wisdom to adjust marketing campaigns, we can’t help but wonder: why spend more money to adjust a marketing campaign in order to sell a non-appealing movie if the movie industry can use Crowd wisdom to co-create value in the first place? In other words, would the Crowd also be good at generating ideas? Pertaining to idea generation, a few studies do show that the Crowd can outperform Experts (Magnusson et al. 2003; Poetz and Schreier 2012) and even highly knowledgeable users (Kristensson et al. 2004) in terms of market value. However, it is interesting to also note that: ••

••

••

Even though studies generally demonstrate the superiority of Crowd wisdom versus other methods in ideaevaluation (Ray 2006; Wagner and Vinaimont 2010; Rauhut and Lorenz 2011; Gaissmaier and Marewski 2011) including in the movie industry (Pennock et al. 2001; Wolfers and Zitzewitz 2004; Karniouchina 2011; McKenzie 2013; Chen and Panaligan 2013; Escoffier and McKelvey 2014, 2015), all of the research pertaining to idea generation used experts to evaluate idea generation from the Crowd. As future research, Ebner et al. (2009: 354) mention that “future work should also aim at developing more mechanisms to support and harvest the Wisdom of Crowds in selecting the best ideas.” We plan to fill this gap in study 3. “There is a conceptual gap between the generation and the selection of ideas and their transformation into innovations” (Ebner et al., 2009: 354). Interestingly, Ebel et al. (2016) use the Crowd to actually create a new product in its final stage as we will do in this present study. In 2006, because of a few relatively minor changes during post-production, based on the use of Crowd wisdom on idea generation, Snakes on a Plane, a B-movie, actually turned a profit (Escoffier and McKelvey 2015).

What nobody mentions, though (as in all the research mentioned above about the ability of the Crowd to generate ideas) is what the movie might have been without the integration of idea generation from the Crowd. Furthermore, even though online reviews are now an important part of the buying process and thus revenues (Luca 2011; Anderson and Han 2016), none of the research above shows proof of concept of an evaluation process in terms of both ratings and comments. With the intention to fill these gaps, we tested both scenarios in Study 3. Based on our literature review, we expected the following: H1: The quantitative value (i.e., ratings) of the New Product Development (i.e., NPD) will be significantly higher after Crowdsourcing than before Crowdsourcing; H2: The qualitative value (i.e., comments) of the New Product Development (i.e., NPD) will be significantly higher after Crowdsourcing than before Crowdsourcing.

252  Nadine Escoffier and Bill McKelvey

••

The Crowd-wisdom effect has been described as the tendency for the average of the independent estimates of all group members to be more accurate than any group member’s individual estimate (Hall 2011). This effect holds only if large estimation errors by individuals are unbiased such that they cancel each other out. Thus, the heterogeneity of numerous decision-makers generates a more accurate aggregate estimate than the estimates of single lay or expert decision-makers (Page 2007).

Therefore, and taking a research perspective, an evaluation of a biased Crowd should be significantly different than an evaluation by a non-biased Crowd. Taking a managerial perspective, however, it is important to see—if a company uses an independent Crowd to evaluate an NPD—whether its evaluation will follow the same pattern relative to a specific new product, which is to ask: will the non-biased customers see the same market value as the customer-Crowd that was integrated into the idea-generation process? With the intention to fill this gap in our study 3, we will also test whether the perception of the market value of the final product after Crowdsourcing will be significantly different between a biased and a non-biased Crowd. Based on our literature review, we expect the following: H3: The quantitative value (i.e., ratings) of an NPD after Crowdsourcing will be significantly different between the biased Crowd and unbiased Crowd. H4: The qualitative value (i.e., comments) of an NPD after Crowdsourcing will be significantly different between the biased Crowd and unbiased Crowd. As mentioned above, with a few relatively minor changes during post-production, based on the use of ideas offered by Internet fans (i.e., the Crowd), the movie Snakes on a Plane actually turned a profit (Escoffier and McKelvey 2015). However, in this example, no evidence of actual improvement (i.e., difference between value before Crowdsourcing and value after Crowdsourcing) has been shown. Consequently, to create a test of value before and after Crowdsourcing, we designed our research accordingly, as described below. We found an independent producer in the LA area who was willing to participate in our experiment with one of his movies at its final version (i.e., “peer reviewed”). It usually takes two or more years to create a two-hour movie, with another year for post-production added on. The movie used in the following research was just a 20-minute movie, which makes it easier for the Crowd to evaluate all aspects of the movie design. Because, as mentioned in our theoretical framework, prior research shows that the Crowd’s evaluation is highly correlated with the new product’s (movie’s) market value and that this evaluation is even more accurate without social influence, we decided to ask our respondents to independently evaluate the movie in addition to generating ideas about how to improve the movie. Thus, our Crowd, who generates the new ideas, could also be biased in their evaluations of their new ideas (i.e., if a person offers new ideas to improve

Using Crowd wisdom via crowdsourcing  253

the value of a product, she/he is likely to also rate her/his ideas as good). Yet, since the Crowd-wisdom effect has been described as the tendency for the average of the independent estimates of all group members to be more accurate than any group member’s individual estimate (Hall 2011), this effect holds only if large estimation errors by individuals are unbiased such that they cancel each other out (Page 2007). This is why we also need a Crowd that will independently judge the value after Crowdsourcing (hereinafter labeled the “unbiased Crowd” of respondents). Therefore, to give us an unbiased evaluation, we divided our Crowd into two panels: •• ••

Panel 1 is our biased Crowd due to its involvement in the movie design; Panel 2 is our non-biased Crowd due to its non-involvement in the movie design.

We asked our Crowds to focus on both the storyline and the cast (i.e., the two fundamental elements of movie design). When comparing means of the ratings before and after Crowdsourcing from the biased Crowd (shown in Table 1 of the article Escoffier et al. 2018), we confirm that the value after Crowdsourcing perceived by our biased Crowd is significantly higher for each movie-design element after just one iteration (i.e., the P-Value for each movie-design element, respectively Story: 0.00007487; Cast: 2.2e-16; Movie Design: 2.2e-16, is significantly lower than our significance level; P-Value < 0.01). Furthermore, when comparing means of the ratings before and after Crowdsourcing from the non-biased Crowd (shown in Table 2 of the article Escoffier et al. 2018), we confirm that the value after Crowdsourcing perceived by our unbiased Crowd is also significantly higher for each movie-design elements after just one iteration (i.e., the P-Value for each movie-design element, respectively Story: 0.005769; Cast: 2.2e-16; Movie Design: 2.2e-16, is significantly lower than our significance level; P-Value < 0.01). However, when comparing the means’ difference of the ratings between the biased and non-biased Crowd (shown in Table 3 of the article Escoffier et al. 2018), we find that, even though and as expected, means from the biased Crowd after Crowdsourcing (i.e., Story: 3.526; Cast: 3.971; Movie Design: 3.796) are higher than the ones from the non-biased Crowd after Crowdsourcing (i.e., Story: 3.36; Cast: 3.901; Movie Design: 3.704), the differences are all non-significant (i.e., the P-Value for each movie-design element, respectively Story: 0.1501; Cast: 0.9522; Movie Design: 0.4055, is higher than our significance level; P-Value > 0.01). Furthermore, when comparing means of the sentiments in the comments before and after Crowdsourcing from the biased Crowd (shown in Table 4 of the article Escoffier et al. 2018), we confirm that the sentiments after Crowdsourcing perceived by our biased Crowd are significantly higher (i.e., more positive) for each movie-design element after just one iteration (i.e., the P-Value for each movie-design element, respectively Story: 5.569e-06; Cast: 3.888e-11; Movie Design: 1.681e-14, is significantly lower than our

254  Nadine Escoffier and Bill McKelvey

significance level; P-Value < 0.01). Moreover, when comparing means of the sentiments in the comments before and after Crowdsourcing from the non-biased Crowd (shown in Table 5 of the article Escoffier et al. 2018), we confirm that the sentiments after Crowdsourcing perceived by our unbiased Crowd are also significantly higher (i.e., more positive) for each moviedesign element after just one iteration (i.e., the P-Value for each moviedesign element, respectively Story: 0.000295; Cast: 2.2e-16; Movie Design: 2.2e-16, is significantly lower than our significant level; p-value < 0.01). However, when comparing the means differences of the sentiments in the comments between the biased and non-biased Crowd (shown in Table 6 of the article Escoffier et al. 2018), even though this time the means from the biased Crowd after Crowdsourcing compared to the non-biased Crowd after Crowdsourcing are slightly higher for the story (i.e., 3.222; 3.106) and the cast (i.e., 3.458; 3.426) and slightly lower for the movie design (i.e., 3.315; 3.356), we find that the means differences between the biased Crowd after Crowdsourcing and the non-biased Crowd after Crowdsourcing are non-significant (i.e., the P-Value for each movie-design element, respectively Story: 0.4714; Cast: 0.8231; Movie Design: 0.8059, is higher than our significance level; P-Value > 0.01). Our qualitative analysis findings follow the same pattern seen in our quantitative analysis findings: ••

••

The Crowds’ evaluations after Crowdsourcing—both in terms of ratings and comments—are significantly higher than before Crowdsourcing, which supports H1 and H2. Since an increase in ratings reflects an increase in revenue (Luca 2011; Anderson and Han 2016), we confirm that Crowdsourcing pertaining to idea generation significantly improves a new product’s market value. We also find that, after Crowdsourcing, positive comments increase significantly and the negative comments decrease significantly. Even though in general, the new product’s market value both in terms of ratings and comments for Panel 1 after Crowdsourcing is higher than Panel 2’s due to a positive biased from our Panel 1, the difference is not significant. Thus H3 and H4 are rejected.

In our Study 3 (Escoffier et al. 2018), by evaluating the market value of our short movie before and after Crowdsourcing, we offer proof of concept that Crowd wisdom pertaining to new-product idea generation improves its market value significantly, both in term of ratings and comments after just one iteration. It is noted that the improvement took place before a large investment was done. More surprisingly, this market value after Crowdsourcing is the same for the Crowd involved in the Crowdsourcing process (i.e., the “biased” Crowd; represented by Panel 1) than for the potential customers of the new product created by using this process (i.e., the “unbiased” Crowd; represented by Panel 2).

Using Crowd wisdom via crowdsourcing  255

Summary of our three studies’ results and opening to further research In Studies 1 and 2, we focus on the ability of the Crowd to evaluate a new product idea. Even though we offer proof of concept that the Crowd (even a small Crowd) is better than experts when evaluating ideas with or without influence, we show that the Crowd is also better at evaluating ideas without social influence. This confirms the Wisdom of Crowd effect’s principle where the tendency for the average of the independent estimates of all group members to be more accurate than any group member’s individual estimate (Page 2007). However, our results show that even though the Wisdom of Crowds effect is more accurate without social influence, its accuracy increases over time under conditions of social influence for opinions or attitudes for which no predefined correct answer exists (i.e., a movie’s quality) which contradicts Lorenz et al.’s (2011) assumption since they: (1) show that the Crowd-wisdom effect tends to decline over time under conditions of social influence when questions have a factually correct answer; and (2) assume that this phenomenon is even more pronounced for opinions or attitudes for which no predefined correct answer exists (i.e., a movie’s quality). More surprisingly, we provide the proof of concept that trailers minimally mislead the Crowd when they are evaluating the market value of a movie. Trailers were actually seen by the Crowd as an information tool. Therefore, we believe that Google’s recommendation to use Crowd wisdom on idea evaluation to improve marketing campaigns (Chen and Panaligan 2013) is not a valid strategy. Furthermore, while “AdBlock,” a popular ad blocker, was identified as a global Internet trend (Meeker 2017), 91% of consumers now actively read online reviews that they trust as much as a personal recommendation (i.e., 84%). The most important factor consumers pay attention to is the overall “star” rating, where 87% of people say that businesses will need a rating between 3 to 5 stars before they will use them. The second most important factor consumers pay attention to are sentiments expressed in reviews where 90% of consumers read about 10 reviews before they feel that they can trust a business. Not surprisingly, positive reviews help impart trust to consumers (i.e., 74%) while negative reviews put consumers off (i.e., 60%) (Bonelli 2016). Finally, studies also show that this proactive trend at the consumer level has an actual impact on revenues (Luca 2011; Anderson and Han 2016). But in the end, even under influence, the best products rarely do poorly and the worst products rarely do well (Salganik et al. 2006). People are more influenced when they are uncertain, but the herding process resulting from their uncertainty can go either way (Salganik, Dodds and Watts 2006). Therefore, how to make sure to create the best product in the first place? In our Study 3, by evaluating the market value of our short movie before and after Crowdsourcing, we offer the proof of concept that Crowd wisdom pertaining to new-product idea generation improves its market value significantly, both in term of ratings and comments after just one iteration. It is noted that the improvement took place before a large investment was done. Our proof

256  Nadine Escoffier and Bill McKelvey

of concept of the efficiency of idea generation is based on (1) an independent Crowd-wisdom evaluation; and (2) before and after Crowdsourcing. First, our Studies 1 and 2, supported by our literature review, show that the performance of the Crowd to evaluate ideas is even better than experts, thus supporting the value of our choice to use the Crowd rather than Experts to evaluate our new product before and after Crowdsourcing. Second, even though evaluations from Experts in previous studies show that the Crowd can outperform Experts on idea generation, our Study 3 is the first one to show the evaluation before and after Crowdsourcing. Furthermore, not only was our Crowd asked to evaluate an actual final product design versus ideas, but also in terms of both ratings and comments. Due to the importance of online reviews versus ads, we believe this evaluation in both rating and comments goes a step further. Finally, and surprisingly, by showing that the mean difference between our biased Crowd (i.e., each member of the Crowd is positively biased) and our non-biased Crowd (random heterogeneity) is not significant, we find that not only the Crowd and the potential customer will see the same value on the market after Crowdsourcing but also that the heterogeneity factor of the Wisdom of Crowd effect does not hold in our example. Even though our three studies clearly show the ability of the Crowd to (1) accurately evaluate new product market value, and then (2) generate valuable ideas as it increases ratings and positive comments (and decreases negative ones), many questions call for additional research, some of which we list below: (1) In our Study 3, in which the Crowd got integrated into the new-product development process, just one iterative process was used. Most likely, several iterations processes should be applied. What is the ideal number between too few and too many? What is the threshold? Study 3 also shows that while the increase in percentage of positive comments has almost quadrupled for one task (i.e., the choice of the cast) it barely doubled for another one (i.e., rewriting a story). Our hypothesis is that the number of iterations might depend on the complexity of the task but it is worth testing. Also, other variables might be implicated. (2) In our Study 3, the creative team played the Crowdsourcing game. However, we note that even though listening to the Crowd for selecting the cast was an easy and simple task, it was not the same for the storyline. Therefore, the creative team did not want to integrate or/and couldn’t integrate all the ideas coming from the Crowd in its new storyline. Does the level of value on idea generation coming from the Crowds depend on the ability and/or willingness of the creative team to incorporate idea generation coming from the Crowds? And if so, we cannot help but wonder: would the Wisdom of Crowds on idea generation be even more efficient by itself? We think not, but it needs testing. (3) The more the members of the Crowd who are involved in idea generation and evaluation, the more one may expect positive word-ofweb if they like the new product design as shown in Study 3 when the

Using Crowd wisdom via crowdsourcing  257

positive comments increase substantially after just one iteration. The more people who are involved in the product design, and the more they like the product before it goes to market, the sooner and greater the impact of word-of-web. Given that the amount of advertising necessary to market a new product is inversely related to the amount of word-of-web that the new product generates, increased positive word-of-web would presumably decrease the cost of a new product development, but this is yet to test. (4) Even though the improved version of the short movie has been produced, and won some awards at movie festivals, we are not able to compare its final value with real market value (i.e., box office receipts)—as we were able to do in our previous research. (5) In our Study 3, none of our respondents had information about anyone else’s response. Our hypothesis was that if our Crowd participants had seen others’ answers, they could have been influenced by their opinions and therefore decreases the efficiency of Crowd wisdom. In fact, in our Study 2, we show that the Wisdom of Crowd’s effect (i.e., idea evaluation) is more accurate without social influence. However, we did not test this phenomenon on idea generation. Would the Crowd wisdom on idea generation be more efficient with or without social influence? (6) In our studies, we use self-selected participants to be the Crowd, but we can’t help but wonder if, by using the same Crowd sample over and over, would the sample become Experts thinking more and more alike and therefore the Crowd could lose its heterogeneity—the main advantage of the Crowd—and therefore lose its wisdom on idea evaluation and idea generation? As a matter of fact, in our Study 3, since one of the main aspects of Crowd wisdom is the Crowd’s heterogeneity, we were quite surprised to see that our supposedly biased Crowd (i.e., each member of the Crowd is positively biased) produced the same results that our nonbiased Crowd did (implying its random heterogeneity showed about the same increase in positive valuation as did the bias of the biased Crowd). Maybe our Crowd was not as positively biased as we assumed it would be or this factor might not be important in some contexts. Furthermore, in our Study 2, a small Crowd is still more efficient than a small group of experts. As the Wisdom of Crowds effect is supposed to hold only if large estimation errors by individuals are unbiased such that they cancel each other out then how can a small Crowd be more correct than a small group of experts? Therefore, instead of focusing only on the undeniable ability of the Crowd on both idea generation and evaluation, we should focus, next, on the reason why experts do not have the same ability to do so.

Conclusion Crowdsourcing has traditionally been pioneered by tech brands (i.e., Microsoft, Google, Samsung). In more recent years though, this trend has been extended

258  Nadine Escoffier and Bill McKelvey

to other sectors such as automobile and energy. For example, General Electric recently funded a Crowdsourcing space called FirstBuild where the Crowd members share ideas, try them out, and then build real products. Nowadays, brands from the fast-moving consumer goods such as Coca-Cola, Danone, and Nestlé are the most active Crowdsourcing users (Roth et al. 2016). How about the entertainment industry? In 2006, Netflix asked the Crowd to find a better way to predict user viewing recommendations by improving on its prediction algorithm. In 2009, our 2000 movie fans predicted The Blind Side would receive highest box office success and Transylmania would receive the lowest box office success two weeks before opening weekend. In 2010, by evaluating the market value of a short movie before and after Crowdsourcing, we offer proof of concept that Crowd wisdom pertaining to new-product idea generation improved its market value significantly after just one integration of our movie fans into the short movie development process. In 2013, Google confirmed this wisdom of the crowds in its white paper “Quantifying Movie Magic” (Chen and Panaligan 2013). In fact, Digital Studios such as Netflix and Amazon are well known to use big data from their users. But how do digital studios use Crowd wisdom? On February 4 2018, Netflix announced during the Super Bowl (the cost for a 30-second Super Bowl commercial in 2018 was at least $5 million) that the Paramount movie titled The Cloverfield Paradox would debut on the streaming service immediately after the game. As part of their digital strategy, Paramount sold The Cloverfield Paradox to Netflix for $50 million because the movie was deemed unsalvageable despite additions to clarify character beats and tie the film to the franchise’s universe (Kit and McClintock 2018). This strategy allowed Paramount to (1) make the movie instantly profitable and (2) avoid a likely misfire and costly marketing campaign. But what did Netflix’s users gain out of this deal? Even though user ratings for this movie are average (i.e., IMDB 2.8/5 for 66,521 votes; Rotten Tomatoes 3/5 for 7,799 votes; Metacritics 2.75/5 for 372) the negative comments about this movie are very strong (i.e., “Disappointing”; “Terror is what you feel when you realize the film will never get better”; “Where was the plot!!”; “Someone has forgotten about a script”; “Not good, just very, very dull”; “Garbage”; “Exceptionally under average”; “Critical scientific flaws everywhere – it’s a sci-fi swiss-cheese”) including on Netflix (i.e., “Garbage”; “I wish I could praise this movie but I can’t”; “This is aweful [sic]. Just dreadful”; “I watched it to the end and I was sorry I did”; “The time I spend on you, I don’t get back, and if you waste it, you aren’t worth more of my time, sorry”; “Just awful”) with some comments that seem to imply that Netflix invested in poor quality content (i.e., “It’s no wonder this went straight to Netflix”; “Well, about 30 minutes in and you realize why Paramount decided to release via Netflix”). Even traditional studios such as Paramount seem to believe it is the case when they sold their unsalvageable movie to the digital studio. When Google’s advice is using crowd wisdom to adjust marketing campaigns in order to sell a non-appealing movie and when Netflix knowingly tries to sell poor quality content to its users, what kind of

Using Crowd wisdom via crowdsourcing  259

message do Digital Studios send to their consumers, employees, and stakeholders in general? More and more companies are up against external pressure such as meeting profitability and investment demands. The main challenge consists in achieving a new kind of social order that would be compatible with human freedom, creativity, and sustainability (Goryunova and McKelvey 2019). This challenge is aligned with the employee 3.0 who wants to work for companies that prioritize employee well-being, allow flexible hours, and imbue them with a strong sense of purpose (PwC 2016a). The same trends such as flexibility, well-being, meaningful purpose, and doing good in general with an actual impact on society are also observed on a consumer level (Trendhunter​.co​m,1 Trendwatching​ .c​om2 2018). Interestingly, in his later years, Abraham Maslow explored a further dimension in its pyramid of needs, self-transcendence, while criticizing his own vision of self-actualization (Maslow 1991). By this later theory, the self only finds its actualization in giving itself to some higher outside goal, in altruism and spirituality. Similarly, we find this last level of the pyramid of needs in the “Theory of Basic Human Values” (Schwartz 1992) that tries to measure universal values recognized throughout all major cultures. Furthermore, studies advocate that in the case that a choice and a value are intertwined, people tend to pick the choice that aligns more with their own values (Piirto 2005) and that values are one of the most powerful explanations of consumer behavior (Beatty 2005). Therefore, and not surprisingly, new trends among some organizations are starting to emerge (supported by nonprofit organizations such as Time Well Spent and B Lab). Time Well Spent was founded by Tristan Harris, a former employee of Google who seeks to reverse the “digital attention crisis” caused by technology companies designing mobile devices and social media features in order to capture as much attention as possible regardless of their impact on users’ quality of life. As a consequence, Silicon Valley’s companies such as Facebook, Instagram, Google are integrating features for users’ awareness on how much time they spent in the digital world for their digital well-being (Schwartz 2018). B Lab, a global nonprofit organization, created a private certification called B Corp where companies must receive a minimum score via an online assessment for social and environmental performance and satisfy the requirement that the company integrate B Lab’s commitment to stakeholders into company governing documents. Consequently, it is not surprising to see that 69% of organizations have a purpose focused on societal value and 67% predict talent will focus more on corporate value than on pay in five years (PwC 2016b). Still, nearly 40% of the US workforce expected to be “freelancers by choice” by 2020 (PwC 2016a). Two out of five people around the world believe that traditional employment will not be around in the future. Instead, people will have their own “brands” and sell their skills to those who need them. While some concerns will be raised for companies that disregard these new trends and see the user as a passive player versus an active one, these same new trends along with the fact that digital beings are active players who want to share, interact, give

260  Nadine Escoffier and Bill McKelvey

their opinions, and create in an efficiently manner predicts a bright future to come for Crowd-wisdom strategy in general. However, some issues are getting in the way of this bright future. For example, since online reviews have an actual impact on revenues (Luca 2011; Anderson and Han 2016), an economy of paid reviews has flourished. Some companies such as ReviewMeta or Fakespot use software algorithms that scrape Amazon, Yelp, Tripadvisor, and Apple App Store for suspicious patterns or attributes of the review or the reviewer. ReviewMeta then gives the product a new star rating based only on the reviews its system deems likely to be authentic. For example, deleting the suspicious reviews on a pair of wireless headphones from ATGOIN, an electronics company based in Shenzhen, dropped its rating from 4.4 stars to 2.6 (Dwoskin and Timberg 2018). The result is that this online review and ratings system that used to help consumers to make smarter choices is now turned into a system in which some consumers are manipulated and misled such that they could unknowingly be purchasing poorer quality products. In the entertainment industry, some data scientists warn the audience of suspicious online movie ratings (Hickey 2015; Olteanu 2017). For example, in December 2017, Star Wars: The Last Jedi received bad reviews on one aggregated movie’s websites most likely due to the fact that user ratings were skewed by bots [a claim from a member of an anti-Disney Facebook group (Spangler 2017)]. Even though the aggregated movie’s website dismissed the claim, we note that this website is part of NBC Universal’s Fandango which acquired the movie-ranking site in 2016 from Warner Bros. In fact, it is important to note that the most recognized aggregated movie’s websites such as IMDB, Rotten Tomatoes, Metacritic, or Fandango are owned by big corporations (Amazon, Warner Bros, NBC Universal, CBS interactive). Another issue is that Crowd wisdom depends on big data but these same data are increasingly becoming a risk for users and companies alike (i.e., privacy, security, manipulation). For example, Cambridge Analytica harvested private information from the Facebook profiles of more than 50 million users mostly without their permission (only 270,000 users had consented to having their data harvested), data that were used to build psychographic profiles in order to influence users’ behavior without their knowledge (Rosenberg et al. 2018). Based on how much risk or damage the breach caused for companies, insurers, and users, CSO, a website that provides news, analysis, and research on a broad range of security and risk-management topics, created a list of the biggest data breaches of the 21st century which includes big corporations such as Yahoo, eBay, Equifax, Target, Uber, Anthem, and Home Depot (Armerding 2018). More recently in the entertainment industry, hundreds of stolen passwords from digital studios such as Netflix, HBO, and Hulu were discovered for sale on the “Dark Web” (Spangler 2018). How to resolve those issues that could be a threat to Crowd wisdom’s efficient solution for decreasing the risk of a New Product Development while respecting users’ best interests? In April 2016, the European Parliament adopted the General Data Protection Regulation (GDPR) which carries provisions that require businesses to protect

Using Crowd wisdom via crowdsourcing  261

the personal data and privacy of EU citizens for transactions that occur within EU member states but also regulates the exportation of personal data outside the EU. This new regulation was actually implemented in May 2018. While regulations are a natural reaction to these real world threats that companies seem powerless to stop and where customers can lose confidence in a brand if they know their data are not safe with them, there is a valid concern in business circles that: (1) cumbersome overly restrictive data protection regulations will suffocate emerging new technologies (i.e., Artificial Intelligence and machine learning); and (2) adding red tape in the form of endless consent prompts for every data process might significantly burden customers in their enjoyment of online services and applications in an age where user-friendliness is one of the key factors in retaining customers. However, does the problem come from the fact that companies seem unable to protect customers’ data or does the problem come from the fact that data, which are the real value in this digital world, are being held, controlled, and monetized, by centralized organizations and thus are easy to hack? How can customers regain ownership on their data without suppressing their digital experience and stopping the new promising digital world to come? How to construct a Crowdsourcing 2.0 experience? Two years after Howe coined the term Crowdsourcing (2006a), an anonymous person (or a group of people) known as Satoshi Nakamoto conceptualized the first blockchain which is an open distributed ledger that can record transactions between two parties efficiently and in a verifiable and permanent way. The ledger itself can also be programmed to trigger transaction automatically (Iansiti and Lakhani 2017). Even though this technology is early on in its development, it is a driving technology supporting the new Web 3.0, which is also called the decentralized Web because it gives real opportunities to decentralized autonomous organizations and by extension to a decentralized economy. A decentralized autonomous organization (i.e., DAO) is an online platform community that works without any requirement for a centralized party to make decisions. Instead, it uses smart contracts which are pre-programmed rules that describe what actions can occur within the organization. A DAO is coordinated through a distributed consensus protocol where decisions regarding the future of the organization are taken by its members (i.e., the Crowd) according to the agreed initial plan and is often funded by a Crowdsale (i.e.; Crowdfunding) via token creation. Although funds may be raised simply for the value token itself, they can also represent equity, bonds, or even “market-maker seats of governance” for the entity being funded (Tapscott and Tapscott 2016). Therefore, tokens represent both the inherent value of the community which is its capital investment and they are also units of exchange within the DAO, thus the people creating the value in the DAO are also getting paid in tokens. Thus, the token system works to better align the incentives of the individuals with the overall system because the value of the tokens they earn is also dependent upon the value of the whole. DAOs work through a process whereby members create a stock of new ideas, decisions, or initiatives and then members (i.e.,, the Crowd) essentially invest

262  Nadine Escoffier and Bill McKelvey

their tokens into those that they believe most viable and receive rewards if the initiative is beneficial to the organization, thus mimicking the process of evolution where new varieties are created and selection is performed based upon the contribution to the whole organization. While DAOs seem to be the new version of Crowdsourcing (i.e., Crowdsourcing 2.0) where challenges of its original version might be solved (i.e., Crowdvoting and big data are secured and decentralized), some other challenges due to the experimental phase of these new form of organizations might emerge (e.g., SEC is scrutinizing DAOs for more regulation, problems due to the immutable system). In one of our previous articles, we were imagining a new economy where economic agents would go from online project to online project according to some personal motivation where he/she can invest, co-create, and get a profit percentage out of it, and then move to the next project. This kind of digital agent becomes a new type of creative shareholder (Escoffier and McKelvey 2014). This time is now. However, our concern toward decentralized autonomous organizations is not about the technology they are based on but more about how their actors will be using it, especially in our field of research (i.e., the entertainment industry) where DAOs seem to be just another way to fund poor quality projects (i.e., The Pitts Family Circus with the Ethereum Movie Venture). While we hope that those projects were created just as an experiment in using the technology behind Crowdsourcing 2.0 versus Crowd wisdom via Crowdsourcing 2.0, we also hope that the entertainment industry will eventually use the full potential of Crowd wisdom via Crowdsourcing 2.0. On December 16 2011, one of the authors (Escoffier) sent an email to the CEO of Netflix, Reed Hasting, suggesting that Netflix should create its own content, (1) in order to avoid studios’ dependency, and (2) by using Crowdsourcing, a long-term strategy to avoid market failure. Even though Netflix listened on the dependency part (i.e., House of Cards, first Netflix original series, February 1 2013), they still did not take into account the ability of the Crowds to help creating high quality content in the first place as well as the rest of the industry. While the entertainment industry in general embraced the financial part of Crowdsourcing (i.e., Crowdfunding) and is embracing the financial part of the Blockchain (i.e., Crowdsale), using the main advantage of Crowdsourcing 1.0 and Crowdsourcing 2.0 (i.e., Crowd wisdom) is yet to come.

Notes 1 https://www.trendhunter.com/trends/free-2018-trend-report. 2 https://trendwatching.com/quarterly/2017-11/5-trends-2018/.

References Afuah, A. and Tucci, C.L. (2012) “Crowdsourcing as a solution to distant search,” Academy of Management Review, 37 (3): 355–375.

Using Crowd wisdom via crowdsourcing  263 Anderson, C. and Han, S. (2016) “Hotel performance impact of socially engaging with consumers,” Cornell Hospitality Report, 16 (10): 3–9. Armerding, T. (2018) “The 17 biggest data breaches of the 21st century,” https​:/​/ww​​ w​.cso​​onlin​​e​.com​​/arti​​cle​/2​​13087​​7​/dat​​a​-bre​​ach​/t​​he​-bi​​ggest​​-data​​-brea​​ches-​​of​-​th​​e​-21s​​t ​ -cen​​tury.​​html Baddeley, M. (2013) “Herding, social influence and expert opinion,” Journal of Economic Methodology, 20 (1): 35–44. Beatty, S.E. (2005) “Alternative measurement approaches to consumer values: the list of values and the Rokeach value survey,” Psychology and Marketing, 181–200. Bonelli, S. (2016) “2016  local consumer review survey,” BrightLocal, November 2. https​:/​ /ww​​w​.bri​​ghtlo​​cal​.c​​om​/20​​16​/11​​/02​/2​​016​-l​​ocal-​​co​nsu​​mer​-r​​eview​- survey/ (Accessed October 6, 2017). Boudreau, K.J. and Lakhani, K.R. (2013) “Using the Crowd as an innovation partner,” Harvard Business Review, 91 (4), 61–69. Brabham, D.C. (2010) “Moving the Crowd at Threadless: motivations for participation in a crowdsourcing application,” Information, Communication & Society, 13 (8): 1122–1145. Chen, A. and Panaligan, R. (2013) “Quantifying movie magic with Google search,” Google Whitepaper. Industry Perspectives and User Insights. http:​/​/ssl​​.gsta​​tic​.c​​om​/th​​ink​/d​​ocs​/​ q​​uanti​​fy- ing​-movie​-magic​_research​-studie​s​.pdf (Accessed May 30, 2014). Cieply, M. and Schwartz, P. (2010) ‘“Blind Side’ finds a path to the Oscars by running up the middle,” The New York Times, http:​/​/www​​.nyti​​mes​.c​​om​/20​​10​/02​​/06​/m​​ovies​​/awar​​ dssea​​son​​/0​​6blin​​d​.htm​l (Accessed February 05, 2010). De Vany, A. (2004) Hollywood Economics, New York: Routledge. DiNucci, D. (1999) “Fragmented future,” Print, 53 (4): 221–222. Dobuzinskis, A.(2009) “Movie studios try to harness Twitter effect,” Reuters, U.S. edition, http:​/​/www​​.reut​​ers​.c​​om​/ar​​ticle​​/2009​​/07​/1​​7​/us-​​twitt​​er​-st​​udios​​-idUS​​T​RE56​​G74H2​​ 00907​​17 (Accessed July 17, 2009). Dwoskin, E. and Timberg, C. (2018) “How merchants use Facebook to flood Amazon with fake reviews,” The Washington Post, April 23. Ebel, P, Leimeister, J.M. and Bretschneider, B. (2016) “Can the Crowd do the job? Exploring the effects of integrating customers into a company’s business model innovation,” International Journal of Innovation Management, 20 (7): 1–24. Ebner, W, Leimeister, M. and Krcmar, H. (2009) “Community engineering for innovations: the ideas competition as a method to nurture a virtual community for innovations,” R&D Management, 39 (4): 342–356. Elberse, A. and Anand, B. (2006) “Advertising and expectations: The effectiveness of prerelease advertising for motion pictures,” Harvard Business School Working Paper Series, No. 05-060. Escoffier, N. and McKelvey, B. (2014) “Using Crowd-Wisdom strategy to co-create market value: proof-of-concept from the movie industry,” Chapter 11 in International Perspectives on Business Innovation and Disruption in the Creative Industries, Northampton, MA: Edward Elgar, 200–222. Escoffier, N. and McKelvey, B. (2015) “The Wisdom of Crowds in the movie industry: towards new solutions to reduce uncertainties,” International Journal of Arts Management, 17 (2): 52–63. Escoffier, N., Tournois, N. and McKelvey, B. (2018) “Using Crowdsourcing to increase new product’s market value and positive comments for both the Crowd involved and customers,” International Journal of Innovation Management, 22 (2): 28 p.

264  Nadine Escoffier and Bill McKelvey Estellés-Arolas, E. and González-Ladrón-de-Guevara, F. (2012) “Towards an integrated Crowdsourcing definition,” Journal of Information Science, 38 (2): 189–200. European Commission (2018) https​:/​/ec​​.euro​​pa​.eu​​/comm​​issio​​n​/fut​​ure​-e​​urope​​/cons​​ultat​​ ion​-f​​​uture​​-euro​​pe​_en​ Faullant, R, Holzmann, P. and Schwarz, E.J. (2016) “Everybody is invited but not everybody will come — the influence of personality dispositions on users’ entry decisions for crowdsourcing competitions,” International Journal of Innovation Management, 20 (6): 1–20. Furby, J. and Randell, K. (2005) Screen Methods, London: Wallflower Press. Gaissmaier, W. and Marewski, J.N. (2011) “Forecasting elections with mere recognition from small, lousy samples: a comparison of collective recognition, wisdom of crowds, and representative polls,” Judgment and Decision Making, 6 (1): 73–88. Galton, F. (1907) “Vox Populi,” republished in Nature, 75 (1949): 450–451. Goldman, W. (1983) Adventure in the Screen Trade, New York: Warner Books. Golub, B. and Jackson, M.O. (2010) “Naïve learning in social networks and the Wisdom of Crowds,” American Economic Journal: Microeconomics, 2 (1): 112–149. Goryunova, E. and McKelvey, B. (2019) “Understanding value conflict between business and society: a new perspective from neuro and complexity sciences.” Chapter 7 in McKelvey (ed) Management in the Age of Digital-Business Complexity. London: Routledge. Hall, A. (2011) “Are crowds wise—or mad?: study confirms that herding undermines the wisdom-of-crowd effect,” Socionomist, 3 (6): 1–4. Hartmans, A. (2016) “17 Google products that bombed, died or disappeared,” Business Insider, September 7. http:​/​/www​​.busi​​nessi​​nside​​r​.com​​/disc​​ontin​​ued​-g​​oogle​​-prod​​​ucts-​​ 2016-​8 (Accessed October 6, 2017). Hickey, W. (2015) “Be suspicious of online movie ratings, especially Fandango’s,” https​:/​/ fi​​vethi​​rtyei​​ght​.c​​om​/fe​​ature​​s​/fan​​dango​​-movi​​​es​-ra​​tings​/ Honthaner, E.L. (2001) The Complete Film Production Handbook (3rd ed.), Oxford: Focal Press. Howe, J. (2006a) “Crowdsourcing: a definition,” June 2. http://crowdsourcing. http:​/​/typ​​ epad.​​com​/c​​s​/200​​6​/06/​​crowd​​sourc​​​ing​_a​​.html​ (Accessed October 6, 2017). Howe, J. (2006b) “The rise of crowdsourcing,” Wired, 14 (6): 1–4. Howe, J. (2008) Crowdsourcing: Why the Power of the Crowd Is Driving the Future of Business, New York: Three Rivers Press. Iansiti, M. and Lakhani, K. (2017) “The truth about blockchain,” Harvard Business Review, January-February. Karniouchina, E.V. (2011) “Are virtual markets efficient predictors of new product success? The case of the Hollywood Stock Exchange,” Journal of Product Innovation Management, 28 (4): 470–484. Kit, B. and McClintock, P. (2018) “Sources Netflix paid Paramount more than $50 million for Cloverfield Paradox”, The Hollywood Reporter, February 6, 2018. Kristensson, P., Gustafsson, A. and Archer, T. (2004) “Harnessing the creative potential among users,” Journal of Product Innovation Management, 21 (1): 4–14. Leimeister, J.M., Marco, J., Huber, M., Bretschneider, U. and Krcmar, H. (2009) “Leveraging Crowdsourcing: activation-Supporting components for IT-based ideas competition,” Journal of Management Information Systems, 26 (1): 197–224. Leonhardt, D. (2012) “When the crowd isn’t wise,” The New York Times, http:​/​/www​​.nyti​​ mes​.c​​om​/20​​12​/07​​/08​/s​​unday​​-revi​​ew​/wh​​en​-th​​e​-cro​​​wd​-is​​nt​-wi​​se​.ht​​ml (Accessed July 7, 2012).

Using Crowd wisdom via crowdsourcing  265 Lorenz, J., Rauhut, H, Schweitzer, F. and Helbing, D. (2011) “How social influence can undermine the Wisdom of Crowd effect,” Proceedings of the National Academy of Sciences of the United States of America, 108 (22): 9020–9025. Luca, M. (2011) “Reviews, reputation, and revenue: the case of Yelp​.com​,” Harvard Business School Working Paper, # 12–016. Lucey, P. (1996) Story Sense: A Screenwriter’s Guide for Film and Television, New York: McGraw-Hill. Lüttgens, D, Antons, D., Pollock, P. and Piller, F. (2014) “Wisdom of the Crowd and capabilities of a few: internal success factors of crowdsourcing for innovation,” Journal of Business Economics (formerly: ZFB), 84 (3): 339–374. Magnusson, P.R., Matthing, J. and Kristensson, P. (2003) “Managing user involvement in service innovation: experiments with innovating end-users,” Journal of Service Research, 50 (1): 111–124. Mannes, A.E. (2009) “Are we wise about the Wisdom of Crowds? The use of group judgments in belief revision,” Management Science, 55 (8): 1267–1279. Maslow, A.H. (1991) “Critique of self-actualization theory,” Journal of Humanistic Education and Development, 29 (3): 103–108. Mavrodiev, P., Tessone, C.J. and Schweitzer, F. (2012) “Effects of social influence on the Wisdom of Crowds.” Paper presented at Collective Intelligence Conference, Massachusetts Institute of Technology, Boston, April 16. McKenzie, J. (2012) “The economics of movies: a literature survey,” Journal of Economic Surveys, 26 (1): 42–70. McKenzie, J. (2013) “Predicting box office with and without markets: do Internet users know anything?” Information Economics and Policy, 25 (2): 70–80. Meeker, M (2017) “Internet trends 2017”—code conference. Kleiner Perkins, May 31. Murray, A (2015) “50  most innovative companies,” Fortune, December 2. http:​/​/for​​tune.​​ com​/2​​015​/1​​2​/02/​​50​-mo​​st​-in​​novat​​i​ve​-c​​ompan​​ies/ (Accessed October 6, 2017). Najar, S., Brunet, J. and Legoux, R. (2011) “Controlled buzz as a marketing strategy in the film industry: the effectiveness of the junkets,” Working paper, HEC Montréal. Ogawa, S. and Piller, F.T. (2006) “Reducing the risks of new product development,” Sloan Management Review, 47 ( 2): 65–71. Olteanu, A. (2017) “Whose ratings should you trust? IMDB, Rotten Tomatoes, Metacritic, or Fandango?” https​:/​/me​​dium.​​freec​​odeca​​mp​.or​​g​/who​​se​-re​​views​​-shou​​ld​-yo​​u​-tru​​st​-im​​ db​-ro​​tten-​​tomat​​oes​-m​​etacr​​itic-​​​or​-fa​​ndang​​o​-7d1​​010c6​​cf19 Page, S.E. (2007) The Difference: How the Power of Diversity Creates Better Groups, Firms, Schools, and Societies, Princeton, NJ: Princeton University Press. Pennock, D, Lawrence, S., Giles, C.L. and Nielsen, F.A. (2001) “The real power of artificial markets,” Science, 291 (5506): 987–988. Piirto, J. (2005) “I live in my own bubble: the values of talented adolescents,” Journal of Secondary Gifted Education, 16 (2–3): 106–118. Poetz, M.K. and Schreier, M. (2012) “The value of crowdsourcing: can users really compete with professionals in generating new product ideas?” Journal of Product Innovation Management, 29 (2): 245–256. Prechter, R. and Parker, W. (2007) “The financial/economic dichotomy in social behavioral dynamics: the socionomic perspective,” Journal of Behavioral Finance, 8 ( 2): 84–108. PwC (2016a) “Work-life 3.0: understanding how we’ll work next,” Consumer Intelligence Series https​:/​/ww​​w​.pwc​​.com/​​us​/en​​/indu​​stry/​​enter​​tainm​​ent​-m​​edia/​​publi​​catio​​ns​/co​​ nsume​​r​-int​​ellig​​ence-​​serie​​s​/ass​​ets​/p​​wc​-co​​nsume​​r​-int​​ellgi​​ence-​​serie​​​s​-fut​​ure​-o​​f​-wor​​k​ -jun​​e​-201​​6​.pdf​

266  Nadine Escoffier and Bill McKelvey PwC (2016b) “Redefining business success in a changing world,” 19th Annual Global CEO Survey, https​:/​/ww​​w​.pwc​​.com/​​gx​/en​​/ceo-​​surve​​y​/201​​6​/lan​​ding-​​page/​​pwc​-1​​9th​-a​​ nnual​​-glob​​​al​-ce​​o​-sur​​vey​.p​​df Rauhut, H. and Lorenz, J. (2011) “The wisdom of crowds in one mind: how individuals can simulate the knowledge of diverse societies to reach better decisions,” Journal of Mathematical Psychology, 55 (2): 191–197. Ravid, S.A., Wald, J.K. and Basuroy, S. (2006) “Distributors and film critics: does it take two to tango?,” Journal of Cultural Economics, 30 (3): 201–218. Ray, R. (2006) “Prediction markets and the financial ‘Wisdom of Crowds’,”Journal of Behavioral Finance, 7 (1): 2–4. Rosenberg, M., Confessore, N. and Cadwalladr, C. (2018) “How Trump consultants exploited the Facebook data of millions,” The New York Times, March 17. Roth, Y,. Petavy, F. and Braz de Matos, M. (2016) “The state of crowdsourcing in 2016,” eYeka Crowdsourcing Report 2016, available at https​:/​/en​​.eyek​​a​.com​​/reso​​urces​​/repo​​rts​?d​​ ownlo​​ad​=cs​​_repo​​r​t​_20​​16​.pd​​f, last accessed April 26, 2019. Rushton, R. 2011. The Reality of Film, Manchester: Manchester University Press. Salganik, M.J., Dodds, P.S. and Watts, D.J. (2006) “Experimental study of inequality and unpredictability in an artificial cultural market,” Science, 311 (5762): 854–856. Schwartz, C. (2018) “Finding it hard to focus? Maybe it’s not your fault. The rise of the new “attention economy,” The New York Times, August 14. Schwartz, S.H. (1992) “Universals in the content and structure of values: theoretical advances and empirical tests in 20 countries,” in M.P. Zanna (ed.) Advances in Experimental Social Psychology, San Diego/London: Academic Press, 1–65. Simonson, I. and Rosen, E. (2014) Absolute Value. New York: HarperBusiness. Simonton, D.K. (2009) “Cinematic success criteria and their predictors: the art and business of the film industry,” Psychology and Marketing, 26 (5): 400–420. Simonton, D.K. (2011) Great Flicks: Scientific Studies of Cinematic Creativity and Aesthetics, New York: Oxford University Press. Sinnerbrink, R. (2011) New Philosophies of Film: Thinking Images, London: Continuum. Spangler, T. (2017) “Rotten tomatoes dismisses claim that ‘Star Wars the last Jedi’ user ratings were skewed by bots,” Variety, December 21. Spangler, T. (2018) “Hundreds of stolen passwords for Netflix, HBO, Hulu and more discovered for sale on dark web,” Variety, August 13. Squire, J.E. (2004) The Movie Business Book (3rd ed.), New York: Simon & Schuster. Surowiecki, J. (2004) The Wisdom of Crowds, New York: Doubleday. Tapscott, D. and Tapscott, A. (2016) The Blockchain Revolution: How the Technology Behind Bitcoin Is Changing Money, Business, and the World, New York: Portfolio/Penguin. Teti, E. (2013) “The dark side of the movie. The difficult balance between risk and return,” Management Decision, 51 (4): 730–741. Velikovsky, J.T. (2012) StoryAlity #22: So – What Actually Makes a Movie Successful? Based on: ‘Understanding and Exploring the Relationship Between: Creativity; Theories of Narratology; Screenwriting; and Narrative Fiction Feature Film- Making Practices’. Unpublished PhD dissertation, University of Western Sydney, Australia. Von Hippel, E. (1976) “The dominant role of users in the scientific instrument innovation process,” Research Policy, 5 (3): 212–239. Wagner, C. and Vinaimont,T. (2010) “Evaluating the Wisdom of Crowds,” Issues in Information Systems, 11 (1): 724–732.

Using Crowd wisdom via crowdsourcing  267 Wang, F., Cai, R. and Huang, M. (2010) “Forecasting movie-going behavior based on online pre-release WOM and opening strength,” Proceedings of Intelligent Systems and Applications (ISA), 2nd International Workshop. White, A. (2010) “Do movie critics matter?” First Things, April. Wolfers, J. and Zitzewitz, E. (2004) “Prediction markets,” Journal of Economic Perspectives, 18 (2): 107–126.

Index

Abell, P. 156 absorptive capacity 142 accelerators 163–164 adaptation 51, 58, 59, 61, 111, 124, 158; firms and 115; institutional 49, 50, 63–64; intelligent 51–52; Law of Requisite Variety 138; natural selection and 115–116; organizational 113–114; positive feedback and 159–160; see also complex adaptive systems (CASs); Lamarckianism; resilience adaptive tension see tension agent-based computational models (ABMs) 56–57, 75, 184, 186, 194, 204 agents 146, 154, 168n1, 187, 191, 205n6, 263; connections 143; heterogeneous 14–15, 141–142, 158; mixing events 143; self-organization 15; see also complex adaptive systems (CASs) Aldrich, H. E. 58, 60, 121 Allen, P. 141 alliance networks 21 Amazon 155, 216, 217, 220, 222, 223 Anderson, P. 117, 197 Andriani, P. 18 Apple 8, 73, 165, 216 Argyris, C. 77 Arita, T. 57 artificial intelligence 139 Ashby, W. R. 137, 138, 147 atomistic ontology 183, 205n3 attractors 140, 141, 144 Auerbach, F. 220 “Automation and productivity: producing ideas” 2

Baldwin, J. M. 48, 49, 50, 51, 55, 57, 58, 59, 62, 63, 74, 80, 113, 123; Darwin and the Humanities 60–61; “A new factor in evolution” 50 Baldwin effect 48, 49, 50, 51, 53–54, 62, 65, 75, 76, 77, 80, 114, 124; confirmation via computational studies 56–57; experimental studies 55–56; firms and 70–73; in organizational evolutionary theory 57–60; routines and 64 banking 24 Barabási, A.-L. 143 Battistella, C. 165 Baum, J.A.C. 18, 28 Bazzan, A.L.C. 194 Bechara, A. 196, 197 Becker, G. S. 13, 138, 139, 142 Bénard cell 12, 137, 140 Bezos, J. 220 “blind variation” 12–13, 113, 141 blockchain technology 32–33, 262 Boisot, M. 183 Bonabeau, E. 143 bottom-up emergence 16 Boudreau, K. J. 9 Boyd, R. 58 Breslin, D. 58, 75 Brown, J. 12 Bullinaria, J. A. 57 Burgelman, R. A. 116 Burt, R. S. 138, 139, 145 butterfly events 15, 221; see also tiny initiating events buyer-supplier networks 21

Badyaev, A. V. 52–54, 53 Bak, P. 157 balanced continuity 111; learning and 121–122

Cadwalladr, C. 32 Cambridge Analytica 31 Campbell, D. T. 12, 48, 58, 121, 141 capability 155

270 Index capital: human 138, 142, 146; social 137 cellular networks 144, 146 central processing unit (CPU) 10 change 115–116, 124; self-organized criticality (SOC) and 158; see also adaptation chaos 16, 192 Chesbrough, H. 49, 59, 60, 66, 67, 68, 69, 70, 74, 77 Child, J., The Evolution of Organizations 59 Clauset, A. 222 coaching 143–144 Cobb-Douglas function 13, 139, 142 coevolution 2, 3, 11, 29, 31, 54, 111, 154, 161–162, 163, 164, 168, 188; accelerated consequences 21, 22; biological 22; of firms 66; managing 145; tags 143 Cohen, J. 7 Colander, D. 203 collaboration networks 19, 21 company networks 21 competencies 111, 155 competitive advantage 139 complementors 8 complex adaptive systems (CASs) 185, 187, 191, 200; schema 188, 189, 197, 201; self-interest and 193, 194, 195; society 191; system disintegration 190; time scale and 190; value conflicts 192, 193 complexity theory 140, 147, 154, 157, 165, 183, 201, 204, 217; absorptive capacity 142; agent-based computational models (ABMs) 185, 186; agents 14, 141–142; attractors 140, 141, 144; Bénard cell 12, 140; “blind variation” 12–13; bottom-up emergence 16; butterfly events 15, 221; coevolution and 161–162; complex adaptive systems (CASs) 185, 186, 187, 188, 189, 190, 191; connections/connectivities 15–16, 158; Digital Age and 166; Digital Age effects on 11; digital effects 18–19; dissipative structures 13–14, 139–140; “edge of chaos” 14, 17, 139, 159, 189; emergence 19, 21, 216; experimentation 185–186; finance and 24; first critical value 13, 140, 189; fractals 17, 18, 19, 21, 160, 219; imposing tension 158–159; incentives and 145–146; MBA terrorists 140; motives to connect 16; network dominance 17; positive feedback 159–160; power laws (PLs) 18, 19; reductionism and 184–185, 186–187, 204; region of emergence 14, 140, 144,

159, 189; second critical value 14, 140; self-organization 15; self-organized criticality (SOC) 157, 158, 159; societies 191, 192; storm cells 12; “strong-tie” effect 15; tension 13, 19, 25–26, 140, 141, 142, 157, 158, 159, 164, 166; tiny initiating events 15, 160, 216; traditional science and 187, 201; value conflicts 192, 193; weak ties 144–145; “weaktie” effect 15; see also connections/ connectivities computers 11, 137; distributed intelligence (DI) 139; see also Internet of Things connections/connectivities 15–16, 19, 21, 143, 154, 157, 158, 183; motivations to connect 16; rewiring 137; see also complex adaptive systems (CASs) consumers 24; product development and 243 Corporate Social Responsibility (CSR) 205n1, 205n2 Coupey, E. 7 creation 75 creativity 13, 137, 141, 260 Crispo, E. 55 Crowdsourcing 244, 246, 247, 253, 255, 256, 258, 262, 263; in the entertainment industry 259 crowdsourcing 2, 9, 23 cultural evolution 93n9 Curran, D. 57 Cusumano, 18; on network effects 8–9 Damasio, H. 192, 197, 198, 199, 200 Darby, M. R. 13, 142 Darwin, C. 48, 75 Darwinism 66, 70, 73, 80, 81, 110, 123, 124, 188; Baldwin effect and 49, 50, 51, 55; death-and-replacement 49, 50, 59, 60, 63, 65, 70, 74, 75, 76, 77, 78, 79; evolution of HDDs in the United States 66–67, 68; firms and 70–73; genetic assimilation 50; natural selection 51, 52, 54, 58, 62, 63, 138, 143; variations 112–113; see also Baldwin effect; institutional adaptation; natural selection; organizational evolution; phenotypic plasticity; variation data 10 Davies, P.C.W. 190 Day, G. 25 DB ecosystems 7, 8; industry platforms and 8–9 De Vany, A., Hollywood Economics 246, 247

Index  death-and-replacement 49, 50, 60, 63, 74, 75, 76, 77, 78, 79, 111; in the HDD industry 69, 70 decentralized autonomous organization (DAO) 262–263 decision-making, emotions and 196, 197 Dedehayir, O. 164 Dennett, D. C. 74 Depew, D. J. 57, 63; Evolution and Learning: The Baldwin Effect Reconsidered 55 Dergarabedian, P. 247 developmental plasticity 48, 54, 61; published studies 58; see also Baldwin effect Digital Age 2, 3, 7, 9, 13, 14, 15, 16, 18, 21, 27, 28, 29, 30, 31, 65, 73, 122, 123, 136, 137, 139, 142–147, 154, 155, 156, 162, 163, 165, 184, 186, 190, 201, 204, 216, 217, 218, 220, 224, 246; coaching 144; complexity dynamics and 11, 166; complexity theory and 157; connections/connectivities 143; information flows 11; information systems (IS) and 10–11; intelligence and 147; strategic principles for competing in 22–23; see also complexity theory; connections/connectivities; information flows digital business (DB) 1, 2, 3, 73, 153, 154, 164, 165, 166, 216; coevolution 3; ecosystem effects 5, 7–8, 78, 112; effects on finance 24; effects on IS 27–28; effects on leadership 29; effects on marketing 24–26; effects on organization change 29–30; effects on production 26–27; effects on strategy 28–29; ethics and 182; market capitalization and 222, 223, 224–225; nonlinearities and 167; organizational learning 122–123; platforms 8–9, 18; published writings on 190; see also Digital Age digital complexity 3 Digital Divide 26–27 digital ecosystems 4, 5 DiNucci, D., “Fragmented Future” 243 dissipative structures 11, 13–14, 139–140 distributed intelligence (DI) 137, 139, 147; see also complexity theory DNA 190 Dominguez-Isidro, S. 57 Dukas, R. 52, 55 Durham, W. H. 74 Durkheim, É. 138

271

dynamic capabilities 110, 155, 168; microprocesses 156, 157; “reconfiguring” 156; “seizing” 156; “sensing” 155 Ebel, P. 252 Ebner, W. 252 Economist, The 2 ecosystems 164 “edge of chaos” 14, 17, 139, 159, 189 efficiency frontier 72 Egol, M. 165 Eisenhardt, K. M. 12, 156 electronic banking 24 emergence 16, 19, 21, 216; business ecosystems and 167 emergent order 137, 138, 147, 160, 167 energy-differentials 137 entanglement ties 145 entrepreneurial firms 15, 21, 124 ethics 182, 183, 202, 203; see also incommensurability problem European Union (EU), General Data Protection Regulation (GDPR) 261–262 Evolutionary Psychology 74 experiments 186 Facebook 18, 21, 31, 167, 216, 217, 220, 260 Federici, D. 48 feedback 10, 193 finance, digital business (DB) and 24 firms 3, 21, 49, 69, 75, 77, 137; adaptation 115; balanced continuity 111; biospecies and 72–73; coevolution 2, 21, 22, 66; competencies 111; competitive advantage 139; Darwin machines 116; Darwinism vs Baldwin effect in 70–73; distributed intelligence (DI) 137; dynamic capabilities 110; efficiency frontier 72; emergence 16; inertia 112; innovation and 117, 144–145; intra-organizational evolution 116; Lamarckianism 113; learning relevant to balanced continuity in competitive ecosystems 121–122; learning relevant to internal selection 118–119; learning relevant to retention 119–121; learning relevant to variation 116–118; MBA terrorists 140; organizational evolution 111; output growth 217; partnerships 165; rents 136, 137, 139, 146; resourcebased view (RBV) 155; routines 111; self-organized criticality (SOC) 158; skunkworks 117; stochastic frontier

272 Index (SF) 217, 218, 220–223, 224; transient advantage 163; variations 116; see also DB ecosystems; digital business (DB) first critical value 13, 140, 189 fractals 17, 18, 19, 21, 160, 219; PLs and 219–220 Francisco, 73 Freeman, 182 Gabora, L. 74 Galton, F. 244 Garzon, M. 139 Gawer, A. 8 Ge, X. 57 Gell-Mann, M. 187 genetic assimilation 50 Ghalambor, C. K. 55 Ghoshal, S. 202 Goldman, W. 246 Goldschmidt, W. 114 Google 7, 216, 217, 220, 222, 260 Gorbis, M. 7 governance, Internet and 7 Granovetter, M. 14–15, 16, 144 Greengard, S. 7 Grund, T. 158 hacking 11 Haidt, J. 198 Haken, H. 17, 19 hard disk drives (HDDs) 77; death-andreplacement 69, 70; evolution of in Japan 68, 69, 70; evolution of in the United States 66–67, 68 hardware 10 Harris, T. 260 Heppelmann, J. E. 154 heterogeneous agents 14–15, 141–142, 158; mixing events 143 Hill, A.: Financial Times 74 Hinton, G. E. 57 Hodgson, G. M. 48, 60, 62, 63; Darwin’s Conjecture 61; The Evolution of Institutional Economics 61 Holland, J. H. 15, 137, 159, 221 Holling, C. S. 75 Hovasse, R. 61 Howe, J. 244, 262 human capital 13, 138, 142, 146 human culture 199, 200 Huxley, J. S. 61 Iansiti, M. 21, 153, 220 IBM 66, 67, 68, 71

incentives 145–146 incommensurability problem 182, 183, 202, 203 Industrial and Corporate Change 60 industry platforms 8; DB ecosystems and 8–9 inertia 112, 115, 118 information flows 2, 3, 11, 27, 29, 137, 142, 153, 184, 192 information systems (IS) 3, 9, 11; components 10; Digital Age and 10–11; digital business (DB) and 27–28; digital effects 3 innovation 144–145, 243, 244; Wisdom of Crowds and 251–255; Wisdom of Crowds effect 246 instinct, learning and 71, 72–73 institutional adaptation 49, 50, 63–64, 78; routines 64 intellectual capital (IC) 16, 217 intellectual property (IP) 32 intelligence 147; artificial 139; as constrained order 137–139; distributed 137, 139 intelligent adaptation 51–52 internal natural selection 111, 116, 121, 123–124; learning and 118–119; variation and 116–117 Internet 1–2, 7, 27, 73, 136, 143, 190; Digital Divide 26–27; dotcombat 7; and governance 7; learning and 137; see also digital business (DB) Internet of Things 4, 10, 23, 122, 153, 162–164; published writings on 136; see also connections/connectivities intra-organizational evolution 116, 124; learning relevant to balanced continuity in competitive ecosystems 121–122; learning relevant to internal selection 118–119; learning relevant to retention 119–121; learning relevant to variation 116–118 Ishikawa, A. 220 Japan 77; evolution of HDDs in 68, 69, 70 Jeppesen, L. B. 9 Jobs, S. 8, 13 Johnson, N. L. 141 Jones, C. 58 Journal of Bioeconomics 74 Journal of Evolutionary Economics 48, 60, 74 Journal of Evolutionary Psychology 48–49 Journal of Natural and Social Philosophy 49

Index  Kaiser, B. N. 31, 32 Kauffman, S. 14, 137, 143, 159, 161, 168 Kenworthy, A. L. 58 Kenya, M-PESA 5 Kerr, S. 145 Khalil, E. L. 48, 64 Knudsen, T. 48, 60 Kotter, J. P. 163, 164 Krishnan, R. 164 Kron, T. 158 Krugman, P. 161 Kumar, P. 163 Laing, A. 26 Lamarck, 75, 113, 123, 124 Lamarckianism 57, 58, 62, 75, 93n6, 110, 124 Lande, R. 55 Langergraber, K. E. 56 Lanier, J. 7 Law of Requisite Variety 138 leadership 30, 77, 148, 154; digital business (DB) and 29; evolutionary theory and 59; variation and 121 learning 28, 48, 58, 63, 64, 113, 114, 115, 116, 123, 124, 136, 188; balanced continuity and 121–122; instinct and 71, 72–73; internal natural selection and 118–119; Internet and 137; managerial 123; organizational 122–123; phenotypic plasticity and 55; retention and 119– 121; variation and 116–118; see also adaptation Leavy, B., “Strategy, organization and leadership in a new ‘transient-advantage’ world” 163 LeBaron, B. 13, 141 Leeflang, P.S.H. 25 Leonhardt, D. 249 Levien, R. 21, 220 Levinthal, D. A. 48, 116, 120 Lineweaver, C. H. 190 Lorenz, E. 15, 250, 251, 256 Madsen, T. L. 49 Maguire, S. 147, 159 Mäkinen, S. J. 164 management 79 management journals, mentions of “digital business” in 3 management theory 154, 155, 201, 204; ethics and 202; incommensurability problem 182, 183, 202, 203 Mandelbrot, B. B. 160; Fractal Geometry 17

273

March, J. G. 115, 121 Marino, A. 48 marketing: digital 25, 26; digital business (DB) and 24–26; platforms and 9 Martin, 4 156 Maruyama, M. 161 Maslow, A. 260 Mayley, G. 57 McCain, R. A. 161 McCormack, R. 5 McKelvey, B. 18, 49, 74, 77, 121, 143, 147, 159, 160, 183, 219 McKinsey Quarterly 28, 30; opportunities for managers 23–24; seven strategic principles for competing in the Digital Age 22–23; seven traits of successful DB managers 23 memes 74 Mezura-Montes, E. 57 microfoundations 162, 168 Microsoft 8, 9, 21, 31, 154, 165, 216, 217, 220 Miller, D. 7, 50, 75, 77; The Icarus Paradox 49, 60, 70–71, 74, 78–79 MIS Quarterly, Special Issue 1 mobile banking 5, 24 Moczek, A. P. 55 modernism 182, 183, 184 moral judgment 198 Morgan, L. 48, 50, 57, 61, 62, 74, 80 Morisse, M. 165 motion picture industry 246; new film production 247; online prediction market 250; trailers 245, 248, 256; Wisdom of Crowds effect 259–260; Wisdom of Crowds effect and 248 M-PESA 5 Murmann, J. P. 49 natural selection 51, 58, 62, 63, 110, 138; adaptation and 115–116; balanced continuity 111; intelligent adaptation 51–52; internal 111, 116, 123–124; phenotypic plasticity and 52–53, 54; principles 112; struggle for existence 112 Nelson, R. R. 60 Netflix 259, 263 network effects 18; Cusumano on 8–9 network(s) 2, 147, 164–165, 216; alliance 21; buyer-supplier 21; cellular 144, 146; collaboration 19, 21; company 21; online 21; organizational 21; peer-topeer 21; PL-distributed 218; rules for managing 141–146; scale-free 21; social

274 Index 21, 138–139, 218; strategy acceleration 163, 164 neuroscience 184, 185, 190, 192, 194, 200; amygdala 197; dorsolateral prefrontal cortex (DLPFC) 199; ventromedial prefrontal cortex (VMPC) 195, 196, 197, 198 Nisbett, R. E. 199 nonlinearities 221 norms 198 Nowlan, S. J. 57 Nozick, R. 48 online networks 21 online reviews 246, 252, 261; see also Internet order 137–138, 158; emergent 137–138, 147; Law of Requisite Variety 138 organic selection 49, 61 organizational theory 114; Baldwin effect and 57–60; coevolution 161; dynamic capabilities 156; evolution and 75, 79, 111, 113–114, 155; networks 21, 144, 146, 147; see also agents; connections/ connectivities; dynamic capabilities; network(s) Osborn, H. F. 48, 50, 57, 61, 74, 80 outsourcing 25 parental plasticity 53, 54 Pareto, V. 221 Parsons, A. 25 PCs 8–9 peer-to-peer networks 21 Penrose, E. T. 60, 113 performance 125n4; retention and 120 personas 64–65 phenotypic plasticity 48, 49, 50, 51, 52, 53, 54, 58, 61, 63, 64, 73, 75, 80, 81; confirmation via computational studies 56–57; evolution of HDDs in Japan 68, 69, 70; experimental studies 55–56; in humans 65, 74; organizational 60; resilience and 75–77; small animal studies 65–66; see also Baldwin effect PL distributions 21, 218, 219–220 plasticity 53, 54, 56, 58, 63, 64, 71, 73, 75, 77; organizational 59–60, 76; simplicity and 79–80; see also adaptation; developmental plasticity; parental plasticity; phenotypic plasticity; resilience platforms 18, 155, 165; crowdsourcing 9; industry 8, 9; marketing and 9; product 8

Plotkin, H. 116 Podobnik, B. 220 Porter, M. 72, 73, 136, 154, 163, 165 positivism 182 post-modernism 182, 183, 202 power laws (PLs) 18, 19; see also PL-distributions prediction market 249 Price, D. J. 57 Prigogine, I. 11, 13, 139, 164, 189 procedures 10 product development 243, 258; motion picture industry and 246–247; Wisdom of Crowds effect 246, 251–255, 256, 257; see also Crowdsourcing product platforms 8 production, digital business (DB) and 26–27 production-oriented design (POD) 27 Prusak, L. 136 rank/frequency (R/F) distributions 50–51, 218, 219–222 rationality 48 Razavi, A. R. 165 reductionism 186–187, 193, 204; complexity theory and 184–185 region of emergence 14, 140, 144, 189 rents 136, 137, 139, 146 resilience 75–-77 resource-based view (RBV) 155 resources 155 retention 116; excessive 120; firm performance and 120; learning and 119–121; see also variation, selection, and retention (VSR) RethinkX 32 Richerson, P. J. 58 Robinson, B. W. 52, 55 Rokeach, M. 198–199 Rollo, C. D. 75 Romanelli, E. 121 Rothstein, J. 138 routines 64, 111 Royle, J. 26 rules 191, 192 Safaricom 5 Salmador Sánchez, M. P. 18, 144, 164 scale-free networks 21 Scarfe, A. C. 49 Scherer, A. G. 183 Schilling, M. A. 144 Schmidt, E. 7

Index  second critical value 13, 140 Selander, L. 164 selection 116, 124; excessive 119; see also natural selection self-actualization 260 self-interest 193, 194, 195 self-organized criticality (SOC) 157, 158, 159 Shepard, J. 74, 77 Shibata, D. M. 75 Simon, H. 189 simplicity, plasticity and 79–80 Simpson, G. G. 50, 55, 59, 64, 74; “The Baldwin Effect” 48, 61 Simula, H. 9 skunkworks 117 smart, connected products 154–155 smartphones 8, 9, 24, 136, 137 Smith, B. 31 social capital 137 social networks 21, 138–139, 218 social science 193, 202 socially responsible business 182 society 204; value conflicts 191, 192, 193 software 10 somatic markers 195–196, 197, 198 Sommerhoff, G. 138 species: firms and 72–73; phenotypic plasticity and 55, 56; self-organization 220; variations 112–113 Spencer, H. 138 start-ups 112; Crowdsourcing 244 Steinmann, H. 183 Stengers, I. 164, 189 stochastic frontier (SF) 1, 216, 217, 218, 220–223, 224; fractals and PLs 219, 220; PL-distributed networks 218; rank/ frequency (R/F) distributions 218, 219–222 Stone, A. 138 storm cells 12 strategy 136, 137, 154; Crowd-wisdom 244; digital business (DB) and 28–29 “strong-tie” effect 15 Surowiecki, J. 244; The Wisdom of the Crowds 247 sustainability 182, 260 Suzuki, Y. 57 Tapscott, D., The Digital Economy 25 Teece, D. J. 155, 156, 157 tension 13, 19, 25–26, 113, 157, 158, 159, 164, 182, 183; adaptive 137, 140, 141, 142, 143, 144, 147; business ecosystems

275

and 166; incommensurability problem 182–183 Theory of Basic Human Values 260 tiny initiating events 15, 160, 216 Turney, P. 57, 71 Tushman, M. L. 117, 121 Twitter 21, 165, 167 Twitter effect 247 United States 13; 2019 Worldwide Threat Assessment 32; evolution of HDDs in 66–67, 68 value conflicts 191, 193–194; chaos and 192; moral judgment and 198; proself motivations 198, 199, 200, 201; prosocial motivations 199, 200; selfinterest 193–194, 195; from a VMPC perspective 195, 196, 197–198, 199 Van Vugt, M., “Evolutionary origins of leadership and followership” 59 variation, selection, and retention (VSR) 111, 112, 115, 116, 121, 123, 124 variation(s) 116, 124n1; blind 141; excessive 118; leadership and 121; learning and 116–118 ventromedial prefrontal cortex (VMPC) 195, 196, 197, 198, 199 Vinaimont, T. 250 Vollmer, C.A.H. 165 Von Hippel, E. 244 Vuori, M. 9 Waddington, C. H. 50, 55, 57, 58, 61, 64, 74, 113 Wagner, C. 250 Walmart 216 “weak-tie” effect 15 Web 1.0 243 Web 2.0 243, 244 Weber, B. H. 57, 63; Evolution and Learning: The Baldwin Effect Reconsidered 55 Weick, K. E. 111, 115, 121, 123 Weill, P. 165 Welch, J. 73, 145, 146 Wells, P. 9 Wicks, A. C. 182 Winter, S. G. 60 Wisdom of Crowds effect 244, 245, 256; idea generation and 251–255, 257–258; motion picture industry and 246–248; online reviews and 246, 252, 261; social influence and 249–251; see also Crowdsourcing

276 Index Witt, U. 49, 60 Woerner, S. L. 165 Wolpert, D., “Strategic choice of preferences: The persona model” 64 World War I 158 Wozniak, S. 8 Wright, S. 161

Zanini, M. 219, 220 Zipf, G. K. 220 Zohar, D., Rewiring the Corporate Brain 137 Zucker, L. G. 13, 142