1 - Intro

UNIVERSITY OF CALGARY

Established Firms’ Strategic Decision Making when Faced with Low-End Disruptive Innovation

by

Oleksiy Osiyevskyy

A THESIS SUBMITTED TO THE FACULTY OF GRADUATE STUDIES IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

HASKAYNE SCHOOL OF BUSINESS CALGARY, ALBERTA APRIL, 2014

© Oleksiy Osiyevskyy 2014

ABSTRACT How should incumbent firms respond to emerging disruptive business model innovations (i.e., substantive changes in the way the value is created and appropriated) in their industries? Despite much discussion, the current literature provides no clear-cut answer. This lack of theoretical understanding is reflected in frequently observed in practice problem when established firms are unable to adapt to their industries’ disruptive changes. This sets the motivation for the current dissertation, aimed at advancing our understanding of the phenomenon of disruptive business model innovations. I intend to achieve this goal by addressing the following two research questions: (1) what are the optimal responses of established incumbent companies to disruptive business model innovations gaining momentum in their industries, and (2) what are the factors causing managers of incumbent companies to make different strategic choices in such situations? To address these questions, I unite existing views in a deductively developed model of incumbent response to disruptive business model innovations. Then, I build a dynamic behavioral model of incumbent firms’ responses. The model describes observed behavioral patterns in disrupted industries and explains incumbent actions and reasons why these actions might deviate from the rational paths. Also, I propose a rational response model, comprising a set of testable propositions regarding the contingency factors determining optimal incumbent actions when facing a disruptive business model innovation. Empirical parts of the dissertation are based on two surveys of incumbents facing low-end disruptive business model innovations in their industries – real estate brokerage and higher education.

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To Jim and Halyna, without whose support this study would never have been accomplished

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Table of Contents Abstract…………………………………………………………………………….. ii Dedication………………………………………………………………………….. iii Table of Contents …………………………………………………………………. iv List of Tables ……………………………………………………………………… vi List of Figures …………………………………………………………………….. vii Introduction………………………………………………………………………. 1 General Research Problem……………………………………………….. 1 Research Question…………………………………………….………….. 2 Definitions of key terms……………………………………….………….. 5 Dissertation Structure………………………………………….………….. 9 Statement of Contribution…………………………………….………….. 10 References…………………………………………………….………….. 12 Essay 1: Explorative versus Exploitative Business Model Change: the Cognitive Antecedents of Firm-Level Responses to Disruptive Innovation…..……………………………………………………………………. 15 Abstract…………….……………………………………………………… 15 Introduction……………………………………………….………………. 16 Conceptual Framework……………………………………….…………… 18 Cognitive Antecedents of Adaptation Strategies…………….……………. 26 Methods…………………………………………………….……………... 36 Results…………………………………………………….……………... 43 Discussion………………………………………………….……………... 47 Conclusion………………………………………………….……………... 54 References…………………………………………………….………….. 55 Appendix. Employed Measures…………………………………………… 62 Appendix 2. Replication Study: The Context of Universities Responding to the Threat of On-Line Education……………………….. 63 Essay 2: Incumbent Dynamic Responses to Disruptive Business Model Innovations: Rational, Behavioral and Normative Perspectives..…………………………. 68 Abstract…………….……………………………………………………… 68 Introduction……………………………………………….………………. 69 Response Model: a Typology……………………………….…………… 73 Behavioral Response Model: a Typical Evolution of a Disrupted Industry…………….………………………………………….………… 84 Rational Determination: Critical Determinants of The Impact of Disruptive Innovations………………………………….……………... 92 Normative Recommendations ..………………………….……………... 102 Discussion………………………………………………….……………... 107 References…………………………………………………….………….. 113

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Essay 3: Resolving the Threat-Rigidity Paradox: the Impact of Urgency and Predictability on Innovativeness of Managerial Strategic Decision Making in Times of Crisis…..……………………………………………………………………… 118 Abstract…………….……………………………………………………… 118 Introduction……………………………………………….………………. 118 Theoretical Background……………………………………….…………… 121 Literature Review……………………………..…………….……………. 124 Hypotheses…………………………………………………….…………... 129 Method…………………………………………………….…………... 136 Results…………………………………………………….……………... 143 Replication Study………………………………………….……………... 150 Discussion………………………………………………….……………... 150 Conclusion………………………………………………….……………... 156 References…………………………………………………….………….. 157 Appendix. Employed Measures…………………………………………… 153 Appendix 2. Replication Study 1: The Context of Universities Responding to the Threat of On-Line Education……………………….. 164 Appendix 3. Replication Study 2: The Context of Strategic Decision Making by Boards of Non-Profit Organizations……………………….. 176 Concluding Note………………………………………………………………… 183

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List of Tables Introduction Table 1. The three-dimensional structure of a business model …………

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Essay 1: Explorative versus Exploitative Business Model Change: the Cognitive Antecedents of Firm-Level Responses to Disruptive Innovation Table 1. Correlations between the study’s constructs…………………… 42 Appendix. Employed Measures………………………….………………. 62 Table A1. Replication study 1: Employed Survey Measures……………… 64 Essay 3: Resolving the Threat-Rigidity Paradox: the Impact of Urgency and Predictability on Innovativeness of Managerial Strategic Decision Making in Times of Crisis Table 1. Descriptive statistics and correlations between the study’s constructs ………………………………………………….…… 146 Table 2. Intention to Innovate: OLS Standardized (β) Coefficients….…… 147 Appendix. Employed Measures………………………….………………. 163 Table A1. Replication study 1: Employed Survey Measures……………… 168 Table A2. Replication study 1: Descriptive statistics and correlations between constructs ……………………………………………….……… 171 Table A3. Replication study 1: Intention to Innovate, Standardized (β) coefficients……………………………………….……………….……… 172 Table B1. Replication study 2: Descriptive statistics and correlations between constructs ……………………………………………….……… 179 Table B2. Replication study 2: Intention to Innovate, Standardized (β) coefficients……………………………………….……………….……… 179

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List of Figures Essay 1: Explorative versus Exploitative Business Model Change: the Cognitive Antecedents of Firm-Level Responses to Disruptive Innovation Figure 1. Typology of Incumbent Firm Responses to Disruptive Innovations……………………………………………………………… 23 Figure 2. Determinants of Response to Disruption: the Hypothesized Theoretical Framework.………………………………………………… 34 Figure 3.The Distribution of Reported Responses in the Study Sample .… 44 Figure 4.Structural Model Tested…………………………………….. .… 45 Figure A1. Structural Model Tested….……………………………….. .… 66 Essay 2: Incumbent Dynamic Responses to Disruptive Business Model Innovations: Rational, Behavioral and Normative Perspectives Figure 1. Typology of Incumbent Firm’s Responses to Disruptive Innovations……………………………………………………………… 82 Figure 2. Three Stages of Industry Evolution in Reaction to Disruptive Business Model Innovation……………………………………………… 85 Figure 3.!Rational Response Model: Expected Outcomes of Explorative and Exploitative Business Model Changes………………… 93 Essay 3: Resolving the Threat-Rigidity Paradox: the Impact of Urgency and Predictability on Innovativeness of Managerial Strategic Decision Making in Times of Crisis Figure 1. The opposing hypotheses in threat-rigidity paradox……….…… 130 Figure 2. The hypothesized association between intention to innovate and critical threat perception ……………………………………….…… 137 Figure 3. The detected moderating effect of urgency on the relationships between perceived critical threat and intention to innovate……….…… 149 Figure 4. The detected moderating effect of predictability on the relationships between perceived critical threat and intention to innovate.. 149 Figure A1. Replication study 1: the detected moderating effect of Urgency on the relationships between Perceived critical threat and Intention to innovate…………………………………………………….. 173 Figure A2. Replication study 1: the detected moderating effect of Predictability on the relationships between Perceived critical threat and Intention to innovate ……………………………………………….. 173 Figure B1. Replication study 2: the detected moderating effect of urgency on the relationships between perceived critical threat and Innovativeness of the strategic decision…………………….………….. 180 Figure B2. Replication study 2: the detected moderating effect of predictability on the relationships between perceived critical threat and Innovativeness of the strategic decision ……………….………….. 181

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INTRODUCTION General Research Problem A new and disturbing trend is gaining momentum and importance for management researchers and practitioners – the inability of established incumbent firms to adjust to industry change caused by disruptive innovations (e.g., Foster & Kaplan, 2001; Christensen, 1997). Aggressive newcomers, lacking traditional pre-requisite capabilities, such as industry experience, technological know-how, market standing or financial resources, are usurping positions of industry leadership through new technological and business model innovations. The scholarly work of Clayton Christensen drew attention of researchers and practitioners to the phenomenon. Scrutinizing the issue of disruptive technology in the earlier works (Christensen & Bower, 1996; Christensen, 1997), he later extended the reasoning to disruptive business models, uniting all these notions under the umbrella term “disruptive innovation” (Christensen & Raynor, 2003). Even though the question of response to disruptive innovation has been scrutinized in the management literature for sixteen years, it is still far from resolved (Christensen, 2006). The long and slow demise of Kodak, notwithstanding heroic efforts of successive managers (The Economist, 2012), is a recent vivid example of practical significance of the problem. Other examples of industries turned upside down by disruptive innovations include steel production (disrupted by mini-mills; Christensen, 1997), mechanical excavators (disrupted by hydraulic excavators; Christensen, 1997), photography (disrupted by digital photography; Sandstrom, Magnusson & Jornmark, 2009; The Economist, 2012), and newspaper publishing (disrupted by e-media; Gilbert, 2005). Yet, disruption is not peculiar to industries dominated by large established corporations;

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indeed, even the industries dominated by small entrepreneurial firms are getting disrupted, as in the cases of real estate brokerage (disrupted by discounted models: Dewald & Bowen, 2010) or travel consulting (disrupted by e-brokering). The underlying problem I am addressing is increasing mortality rate of established incumbents caused by inability to respond effectively – in terms of making feasible strategic decisions – to disruptive business model innovations. In recent years the problem became particularly salient, fueled largely by a high pace of technological change (particularly in IT sphere), rising globalization, and an increase of turbulence within the business environment (Foster & Kaplan, 2001).

Research Question The recent editorial article by Powell, Lovallo and Fox (2011), opening the Strategic Management Journal special issue on the psychological foundations of strategic management, offered an insight that lays the ground for the proposed research. According to Powell and colleagues (2011), up to this date the dominant theme in strategy research has been explanation of firm heterogeneity using three basic pillars: Bainian market power, Penrosian resource advantage, and Schumpeterian rents. Yet, the authors assert that many observable phenomena in business practice (such as the failure of Lehman Brothers, performance shock of BP, failed merge of HP and Compaq) cannot be explained by any of these three pillars, but rather by the poor executive judgment. Therefore, there is a need for the fourth pillar of strategic management research – a behavioral strategy – dealing with the effectiveness of executive judgment:

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“The facts overwhelmingly implicate poor executive judgment, or larger macro-cultures of poor judgment. Conversely, sound executive judgment, and contextual architectures that promote sound judgment, can enhance firm performance. Until strategy theory builds stronger foundations in psychology, it will struggle to explain the facts of firm performance. To achieve empirical fidelity, we believe the field needs a robust subfield of behavioral strategy to serve as the fourth pillar of strategic management theory.” (Powell et al., 2011, p.1370) I develop this idea further, demonstrating that the failure of incumbent firms to respond appropriately to gaining momentum disruptions in their industries is an example of the flawed executive judgment. As such, improper incumbents’ responses to disruptive innovations cannot be explained only by the abovementioned three mainstream frameworks in strategic management research. Rather, the holistic answer to the question should include the strategic decision making considerations as well. However, the listed above three established pillars of strategic management thinking should not be dismissed, even when explicitly concentrating on strategic decision making process. First, the three pillars provide an understanding of rational (optimal) firm’s behavior in each particular situation. Second, when making strategic decisions, decision makers usually base their reasoning on one or more of the aspired competitive factors – Bainian market power, Penrosian resource advantage, or Schumpeterian rents. Hence, models that take into account flawed executive judgment must take into account both objective (the three pillars) and cognitive considerations, with the latter being a mediator between the objective reality and the behavioral actions, causing deviations from the appropriate path.

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Therefore, in the dissertation I concentrate on the two research questions: 1) what is the appropriate response for an established incumbent company to an ongoing low-end disruptive business model innovation, and 2) what cognitive factors cause managers to make different strategic choices? Answering these questions contributes to the strategic management literature by enhancing our understanding of disruptive innovations phenomenon. Particularly, I address the gaps in the existing literature concerning the rational decisions to be made in response to low-end disruptive business model innovations and the actual drivers causing decision makers to deviate from the appropriate path. The conceptual and empirical models explaining incumbents’ flaws in strategic decision making opens potentially interesting avenues for further development of research topics of innovation studies, cognition in strategy, and change management. In addition to this, I propose a theoretical resolution to the ongoing debate regarding the “threat-rigidity” paradox, supplementing it with empirical evidence (see the Essay 3). The latter paradox, explaining the risk preference of companies facing severe adversity (such as low-end disruptions that gained momentum in established industries), not only has significant implications for studies of disruptive innovation phenomenon, but also provides more broad implications for a wide set of management science research topics, such as organization decline and leadership studies. The chosen topic is of interest and importance to the academy, as evidenced by the awarding of the 2012 Journal of Small Business Management Editor’s Choice Award to the initial ideas that laid the ground for the current research (Osiyevskyy & Dewald, 2012; Osiyevskyy & Dewald, 2014).

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This is a topic of importance to business practitioners, in that the developed conceptual disruption-response framework and supporting empirical models provide normative guidance for managers of incumbent firms, improving their strategic decision making to maximize the chances of their companies’ survival and growth in times of substantive environmental change caused by disruptive innovations. The study’s results will help in recognizing and overcoming the cognitive biases preventing organizational decision makers from making appropriate decisions in times of disruption. The practitioners’ interest in this topic was indicated when the Canadian Business Magazine included the study in the list of “five research projects taking place at Canadian universities that teach important lessons for today’s business leaders” (McCullough, 2011).

Definitions of key terms Before proceeding to the structure of the dissertation and discussing how each research question is addressed in different parts of this document, it is essential to clarify the terms used throughout all essays. Business model. The first concept needing clarification is a business model, still lacking consensual definition in existing literature (Zott, Amit, & Massa, 2011). I propose a novel definition of a business model that unites the essential salient features of prior conceptualizations (based on the reviews of Zott et al., 2010; DaSilva & Trkman, 2013), while at the same time providing a clear link to established management theories. In its essence, a firm’s business model is a routine for: (i) creating economic value for firm’s stakeholders, and (ii) appropriating part of this value for the firm itself and its

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shareholders. In this definition, the term routine is used in a sense of the evolutionary theory of the firm (Nelson & Winter, 1982), as a regular behavioral pattern within a firm. Notably, this conceptualization allows a firm to have more than one business model (if the value is created and appropriated in more than one distinct way), or have no business model at all (if no regular behavioral pattern for value creation and appropriation is established). As any other routine, a business model can become a capability underpinning the firm’s competitive advantage (Markides & Charitou, 2004; CasadesusMasanell & Ricart, 2010), provided that a set of conditions are met (e.g., the VRIN framework of Barney, 1991). Structurally, a business model as a routine is characterized by three major interrelated components [dimensions] (George & Bock, 2011; Osiyevskyy & Dewald, 2014): value dimension (value propositions for each stakeholder), transactive dimension (organization and governance of exchanges within and across the firm boundaries), and resource dimension (unique combination and organization of resources through which transactions create value – DaSilva & Trkman, 2013). The widely used in practice ‘9 Building Blocks’ of a business model canvas (Osterwalder & Pigneur, 2010) add details to the provided above structural definition of a business model, being accurately mapped onto each of the three generalized dimensions: (1) customer segments and (2) value propositions (value dimension), (3) channels, (4) customer relationships, (5) revenue streams, (6) key activities, (7) cost structure, and (8) key partnerships (transactive dimension), and (9) key resources (resource dimension) – see Table 1 below.

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Table 1. The Three-Dimensional Structure of a Business Model Dimension

Key Components

Value dimension

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customer segments value propositions

Transactive dimension

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channels customer relationships revenue streams key activities cost structure key partnerships

Resource dimension

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key resources

Business model change and innovation. As any type of routine (Nelson & Winter, 1982), business models are in a state of constant change. Hence, a static view of business models as interrelated value, resource and transactive dimensions tells only half of the story; the other essential half is the dynamic, transformational view of the business model evolution (Demil & Lecocq, 2010). In line with this reasoning, throughout this dissertation the term business model change means any alteration of the existing business model of a firm, either radical (major shift in one or more dimensions of a business model), or incremental (progressive refinement of individual components). In terms of novelty, the general business model change concept includes both business model innovations (“new to the world” changes introduced in the industry for the first time) and imitative business model changes (“new to the firm” changes that copy approaches of competitors or firms from other industries). The latter distinction allows differentiating the broad term of business model change from its partial exemplar, business model innovation, which represents !

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intentional, unique for the industry change of the firm business model in response to perceived opportunity to make it more effective or efficient. Finally, this dissertation’s focal phenomenon of disruptive business model innovations represent a particular type of business model innovations that are at first “financially unattractive for the leading incumbent to pursue, relative to its profit model and relative to other investments that are competing for the organization’s resources” (Christensen, 2006, p.49). Being perceived by incumbents and their most valued customers as inferior, such innovations seeks to find new customer bases, often among price-sensitive non-consumers who cannot pay for the full-featured products or services, or among consumers who value different product or service attributes comparing to those emphasized by traditional business models (Markides, 2006; Christensen, 1997). As Christensen(1997) demonstrates, with time the prior ‘inferior’ disruptive innovations gain momentum, develop, and ultimately surpass the requirements of mainstream customers, who eventually switch to the new alternatives. As such, a disruptive business model innovation manifests a discovery of a fundamentally different routine for value creation and appropriation, which usually enlarges the existing market by either attracting new customers or encouraging the existing ones to consume more (Markides, 2006). Given the definitions above, in the dissertation I scrutinize the rational and behavioral antecedents of incumbent companies’ business model change in response to disruptive business model innovations gaining momentum in their industries. These induced business model changes of incumbents can go along two paths: exploitative or explorative. The former represents incremental strengthening a firm’s existing business model to protect from the disruption; in this case, all essential elements (see Table 1) and

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links between them remain unchanged. The latter, explorative business model change, implies adopting a disruptive business model or some elements, with adaptations to match the company’s existing competences. A more detailed discussion of the explorative and exploitative business model change paths is presented in Essays 1 and 2.

Dissertation Structure To comprehensively address the chosen research topic, the dissertation consist of three essays that are closely tied together and complement each other. Exploring the factors causing actually observable (usually improper) behavior of incumbents, the first (empirical) essay addresses the following gap in the existing literature: What are the cognitive determinants of managerial strategic decision making in response to salient disruptive business model innovations gaining momentum in their industries? Drawing upon empirical evidence gathered from two different industries, Canadian residential real estate brokerage and higher education, both facing today the changes caused by disruptive innovations, I elucidate the cognitive predictors of managers’ response to disruptive innovation. The second (conceptual) essay scrutinizes the link between rational and actual responses of incumbent firms to disruptive business model innovations, integrating the established in strategic management theories with cognitive studies into a comprehensive dynamic behavioral theory. Particularly, I analyze the possible (typology), optimal (rational), and actually observed behavior of organizations. The third (empirical) essay is aimed at resolving (theoretically and empirically) the “threat-rigidity paradox” – the conflict that has plagued strategy and organization

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researchers for decades – respecting organizational response to severe adversity. The latter can be caused, particularly, by gaining momentum disruptive innovations in established industries, and a better understanding of incumbents’ strategic decision making in the situation of a critical threat on the latter stages of disruptive change will advance our understanding of the disruption phenomenon in general. The essay’s empirical part is based on the data gained from the survey of real estate brokers in Canada and universities worldwide, triangulated with the data on strategic decision making by boards of nonprofit organizations (survey conducted by the North Carolina State University’s Institute for Nonprofits and the Center for Association Leadership of the American Society of Association Executives). The concluding note summarizes key findings and contributions from the three essays, showing how they are linked together and how their results supplement each other, to form the holistic understanding of the topic of established firms’ strategic decision making in response to a low-end disruptive business model innovations.

Statement of Contribution The prior editions of the essays included in this dissertation undergone substantive peer-review process, and were presented at top academic conferences, including: 1. Osiyevskyy, O., Dewald, J. (2013). Explorative versus Exploitative Business Model Change: The Cognitive Antecedents of Firm-Level Responses to Disruptive Innovation. Strategic Management Society & SEJ Special Issue Conference on Business Models (Barcelona, Spain). 2. Osiyevskyy, O., Dewald, J. (2013). Explorative/Exploitative Business Model Change: The Antecedents of Responses to Ongoing Disruption. Academy of Management (AoM) Annual Meeting, Lake Buena Vista (Orlando, Florida).

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3. Osiyevskyy, O., Dewald, J. (2013). Adversity as a Driver of Innovation in Small Businesses. United States Association for Small Business and Entrepreneurship (USASBE) Annual Meeting, San Francisco. 4. Osiyevskyy, O. & Dewald, J. (2012). Beyond rational reasoning: cognitive factors shaping entrepreneur’s strategic decision making when facing industry’s disruptive change. United States Association for Small Business and Entrepreneurship (USASBE) Annual Meeting, New Orleans. 5. Osiyevskyy, O. & Dewald, J. (2012). Ignoring, adopting, incremental innovating or integrating: typology and determinants of incumbent entrepreneurs’ actions in response to industry’s disruptive change. United States Association for Small Business and Entrepreneurship (USASBE) Annual Meeting, New Orleans. 6. Osiyevskyy, O. (2012). When crisis leads to innovation: the moderating effect of perceived urgency and predictability resolving the threat-rigidity paradox. Strategic Management Society (SMS) Annual Meeting, Prague (competitive paper session). 7. Osiyevskyy, O. & Dewald, J. (2012). Typologies of incumbent response to disruptive innovation: Why they do what they do. Strategic Management Society (SMS) Annual Meeting, Prague (competitive paper session). 8. Dewald, J., & Osiyevskyy, O. (2012). Dynamic positioning framework: explaining dominant migration pattern of incumbent firms in response to emerging disruptive innovation. Decision Sciences Institute (DSI) Annual Meeting, San Francisco. 9. Osiyevskyy, O. & Dewald, J. (2012). Innovating in crisis times? The model of organizational strategic decision making in the situation of critical threat. Decision Sciences Institute (DSI) Annual Meeting, San Francisco. 10. Osiyevskyy, O. & Dewald, J. (2011). Cognitive factors shaping entrepreneur’s strategic decision making: Evidence from real estate brokerage industry. Decision Sciences Institute (DSI) Annual Meeting (featured session), Boston.

All listed above papers were co-authored with the dissertation supervisor, Dr. Jim Dewald. Yet, the author of the current dissertation (Oleksiy Osiyevskyy) is the primary contributor to the research and has done most of the writing, as required by the regulations of the Faculty of Graduate Studies of the University of Calgary for including the co-authored papers to the dissertations of graduate students.

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REFERENCES Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99-120. Casadesus-Masanell, R., & Ricart, J. E. (2010). From strategy to business models and onto tactics. Long Range Planning, 43(2), 195-215. Christensen, C.M. (1997). The innovator’s dilemma: When new technologies cause great firms to fail. Boston: Harvard Business Press. Christensen, C.M. (2006). The ongoing process of building a theory of disruption. Journal of Product Innovation Management, 23, 39-55. Christensen, C.M. & Bower, J. (1996). Customer power, strategic investment, and the failure of leading firms. Strategic Management Journal, 17(3), 197–218. Christensen, C.M. & Raynor, M.E. (2003). The innovator’s solution: Creating and sustaining successful growth. Boston: Harvard Business Press. DaSilva, C.M. & Trkman, P. (2013). Business model: What it is and what it is not. Long Range Planning, in-press. http://dx.doi.org/10.1016/j.lrp.2013.08.004 Demil, B., & Lecocq, X. (2010). Business model evolution: in search of dynamic consistency. Long Range Planning, 43(2), 227-246. Dewald, J., & Bowen, F. (2010). Storm clouds and silver linings: Responding to disruptive innovations through cognitive resilience. Entrepreneurship Theory and Practice, 34(1), 197-218. Foster, R., & Kaplan, S. (2011). Creative Destruction: Why Companies That Are Built to Last Underperform the Market – and How to Successfully Transform Them. Random House.

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George, G., & Bock, A. J. (2011). The business model in practice and its implications for entrepreneurship research. Entrepreneurship Theory and Practice, 35(1), 83-111. Gilbert, C. (2005). Unbundling the structure of inertia: Resource versus routine rigidity. Strategic Management Journal, 48(5), 741-763. Markides, C. (2006). Disruptive Innovation: In Need of Better Theory. Journal of Product Innovation Management, 23(1), 19-25. Markides, C., & Charitou, C. D. (2004). Competing with dual business models: A contingency approach. Academy of Management Executive, 18(3), 22-36. McCullough, M. (2011). What’s your thesis? Canadian Business, 20 October. http://www.canadianbusiness.com/article/51894--what-s-your-thesis (Accessed Jan/15/2012). Osiyevskyy, O. & Dewald, J. (2012). Beyond rational reasoning: cognitive factors shaping entrepreneur’s strategic decision making when facing industry’s disruptive change. Proceedings of 2012 Annual Meeting of United States Association for Small Business and Entrepreneurship. Osiyevskyy, O. & Dewald, J. (2014). Inducements, impediments, and immediacy: Exploring the cognitive drivers of small business manager's intentions to adopt business model change. Journal of Small Business Management, in press. Osterwalder, A., & Pigneur, Y. (2010). Business model generation: a handbook for visionaries, game changers, and challengers. John Wiley & Sons. Powell, T.C., Lovallo, D., & Fox, C.R. (2011). Behavioral Strategy. Strategic Management Journal, 32,1369-1386.

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Sandstrom, C., Magnusson M., & Jornmark, J. (2009). Exploring factors influencing incumbents’ response to disruptive innovation, Creativity and Innovation Management, 18(1), 8-15. The Economist (2012). The last Kodak moment? January 14, 63-64. Zott, C., Amit, R., & Massa, L. (2011). The business model: recent developments and future research. Journal of Management, 37(4), 1019-1042.

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Essay%1:%Explorative%versus%Exploitative%Business%Model%Change%

EXPLORATIVE VERSUS EXPLOITATIVE BUSINESS MODEL CHANGE: THE COGNITIVE ANTECEDENTS OF FIRM-LEVEL RESPONSES TO DISRUPTIVE INNOVATION ABSTRACT How do incumbent companies respond to entrepreneurial disruptors entering their industries? Focused on disruptive business models, we develop a typology of heterogeneous incumbent adaptations, based on juxtaposing two generic strategic paths: i) explorative adoption of the disruptive business model, ii) exploitative strengthening of the existing business model. Applying theories of managerial decision making, we derive hypotheses concerning situational antecedents of managerial intentions to embrace each of the two adaptation strategies. Empirically, we study Canadian realtors at a time when a salient disruptive business model innovation was gaining momentum. We show that explorative business model change intentions are positively influenced by recognition of opportunity, perceived performance-reducing threat, and the incumbent manager’s prior successful risk experience. On the other hand, exploitative intentions are negatively associated with perception of critical threat and tenure within the industry, and positively associated with prior successful risk experience. We contribute to the growing literature on disruptive business model innovations by collecting prior research into one definable framework of incumbent responses, and by developing and testing replicable hypotheses of cognitive influences on incumbent strategic responses in a dynamic setting.

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INTRODUCTION In describing the phenomenon of disruptive technologies, Christensen’s (1997) early brilliance was in recognizing that firms that own or control these disruptive technologies (often the inventors of such technologies) face a dilemma between sticking with the “old” suite of performance metrics embedded in existing products/services, and adopting “new” measures offered through the disruptive technology. Research has since developed to reveal that disruptive technologies act as mere antecedents to disruptive business model innovations (Christensen, 2006; Markides, 2006), which extends the dilemma well beyond firms that own disruptive technologies. Indeed, as disruptive business model innovations gain momentum, all incumbent managers within established industries face a strategic dilemma of whether to explore new disruptive business models, or exploit existing models that provided past success. And there is no reliable answer as the choice of the optimal response is contingent upon numerous contextual factors. For example, even though enthusiastic adoption of the disruptive approach can sometimes lead to disastrous consequences for incumbents (Markides, 2006), refusal to adopt may be equally harmful as a rigid response (Casadesus-Masanell and Ricart, 2010, 2011; Christensen, 1997; LeonardBarton, 1992). At first, disruptive business model innovations are ‘financially unattractive for the leading incumbent to pursue, relative to its profit model and relative to other investments that are competing for the organization’s resources’ (Christensen, 2006, p.49). Being perceived by incumbents and their most lucrative customers as inferior, the innovation seeks to find a new customer base, often initially among price-sensitive non-consumers, who do not need or cannot pay for the full-featured products or services (Christensen, 1997; DaSilva

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et al., in press). However, with time the prior ‘inferior’ disruptive innovation might gain momentum, develop, and ultimately surpass the requirements of mainstream customers, who eventually switch to the new alternative. Examples of industries turned upside down by disruptive business model innovations are numerous, ranging from film photography (disrupted by digital photography; Tripsas & Gavetti, 2000), and newspaper publishing (disrupted by e-media; Gilbert, 2005), to industries dominated by small entrepreneurial firms, such as real estate (disrupted by discounted models; Dewald & Bowen, 2010; Osiyevskyy & Dewald, in press) and travel brokerages (disrupted by e-brokering). The challenge of incumbent response to disruptive business model innovations gaining momentum has received consideration in management literature, however largely without theoretical grounding. Christensen (1997) observed a primary response to disruptive technology implying sticking with the existing technology and going up-market to high-end segments, driven by the need to avoid the conflict between traditional and newborn approaches. Markides advanced the field by concentrating explicitly on the dynamism of incumbent responses as disruptive business model innovations gain momentum (Markides, 1997, 1998; Markides & Charitou, 2004; Markides, 2006). An inductively developed actionresponse framework provided the wider scope of possible incumbent actions, embracing five basic types of business model change in response to disruption (Charitou & Markides, 2003); however, their framework does not address the question of what drives incumbent behavior. The research stream of Gilbert (2005) is a fundamental block toward explaining the determinants of actual incumbent behavior in times of industry-level disruptive change. He demonstrated how cognitive framing of the issue as a threat or an opportunity drives further

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organizational actions, in terms of committing resources and overcoming resource and routine rigidities. We expand the Gilbert’s model, exposing perceptions of threat and opportunity as drivers for change; but rather than concentrating on a micro-level actions (resource commitment and routines change), we explore the more aggregate outcome – business model change as an alteration of the meta-routine of value creation and appropriation (e.g., Zott & Amit, 2007; Chesbrough, 2007; Zott et al., 2011). More specifically, we complement and build on prior research by applying decision making theories to explain an incumbent firms’ business model change in response to disruptive business model innovation gaining momentum in an established industry. Firstly, we propose a parsimonious typology of incumbent adaptation strategies, embracing prior frameworks within a two-dimensional representation of the business model evolution process. Secondly, we scrutinize the research question of what cognitive antecedents determine specific intentions (within established firms) to respond to an ongoing disruption. We concentrate on the managerial decision making perspective, explicitly assuming that the decisions of strategic leaders can provide insights concerning the ultimate behavior of their organizations in line with strategic agency perspective (e.g., Gavetti, 2012). We then derive and test hypotheses concerning situational and dispositional antecedents affecting manager’s intentions to embrace each of the two adaptation strategies, using data collected through a survey of Canadian realtors at a time when a salient disruptive business model innovation was gaining momentum. CONCEPTUAL FRAMEWORK Disruptive innovations as a business model phenomenon

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Initial studies of disruption concentrated on discontinuous technological innovations (Christensen & Bower, 1996; Christensen, 1997). Christensen and Raynor (2003) extended the concept to include disruptive business models, uniting both technological and business model disruptions under the umbrella term “disruptive innovation” (Christensen & Raynor, 2003). The emphasis on business model disruptions was central to the work of Markides, scrutinizing the responses of established firms to disruptive business model innovations [originally named ‘disruptive strategic innovations’ – see Markides (2006), pp.19-20, for clarification of this term] gaining momentum in established industries. Markides explicitly points out that ‘the companies succeed dramatically in attacking an established industry leader without the help of radical technological innovation’ (1997, p.9, italics added) and that ‘the common element in all the successful attacks is strategic innovation’ (p.10). Disruptive strategic (business model) innovation draws heavily on identification and exploitation of gaps in the industry positioning, often addressing the needs of unserved customers through low-cost offerings that may ultimately overtake established markets (Charitou & Markides, 2003; Markides, 1997, 1998, 2006). Christensen (2006) elaborated further that a disruptive technology becomes an antecedent to the disruptive impact of business model innovations, noting that, ‘it is a business model problem, not a technology’ (p.48). The economic value of any invention (including new technology) can be materialized only through commercialization via a business model (Chesbrough, 2010), and hence the notion of innovations must be broadened, embracing new business models along with mere new technologies or R&D process (Chesbrough, 2007). This argument leads to a conclusion that a single new technology can be commercialized in multiple ways through different

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business models (Chesbrough, 2007), and hence the incumbent responses to a technology that has a disruptive potential do not have to be homogenous.% Research regarding the business model construct is still evolving (Zott, Amit, & Massa, 2011), however the emerging patterns in the strategic management literature align with studies from disruption studies. Functionally, a firm’s business model is a meta-routine for: i) creating and ii) appropriating economic value (e.g., Zott & Amit, 2007; Chesbrough, 2007; Zott et al., 2011). From the competitive analysis perspective, the business model underpins the firm’s competitive advantage (Markides & Charitou, 2004), being one of the major facets of competition within an industry (Casadesus-Masanell & Ricart, 2010), distinct from the market positioning (Christensen, 2001) or market strategy (Zott & Amit, 2008). From a transaction point of view, the business model describes the company’s economic exchange with all its stakeholders, including the value proposition for each (Zott & Amit, 2008; Zott et al., 2011); these transactions are based on a firm’s unique combination of resources, and are aimed at generating value for both stakeholders and the organization (DaSilva & Trkman, 2013). Structurally, a business model can be represented as a three-dimensional construct comprising interrelated value, transactive, and resource structures (George & Bock, 2011). Incumbent adaptation to ongoing disruption When faced with a gaining momentum disruptive business model innovation in its industry, an established company can either respond or not. Active response follows along two generic strategies: i) strengthening the existing business model, and ii) adopting a disruptive business model (or some elements, with adaptations to match the company’s existing competences). This choice is consistent with the traditional divergence between the

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exploration of new opportunities and the exploitation of established certainties in organizational learning (March, 1991), or distant search and local search (Stuart & Podolny, 1996; Rosenkopf & Nerkar, 2001). We base our typology of incumbent firm responses to a disruptive innovation on these two fundamental generic strategies, which are distinct, but not mutually exclusive to one another. Each generic response strategy implies alteration of the existing business model, including reconsidering the underlying choices of policies, assets and governance (Casadesus-Masanell & Ricart, 2010, 2011) to match the explorative or exploitative approach. Tripsas & Gavetti (2000) correctly point out that development of new organizational capabilities and the direction of search activities (local or distant) in response to the disruptive change is driven to a large extent by managerial cognition (namely, managerial beliefs). We extend this thinking to explorative/exploitative business model change as a major alteration of business’s meta-routine for value creation and appropriation, arguing that heterogeneous incumbent strategies are driven by divergent managerial beliefs and perceptions of the disruptive approach. The first generic response strategy – explorative adoption of a disruptive business model – generally includes offering different products or services, and potentially reengineering the existing business processes and rethinking the way value is created and distributed by the firm. For instance, the cases of low-end disruptions in established service industries might requires a switch from fewer complex customized transactions to higher number of standardized transactions, a step implying change not merely in organizational resources (such as an IT system), but also a major change in the business mindset (Tripsas & Gavetti, 2000). Exploration may also include forms of experimentation with a new business model or

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some of its components, searching for elements that work as a specific fit to the unique capabilities of the existing company1. Notably, the recent studies of business planning point out the fact that such experimentation with the elements of the new business model might be more effective than the formal business planning process (e.g., see the discussion in Osiyevskyy et al., 2013). Within the Casadesus-Masanell and Ricart’s (2010, 2011) understanding of business models, the explorative choices can lead to either flexible or rigid consequences (or both). Whereas the company managers might be more inclined to make the choices with flexible consequences, which are relatively easy to revert, the need to embrace a totally new disruptive approach might require hard choices with rigid consequences, such as major change in organizational culture, or major investments into development of new capabilities, which are usually rigid and not easy to revert (see Casadesus-Masanell & Ricart, 2010, footnote 7). The second strategy – exploitative strengthening of the existing business model – is a typical reaction of established companies to an inferior, from their point of view, disruption. Following their most valuable customers, the incumbent companies usually migrate to a high-end market, incrementally augmenting their products or services by adding sophisticated features that up-market clients should appreciate (Christensen & Bower, 1996). These steps are intended to create exclusive or monopoly rents in a certain market segment (Porter, 1980), isolating the incumbent firm from discounting disruptors. In most cases, the exploitative strengthening of the existing business model will lead to reinforcement of the existing rigid consequences of the choices within the existing business model (Casadesus%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 1

%Markides%&%Oyon%(2010)%explicitly%warn%incumbent%companies%against%mere%mimicking%the%disruptive%approach,%arguing%that%pure% imitation%will%deprive%them%of%the%firstKmover%advantage%in%the%emerging%new%market;%instead,%the%argument%goes,%they%should%“disrupt% the%disruptor”%by%coming%out%with%original%model%tailored%to%their%competencies.%In%the%descriptive%context%of%the%current%paper%both% approaches%(adoption%of%the%new%business%model%the%way%it%is%or%with%modifications)%are%united%under%the%broad%term%of%explorative% adoption.%

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Masanell and Ricart, 2010, 2011). Notably, the incumbents’ choice of exploitative (incremental) rather than explorative (radical) business model change can be a rational choice, particularly when the disruptive business model is driven by a discontinuous technological change (Tripsas, 1997). Applying these two strategies as orthogonal axes yields the 2x2 matrix of incumbent reaction to disruptive business model innovations gaining momentum (Figure 1).

Figure 1. Typology of Incumbent Firm Responses to Disruptive Innovations

%

% Yes+

%

Exploitative+strengthening+of+the+existing+business+model+ No+

Yes+

Group+2:+

Group+3:+

Pure+exploration:%adoption% of%the%new%approach%

Integration%(in%one%company% or%spinKoff)%

Group+1:+ No+

Explorative+adoption+of+ the+disruptive+business+ model+

%

Defiant+resistance:+Defend% habitual%routines%% +

Group+4:+ Pure+exploitation:%incremental% innovating%

The companies in Group 1 defend the status-quo (Defiant Resistance), sticking with the existing business model. Examples of such response are numerous, particularly in the early stages of disruption: consider IBM ignoring the market for personal computers, recognizing that serving the new, perceived inferior market would require complete rethinking of the business model – pricing, production, distribution, service, and profit formula (Christensen, 1997). Today, the same defiant resistance generic strategy is observed, for instance, among commercial banks ignoring the peer-to-peer lending (The Economist, 2013), or in the US public school system, which mostly ignores the on-line models of class delivery

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(Christensen et al., 2008). This reaction, akin to ‘Ignore the Innovation – It’s Not Your Business’ (Charitou & Markides, 2003; p.59), is a predictable protectionist reaction for incumbents in the early emergence of a disruptive business model innovation. Charitou and Markides (2003) cite a focus on prior huge investments in existing business (omnipresent problem of sunk costs), having more important issues to deal with in existing business, a need for further analysis of the situation, a lack of necessary resources for entering new business, or simply bad timing to enter as specific reasons for incumbent firms to adopt a defiant resistance position. Christensen (1997) found that incumbents rely on the advice of existing customers who do not value the specific benefits of the disruptive innovation. Dewald & Bowen (2010) even demonstrated that some managers are likely to proactively resist change, lobbying for legislative prohibition of disruptive innovations. Group 2 consists of companies pursuing a Pure Exploration strategy. These firms see the future in the new business model and embrace it without simultaneous development of the existing business model or attempting to integrate both models to find some synergy between them. This can be done either by switching to the new business model completely, or by simply neglecting the existing business model, depriving it of further investment and instead focusing on developing the new one. Yet, as Teece (2010) succinctly summarize a large body of literature, “changing the firm’s business model literally involves changing the paradigm by which it goes to market, and inertia is likely to be considerable” (p.187); ergo, Pure Exploration strategy is usually pursued exclusively by newcomers or very young incumbents who still remain flexible. This is the generic strategy of the disruptive newcomers – agents of the Schumpeterian creative destruction. To illustrate the point: Today, in the GPS navigation industry, the established business model hinges upon selling

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expensive standalone GPS devices and (usually) charging a subscription fee. The nascent business model (free application run on smartphone and tablets, and value appropriation through advertising) comes from industry newcomer – Google, not from the established players. Notably, incumbents’ low likelihood of embracing the Pure Exploitation strategy is usually based on good grounds, as the existing business model with matching fine-tuned competences, routines and architectures usually handicaps the incumbents when trying to embrace the disruptive approach (Tripsas, 1997). Group 4 is termed the Pure Exploitation strategy, wherein firms strengthen their existing business model without adopting in whole or in part the disruptive approach. Prior research indicates that this is the group where the majority of industry incumbents migrate after the disruption becomes too visible to ignore (Christensen, 1997). In an attempt to defend the status-quo in the way business is done, and exploit the previously effective resources and capabilities possessed by the firm, management looks for incremental improvements, such as going to upper segments of the market (protected from inferior disruptors) or differentiating (to enjoy monopoly power). These firms exploit their existing business model, consumed in the belief that what they offer their customers is value-added and superior to the offering through the disruptive business model innovation. It is essential to note, nonetheless, that in some cases such behavior can be perfectly rational: some business model innovations might not have an economic sense for incumbents, and responding by developing the existing business model might be the optimal strategy for incumbents (Markides, 2006). In other words, Christensen’s recommendation favoring innovating in response to technological disruption should be considered with caution in the domain of business models disruptions: in most latter cases, despite the disruption, there will

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still remain considerable market for the old approach, lucrative enough for the best incumbents (see Markides, 2006, for a detailed discussion of this issue). Low-cost airlines (Pure Exploration) co-exist with traditional, full-service ones (Pure Exploitation); discounted stock brokers (Pure Exploration) did not displace traditional ones completely (Pure Exploitation); similarly, on-line education will unlikely eliminate the role of traditional universities, even if the latter institutions do not embrace the on-line education models, but concentrate on improving their current offerings instead. Finally, members of Group 3 attempt to benefit from both models, simultaneously adopting the innovation (in its entirety or its major components) and leaving the door open for other opportunities in the existing business model (‘Playing Both Games at Once’, as described by Charitou & Markides (2003)). This can be achieved either though integration of both models in the same business unit, or through unrelated diversification (spin-off). The integration approach implies business models reinforcing each other by leveraging the synergies between traditional and disruptive approaches (for instance, on-line and in-class MBA programs co-existing in the same business school). The spin-off approach, on the other hand, supposes that the new business is developed simultaneously but independently from the traditional one (the early recommendation proposed for responding to a technological disruption – see Christensen, 1997). COGNITIVE ANTECEDENTS OF ADAPTATION STRATEGIES Within the literature on disruptive innovations, incumbent responses are explained by describing the obstacles to embracing the new business model, which most directly draws to conflicts between the existing and the new business model. Researchers contend that

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incumbent managers enter with a fixed mindset, unable to conceive the opportunity offered by the disruptive innovation, and hence tend toward displaying strong resistance (e.g., Christensen, 1997; Gilbert, 2005). The nascent disruptive business model might contradict the managerial beliefs about success factors in the industry (Tripsas & Gavetti, 2000), resulting in strong opposition from the senior leaders.

Similar arguments have been

developed in the business model innovation literature (Amit & Zott, 2001), demonstrating that conflicts between the key aspects of the established and new business models (novelty, lock-in, complementarities and efficiency) can hamper exploration. Others point out potential cognitive conflicts between the traditional and new business models (Chesbrough, 2010; Gilbert, 2005), which are dispositional in nature, and act as filters, selecting only the information related to the firm’s dominant logic (Prahalad & Bettis, 1995). Hence, when analyzing the determinants of incumbent behavior in response to emerging disruptive innovation, managerial cognition and decision making should play the major role (Tripsas & Gavetti, 2000; Benner & Tripsas, 2012). This resonates with the ideas of the classic management work of Edith Penrose (1959), stating that “the environment has been treated not as an objective ‘fact’ but rather as an ‘image’ in the entrepreneur’s mind; the justification for this procedure is the assumption that it is not the environment ‘as such’, but rather the environment as the entrepreneur sees it, that is relevant for his actions” (p.215). Recently, the students of disruption phenomenon devoted particular attention to the situational determinants of established firms’ response to ongoing disruptive innovation – namely, the opportunity- or threat-related framing of the disruptive business model innovation in the minds of managers of incumbent companies (Osiyevskyy & Dewald, inpress; Dewald & Bowen, 2010; Gilbert, 2005). Gilbert (2005) demonstrated that the

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perception of threat induced by disruption attenuates the rigidity in the patterns of resource allocation, but simultaneously amplifies the routine rigidity. Dewald and Bowen (2010) expanded this reasoning, showing that the situational threat perception leads to proactive resistance to change (such as erecting the legislative barriers against the disruption), while the opportunity framing drives adoption of the disruptive business model innovation. Along these lines, Osiyevskyy and Dewald (in-press) scrutinize the role of opportunity perception as a driver for changing the firm business model (resource, transactive and value structure dimensions); the authors show that perceived opportunity induces alterations in all three aspects of the firm’s business model. Unique to this paper, we specifically concentrate on the cognitive drivers of the focal two adaptation strategies – explorative and exploitative intentions for business model change. Concurring with the cited above studies, we explore situational cognitive factors (i.e., perceiving the disruption as a threat or an opportunity) that drive incumbent’s intentions to act along the exploration and exploitation dimensions. We assume that managerial cognitive framing acts as a key mediator between actual environment and the intentions to act (Penrose, 1959). Two other critical assumptions underlying our approach are: i) the strategic agency perspective, and ii) intention as the critical predictor of further behavior. The strategic agency perspective implies that the decisions of strategic leaders precede the behavior of the organization (e.g., Gavetti, 2012), and hence individual decision making frameworks can explain firm actions. Miles and Snow (1978) stressed the strategic choice as adaptation strategy when studying the firm reactions to changes in external environment, while March & Shapira (1982) pointed out the need to consider the insights from an

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individual decision making level in order to understand the processes of innovating and risktaking at the organizational level. The strategic agency perspective embraces the employment of existing theories and frameworks of managerial decision making (entrepreneurship studies, threat-rigidity, prospect theory) to effectively derive propositions concerning situational and dispositional antecedents affecting manager’s intentions to embrace each of the two adaptation strategies. These intentions will lead to firm actions. We use the intentions to innovate (our dependent construct) as an antecedent of observable innovative behavior because intentions are the key aspect in scrutinizing the human behavior (Tubbs & Ekeberg, 1991; Fini, Grimaldi, Marzocchi, & Sobrero, 2012). Numerous studies of human behavior demonstrated that intentions are reliable predictors of actions (e.g., Ajzen & Fishbein, 1980; Bagozzi, Baumgartner, & Yi, 1989; Ajzen, 1991; Sutton, 1998), including innovation and entrepreneurship (Krueger, Reilly, Carsrud, 2000). Therefore, by understanding the determinants of managerial intentions to innovate, we can identify antecedents for further strategic actions of their firms. Opportunity The first hypothesized situational factor is the capstone of research in entrepreneurial decision making – perceived opportunity. Perceived opportunity is a primary driver of strategic entrepreneurial actions (Shane and Venkataraman, 2000; Christensen at al., 1994). The positive link between perceived opportunity and incumbent adoption of a new disruptive business model was theoretically discussed by Christensen and Raynor (2003), and empirical demonstrated by Dewald and Bowen (2010).

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Following this line of reasoning, we argue that when incumbent firm managers perceive an opportunity for business growth or additional profit in the disruptive approach, they are likely to chase the opportunity by engaging the firm in some form of exploration, leading to further business model change. The second argument supporting the opportunity-induced exploration stems from the recent critique of the classic behavioral theory of the firm (Cyert & March, 1963) for concentrating on problemistic threat-based search and neglecting the opportunity-driven one (Barreto, 2012; Iyer & Miller, 2008). This research confirms that search can clearly be stimulated by perceived opportunity, inducing organizational attention and actions towards experimenting through the explorative adoption of a disruptive business model. Being oriented towards the opportunities created by the new approach, we anticipate that intentions to innovate will be explorative. These arguments converge to the same conclusion: Hypothesis 1: perceived opportunity in the disruptive business model is positively associated with explorative intentions to adopt the new business model. It is important to note that in response to the ongoing disruption in their industry, incumbents can potentially come out with different business models, each tailored to their idiosyncratic competences and context. So, several business models can emerge in a disrupted industry. On the level of a particular incumbent, however, mimicking the original disruptive approach or adjusting it to match available competencies still constitutes an explorative adoption of the disruptive approach (in a pure or modified form). Threats %

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Opposite to opportunity is the perception of threat from the emergence of the disruptive business model; this factor determines firm’s actions in response to any ongoing major change in environment (Dutton & Jackson, 1987; Gilbert, 2005). We assert that the construct of threat is multi-faceted, with each facet leading to different outcomes in terms of strategic decisions made. Particularly, we differentiate between perceived performance-reducing and critical threat. Perceived performance-reducing threat is stimulated by anticipated losses (or expected results falling below the aspired goals) in revenues and profits, making companies riskseeking and innovative (Bowman, 1982; Fiegenbaum & Thomas, 1988; Lehner, 2000). Two streams of literature explain this response to performance-reducing threat. The first stream draws on individual-level prospect theory (Kahneman & Tversky, 1979) applied to the context of managerial decision making in organizations (e.g., March & Shapira, 1987, 1992; Kahneman & Lovallo, 1993; Holmes, Bromiley, Devers, Holcomb, & McGuire, 2011). The theory implies that when faced with the situation framed as potential losses, the human decision makers demonstrate risk-seeking tendency. Decision makers perceive any innovation as a risky action because of its uncertain results compared to the status quo (Latham & Braun, 2009; Wiseman & Bromiley, 1996), and hence the human natural risk aversion hampers innovativeness without some external stimulus. Performancereducing threat can be this stimulus: according to prospect theory, perceived threat from the disruption will make a decision-maker more risk-seeking, offsetting the perceived riskiness as the obstacle for innovation.

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The second stream of literature stems from the behavioral theory of the firm, arguing that perceived threat (expected results falling below the aspired goals) triggers the problemistic search reaction (Simon, 1959; Cyert & March, 1963), aimed at making incremental changes in the current business model in order to fix the situation. The first mechanism, prospect theory, will make decision makers more likely to embrace the explorative adoption of disruptive business model. On the other hand, the second mechanism – behavioral theory of the firm – will likely lead to the incremental exploitative change, caused by the problemistic search that is always local and close to the existing solution. Therefore: Hypothesis 2a: perceived performance-reducing threat from the disruptive business model is positively associated with explorative intentions to adopt the new business model. Hypothesis 2b: perceived performance-reducing threat from the disruptive business model is positively associated with exploitative intentions to strengthen the existing business model. Critical threat refers to situations of anticipated severe adversity that cause cessation of the entire business (bankruptcy, forced sale, loss of license to operate). Critical threat triggers reaction predicted by threat-rigidity thesis (Staw, Sandelands & Dutton, 1981). At its core, threat-rigid reaction is grounded in individual support of the most learned, habitual, or dominant response given the dispositional setting. This may constitute a complete inability to adapt (‘rigidity’) or concentration on efficiency by exploiting existing resources (‘resource conservation’). The theoretical propositions of Staw and colleagues (1981) are in line with results of numerous empirical studies, demonstrating the hampering effect of perception of critical threat on organizational change, innovation, and risk-taking (e.g.,

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Schendel et al., 1976; Laughhunn et al., 1980; D’Aunno & Sutton, 1992; Shimizu, 2007; Iyer & Miller, 2008). In the situation of ongoing disruptive business model innovation, particularly in a period of growing momentum when customers start switching to the new approach, the managers of established firms might perceive the new business model as a critical threat to their business. They can neither compete with it (it’s too late), nor embrace it because of the conflict with existing business model (Christensen, 1997); instead, they fall into a state of resource conservation, trying to do harder what they have always been doing (exploitative incremental improvement of existing business model). Therefore: Hypothesis 3a: perceived critical threat from the disruptive business model is negatively associated with explorative intentions to adopt the new business model. Hypothesis 3b: perceived critical threat from the disruptive business model is positively associated with exploitative intentions to strengthen the existing business model. Controls In addition to the situational factors, we expect at least three substantive dispositional (long-standing) factors to have an impact on both the decision making outcome (direct effect) and on the situational factors (indirect effect). Hence, we include prior successful risk experience, tenure within the industry and other industries experience as controls in our model (Figure 2).

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Figure 2. Determinants of Response to Disruption: the Hypothesized Theoretical Framework Disposi8onal* controls*

disrup0ve&approach)&

Prior*risk* experience*

Opportunity* Impact*on* percep8ons&

Industry* experience*

Business*model*change* inten8ons*

Situa8onal* percep8onal* factors*(view&of&the&

+H1$ +H2a$

Performance 1reducing* threat*

+H2b$ (H3a$

Other* industries* experience*

Cri8cal* threat*

Explora8ve*adop8on* of*disrup8ve* approach*

+H3b$

Exploita8ve* strengthening*of* exis8ng*approach*

Direct*impact*on*decision*making&

%

Prior risk experience. Different experiential backgrounds of managers can lead to different patterns of reactions to disruptive business model innovation. Particularly, entrepreneurship studies show that prior positive experience of acting in risky situations can increase a person’s belief in her own abilities and judgment, driving up the overconfidence and representativeness biases, which have a substantive effect on innovation activity (Curseu & Louwers, 2008; Camerer & Lovallo, 1999). In particular, Pablo (1997) demonstrates that prior risk outcome history determines individual’s risk propensity, with perceived success augmenting the tolerance towards risks in the future. Since innovation is perceived as a risky action, prior success in a risky situation will arguably lead to an increase in the likelihood of emergence of innovation intentions. This statement was tested empirically in Dewald and Bowen (2010), finding that entrepreneur’s intentions to adopt a new business model is determined to a large extent by her prior risk experience. We test both direct impact of prior

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risk experience on innovation intentions, and its indirect effect, mediated by the three focal situational factors (perception of opportunity, performance-reducing threat, and critical threat). We expect positive direct impact of this factor on both explorative and exploitative business model change, and do not set any a priori hypotheses about its impact on perception of opportunity and two facets of threat. Work experience. An important dispositional determinant of both situational antecedents (opportunity, performance-reducing and critical threats) and the ultimate result (explorative or exploitative business model change intentions) of decision making in response to disruptive innovation is the manager’s prior experience of work in the particular industry and exposure to other industries (e.g., Benner & Tripsas, 2012; Tripsas & Gavetti, 2000). For instance, when studying the firms’ technology changes, Furr, Cavarretta and Garg (2012) demonstrate that ‘while all teams make minor changes, only teams with some novelty (domain outsider top management team members or CEOs) make moderate changes and only teams with extra-domain CEOs make major, competence-destroying changes’ (p.237). This result aligns with the broad studies of top management teams, revealing that a manager’s background may influence her reactions to major changes in external environment, such as ongoing disruption. Particularly, experience of working in other industries improves cognitive flexibility (Harris & Helfat, 1997; Wiersema & Bantel, 1992), facilitating an ability to conceive innovative strategies. Similarly, experience of working in the current industry could lead to cognitive rigidity, commitment to status quo and reluctance to making strategic changes (Wiersema & Bantel, 1992; Finkelstein & Hambrick, 1996). Hence, we expect that these two control variables will impact the intentions to change

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the business model, either directly or indirectly (mediated by perception of opportunity, performance-reducing and critical threats). METHODS Context To test the proposed theoretical model of intentions, we selected the homogenous sample of the single industry, eliminating industry effects and increasing internal validity. More specifically, we derive empirical data by investigating the responses to the natural experiment that took place in Canadian real estate brokerage industry in 2010-2011. Traditionally, the business model adopted by real estate brokerage firms in North America is built around the monopoly access to a valuable resource – the Multiple Listing Service (MLS) system – owned and supported by local industry associations in each area. This system provides an exclusive source of crucial information about local real estate market that is not available elsewhere. The potential substitutes for MLS, such as specific Internet real estate sites or advertisements of “For Sale by Owner” (FSBO), could not match the richness of the MLS data. Potential clients, either sellers or buyers, cannot access MLS directly, but only through engagement of a broker. To protect this stream of clients and revenues, in many jurisdictions across North America, regulators (incumbents’ associations) have imposed a legal ban on unbundling of brokerage services, explicitly prohibiting the industry players from proposing the discounted services (such selling the access to MLS for a flat small fee) instead of the full-priced bundle of expensive brokerage offering (comprising showing the objects, negotiations, documents work, etc.). This legislative barrier kept numerous aspiring disruptive business model innovations – either offered by entrepreneurial newcomers or incumbents willing to offer discounted services – at bay. The %

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brokerage industry in Canada was protected from disruptive technologies (e.g., Internet sites listing property for sale) and disruptive business models (e.g., discounted brokers, mere posting service, services for FSBO-“For Sale by Owner”), which flourished in related sectors and in jurisdictions without the unbundling ban. In the Fall of 2010 the situation in Canadian real estate brokerage changed substantively: pressed by the federal Competition Bureau, the Canadian Real Estate Association (CREA) signed a consent agreement allowing (but not forcing!) the brokers (those who wish) to offer mere posting service (listing on MLS for a flat fee), clearing the path for disruptive business models and eliminating the protective shield for traditional real estate brokers. Notably, the discounted real estate brokerage model was enabled by new technology (Internet, e-business platforms). Still, the emerging disruption should not be scrutinized as a mere adoption of technological innovation, as it represents a substantial business model change (Chesbrough, 2007; Teece, 2010). The switch to disruptive discounted brokerage by an established firm would result in substantive change of the source of competitive advantage, business processes, expertise, human resource management and information systems. Therefore, although the focal regulatory change is applicable to the incumbents and newcomers alike, it is usually newcomers who are the enthusiastic proponents of the disruptive business model, as they do not bear the legacy costs of the old approach and are not threatened by the potential to cannibalize the existing sales and profit (Teece, 2010). Hence, incumbents usually respond to (or adopt to) disruption, but rarely cause it; in this sense, our choice of the real estate brokerage context is justified, as it allows studying the incumbents’ responses to disruptive business model in their industry.

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The timing of our study is also appropriate: in the early 2011 (the time of our data collection), the focal legislative change triggered the growth of discounted brokerage sector. Numerous industry observers pointed out that incumbents felt a major threat caused by the situation (Ladurantaye, 2011a). Reports indicated that some brokers experimented with adopting new business models (Roberts, 2011), while most either rigidly ignored it or proactively resisted (Ladurantaye, 2011b). Even though traditional realtors can choose to ignore a disruptive innovation by strengthening their existing business models through a wide set of additional services valued by their most lucrative customers (Saber & Messinger, 2011), any change of the established business model is risky (Roberts, 2011), and hence managers postpone it as long as possible. Data collection and sample The main object of the current study, the association between perceived characteristics of current situation and intended actions, is the mechanism operating on the level of individual cognition. The most appropriate way for measuring such unobserved constructs of interest is direct inquiry based on self-report measures. Therefore, following the example of Dewald and Bowen (2010), we employed survey as the primary data collection method, assuming that peoples’ actual actions are purposeful and are based upon the decisions made, which are contingent upon perception of the situation. The acknowledged vulnerability of self-reports for capturing both independent and dependent variables to common method variance bias will be addressed below. Six months after the legislative change, in May/2011, the real estate brokerage regulatory authorities of two Western Canadian provinces invited their brokers via e-mail to

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fill in the voluntary anonymous electronic survey about their view on the situation in the industry and response plans. The survey was targeted at real estate brokers (owners and managers of the business). An additional reminder was sent one week after the initial e-mail message. We received 288 electronic surveys; 266 of which were usable, revealing a 15 percent response rate for the province of Alberta (similar to result reported by Dewald & Bowen (2010)). After deleting 25 observations with substantial number of missing values (particularly, missing values for the dependent constructs), we obtained the final sample with 241 cases to be used in all other analyses. The general characteristics of our sample’s participants

(urban/rural

clientele

split,

gender,

independent

brokers/franchise

operators/corporate brokers) were similar to those reported in prior studies of the real estate brokerage context (Dewald & Bowen, 2010; AREA, 2004). Measurement The survey questionnaire was developed on the basis of theoretical reasoning about the underlying constructs, employing wherever possible the scales with tested reliability and validity found in existing literature (see table in Appendix). To match the empirical context, the survey questions were adapted for peculiarities of real estate brokerage business (Dewald & Bowen, 2010). The survey instrument was validated by three industry experts, who made sure that the questions made sense for the respondents and captured key aspects of measured constructs in real estate brokerage business (e.g., what things can we reasonably expect to be changed in brokerage business model).

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All cognitive constructs were self-reported by respondents on the Likert-type 5- and 6point scales, with factors scores estimated using the Anderson-Rubin method implemented in LISREL 8.80 software (Joreskog et al., 2006). The reliability testing (see Appendix) reveals the appropriateness of all composite indexes (Cronbach’s α statistics and Composite reliabilities (CR) above the acceptable threshold of .7). We measured the dependent constructs of business model change using the scales specifically adopted for real estate brokerage context. The explorative adoption of the disruptive business model captured the intentions to embrace the new approach – discounted brokerage services – either holistically (as a switch to the new approach) or as a part of the existing offering. On the other hand, exploitative strengthening of existing business model captured the intentions to add supplementary value-added services, complementary to traditional brokerage offering, aimed at obtaining the differentiating advantage over the discounting disruptors to attract the best customers. Even though ‘business model is not the same as product market strategy (i.e., it does not refer to firm positioning in product market based on differentiation or cost leadership in certain activities)’ (Zott et al., 2011, p.1032), as it was argued before, in the case of real estate brokerage the market positioning can be used as a proxy for business model change, in that embracing the discounted approach requires altering all other aspects of business (namely, resource, transactive and value structures of the business model – Osiyevskyy & Dewald, in press; George & Bock, 2011). The difference between the constructs of critical and performance-reducing threats stems from their theoretical distinction: the former relates to perceptions of potential business failure, while the latter to a potential drop in revenues and/or profit.

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Validity Measurement Model. To validate the measurement model for the scales employed, the confirmatory factor analysis was conducted in LISREL 8.80. The results reveal appropriateness of used construct measures: overall factor model fits the data reasonably well (χ2 = 312.87, df = 163, p < .01; χ2/ df = 1.92; RMSEA = .06, NFI = .92, Non-Normed Fit Index (NNFI) = .95, CFI = .96, RMR = .06, GFI=.89, AGFI=.84). Convergent Validity. Three basic tests to assure the convergent validity of the employed measures were used (Hair et al., 2010). First, for latent constructs with multiple indicators, all hypothesized factor loadings are significant at .05 level, and all standardized loading values highly exceed the recommended threshold of .5 (all being higher than .6 and with majority being in >.75 range). Second, all average variance extracted (AVE) indicators for composite scales are above the recommended threshold of .5, suggesting an adequate convergence (see Appendix). The only construct that fails this test is exploitative strengthening of the existing business model, with AVE=.462, very close to the recommended minimum. Third, all Composite Reliability (CR) indices turn out to be well above the stipulated cut-off point of .7 (see Appendix), suggesting good reliability of measured constructs. Discriminant Validity. All AVE indicators (see Appendix) are substantively less than the estimated squared correlation between each variable (see Table 1), suggesting that the factor loadings are higher than the correlations between the factors, i.e., that the constructs are empirically discriminated (Hair et al., 2010). The only concern emerges because of the predicted correlation between performance-reducing and critical threats (r=0.83, p.05) CONCLUSION Overall, the replication study completely corroborated the key results of the main study in a new context. Similarity of the results substantiates the generalizability (external validity) of the proposed theoretical framework across different settings (industries, countries, organization types). Moreover, alignment of empirical results adds to the robustness (internal validity) of the main analysis and mitigates risks caused by the limitations of the main study.

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Essay 2: Incumbent Responses to Disruptive Business Model Innovations

INCUMBENT DYNAMIC RESPONSES TO DISRUPTIVE BUSINESS MODEL INNOVATIONS: RATIONAL, BEHAVIORAL AND NORMATIVE PERSPECTIVES ABSTRACT How should incumbent firms respond to emerging disruptive business model innovations introduced in their industries by innovative startups, newcomers from adjacent industries, or entrepreneurial established players? Despite much discussion, the current literature provides no clear-cut answer. One view suggests establishing autonomous business units to explore disruptive business model innovations; the other approach implies ambidextrous integration of two business models in the same firm, or even ignoring the disruptive innovation to concentrate on the core business model. In this paper, we unite existing views in a deductively developed model of incumbent response to disruptive business model innovations in their industries, manifested in an original, holistic typology of response strategies. On its basis, we build a dynamic behavioral model of incumbent firms’ responses to disruptive business model innovations, describing observed behavioral patterns in disrupted industries and explaining incumbent actions and reasons why these actions might deviate from the rational path. Then, we propose a rational response model, comprising a set of testable propositions regarding the contingency factors determining optimal incumbent actions when facing a disruptive business model innovation. Finally, we supplement the insights of rational and behavioral models with the real options lens to formulate a set of normative recommendations for managers of established real-world firms having to make decisions regarding nascent or gaining momentum disruptive business models in their industries. !! 68! !


INTRODUCTION Back in 2007, Garmin was on its rise in the booming GPS navigators market. The company was considered “the next Apple” by stock analysts (Leber, 2013), with excited customers, increasing sales, and skyrocketing stock price. Yet, five years after, the momentum seems to be lost: revenues are shrinking (-15% comparing to 2007: Leber, 2013), customers are switching to alternatives, and the market capitalization is less than a half of the one in 2007. The reason for this decline is simple: a major disruptive business model innovation is gaining momentum, taking away the market share from existing industry players (Downes & Nunes, 2013; Leber, 2013). The disruptive business model is reflected in new products, free map applications for smartphones and tablets (such as Google Maps or its analogues). The problems of Garmin are not idiosyncratic; they manifest a new and disturbing trend today’s business environment – the inability of established incumbent firms to adjust to industry change caused by disruptive innovation. The incumbents face a major two-staged problem: (1) having to foresee if the disruptive innovation will transform the industry; (2) having to come out with a proactive response strategy, even without knowing ex ante the answer to the first question. The long and slow demise of Kodak, notwithstanding heroic efforts of successive managers (The Economist, 2012), is a vivid and identifiable example of practical significance of the topic. There are myriad of industries in which leading firms were dethroned or forced out of business by disruptive innovators: steel production (disrupted by mini-mills; Christensen, 1997), photography (disrupted by digital photography; Sandstrom, Magnusson & Jornmark, 2009; The Economist, 2012), or newspaper publishing (disrupted by e-media; Gilbert, 2005). The !! 69! !


other group of industries underwent substantive change; although most incumbents survived, they have to adapt to totally new industry structure: e.g., traditional airlines now having to co-exist with low-cost disruptors, or traditional stock brokers competing with on-line discounters (Markides, 2006; Christensen, 2006). Finally, there are industries where the disruptive innovations are just starting to gain momentum, and their impact is yet to be discovered: e.g., traditional banking facing peer-to-peer lending (The Economist, 2013), or traditional universities facing on-line models of education. In both latter cases the disruptive innovations can either completely change the existing industries, or never materialize. The scholarly work of Clayton Christensen drew attention of researchers and practitioners to the phenomenon of incumbent failure to properly respond to gaining momentum disruptive innovations. Scrutinizing the issue of disruptive technology in the earlier works (Christensen & Bower, 1996; Christensen, 1997), he later extended the reasoning to disruptive business models, uniting all these notions under the umbrella term “disruptive innovation” (Christensen & Raynor, 2003). Moreover, reflecting on the further elaboration of the topic in subsequent works of strategic management scholars, Christensen (2006) explicitly emphasized the business model facet of the phenomenon (“it is a business model problem, not a technology”, p.48). Arguably, it is the evolution of disruptive business models that should receive the primary attention of researchers in this field. Indeed, “a disruptive innovation may or may not even represent a technical breakthrough. Rather, it may simply involve…the changing of a firm’s business model” (Crockett et al., 2013, p.858).

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How should incumbent firms respond to emerging disruptive business model innovations introduced in their industries by innovative startups, newcomers from adjacent industries, or entrepreneurial established players? Although discussed above examples of Garmin and Kodak paint a gloomy picture for established firms, in many instances incumbents can adapt successfully to protect themselves from disruptive innovations, or even to thrive on them, preserving the market leadership (Ansari & Krop, 2012; Yu & Hang, 2010). Even though the question of optimal response to disruptive innovation was extensively scrutinized in the management literature, it is still far from being resolved (Christensen, 2006), particularly for the case of disruptive business models rather than disruptive technologies. One view suggests establishing autonomous business units to explore disruptive business model innovations (Christensen & Raynor, 2003); the other approach implies ambidextrous integration of two business model in the same firm (O’Reilly & Tushman, 2008), ignoring the disruptive innovation to concentrate on the core business model, or even coming out with a new disruption in response (Charitou & Markides, 2003). Most of the existing models of incumbent response to disruptive business model innovations are developed inductively from cases or industry data (most notably, Christensen, 1997; Christensen & Raynor, 2003; Charitou & Markides, 2003; Markides & Oyon, 2010). The inductive origins of these frameworks leave unanswered the question of their scope and parsimony: One cannot say for sure whether the proposed sets of actions embrace all possible responses, whether the proposed actions are not overlapping, and whether they are really responses to disruption. Also, the proposed in different studies contingency factors determining proper incumbent response must be unified in a single model. Taking into account the importance of the

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topic and the stage of our understanding of the disruptive business model innovations phenomenon, the further progress of the field would benefit from deductive generalizing of available theoretical insights and observed empirical regularities into a holistic theoretical model. This sets the motivation for the current study. Therefore, in this paper we intend to address the following research questions: i) what is the rational incumbent response to gaining momentum disruptive innovations? ii) which factors explain observable patterns of incumbents behavior when faced with disruptive business model innovations?; iii) what are the normative recommendations for managers of incumbent firms in such situations? Uniting the literatures on business models and disruptive innovations, we address the listed research questions in the following way. First, we clarify the conceptualization of key terms (business model, business model innovation/change, and disruptive business model innovation), which are highly ambiguous in the current literature. From this, we deductively develop a holistic typology of incumbent responses to disruptive business model innovations in their industries, embracing two generic business model change strategies: explorative adoption of the disruptive business model, and exploitative strengthening of the existing business model. Then, we propose a behavioral model of incumbent firms’ responses to disruptive business model innovations gaining momentum in their industries; the model describes the observed behavioral patterns in disrupted industries, explaining incumbent actions and reasons why these actions might deviate from the rational path. The behavioral response model is supplemented by a rational response model, comprising a set of testable propositions regarding the contingency factors determining optimal incumbent actions when facing a disruptive business model !! 72! !


innovation. Finally, we enhance the insights of the rational and behavioral models with the real options lens to formulate a set of normative recommendations for managers of real-world established firms having to make decisions regarding nascent or gaining momentum disruptive business model innovations in their industries. Overall, the developed models stress that the firm’s response is an appropriate unit of analysis of situations of ongoing disruptive business model innovations, and that the actual responses are determined by managerial cognition, organizational context and market factors. RESPONSE MODEL: A TYPOLOGY Disruptive Business Model Innovations: Clarification of the Concepts Business model. Before developing the theoretical models, we must first clarify the definitions of concepts used in further theoretical reasoning. The first and most important concept needing clarification is a business model, which still lacks consensual definition in existing literature, despite growing understanding of its importance (see, e.g., the review by Zott, Amit, & Massa, 2011). Authors of papers of the business model concept often study it without explicit definition (see the review by Zott, et al., 2011), or propose competing definitions. For instance, the essence of a business model was defined as a “design or architecture” (Teece, 2010), “articulation between different areas of activity” (Demil & Lecocq, 2010), “specific combination of resources” (DaSilva & Trkman, 2013), among other things (Zott et al., 2011). This lack of clear definition of the key construct is typical for a pre-paradigmatic stage of science, in which the literature on business models is currently situated. Yet, these conflicting definitions of the essence of a business model makes its theoretical development problematic; moreover, most of the

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proposed definitions (such as “architecture”, “design”, “conceptual tool or model”) prevent conceptual linking of the business model concept with the existing body of knowledge in management studies, a necessary step for deductive development of models involving business models concept. To overcome this obstacle, we propose a definition of a business model that unites the essential salient features of prior conceptualizations (based on the reviews of Zott et al., 2010; DaSilva & Trkman, 2013), while at the same time providing a clear link to established management theories. Functionally, a firm’s business model is a meta-routine for: i) creating economic value for firm’s stakeholders, and ii) appropriating part of this value for the firm itself and its shareholders. In this definition, the term meta-routine is used in the sense of the evolutionary theory of the firm (Nelson & Winter, 1982), as a complex multilevel routine – a regular behavioral pattern within a firm – used for value creation and appropriation. Notably, this conceptualization allows a firm to have more than one business model (if the value is created and appropriated in more than one distinct way), or have no business model at all (if no regular behavioral pattern for value creation and appropriation is established). As any other routine, a business model can become a capability underpinning the firm’s competitive advantage (Markides & Charitou, 2004; Casadesus-Masanell & Ricart, 2010), provided that a set of conditions are met (e.g., the VRIN framework of Barney, 1991). Structurally, a business model as a routine comprises three major interrelated dimensions (George & Bock, 2011; Osiyevskyy & Dewald, 2014): value structure (value propositions for each stakeholder), transactive structure (organization and governance of exchanges within and across the firm boundaries), and resource structure (unique

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combination and organization of resources through which transactions create value – DaSilva & Trkman, 2013). Business model change and innovation. As any type of routine (Nelson & Winter, 1982), business models are in a state of constant change. Hence, a static view of business models as interrelated value, resource and transactive structures tells only half of the story; the other essential half is the dynamic, transformational view of the business model evolution (Demil & Lecocq, 2010). In line with this reasoning, we conceptualize business model change as any alteration of the existing business model of a firm, either radical (major shift in one or more dimensions of a business model), or incremental (progressive refinement of individual components). In terms of novelty, the general business model change concept includes both business model innovations (“new to the world” changes introduced in the industry for the first time) and imitative business model changes (“new to the firm” changes that copy approaches of competitors or firms from other industries). The latter distinction allows differentiating the broad term of business model change from its partial exemplar, business model innovation, which represents intentional, unique for the industry change of the firm business model in response to perceived opportunity to make it more effective or efficient. Business model innovations can be introduced by newcomers (either startups (Christensen, 1997) or diversifying entrants from adjacent industries (Tripsas, 1997)), or by entrepreneurial established players (“Schumpeter Mark II” – Schumpeter, 1943). If the introduced business model innovation proves its potential, the remaining incumbents often learn about this, and respond by imitating and copying it (Casadesus‐Masanell & Zhu, 2013). A relatively !! 75! !


broad topology of business model innovations (according to their essence) was proposed in Giesen et al. (2007), distinguishing among industry model innovations (redefining the industry or creating a new one), revenue model innovations (reconfiguration of product offering and pricing), and enterprise model innovations (changing the role of a firm in the value chain). Disruptive business model innovations represent a particular type of business model innovation that is initially “financially unattractive for the leading incumbent to pursue, relative to its profit model and relative to other investments that are competing for the organization’s resources” (Christensen, 2006, p.49). Being perceived by incumbents and their most valued customers as inferior, the innovation seeks to find a customer base, often among price-sensitive non-consumers who cannot pay for the fullfeatured products or services, or among consumers who value different product or service attributes comparing to those emphasized by traditional business models (Markides, 2006; Christensen, 1997). As Christensen(1997) demonstrates, with time the prior ‘inferior’ disruptive innovation gains momentum, develops, and ultimately surpasses the requirements of mainstream customers, who eventually switch to the new alternative. As such, disruptive business model innovation manifests a discovery of a fundamentally different routine for value creation and appropriation, which usually enlarges the existing market by either attracting new customers or encouraging the existing ones to consume more (Markides, 2006). One important fact differentiates disruptive technological innovations from disruptive business model innovations: whereas the materialization of a disruptive technology in most cases leads to displacement of prior, established technology !! 76! !


(Christensen, 1997), the rise of disruptive business models rarely eliminate prior business models in their industries (Markides, 2006). In most cases, disruptive business models gain some market share, extend the overall market, and then compete and co-exist with established ones. Indeed, “several value propositions may coexist within a specific industry” (Sabatier et al., 2012, p.950), with several business models successfully coexisting. Responses to Disruptive Business Model Innovations: Existing Approaches When facing disruptive business model innovations introduced and gaining momentum in their industries, managers of incumbent firms must make decisions regarding appropriate responses to them. As it was stressed at the beginning of the paper, even though the question of optimal response to disruptive innovations was extensively scrutinized in the management literature, it is still far from being resolved (Christensen, 2006), particularly for the case of disruptive business models. The early approaches suggest establishing autonomous business units (spin-offs) to explore disruptive business model innovations (e.g., Christensen & Raynor, 2003; Christensen, 2006). Yet, this model implicitly assumes that incumbents should respond to every potential disruption in their industries, which is far from optimal (Charitou & Markides, 2003; Markides, 2006). Some disruptive business model innovations never gain momentum, and pursuing each of them might be a waste of resources. Moreover, the discussed above fact that more than one business model can co-exist in the same industry suggests other possible incumbent responses, such as ignoring the disruption and

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concentrating on existing business model (e.g., not all universities should switch to online delivery, and not all airlines should become low-costers). The action-response framework of Charitou & Markides (2003) moves the discussion much closer to a desired theoretical explanation of the optimal incumbent response to disruption. Starting with the observation that incumbents do not have to respond to every disruptive business model innovation by adopting it, the researchers propose a more sophisticated variation of incumbents’ responses. The inductively developed action-response framework embraces five basic responses: 1) focusing on traditional business; 2) ignoring the innovation; 3) attacking back through new disruption; 4) adopting the innovation by playing both games at once; 5) embracing the innovation and scaling it up. This work provides an important comprehensive perspective on incumbent responses, and hence represents a huge step towards building the theory of response to disruptive innovations. Yet, the inductive origins of the action-response framework leave unanswered the question of its scope and parsimony: One cannot say for sure whether the proposed sets of actions embrace all possible responses, whether the proposed actions are not overlapping, and whether they are really responses to disruption. Moreover, the normative implications of Charitou and Markides (2003) need further elaboration. Based on juxtaposing of only two factors (ability to respond and motivation to respond), the recommendations leave too much ambiguity: e.g., in the case of high levels on both factors, we have three plausible solutions (adopt and separate, adopt and keep internal, and attack back). Obviously, additional contingencies must be taken into account to resolve these ambiguities. To some extent, these were resolved in further

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studies of organizational ambidexterity (O’Reilly & Tushman, 2008); yet, these separate bodies of literature are still not united in a holistic deductive model. Typology of Responses The first step in developing a model of incumbents’ responses to disruptive business model innovations is in clarifying the potential types of responses. Such typology provides a context from which to describe and discuss the dynamic process of responding to a business model innovation. On a very broad level, when faced with a gaining momentum disruptive business model innovation, an established company can either respond or not. The active response choice can follow two generic strategies: i) strengthening the existing business model, or ii) adopting a disruptive business model (pure imitating, or imitating some elements with adaptations to match the company’s existing competences and capabilities). This choice is consistent with the traditional distinction between the exploitation of established certainties and exploration of new opportunities in organizational learning (March, 1991), or distant and local search (Stuart & Podolny, 1996; Rosenkopf & Nerkar, 2001). Notably, these two generic response strategies are distinct, but not mutually exclusive to one another. Each strategy implies a change of the existing business model, since to match the explorative or exploitative approach, the firm has to reconsider the business model’s value, transactive and resource structures (Osiyevskyy & Dewald, 2014), including alteration of established policies, assets configuration and choice of governance (Casadesus-Masanell & Ricart, 2010, 2011).

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The first generic response strategy – explorative adoption of the disruptive business model (explorative business model change) – implies imitating the disruptive business model or some of its essential elements. This change is radical in nature, putting a company on a change trajectory outside linear (incremental) refinement of the existing business model. This often leads to changing the value proposition to firm customers, manifested in offering different products or services, changes in the way of delivering them, rethinking the methods of payment or price policy. In its turn, change in value proposition usually demands reconsidering the supporting transactive and resource structures of the business model (Osiyevskyy & Dewald, 2014). The explorative adoption of the disruptive business model has two important features. First, finding the right way to adopt the disruptive business model does not happen overnight; rather, it is a learning and experimentation process requiring major changes with following progressive refinements to achieve internal and external consistency – among business model elements and with external environment, respectively (Demil & Lecocq, 2010; McGrath, 2010). Indeed, “an emerging dynamic perspective sees business model development as an initial experiment followed by constant revision, adaptation, and fine tuning based on trial-and-error learning” (Sosna et al., 2010, p.384). Second, explorative adoption of the disruptive business model often implies searching for elements that work as a specific fit to the unique context of the existing company (peculiar assets and capabilities), rather than mere imitation of the disruptive approach. Pure imitation usually deprives incumbents of the first-mover advantage in the emerging market; instead, the rational response supposes coming out with original model tailored to a particular firm (Markides & Oyon, 2010). !! 80! !


The second strategy – exploitative strengthening of the existing business model (exploitative business model change) – implies changing the established business model in an incremental way (along the linear trajectory), to protect from disruptive newcomers. The goal of this strategy is in creating exclusive or monopoly rents in a certain market segment (Porter, 1980) and isolating the firm from the impact of disruptors. Christensen & Bower (1996) provide an example of such strategy, a typical response of incumbents when facing a disruptive innovation: Following their most valuable customers, the firms usually migrate to a high-end market, incrementally augmenting their products or services by adding sophisticated features that up-market clients should appreciate. Notably, the incumbents’ choice of exploitative rather than explorative business model change can be a rational choice: as it was stresses before, even if a disruptive business model innovation gains momentum, in most cases it does not overtake the whole market (Markides, 2006). In other words, a disruptive business model innovation might leave lucrative niches for incumbents to occupy, and hence exploitative strengthening of the existing business model to build strong positions in these niches might be a rational strategy. Applying these two strategies as orthogonal axes yields the 2x2 matrix of incumbent responses to gaining momentum disruptive business model innovations (Figure 1). Notably, the generic response strategies (with regards to a disruption) are similar to firm’s positioning (with regards to competition in its industry – e.g., frameworks of Porter (1980) or Miles & Snow (1978)), being dynamic and pre-determined by market forces and organizational characteristics.

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Figure 1. Typology of Incumbent Firm’s Responses to Disruptive Innovations

!

!

No!

Yes!

!

Explorative!adoption!of! the!disruptive!business! model!(explorative! change)!

!

Exploitative!strengthening!of!the!existing!business!model! (exploitative!change)! No!

Yes!

Group!2:!

Group!3:!

Pure!exploration:!adoption! of!the!new!business!model!

Integration!(in!one!company!or! in!autonomous!unit)!

Group!1:!

Group!4:!

Defiant!resistance:!Defend! habitual!routines!! !

Pure!exploitation:!incremental! innovating!of!the!existing! business!model!

The companies in Group 1 defend the status-quo (Defiant Resistance), sticking with the existing business models without any changes. Examples of such response are numerous, particularly in the early stages of disruption: consider commercial banks ignoring the peer-to-peer lending (The Economist, 2013), or the US public school system, mostly ignoring the on-line models of class delivery (Christensen et al., 2008). Companies from this group not only decline to change their business models, but also are likely to proactively resist changes, lobbying for legislative prohibition of disruptive innovations (Dewald & Bowen, 2010). Notably, the Defiant Resistance response strategy is a natural position for firms to adopt before a disruptive innovation has proven its market potential. Group 2 consists of companies pursuing a Pure Exploration strategy. These firms adopt the disruptive business model or its essential elements without simultaneous development of the existing business model or attempting to integrate the development of both models. The adoption of a disruptive business model can be done either by switching to it entirely, or by simply neglecting the existing business model, depriving it !! 82! !


of further investment and instead focusing on developing the new one. This is a generic strategy of agents of the Schumpeterian creative destruction, either entrepreneurial incumbents or newcomers. Group 4 includes the companies pursuing a Pure Exploitation strategy, which strengthens their existing business models without adopting any elements of the disruptive approach. Unlike members of the Defiant Resistance group, companies pursuing pure exploitation are engaged in incremental changing of their established business models. In an attempt to defend the status-quo in the way business is done, and exploit the previously effective resources and capabilities possessed by the firm, managers of these firms look for incremental adjustments and refinements, such as going to upper segments of the market (protected from inferior disruptors) or differentiating (to enjoy monopoly power). Finally, members of Group 3 (Integration) attempt to benefit from both models, simultaneously adopting the elements of the disruptive innovation and leaving the door open for other opportunities in the existing business model (‘Playing Both Games at Once’, as described by Charitou & Markides (2003)). This can be achieved either though integration of both models in the same business unit, or through unrelated diversification using an autonomous business unit (spin-off). The former approach implies leveraging the synergies between business models by reinforcing their strengths. The spin-off approach, on the other hand, supposes that the new business is developed simultaneously but independently from the established one (the early recommendation proposed for responding to a technological disruption – see Christensen, 1997; Christensen & Raynor, 2003). !! 83! !


BEHAVIORAL RESPONSE MODEL: A TYPICAL EVOLUTION OF A DISRUPTED INDUSTRY Further development of the response model will proceed along two directions: behavioral and rational paths. Whereas the rational response model concentrates on the question of what incumbent firms should do when facing a disruptive business model innovation in their industries, the discussed in this section behavioral response model studies their actual behavior, in terms of a typical pattern of disrupted industry’s evolution. The key premise of the developed behavioral response model is considering disruption as a dynamic process rather than as one-time event. Indeed, from all disruptive business model innovations that materialized (overtaking substantive part of established markets), not a single one did so overnight; rather, any nascent disruption needed time to gain momentum: “it indeed is a process, not a cataclysmic event” (Christensen, 2006, p.50). We assert that the typical incumbent response to an emerging disruptive business model innovation is contingent upon the stage of disruptive innovation’s development, and hence the typical established industry’s response should be considered from a dynamic evolution perspective. In majority of cases this evolution will include three stages (Figure 2).

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Figure 2. Three Stages of Industry Evolution in Reaction to Disruptive Business Model Innovation Stage%1%

Stage%2%

Stage%3%

Pure% Explora,on%

Integra,on%

Pure% Explora,on%

Integra,on%

Pure% Explora,on%

Integra,on%

Deﬁant% Resistance%

Pure% Exploita,on%


Pure% Exploita,on%


Pure% Exploita,on%

Stages of Disrupted Industry’s Evolution Stage one. In the first stage, the emerging disruptive business model innovation does not have the necessary momentum to become salient enough to be perceived seriously by major incumbents. Managers of incumbent firms, so as their best customers, tend to focus on inferior aspects of the disruption, making it easy to ignore. The disruptive offering seems to appeal to only a small subset of customers, promise insufficient financial returns, and looks like a fleeting, short-term, non-serious threat. As a result, majority of incumbents stick with the traditional business model. The rationale behind these actions is obvious: while requiring rethinking of the whole business model, the disruptive offering is simultaneously “inferior” in terms of traditional performance measures, appeals to different (or unknown yet) group of customers, and does not fit traditional business performance evaluation models. Therefore, on the first stage of disrupted industry’s evolution, the dominant incumbent response is Defiant Resistance (akin to ‘Ignore the Innovation – It’s Not Your Business’ in Charitou & Markides, 2003). As an example, consider the traditional book retail industry in 1995-1997, with Borders !! 85! !


and Barnes & Noble being major specialized competitors, sharing the book market with generalist retailers like Costco and Walmart. In those days, the emergence of a pure online book retailer with totally new disruptive business model – Amazon (founded in 1995) – did not lead to any substantive reaction of incumbents (i.e., Defiant Resistance response). Barnes & Noble opened its on-line bookstore only in 1997 (although without major emphasis on it); while Borders allowed Amazon to handle its Internet sales – a mistake that would lead to fatal consequences later (e.g., Hitt et al., 2011). There are two basic mechanisms which lead to Defiant Resistance as a dominant response of most incumbents in the early stage of disruptive business model innovation emerging in their industry: cognitive and structural ones. On the level of managerial cognition, in the first stage of industry disruption there is a set of factors that were shown to cause rigidity (or reduce intentions to change the business model in an explorative way) in the decision making of the managers of incumbent firms. First, the framing of the market potential for the disruptive business model is formed by managers’ prior industry affiliations, beliefs, and experiences (Benner & Tripsas, 2012; Tripsas & Gavetti, 2000), biasing their search activities in favor of existing approaches and business models (Tripsas & Gavetti, 2000). Indeed, “managers of established firms, whose perspectives are deeply entrenched and largely shaped by their current experiences, tend to ignore these new low margin segments and focus on their existing customers and markets” (Crocket et al., 2013, p.859). Second, the disruptive business model is not salient enough from the point of view of managers of established businesses; hence, they perceive no major threat or opportunity from it, while these two latter factors were found to be significant drivers of explorative business model change intentions (Osiyevskyy & !! 86! !


Dewlad, 2014; Dewald & Bowen, 2010). Finally, any nascent market sparked by a radical change, including the market of a disruptive business model, is characterized by major ambiguity and uncertainty (Benner & Tripsas, 2012). This low level of predictability also constrains the intentions to explore the new business model. From the perspective of structural (non-cognitive) impediments for business model change on the first stage of disruption, Christensen (1997) found that incumbents rely on the advice of existing customers who do not value the specific benefits of the disruptive innovation. In addition, Charitou and Markides (2003) cite a focus on prior huge investments in existing business (omnipresent problem of sunk costs), having more important issues to deal with in existing business, an objective need for further analysis of the situation, a lack of necessary resources for entering new business, or simply bad timing to enter as specific reasons for incumbent firms to adopt a Defiant Resistance position. Yet, despite the cognitive and structural barriers, the other incumbent responses are possible in the early, first stage of disrupted industry’s evolution. They may take the form of a Pure Exploration, embraced by rare incumbents that are predisposed towards the disruptive business model. An example of the latter non-dominant response is proactive engagement of University of Phoenix in the U.S., Open University in the U.K., and Athabasca University in Canada in on-line education. All these institutions were predisposed towards on-line education, because of the prior experience of correspondence course delivery and the emphasis on increased enrollment of diverse students from other geographical regions as an engine for further development.

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Stage two. If the disruptive business model innovation does not die or flatten out on the first stage and keeps on gaining momentum, with time this disruption becomes too visible for incumbents to ignore; this change manifests the beginning of the second stage of the disrupted industry’s evolution. At this point, the disruptive approach starts threatening the managers of incumbent firms, requiring proactive response. In most cases, this proactive response usually implies the dominant reaction of exploitative development of the traditional business model (Pure Exploitation), aiming at protecting from the threat posed by the disruption by increasing the customers’ value to prevent the latters from switching to the disruptive alternative. This development is usually done through marginal incremental changes, rather than drastic radical innovations (Demil & Lecocq, 2010). For instance, in traditional real estate brokerage, disrupted by discounted offerings and Internet technologies, these incremental innovations could include supplementing traditional broker’s services of facilitating the transaction (search, negotiations, and legal details) with help in getting mortgage, in renovation of newly bought houses, or in preparing the real estate for sale (Osiyevskyy & Dewald, 2014). Prior research demonstrates that Pure Exploitation is the quadrant where the majority of industry incumbents migrate after the disruption becomes too visible to ignore (Christensen, 1997). The rationale for the Pure Exploitation response suggests that the firms protect their “premium” position from discounting newcomers through enhanced differentiation (Porter, 1980). In the case of retail book selling, the Pure Exploitation as a dominant response in Stage two of the disrupted industry’s evolution was manifested in the Borders’ strategy in 1997-2009: indeed, while major visible changes were happening in the industry (customers shopping for books online, growing !! 88! !


popularity of electronic books for e-readers), “Borders invested heavily to enhance the marketing for traditional book selling” and “tried to lure customers to its stores with promises of enriching experience” (Hitt et al., 2011, p.3). No major attempt to explore the disruptive business model by Borders was noted; all this time, the company’s online sales operations were outsourced to Amazon. Christensen (1997) clearly described the flaws of this response, noting that at a certain point mainstream customers become “overserved” and readily switch to the less costly disruption, once it meets their minimum performance requirements (“performance overshooting” – e.g., Bergek et al., 2013). This fulfills the innovators “dilemma”. Tom make the things worse, concentrating on strengthening the existent business model distracts managers from scrutinizing the benefits of disruptive innovation and from getting experience of working with it.!! The second stage of the industry disruption requires proactive response from incumbents. Two main mechanisms drive the Exploitation (rather than Exploration or some combination) as this proactive response. From the managerial cognition perspective, the main obstacle for explorative adoption of the disruptive business model is the cognitive inability of managers of incumbent firms to understand the opportunity and value potential of the disruptive approach (Zott et al., 2011; Tripsas & Gavetti, 2000). Yet, managerial cognition is not the only obstacle; there is also a set of structural factors preventing explorative change: as Teece (2010) succinctly summarize a large body of literature on the topic, “changing the firm’s business model literally involves changing the paradigm by which it goes to market, and inertia is likely to be considerable” (p.187). This inertia is sometimes referred to as handicap of the incumbents !! 89! !


(Tripsas, 1997), when the organizational structures, routines and procedures fine-tuned for the existing business model constrain any explorative business model change. Nevertheless, despite the hardships, some incumbents demonstrate non-dominant responses in Stage two, of which the most important one is Integration, embracing both explorative and exploitative changes of the firm’s business model. A salient example of this strategy is that of Barnes and Noble in 1997-2009, implying integration of the established business model (brick-and-mortar retail stores) with on-line selling (through Barnesandnoble.com) and selling e-books (using original Nook eReader). Stage three. Finally, the last, third stage of disrupted industry’s evolution, starts when the disruptive business model becomes too lucrative or threatening to procrastinate or delay its adoption any more. At this time, the disruption’s market potential becomes obvious, so as the performance overshooting of the traditional business model (Crocket et al., 2013; Christensen, 1997). At this time the majority of incumbents start looking for the ways to integrate both models, by pursuing the Integration strategy (combination of both explorative and exploitative business model change), either through internalization of both models within one unit, or through a related diversification (spin-off or autonomous business unit for pursuing disruptive opportunity). It is the Stage three when the flaws of the dominant response in the prior Stage two become obvious, as the previously pursued Pure Exploitation did not allow building competences and acquiring the resources necessary for exploring the disruptive business model. Consider the case of Borders: at the Stage three of the evolution of disrupted retail book selling industry (in 2010-2011), it was too late to start selling books on-line; the chance to develop this capability was lost in 1995-2009, when these operations were outsourced to Amazon. As a result, in 2011 !! 90! !


Borders declared bankruptcy. Barnes and Noble, on the other hand, was relatively successful pursuing the Integration strategy in Stage three of the disrupted industry’s evolution, as the potential for this was developed in Stage two, through experimenting with disruptive business model (through company’s online bookstore and eBook reader). However, the anecdotal evidence of the retail book industry does not imply that the dominant behavioral response in Stage three – Integration – is always the optimal one. Arguably, other three response strategies (including even Defiant Resistance) could be viable for particular incumbents, contingent on their internal situation and market conditions. This issue is elaborated in detail in the next section, discussing the rational response model. One last observation is worth noting with regards to non-dominant responses, particularly Pure Exploration. On any of the three stages of the disrupted industry’s evolution, this strategy of incumbents (i.e., embracing the new business model while neglecting the old one) is highly unlikely. First, it is hard to abandon core business model, which can still be profitable, in order to switch to something new. Secondly, this transition would require significant alteration of all aspects of business, including resource structure, processes organization, and even ways of thinking. One more important reason for not pursuing this option is strategic consideration: When making this switch, a firm will not be able to leverage the existing resources and competences, having to build new competences and to acquire necessary resources from scratch, without any advantage over disrupting newcomers.

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RATIONAL DETERMINATION: CRITICAL DETERMINANTS OF THE IMPACT OF DISRUPTIVE INNOVATIONS Having provided descriptive overview of the behavioral response patterns of incumbents in disrupted industries, in this section we develop propositions and provide a richer understanding of rational responses to disruptive business model innovation, acknowledging the complexity and dynamics of change, and the cognitive and structural drivers of firms’ actions. The proposed in this section Rational Response Model is intended to provide testable propositions concerning the appropriate response of an incumbent firm when facing a disruptive business model innovation introduced in its industry. The unit of analysis within the proposed model is disruptive business model innovation by company. This statement stresses the fact that a single disruption can require heterogeneous responses from different companies (Bergek et al., 2013; Yu & Hang, 2010), as well as that a single company cannot have a unified approach towards all disruptive business model innovations (Charitou & Markides, 2003). Therefore, the model links drivers of responses (innovation-related – market drivers, company-related – organizational enablers) with optimal response strategy, formulated in terms of explorative or exploitative change of the existing incumbent’s business model. The two dependent variables are expected performance outcomes of explorative and exploitative business model changes in response to disruption, reflecting the impact of embracing each of these response strategies on the long-term financial performance of an incumbent firm. Schematically, the discussed in the next sections model is presented in Figure 3.

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Figure 3. Rational Response Model: Expected Outcomes of Explorative and Exploitative Business Model Changes +((P1)(

Market'Drivers:' ① Market(propensity( ② Innova2on( legi2macy(

+((P2)(

Expected'outcome' of'explora(ve' change!

+((P3)(

+((P5)(

L((P4)(

Organiza1onal'Enablers:'

Expected'outcome' of'exploita(ve, change!

Complementarity'of' Business'Models:' Complementary( capabili2es(

① Slack(Resources( ② Specialized(Complementary( Assets( ③ Organiza2onal(Learning( Capabili2es( ④ Resource(ﬂexibility(

The Role of Market Drivers The innovation-related determinants of the appropriate incumbent response are summarized in the concept of market drivers. The first determinant, market propensity, reflects the potential demand for the offering of the disruptive business model innovation (demand for its value proposition to customers), comprising the demand from prior non-consumers and the demand from part of the customers served by the existing business model who migrate to the offering of the disruptive business model. The first component of the demand for the disruptive business model innovation is particularly noteworthy, since, as it was noted before, the disruptive innovations usually expand the customer base, attracting former non-consumers (Markides, 2006; Christensen, 1997). The second component of the demand for the

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disruptive business model innovation, driven by the migration of consumers of conventional business model, is determined by the propensity of existing customers to switch to the disruptive approach. The ultimate characteristic of the market propensity is the potential for economic value appropriated by an incumbent when serving the customers using the disruptive business model, compared to the value appropriated using the existing business model. The higher the market propensity of the disruptive business model innovation for a particular incumbent, the higher is the expected outcome of responding to it.!Market propensity, in its turn, is influenced primarily by the evolution of customer needs (in the dimensions of performance, functionality, convenience, simplicity, price – e.g., Christensen & Raynor, 2003). Yet, market propensity is not the sole essential factor determining the appropriate incumbent response, as not all disruptive innovations can be exploited despite their market potential. Consider numerous disruptive innovations in healthcare (such as empowering professional nurses) that cannot be implemented despite the obvious market potential for them; these innovations first need to gain legitimacy in the eyes of powerful stakeholders in the healthcare industry. Hence, the second crucial factor, innovation legitimacy, determines whether the offering of the disruptive business model (value proposition to customers) is perceived as legitimate by key stakeholders in the industry. Any changes or innovations in organizational practices, forms, or policies require legitimization before being perceived as an objective reality (Tost, 2011) and being allowed to be implemented. In addition to individual-level legitimacy from customer perspective, a disruptive business model innovation also often needs explicit endorsing by institutional environment through regulatory actions (Kaplan & Tripsas, 2008), such !! 94! !


as implicit or explicit approval by regulators, who “measure, credential, or certify innovations” (Ansari & Krop, 2012, p.1363). Hence, on the most basic level, the innovation legitimacy comprises to components – subjective legitimacy (the evaluation of the innovation by important decision makers (namely, customers) and their influencers), and legal legitimacy (the endorsement of the innovation by authorities, such as government regulators or accrediting bodies). Therefore, the combination (mathematically: interaction) of market propensity and legitimacy of the disruptive business model determines the salience of market drivers of response to the emerging innovation. Low level on either of these two components prevents the disruptive business model innovation from gaining momentum; moderate or high levels of components reinforce each other. Obviously, ceteris paribus, the higher the market drivers of the disruptive innovation for a particular incumbent firm, the higher is the expected outcome of its embracing: Proposition 1: Market drivers are positively associated with the expected performance outcome of the incumbent’s explorative business model change in response to a disruptive business model innovation However, market drivers of a disruptive business model innovation facilitate not only its explorative adoption. In fact, even the incumbents who respond in an exploitative manner will still benefit from doing so when facing a disruptive business model innovation with high market drivers. First, as it was already argued, disruptive business model innovations usually expand the industry by appealing to non-consumers, and exploitative strengthening of the existing business model can help an incumbent to

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benefit from this expansion. Second, if the disruptive business model gains momentum (which is more likely in the case of innovations with high market potential), an incumbent will have to compete with disruptors in the future, and exploitative strengthening of the existing business model will help to protect its market position. Therefore, all other things being equal, exploitative strengthening of existing business model in response to a disruptive innovation with high market drivers is a rational action: Proposition 2: Market drivers are positively associated with the expected performance outcome of the incumbent’s exploitative business model change in response to a disruptive business model innovation. The Role of Organizational Enablers In the prior section we postulated that external market drivers make a proactive business model change (explorative or exploitative) the optimal incumbent strategy when faced with a disruptive business model innovation. The exact nature of the best response – explorative change, exploitative change, or integration – is determined by internal organizational factors – enablers of disruptive business model change. The latter term refers to internal contextual factors that make embracing the disruptive business model a viable option. The first crucial organizational enabler is availability of slack resources necessary for explorative business model change (Hill & Rothaermal, 2003). Explorative business model change often requires long-term investment with uncertain outcomes, and unless an incumbent possesses or can acquire the necessary resources (from existing operations or external sources), explorative change cannot be a viable option. Indeed, the high level !! 96! !


of available slack allows an organization to take additional risks and experiment (Mone et al., 1998). This argument is supported by the behavioral theory of the firm (Cyert & March, 1963), arguing that, ‘slack provides a source of funds for innovations that would not be approved in the face of scarcity but that have strong subunit support’ (p.189). Moreover, slack buffers an organization from the downside risk associated with a major change, and increases the change’s legitimacy in the eyes of powerful stakeholders (Singh, 1986). In the case of explorative business model change, the slack resources can be utilized for acquiring generic complementary assets necessary for the change, and for initial launching (experimenting, refinement) of the new business model, until it becomes sustaining. The second organizational enabler of explorative business model change is availability of specialized complementary assets necessary for the new business model. Adding this enabler is justified by the assumption that business model innovation requires additional complementary assets (or capabilities) for its deployment in a particular company. Unlike generic complementary assets (which are freely available on the market and are not company-specific), specialized complementary assets are company-specific, and hence must be developed internally. Specialized complementary assets necessary for disruptive business model can buffer incumbents from competition and facilitate value appropriation from this innovation (e.g., Teece, 1986; Tripsas, 1997; Ansari & Krop, 2012). Examples of such complementary assets include, inter alia, specialized manufacturing capabilities and sales/service networks (Tripsas, 1997), strong brand name and corporate reputation (which will make innovation’s legitimization and endorsement

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more easy), or unique network of partners (alliances, joint ventures) supporting the value chain activities necessary for the new disruptive business model. An explorative business model change is possible only if an incumbent possesses the organizational learning capabilities, in that the proper new business model, suiting both external conditions and internal context, can rarely be apparent early on, and those organizations that are well positioned to learn and adjust are more likely to succeed (Teece, 2010; McGrath, 2010). We argue that two key organizational learning capabilities are absorptive capacity and learning orientation. Absorptive capacity (Cohen & Levinthal, 1990; Zahra & George, 2002) represents the incumbent’s ability to recognize the value and essential features of the disruptive business model, to assimilate the available knowledge about it, and to apply this knowledge for planning and executing the actual explorative business model change. As such, absorptive capacity is essential for triggering the explorative business model change. Yet, as it was argued before, explorative business model change is not only a onetime radical change, but also a following process requiring continuous refinements, adjustments and learning, as new sustaining business model cannot be conceived and created at once. Hence, whereas the conceiving of the explorative change is facilitated by absorptive capacity, the crucially important further adjustments require organizational commitment to learning, open-mindedness, and knowledge-questioning values – a culture of learning orientation (Sinkula et al., 1997; Baker and Sinkula, 1999). Finally, an explorative business model change is facilitated if an incumbent has resource flexibility, or the ability to redeploy the available resources for the purposes the

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disruptive business model innovation. In some cases, the lack of resource flexibility might lead to lock-in and pursuing the conventional business model. For instance, the traditional airlines might be prevented from rapid switch to discounted models by existing union agreements, or the traditional steel plants might be stuck with the old, integrated mills technology because of the prior major investments into the plants (which cannot be easily redeployed to pursue the disruptive mini-mills model). In summary, the high level of organizational enablers makes explorative business model change the better option in cases of high market drivers of disruptive business model innovation: Proposition 3: Organizational enablers strengthen the positive association between market drivers and expected performance outcome of the incumbent’s explorative business model change in response to a disruptive business model innovation. On the other hand, in case of exploitative strengthening of the existing business model in response to high market drivers of disruption, the organizational enablers (which are aimed at facilitating explorative adoption of the disruptive business model) become wasted resources, reducing the overall performance outcome. In other words, in the situations of high market drivers, the exploitative business model change becomes an appropriate proactive response only if the organizational enablers are low: Proposition 4: Organizational enablers weaken the positive association between market drivers and expected performance outcome of the incumbent’s exploitative business model change in response to a disruptive business model innovation.

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The formal Propositions 1-4 can be briefly summarized as follows: If the market drivers are low, the only optimal incumbent response to a disruptive business model innovation is Defiant Resistance; if the market drivers are high, the optimal response is Explorative business model change (in case of high enablers) or Exploitative business model change (in case of low enablers). In other words, as the moderator (organizational enablers) changes on a continuum from low to high, the slope of the “Market Drivers -> Outcome of the explorative change” line goes up (counterclockwise), while the slope of the line “Market Drivers -> Outcome of the exploitative change” goes down (clockwise). Yet, this framework ignores one important response – Integration (both explorative and exploitative changes) – that becomes a viable option in the case of substantive synergy effects between existing and disruptive business models in an incumbent firm. The Role of Synergy Sometimes, each business model (existing and disruptive) can reinforce the performance outcome of the other one. Both models can leverage the same complementary assets and capabilities (e.g., sales and support channels, brand name, corporate reputation of the parent company, technological expertise), achieving by this means economies of scale and scope. An example of this mechanism can be found in airlines industry, where conventional and low-cost business models can share substantive amount of complementary capabilities (airport infrastructure, trained personnel, IT systems, etc.), leading to a synergistic effect from both explorative and exploitative business model changes. In such cases, simultaneous pursuit of both exploitative

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strengthening of the existing business model and explorative adoption of the disruptive business model will reinforce the positive performance outcome of both actions: Proposition 5: Synergy between existing and disruptive business models (achieved through exploitation of complementary capabilities) increases expected performance outcomes of both exploitative and explorative business model changes Notably, provided the substantive synergy between existing and disruptive business models, the optimal incumbent response to disruptive business model innovation in cases of high market drivers becomes Integration, comprising both explorative adoption of the disruptive business model and exploitative strengthening of the business model, regardless of the level of organizational enablers. In case of low synergy, on the other hand, the optimal response to disruption is governed by Propositions 1-4. The Dynamic Perspective The whole prior argument is based on the static analysis of the determinants of the rational response of incumbents to gaining momentum disruptive innovations; in other words, the propositions are formulated without considering the temporal dimension of the disruption phenomenon. Yet, in real world, both market drivers and organizational enablers are not time invariant. Particularly, most salient changes with time happen along the three dimensions. First, the market propensity develops, driven by the evolution of customer needs (performance, functionality, convenience, simplicity, price; Christensen & Raynor, 2003). Second, the innovation legitimacy changes over time, particularly its regulatory aspect, as sooner or later the authorities endorse the more efficient disruptive approach. Finally, the organizational enablers are not time-invariant, in that with time the !!101! !


organization might acquire or develop the necessary specialized complementary assets, needed for embracing the disruptive approach. These three types of change shift the market drivers and organizational enablers of a disruptive innovation; ergo, the rational response of an incumbent can change with time. This warrants the dynamic investigation of the rational response, depending on the time-variant parameters. NORMATIVE RECOMMENDATIONS Real options to manage contingency As all other models, the discussed in this paper rational and behavioral response models

have

their

shortcomings

and

limitations

when

providing

normative

recommendations for the managers of real-world companies having to choose a response strategy when facing a gaining momentum disruptive business model innovation. The behavioral model explains the typical response evolution within an industry as the disruption gains momentum, remaining ignorant about performance implications of particular actions. The rational model provides important insights regarding the optimal response, but demands the ability to accurately predict the exact values of contingency factors (market drivers, organizational enablers and synergy). As such, it can be very valuable for explaining the performance differential of heterogeneous incumbent responses ex-post (after the disruption gained momentum or flattened out), but has limited use for providing ex-ante guidance for managers of established incumbent firms. In fact, it is the rational model’s unrealistic demands for available information and decision maker’s cognitive abilities that leads to prevalent patterns of incumbent actions

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in the behavioral model, which usually deviate from the optimal path suggested by expost rational response model’s analysis. Hence, the main problem of the rational response model for providing the normative ex-ante guidance is the essential inability of humans to predict the dynamics of the industry disruption (Benner, & Tripsas, 2012; Demil & Lecocq, 2010; Yu & Hang, 2010). Whereas some of the crucial contingencies (particularly organizational enablers: learning capabilities, slack resources, specialized complementary assets) can be estimated ex-ante with enough accuracy, others (market drivers: market propensity and innovation legitimacy) cannot be forecasted with the precision enough for strategic decision making. Moreover, some market contingencies are dependent upon the firm’s own actions (such as the subjective legitimacy of an innovation going substantively up after its endorsement by a major incumbent), which adds complexity and dynamics to the disruption process. Consequently, to be able to infer normative ex-ante recommendations on the basis of the rational response model, it must be supplemented with an uncertainty resolution mechanism, such as a real options perspective (McGrath, 2010; Raynor, 2007; Hill & Rothaermel, 2003). According to real options logic, investments in the development of the disruptive business model are treated as paying the premium for a real option on this approach. This allows managers of the incumbents to possess a call option on the disruptive approach – the ability to execute the option by embracing the disruptive business model and scaling it up if in the future the market drivers for the disruption turn out to be substantive (McGrath, 2010; Hill & Rothaermel, 2003), or the resolution of uncertainty turns out to

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be in favor of the disruption (Raynor, 2007). Moreover, additional information regarding the disruption is often collected as a by-product of experimentation and implementation of the disruptive approach; this additional information improves the incumbent managers’ ability to forecast the market drivers and organizational enablers (of the rational response model), and to build the absorptive capacity with regards to the disruptive approach (Hill & Rothaermel, 2003). Therefore, on the early stage of the evolution of the disrupted industry (when major uncertainty still makes impossible the accurate estimation of the market potential of the disruptive approach), the incumbents should invest in both explorative and exploitative business model changes, treating these investments as payment for real options on uncertainty of the future, which limit the downside risk of the change (McGrath, 2010) and prevent the lock-out Hill & Rothaermel, 2003). On these stages, the contingency factors of the rational response model (market drivers, organizational enablers, synergy) are not known yet; rather, they have to be treated as dimensions to build scenarios of the possible futures (Raynor, 2007). Within each scenario the contingency factors are assumed to be certain, and the rational response model will yield the optimal response strategy, contingent upon the scenario being materialized. Since in the future only one scenario will become reality, and there is no way to find out ex-ante which one, this optimal strategy for each scenario must be supported by a portfolio of real options. The latter will make sure that no matter which scenario of the future actually materializes, the incumbent is not locked out from the winning business model approach at a later stage of the disruption, when all major uncertainties are resolved.

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In most cases, the real options approach implies simultaneous investment of small amounts into both explorative and exploitative business model changes; these investments with limited downside potential allow gathering the information about both approaches as the disruption gains momentum, as well as building the portfolio of real options to make sure that regardless of the actual future, the company has the ability to acquire the necessary capabilities to embrace and scale up the winning approach. Proper Cognitive Framing of the Disruptive Innovation In the prior section, we discuss how embracing the real options lens for analyzing the investments into exploring the disruptive innovation can remediate the uncertainty of structural factors driving the rational response. In business practice, this rational reasoning should be supplemented by proper cognitive framing of the disruptive innovation, to remove the cognitive barriers of the rational response. We suggest that from the very beginning, the disruptive innovation must be framed in terms of both threat (to existing business model) and opportunity (to profit from the disruptive change). Such dual framing of the disruption in the minds of organizational decision making will lead to cognitive resilience (Dewald & Bowen, 2010), a necessary precondition for experimenting with or embracing the disruptive innovation. As such, the dual threatopportunity framing of the innovation must supplement the discussed above real options mechanisms when responding to the gaining momentum disruptive innovations. Internalization or Autonomy? The discussed in this paper Integration response implies simultaneous development of two business models, existing and disruptive ones, aiming at leveraging the synergies !!105! !


between them. Moreover, even in the cases of Pure exploration response (explorative adoption of the disruptive business model only), both business models have to co-exist, at least for some time, before the existing model is abandoned. This raises the question of proper way of combining and managing the two models in the same organization. Existing extensive literature on the topic of combining multiple business models in one organization stresses two basic approaches for this: internalization of both models in one organization, or creating an autonomous business unit (spin-off) for developing the disruptive business model independently from the existing business model (e.g., Christensen & Raynor, 2003; Markides & Charitou, 2004; Hill & Rothaermel, 2003; O’Reilly & Tushman, 2008; Markides, 2006), with most recommendations inclined towards the latter approach (Crockett et al., 2013; Markides & Charitou, 2004). The internalization approach implies tight coupling of both business models within the same organizational context (organizational structure, incentives system, system of market information collection, organizational culture), which facilitates maximal exploitation of the synergies between business models. Indeed, keeping both models tightly coupled, as opposed to keeping them independent, facilitates more free flow of information between managers responsible for their development, as well as ensuring the necessary support of the nascent disruptive offering. Yet, with benefits, the internalization approach brings potential costs, caused by inherent conflicts between the existing and disruptive business models. These conflicts are caused by different value propositions of the two models, which result in major incongruities in their supporting transactive and resource structures (e.g., different approaches to serving the customer bases, different cost structures, and different emphasis on activities in value chains). !!106! !


Some authors even argue that these conflicts are so severe and frequent, that “the simultaneous pursuit of different business models within the same organizational unit will lead to a failure to execute one or perhaps both models” (Hill & Rothaermel, 2003, p.267). Loose coupling of the existing and disruptive business models through establishing an autonomous business unit for the disruptive approach eliminates the conflicts (Christensen & Raynor, 2003; Markides & Charitou, 2004; Hill & Rothaermel, 2003), but also reduces the benefits form the economies of scale and scope which result from coordinated activities between the two business models. Therefore, the optimal choice of governance mechanism for managing two business models is contingent upon the benefits of coordinated activities, achieved within the internalization approach, and costs associated with them. As the prior literature suggests, the costs of keeping both business models in the same organizational context usually substantively exceed the benefits from synergy; hence, the most frequent solution to the Innovator’s Dilemma is establishing a spin-off unit for pursuing disruptive opportunities (Christensen & Raynor, 2003; Markides, 2006; Hill & Rothaermel, 2003). DISCUSSION Key insights: summary We start the current paper by posing the question of how incumbent firms should respond to emerging disruptive business model innovations introduced in their industries, and why the observable actions are usually deviant from the rational path. To provide a comprehensive answer to these broad research question, we draw heavily on the literature !!107! !


streams on business models and disruptive innovations. We propose a rational response model, embracing an original typology of incumbent responses (explorative adoption of the disruptive business model versus exploitative development of the existing one), supplemented by a set of testable propositions regarding contingency factors determining optimal incumbent actions when facing a disruptive business model innovation. We demonstrate the essential groups of contingency factors (market drivers and organizational enablers of the disruptive innovation, synergy, complementarity of the business models), which determine the expected performance outcomes of explorative and exploitative business model changes. In addition, we develop a behavioral model of incumbent firms’ responses to disruptive business model innovations gaining momentum in their industries, describing and explaining the typical pattern in incumbent response (Defiant Resistance -> Pure Exploitation -> Integration), contingent upon the stage of the disruptive innovation’s development. Finally, we supplement the insights of rational and behavioral models with normative recommendations for managers of real-world established firms having to make decisions regarding nascent or gaining momentum disruptive business model innovations in their industries. Implications for theory and future research We see a set of theoretical contributions of the paper for the nascent literature on business models (Zott et al., 2011), as well as for the more established literature on disruptive innovations. To the former stream of literature, we add new insights regarding business model change and innovations; while for the latter stream our contribution stems from emphasizing the peculiar features of disruptive business model innovations, as opposed to traditional disruptive technologies. !!108! !


First, we clarify the conceptualization of the essential terms, most notably, business model innovation and business model change, providing clear theoretical distinction between the two. This clarification of the key terms is necessary for further development of the business model innovation field. Second, we study the complexity of the phenomenon of business model change in response to emerging disruptive innovation, showing two distinct dimensions of this process: explorative and exploitative change. Being developed deductively from existing theories (rather then inductively from the data), the proposed typology of responses is both parsimonious and all-inclusive. None of the two generic approaches is ultimately superior in all situations; rather, it is a set of contextual factors that determine the optimal response (or their combination). Third, the developed rational response model provides a set of testable propositions (P1-P5) regarding the contextual factors determining the performance outcomes of different types of incumbent responses, uniting prior disparate studies suggesting particular responses into a holistic contingency-based model. Fourth, in the developed behavioral response model we elaborate on the question of the dynamics of the pattern of incumbent responses to disruptive business model innovations introduced in their industries. First time in the literature, the model stresses the temporal aspect of a disruptive business model innovation process, explaining the reasons for observable behavior in different stages of the disruption. The obtained theoretical insights open a set of future research streams. The current study stresses the importance of understanding disruptive innovations in today’s business !!109! !


environment and the infancy of the literature on the topic, which open the rich opportunity available to researchers. The proposed typology of business model changes can be employed in further studies of business models and disruptive innovations within the descriptive and prescriptive theoretical perspectives, as a parsimonious holistic conceptualization of the variation of business model change phenomenon. The developed contingency-based rational responses model opens a fruitful research agenda with the potential for conceptual and empirical studies of the determinants of optimal incumbent actions when facing a disruptive business model innovation. Particularly promising would be conceptualizing and empirical investigating the impact of crucial contextual factors, market drivers or organizational enablers. The richest further research agenda opens in relation to the warranted fine-grain analysis of organizational enablers of embracing the disruptive approach, which were only briefly outlined in the current paper. Potential studies could discriminate between the impact of different types of slack (e.g., absorbed, unabsorbed, potential), or diverse types of organizational learning capabilities (e.g., learning orientation and absorptive capacity).! The other important understudied question is related to the drivers of the contingency factors, most important, what are the determinants of the market drivers (market propensity and innovation legitimacy), and how the actions of established incumbents influence these drivers? How do different aspects of innovation legitimacy (subjective, regulatory) evolve over time? How do incumbents’ actions influence

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innovation legitimacy and market propensity? Answering these questions would make the dynamics of the disruption process more predictable. Finally, the behavioral view of response also requires further clarification, using the multi-dimensional analysis of strategy decisions and stressing the role of cognition and decision-making in expanding the behavioral stream of strategy research. Particularly interesting is studying the few incumbents who respond to the disruption by Pure Exploration response, or by switching to the new business model without development of the established one. We argued that such responses are very rare among established players, because of the cognitive barriers and structural inertia preventing radical business model change. Yet, despite being rare, such companies still exist. What drives such responses, and what are their performance outcomes? Implications for practice For managers of incumbent firms, the paper provides guidance for making appropriate decisions when faced with emerging disruptive business model innovations in their industries. The discussed rational response model provides explicit advice regarding choosing the optimal response (with highest expected performance outcome), provided full information is available (namely, the future values of contingency factors). The proposed later real options and cognitive resilience lenses provides normative guidance when the assumption of full available information is relaxed. Particularly, the dual framing of the disruptive innovation – as both an opportunity and a threat – allows removing the cognitive barriers of the appropriate response.

!!111! !


The other crucial for practice insight of the current paper is the emphasis on internal contingency factors (organizational enablers) when adopting a disruptive business model. We provide a comprehensive (although probably not exhaustive) list of crucial internal success factors for disruptive innovating of the firm, all being within the managerial

control:

slack

resources,

specialized

complementary

assets,

and

organizational learning capabilities. Strengthening these (most important, having the buffer of slack resources and enhancing organizational learning capabilities) opens up opportunities for real-world companies to increase the likelihood of success of the radical business model change projects.

!!112! !


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Essay 3: Resolving the Threat-Rigidity Paradox

RESOLVING THE THREAT-RIGIDITY PARADOX: THE IMPACT OF URGENCY AND PREDICTABILITY ON INNOVATIVENESS OF MANAGERIAL STRATEGIC DECISION MAKING IN TIMES OF CRISIS

ABSTRACT Does perception of critical threat so severe as to risk business continuity hamper or stimulate organizational innovation? This question remains unresolved after more than two decades of contradicting theoretical arguments and inconsistent empirical results. One view argues for and finds amplified innovativeness within threatened organizations. The other view supports threat-rigidity phenomenon, reinforcing resistance to innovation through a focus on habitual practices. We address the question from a managerial decision making perspective, uniting theoretical perspectives that have been used to support both views. While showing that both views hold true in certain situations, we partly resolve the paradox by demonstrating that cognitive moderators, predictability and urgency, lead to diverging results. The model is tested in the context of real estate brokerage industry, finding strong empirical support. INTRODUCTION How do companies react to adversity so severe as to threaten their survival? Do they become more innovative, changing the established methods of doing business in an attempt to escape from, adapt to, or even thrive on new adversarial circumstances? Or do they stubbornly stick with the old solutions – products, business models, and routines – avoiding any innovation and trying to rigidly ignore the adversity in the hope of a return !

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to prior stability? There is little agreement in the literature on this question. Some researchers argue for innovative actions (e.g., Bowman, 1982; Bromiley & Wiseman, 1989; Mayhew, 1979; Gooding, Goel & Wiseman, 1996; Miller & Chen, 2004), while others suggesting rigidity and risk-aversion (e.g., Staw, Sandelands, & Dutton, 1981; Schendel, Patton, & Riggs, 1976; Laughhunn, Payne, & Crum, 1980; Iyer & Miller, 2008; Shimizu, 2007). The polar views of this paradox can be summarized as “necessity as the mother of invention” versus “necessity as the mother of rigidity”1. For over two decades, the strategic management literature has struggled with this paradox (e.g., Hu, Blettner & Bettis, 2011; Latham & Braun, 2009; Mone, McKinley, & Barker, 1998; Occasio, 1995; McKinley, 1993; March & Shapira, 1992; Singh, 1986). With the growing number of papers within each camp, as well as the high rigor and broad variety of methods employed, it is inconceivable that the lack of resolution is a function of flawed design or execution of studies. Finding a solution to this research problem is important for both strategic management scholarship and practice, in that it would provide insights on what behavior to expect from organizations in crisis times, and, to some extent, how to influence it. We confront the likelihood that there are factors not addressed in the literature; moderators that drive the decision maker within organizations to formulate intentions to either adopt new ways, or reinforce old ways. We address the unresolved paradox by applying the literature on cognition to determine and test plausible cognitive moderators that explain the decision making process resulting in innovative or rigid intentions. To accomplish our task, we firstly analyze the existing theoretical underpinning of each of !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! 1

!

!Designations!proposed!in!McKinley!(1993).!

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the two contradictory views. Despite the abundance of conceptual papers attempting to unify the opposing viewpoints, the field is surprisingly underdeveloped empirically (Audia & Greve, 2006), with only two contingencies being investigated on observed data: size (Greve, 2010; Audia and Greve, 2006) and slack (Chattopadhyay, Glick, & Huber, 2001; Singh, 1986). In pursuit of advancing the field, we briefly review these previous attempts to resolve the paradox, and unveil the shortcomings within. Previous models largely ignored the role of individual managerial decision making in determining organizational actions in crisis times. The need to consider insights from the decision making literature, including cognition, perceived risk and innovation, was emphasized in the seminal paper by March and Shapira (1982). This insight has led to a new emphasis within recent developments such as the behavioral theory of strategy (Powell, Lovallo, Fox, 2011), stressing the phenomenon of strategic agency (the role of strategic leaders – e.g., Gavetti, 2012). To understand managerial decision making in crisis times, we draw heavily on the theories of individual decision making, such as prospect theory (Kahneman & Tversky, 1979), threat-rigidity thesis (Staw, Sandelands & Dutton, 1981), and recent advances in cognitive and social psychology. This allowed us to derive a set of cognitive moderating factors that, according to past theory, will influence the decision maker’s intentions when facing severe adversity (critical threat). We show that the ultimate decision to innovate or be rigid is determined by the interaction of perception of urgency and predictability with the decision-makers’ perception of critical threat. We test our theoretical models in the context of Canadian real estate brokerage industry right after a major regulatory change threatening the incumbent survival. The

!

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first model – based solely on the main effect of critical threat perception, found no empirical support for intentions to innovate, while the unifying moderating model that included perception of urgency and predictability as moderators was strongly supported. A replication study – conducted in the context of universities responding to the threat from on-line education models (see electronic supplement to this paper) – corroborates the conclusions obtained in the main study. This paper provides three specific contributions. We enhance the understanding of organizational actions in crisis times, uniting both innovative and rigidity views in a single coherent moderation-based theoretical model grounded in managerial decision making theories. Secondly, we propose a set of specific moderators that influence organizational response to adversity. Both hypothesized moderators, perception of urgency and predictability, are empirically supported. Finally, the proposed model augments our knowledge of organizational decline, innovation studies, and change management phenomena, by answering the question of what are the determinants of innovation and change in crisis times. THEORETICAL BACKGROUND To position our study, we begin with an overview of key constructs we applied to the development and testing of our model. We outline the specific form of strategic innovation being assessed, indicators of risk taking and organizational adversity, and discuss the appropriate unit of analysis (individual decision making leading to organizational actions).

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Innovation as a response to adversity. While most of previous research has concentrated on a narrow view of innovation as R&D investments (e.g., Latham & Braun, 2009), in this paper we scrutinize a more general type of change – the business model innovation – requiring profound rethinking of all aspects of business and implying the need to alter its resource, transactive and value structures (George & Bock, 2011). From the business strategy perspective, the business model innovation would imply ‘action that either puts the organization into new strategic domains or significantly alters the way the organization attempts to serve existing customers or constituents’ (Mone et al., 1998, p.117). Previous research supports the study of intentions to innovate versus harder to assess behavioral actions (Tubbs & Ekeberg, 1991; Fini, Grimaldi, Marzocchi, & Sobrero, 2012). Prior studies have demonstrated that intentions are reliable predictors of most crucial actions (e.g., Ajzen & Fishbein, 1980; Bagozzi, Baumgartner, & Yi, 1989; Ajzen, 1991; Sutton, 1998), including innovation and entrepreneurship (Krueger, Reilly, Carsrud, 2000). Although intentions do not guarantee specific behaviors (the intentionsactions gap), the existing literature provides strong support for the position that behaviors seldom occur without prior formulation of intentions: ‘behavioral intention is both the immediate determinant and the single best predictor of behavior’ (Sutton, 1998, p.1318). By scrutinizing the emergence of managerial individual intentions to innovate, we study the major necessary condition for further organizational actions (a similar argument was applied in a seminal paper by Krueger et al., (2000) with regards to entrepreneurship process).

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Intention to innovate as an exemplar of risk-taking. Prior studies have demonstrated that intentions to innovate are perceived by decision-makers as risk-taking due to uncertain results comparing to status quo (Latham & Braun, 2009; Wiseman & Bromiley, 1996; Whetten, 1987; Greve, 1998). In some cases, a rational argument or proforma analysis, based on predictive assumptions, may indicate that the rigid choice is rationally more risky than innovation; yet, this does not nullify the cognitive framing that causes decision makers to perceive more risk in adopting new versus tried and know old ways. Uncertainty about future outcomes is fueled by bounded rationality (Simon, 1955) and risk-aversion (Arrow, 1965) to inhibit unbiased comparison of all possible alternatives. We rely on numerous studies that scrutinized the association between adversity and organizational risk-taking actions, wherein innovation is an exemplar of risk-taking behavior (Lehman et al., 2011). Perception of adversity. Previous research links low performance to organizational adversity or critical threat (both terms are used interchangeably) as a stimulus for risk-taking or risk-averse behavior (e.g., Hu et al, 2011; Boeker, 1997; Bromiley, 1991; Greve, 1998,2010; Iyer & Miller, 2008; Shimizu, 2007; Audia & Greve, 2006; Wiseman & Bromiley, 1996; Singh, 1986). In a similar manner, the studies concentrating on organizational decline phenomenon consider risk of bankruptcy as the cause for perception of critical threat (e.g., Miller & Chen, 2004). We argue that this view of severe financial adversity as the driver for organizational perception of critical threat is too narrow. Other possibilities are easily conceivable, such as changes in business’s regulatory environment, emergence of disruptive innovations (Christensen & Raynor, 2003), loss of license to operate, environmental jolts (Meyer, 1982), industry revolutions

!

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and discontinuous changes (Meyer, Brooks, & Goes, 1990), or even the conflict among owners of a family firm. Each of these drivers rarely manifests through short-term financial adversity, but nevertheless can lead to critical threat. In this study, we concentrate our theoretical reasoning on perceived critical threat caused by any reason (financial adversity being only one of them), which is similar to the perspective adopted in initial studies of critical threat (e.g., Laughhunn et al., 1980), and more recently by Chattopadhyay and colleagues (2001). Individual decision making perspectives. March and Shapira (1982) drew attention to the salient difference between the fields of behavioral (individual) and organizational decision making. The authors argued that these two research fields cover different domains with different methods and central concerns, with neither being ‘a special case, nor an application of the other’ (p.95). Even though organizational innovativeness in times of adversity is obviously an organization-level phenomenon, March and Shapira (1982) revealed that this phenomenon cannot be properly explained by simplistic organization decision making theories. They argued that the work in behavioral (individual) decision making research would lead to models of innovation and risk-taking ‘in new ways that might make some of the relations a bit clearer’ (p.108), a view supported by Hambrick and Mason (1984) in their upper echelons perspective, and more recently in the behavioral theory of strategy (Powell, Lovallo, & Fox, 2011; Gavetti, 2012). To understand the managerial decision making in crisis times, we follow the lead of others and draw on the theories of individual decision making as a driver of organizational actions – whether innovative or rigid. LITERATURE REVIEW

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Prospect theory considerations. The most established theory in individual decision making is arguably the Kahneman and Tversky’s (1979) prospect theory (Holmes et al., 2011). Applied to the cases of managerial decision making within organizational settings (e.g., March & Shapira, 1987, 1992; Kahneman & Lovallo, 1993; Holmes, Bromiley, Devers, Holcomb, & McGuire, 2011), studies have found that managers demonstrate risk-averse tendencies when facing a situation framed in terms of potential gains, and risk-seeking when facing potential losses. As innovation usually portrays a risky endeavor, if the business forecast is framed as a potential gain, riskaversion will prevent decision makers from embracing innovations. On the other hand, if the business forecast is likely to lead to an adversity (perceived extreme potential losses), this framing should make a decision-maker extremely risk-seeking, removing the obstacles for innovation posed by its perceived riskiness. Consequently, from the prospect theory perspective, decision makers will demonstrate risk-seeking – and hence innovative – tendencies when faced with a major adversity, regardless of its severity (e.g., Gooding, Goel & Wiseman, 1996). Behavioral theory of the firm view. A related stream of literature is rooted in the behavioral theory of the firm (BToF) (Simon, 1955; Cyert & March, 1963)2. The theory predicts the activation of the problematic search mechanism – ‘particularly search for new alternatives of action’ (Simon, 1955, p.263) – when an organization cannot reach the aspired performance level. When facing a critical threat, the severe discrepancy between

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! 2

!An!important!issue!must!be!made!explicit!here.!BToF!explains!the!behavior!at!the!level!of!organization! rather!than!individual.!Yet,!BToF!can!be!applied!to!individual!decision!making!of!managers!within! organizations!in!the!realms!of!risk!taking!and!problemistic!search!(March!&!Shapira,!1987,!1992),!as!it!is! done!in!the!current!study.!

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the actual and aspired state of affairs will potentially lead to innovation (Gavetti et al, 2012). Although the prospect theory and the behavioral theory of the firm draw from differing foundations and different units of analysis (individual versus organization), both theories provide the same prediction: the higher the perception of adversity, the more innovation will happen. Threat-rigidity counterargument. The opposite view of organizational behavior (and corresponding pattern in managerial decision making) stems from the threat-rigidity thesis of Staw, Sandelands and Dutton (1981). They predict that the situations threatening the business’s existence limit the decision makers’ ability to even conceive of actions that are different from traditional ones (Shimizu, 2007; D’Aunno & Sutton, 1992). Stress and anxiety impede an individuals’ ability for rational reasoning and proper processing of information; therefore, decision makers limit their scope of analyzed alternatives to only familiar solutions, and become ‘rigid’, choosing not to act at all. Having historical roots in organization science research, the threat-rigidity thesis is in line with studies from cognitive and social psychology. For instance, Nash, McGregor and Prentice (2011) demonstrated that perception of threat leads to reinforcement of an individual’s conviction in prior beliefs, which may inhibit intentions to innovate. Notably, suppression of innovation is only one facet of threat-induced rigidity, which can also be manifested in other actions reinforcing the habitual routines, such as concentrating on efficiency of existing business (resource conservation phenomenon – Staw et al., 1981), incremental innovations, or proactive resistance to change (e.g., Dewald & Bowen, 2010).

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The focal paradox: Empirical evidence. The theoretical controversy is echoed by inconsistency of empirical results in the studies of both organizational behavior and managerial decision making in times of major adversity (e.g., Hu et al., 2011). The innovative and risk-seeking tendencies were demonstrated in numerous rigorous studies (e.g., Bowman, 1982; Fiegenbaum & Thomas, 1988; Lehner, 2000; Miller & Chen, 2004; Boeker, 1997; Bromiley, 1991; Wiseman & Bromiley, 1996; Gooding, Goel & Wiseman, 1996), corroborating the claim for the positive association of managerial perception of adversity with emergence of intention to innovate. On the other hand, the theoretical threat-rigidity propositions are in line with the results of multiple other studies, particularly Schendel et al. (1976), Laughhunn et al. (1980), D’Aunno & Sutton (1992), Iyer and Miller (2008), and Shimizu (2007), resulting in the contradictory conclusion that adversity threatening organization survival makes the decision makers in it extremely risk-averse, impeding their intentions to innovate. Contingencies from organizational decision making studies. Some studies (Audia & Greve, 2006; Greve, 2010; Shimizu, 2007) have postulated that the explanation of inconsistent behavior of firms stems from moderating factors that influence the association between the stimulus (perception of critical threat) and the reaction (riskseeking or risk-averse behavior, or innovativeness and rigidity). These proposed moderators create diverging results, hinged upon the organizational decision making perspective. One previously studied moderator of adversity-innovation association is organizational slack, although the effect of this factor on threatened organizations’ innovativeness remains contentious. Mone and colleagues (1998) provide a theoretical

!

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argument for uncommitted resources serving as the positive moderator for “organizational decline – innovation” association. They contend that the high level of available slack allows an organization to take additional risks and experiment. This argument is supported by the behavioral theory of the firm (Cyert & March, 1963), arguing that, ‘slack provides a source of funds for innovations that would not be approved in the face of scarcity but that have strong subunit support’ (p.189). Moreover, slack buffers an organization from the downside risk associated with innovation, and increases the legitimacy of the change in the eyes of powerful stakeholders (Singh, 1986). Unlike most previous papers, which are purely conceptual, Singh (1986) provided empirical evidence for the significant mediating role of slack in the “performance-risk taking” association. He found that better performance increased organizational slack, which in turn drove greater risk taking. Despite the dominance of the conceptual argument for the positive association between organizational slack and innovation in crisis times, some scholars challenge this theory. Latham and Braun (2009) stress that, in line with agency theory, organizational slack is used by risk-averse managers to secure their position by ‘opting to stockpile slack resources instead of investing in innovative activities’ (p.265). Finally, in the seminal paper discussing environmental jolts, Meyer (1982) found out that organizational slack is a relatively unsubstantial predictor of organizational adaptation. A second scrutinized moderator of the adversity-innovativeness association is firm’s size, in many ways similar to slack. In the study of Japanese shipbuilding industry, Audia and Greve (2006) showed the positive interaction of underperformance and firm size: the performance shortfall led to rigidity in small firms, but did not affect or

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increased the risk taking by large firms. Firm size, the authors theorized, is linked with resource availability, which affects the managers’ risk tolerance: Managers of large firms are buffered from the threat of failure, and are hence inclined towards experimenting in response to underperformance. Managers of the smaller firms, on the other hand, view performance shortfall as a step towards failure, and therefore decrease risk taking. This result was further corroborated in Greve (2010), demonstrating that low performance had a more pronounced effect on rigidity in small companies. Acknowledging the useful insights gained from this slack/size view, in this paper we intend to complement it using the moderators derived from managerial individual decision making perspective, which sets the ground for hypothetical framework presented below. HYPOTHESES The main effects model. Appreciating the arguments of both camps – “necessity as the mother of invention” and “necessity as the mother of rigidity” – we formulate the first two opposing hypotheses in a form of a critical experiment (see Figure 1).

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Figure 1. The Opposing Hypotheses in Threat-Rigidity Paradox Intention(to( Innovate

Prospect)theory,)BToF predictions)(Hypothesis+1a)

Contingency*factors Threat4rigidity)thesis) predictions)(Hypothesis+1b) Critical( Threat

Hypothesis 1a stems from the reasoning of prospect theory and behavioral theory of the firm, supporting the view that the higher the perception of critical threat, in the mind of managers, the more their decisions will be inclined towards innovation: Hypothesis 1a: perception of critical threat has a positive effect on intention to innovate. The opposing Hypothesis 1b hinges on the theoretical propositions of threat-rigidity thesis, suggesting that severe adversity threatening survival of the organization makes the decision makers extremely risk-averse and hampers their intentions to innovate: Hypothesis 1b: perception of critical threat has a negative effect on intention to innovate We propose two specific moderating factors, perceived urgency and predictability, to resolve the adversity-innovation paradox. Perceived urgency. The complex interplay of diverging outcomes explained by prospect theory and threat-rigidity thesis (risk-seeking and risk-averse behavior of threatened firms, respectively) is governed, to a large extent, by the third mechanism

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overlooked in prior studies – time pressure when making the response decision, or perceived urgency3 in the decision making process. We specifically conceptualize perceived urgency as the construct manifesting the perceived lack of time (pressure for immediacy) in the process of individual decision making; as such, it is distinct from the construct of perceived critical threat, which manifests the perceived severeness of anticipated adversity. Conceptually, the dimensions of time for responding and the magnitude of crisis are decoupled and can vary independently4. It is worth noting that the proposed perceived urgency construct is situational (occurrent), different from the constructs of time urgency (e.g., Landy et al., 1991; Conte et al., 1998) and positive/negative urgency (Cynders and Smith, 2007), all of which are dispositional personality traits or persistent patterns in human behavior. Indeed, time urgency represents the an individual’s “frequent concern with the passage of time”(Conte et al., 1998, p.2), positive urgency is “the tendency to act rashly while in a positive mood” (Cynders and Smith, 2007, p.840), negative urgency is “the tendency to act rashly when distressed” (ibid) . Perceived urgency prevents decision makers from carefully performing a thorough situation analysis, generating and assessing all possible alternatives, and experimenting !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! 3

!When!defining!the!word!“urgency”,!The!MerriamUWebster’s!Dictionary,!The!Oxford!American!Dictionary!and!The!American! Heritage!Dictionary!agree!with!its!first!meaning!–!time!pressure!–!and!disagree!concerning!its!second!meaning.!The!first!two! dictionaries!define!it!also!as!insistence,!while!the!last!one!U!as!importance.!!In!management!literature!some!authors!embrace!both! meanings!of!the!word:!e.g.,!Mitchell,!Agle!&!Wood!(1997)!consider!urgency!as!timeUsensitivity!and!importance.!Yet,!this!usage!adds! ambiguity!by!lumping!together!two!different!factors,!and!is!not!typical!for!other!fields!of!knowledge.!For!instance,!the!studies!in! psychology!(e.g.,!Cyders!&!Smith,!2007;!Landy!et#al.,!1991),!organizational!behavior!(Conte!et!al,!1998)!and!neuroscience!(Jones!et# al.,!2011;!Reddi!&!Carpenter,!2000)!all!explicitly!link!urgency!with!time!pressure!only.!In!this!paper!we!take!the!side!of!the!latters,! defining!urgency!as!the!perception!of!time!pressure!for!making!a!decision!(immediacy,!time!sensitivity).! 4 !Available!empirical!evidence!corroborates!the!distinction!between!perceived!urgency!and!critical!threat:!e.g.,!in!the!current!study,! the!decoupled!variables!are!significantly!correlated!with!each!other,!but!this!correlation!is!far!from!unity:!r=0.195!(approximately!4%! of!the!common!variance).!

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with unfamiliar solutions. As reported in studies of crisis decision making, perceived urgency is one of the cognitive factors reinforcing the rigidity effect of crisis in the minds of the mangers, leading to ‘hypervigilance, a decline of information processing capabilities, and panic’ (Billings, Milburn, & Schaalman, 1980, p.306; see also Janis & Mann, 1977). Lack of time needed to gather all necessary information to evaluate all feasible alternatives forces decision-makers to make fast decisions, employing heuristics and intuition as shortcuts in reasoning. In the face of urgency coupled with crisis, decision-makers analyze only the most salient factors and evaluate only a limited number of options closely linked to the existing solution. In line with this reasoning, Whyte (1991) points out: ‘in responding to threats and crises, the urgency of the situation encourages a tendency to minimize response time and to go with first impulses’ (p.28). Therefore, when reacting to severe adversity coupled with urgency, decision-makers limit the scope of conceivable alternatives, thereby limiting consideration of innovative choices. On the other hand, provided enough time, even a major threat to the business will not cause rigidity as the manager will sense the ability to rethink the situation and adapt in the most rational way, embracing innovations when needed – according to prospect theory predictions. Recently, neuroscientists have provided a physiological explanation to this phenomenon, arguing that urgency has a detrimental effect on decision-making performance because of the mediating role of striatum influencing the behavioral effect ‘by releasing motor circuit inhibition to facilitate fast but possibly premature responses’ (Jones et al., 2011, p.6). Lehman et al. (2011) reinforce the provided above argument for the detrimental impact of the interaction of perceived urgency and perceived critical threat on risk-

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seeking (and hence innovativeness) of managerial decision making. The researchers show that the time pressure in times of crisis is the factor shifting the managerial attention from reaching the aspired level of performance, a state supporting risk-seeking, to ensuring the survival, a state where risk-averse threat-rigidity mechanism gets triggered (March & Shapira, 1987, 1992; Hu et al, 2011; Iyer & Miller, 2008; Shimizu, 2007). In other words, perceived urgency determines the nature of reaction to crisis, in terms of risk-seeking prospect theory to risk-averse threat-rigidity mechanisms: ‘when more time is available…attention is more likely to be directed at attaining and maintaining an aspired level of performance’, yet, ‘as less time is available and the deadline draws nearer, attention is increasingly likely to be redirected at ensuring survival…’ (Lehman et al. (2011), p.1614). In summary, there is a strong theoretical case for expecting perceived urgency to have a moderating effect on the association between decision makers’ perception of the situation-based critical threat and intentions to innovate. High level of urgency suppresses risk-taking and innovativeness of decision making in response to adversity. If Hypothesis 1a is true, i.e., the critical threat perception has a positive impact in intention to innovate, this positive impact will get attenuated by high level of perceived urgency; or it can even flip to negative, reflecting the shift of the attention from the aspired performance to the survival reference points, triggering the threat-rigidity mechanisms (Lehman et al., 2011). If Hypothesis 1b is true – the critical threat perception has a negative impact in intention to innovate – this negative impact will be reinforced by high level of perceived urgency. In other words, regardless of the actual impact of perceived critical threat on intention to innovate (positive – Hypothesis 1a or negative – Hypothesis 1b), high level of perceived

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urgency of decision making will trigger the threat-rigidity mechanisms. Therefore, the interaction of perceived urgency and perceived critical threat in their effect on intention to innovate is always negative5: Hypothesis 2: The interaction of perceived urgency and perceived critical threat has a negative effect on intention to innovate Predictability of situation. The second factor influencing the weights – and hence the results – of the interplay of mechanisms of prospect theory and threat-rigidity is perception of predictability of the situation. We intentionally employ the term “perceived predictability” to make the argument as precise as possible, in that the more traditional opposite term of “uncertainty” has too many different definitions in the existing literature6. Conceptually, the perception of predictability of situation is the situational (occurrent) factor, indicating the confidence of a decision maker in his or her ability to foresee and predict how the state of the external environment will develop in the future. As such, perceived predictability of situation is essentially opposed to the construct of state uncertainty (perceived environmental uncertainty) in the classification of Milliken (1987). Low level of perceived predictability implies inability to predict “what actions relevant organizations or key organizational constituencies (i.e., suppliers, competitors, consumers, the government, shareholders, etc.) might take”, or lack of certainty about “the probability or nature of general changes in state in the relevant environment (i.e., !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! 5

!Graphically,!this!conjecture!implies!that!the!“critical!threat!–!innovation”!line!always!turns!clockwise!with!rising!perceived!urgency! !For!instance,!the!review!of!literature!on!uncertainty!by!Jauch!&!Kraft!(1986)!demonstrated!that!25!years!ago!there!was!no! consensus!on!the!term,!which!was!used!to!manifest!unpredictability,!turbulence,!complexity,!cognitive!rate!of!change,!diversity,!and! interdependence.!In!the!related!fields,!such!as!economics,!the!term!uncertainty!has!different!meanings:!e.g.,!Oliver!Williamson! (1996)!describes!three!kinds!of!uncertainty:!primary!(stateUcontingent),!secondary!(due!to!lack!of!timely!communication),!and! tertiary!(behavioral).!In!recent!years!the!situation!became!even!worse,!wherein!the!lack!of!consensus!concerning!uncertainty! construct!led!to!the!situation!when!‘environmental!uncertainty!construct!may!soon!be!stretched!beyond!usefulness,!as!it!becomes! so!broad!as!to!be!fundamentally!meaningless’!(Kreiser!&!Marino,!2002,!p.895).!! 6

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sociocultural trends, demographic shifts, major new developments in technology)” (Milliken, 1987, p.136). On the other hand, high level of perceived predictability of situation reflects the decision maker’s confidence in the ability to forecast the essential features of the future state of the environment. Perception of predictability will facilitate emergence of intention to innovate in crisis times through a set of mechanisms reinforcing each other. First, the impact of predictability on decision making in organizational context stems from the mechanisms explained by the behavioral theory of the firm: Low predictability of the future state of the environment coupled with crisis leads individuals to ‘resort to coping mechanisms that spare them the need to anticipate the distant futures’, usually taking the form of ‘automatic rules (standard operating procedures in organizations)’ (Gavetti et al, 2012, p.5); these standard procedures attenuate any innovativeness outside their boundaries. In other words, in crisis times an intention to innovate will emerge only if it is coupled with low state uncertainty (high predictability); otherwise the decision makers will resort to old ways of doing business institutionalized in routines or standard operating procedures. The second mechanism deals with the perceived need for gathering and analyzing the additional information without actual acting: the low predictability makes organizational decision-makers trapped in “analysis-paralysis”, and hence more rigid. The cognitive science scholars reach this conclusion for individual decision making models: ‘Extreme uncertainty is paralyzing under dangerous circumstances’ (Kahneman, 2011, p.263).

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The two discussed above mechanisms link predictability of situation with perception of the critical threat and intention to innovate. If Hypothesis 1a is true, i.e., the critical threat perception has a positive impact in intention to innovate, this positive impact will get reinforced by high level of perceived predictability of situation. If Hypothesis 1b is true – the critical threat perception has a negative impact in intention to innovate – this negative impact will be attenuated by high level of perceived predictability. Hence, regardless of the actual impact of perceived critical threat on intention to innovate (positive or negative), high level of perceived predictability of situation will facilitate innovativeness in times of crisis. In other words, the interaction of perceived predictability of situation and perceived critical threat in their effect on intention to innovate is always positive7: Hypothesis 3: The interaction of perceived predictability of situation and perceived critical threat has a positive effect on intention to innovate. The theoretical model proposed in this paper is presented in Figure 2. METHOD Data Collection. We employed self-report survey as the primary data collection method. Using self-report measures of key constructs (particularly, perception of critical threat, predictability and urgency) allowed us to mitigate the shortcomings of approaches employed in most recent studies: Iyer & Miller (2008), Shimizu (2007), Miller & Chen (2004), Greve (1998, 2002, 2010). These studies sought to infer the antecedents and strategic decisions by applying observed behavior and measures of business environment. !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! 7

!Graphically,!this!conjecture!implies!that!the!“critical!threat!–!innovation”!line!always!turns!counterclockwise!with!rising!perceived! predictability!

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These traditional indirect measures are inaccurate proxies for decision-makers’ perceptions and intentions, and by using the survey design we consciously trade off depth of data in recognition of possible bias due to self-reporting, as self-reports in such cases reveal more accurate estimates of the population parameters than behavioral measures (Podsakoff & Organ, 1986). Since the hypothesized model, linking cognitive stimuli with intention to innovate, is based on static rather than dynamic argument, we used crosssectional design (Singh, 1986). Figure 2. The Hypothesized Association between Intention to Innovate and Critical Threat Perception

Perception

Critical( Threat

H1(+a,;b) ;H2 +H3

Intention(to( Innovate

Urgency

Predictability

Control'for: ! Number(of(employees ! Prior(successful(risk(experience ! Industry(experience ! Other(industries(experience

Context. Selecting the proper site for empirical testing of the proposed theoretical moderation model was a major antecedent of the current study. The natural experiment due to major regulatory changes – perceived by many as a major adversity for the business – taking place in 2010-2011 in the Canadian residential real estate brokerage industry created the perfect condition for this kind of inquiry. Previously, the dominant business model for real estate brokers in North America hinged upon a unique valuable

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resource – monopoly access to Multiple Listing Service (MLS) system operated by local realtors associations, which contains the best source of information about the local real estate market. MLS databases include a lot of practically useful information, such as the period a certain home is on sale, that potential buyers cannot find elsewhere. Since clients are not able to access MLS directly, they were forced to find a broker for access, and this need drove the constant stream of clients to realtors. Moreover, to protect their revenues, in many jurisdictions realtors lobbied the legal authorities for prohibition of ‘unbundling’ of brokerage services, meaning that a client was unable to buy only access to MLS without first committing to a full-priced set of other services, such as searching for homes, showing/looking, negotiations, preparing the documents, etc. Market pressures to change the previous situation created opportunities for out-ofindustry entrepreneurs to develop disruptive innovations in the real estate brokerage industry. Particularly, in the last decade the emergence of a disruptive technology (Internet sites listing property for sale) led to disruptive business models (discounted brokers, mere posting service, services for FSBO-“For Sale by Owner”). As expected, traditional brokers resisted collectively by ‘pursuing anti-competitive policies and laws’ (Miceli, Pancak, & Sirmans, 2007, p.7), such as limiting the access to MLS (e.g., Ladurantaye, 2011b) or banning the unbundling of brokerage services. On individual level, traditional brokers resist change, for instance, by refusing to work with brokers pursuing new business models (Roberts, 2011). In the Fall of 2010 the situation in the Canadian real estate brokerage industry changed dramatically: the industry’s regulator and the federal Competition Bureau legalized discounted brokerage services (such as listing on local MLS system for a flat

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fee), by this means depriving traditional real estate brokers of their protective shield, monopoly access to MLS and bundling of services. The established industry players seem to have perceived the situation threat (Ladurantaye, 2011a); some tried to innovate by experimenting with new business models (Roberts, 2011), while the majority of realtors rigidly resisted (Roberts, 2011; Ladurantaye, 2011b). Even though traditional realtors do have an array of value-added services, besides access to MLS, to offer their clients (Saber & Messinger, 2011), and including additional services to broker’s offering improves revenues and net profit (Benjamin, Chinloy, Jud & Winkler, 2008), innovating to alter the established business model is perceived as an extremely risky endeavor (Roberts, 2011). This made the Canadian real estate market a perfect site for studying cognitive factors shaping managerial decision making in crisis times: the ongoing obvious disruptive changes make the predictors from the developed model (critical threat, urgency, predictability) salient; moreover, the observed variability in incumbents’ strategic intentions enable empirical testing of the model. The empirical data for the study were obtained from the industry players (real estate brokers in Canada) six months after the discussed above dramatic change in regulation took place, allowing disruptive innovations to gain momentum. Sample. The survey, aimed at brokers (owners-managers of business), was administered electronically in two Canadian provinces; the participation was anonymous and voluntary. An invitation to participate in the study was sent by regulatory organizations to brokers via e-mail twice. The response rate was 15 percent, with demographic characteristics of respondents in obtained sample similar to those in other studies (e.g., Dewald & Bowen, 2010). The final sample contained 241 observations, and

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included the responses from independent brokers, franchise operators, and corporate brokers (65, 33 and 9 percent, respectively8). Measures. The survey questionnaire was developed employing, where possible, the existing scales with satisfactory characteristics of reliability and validity (table in Appendix summarizes all variables and original scales used). The questions were specifically adapted for real estate brokerage business, similarly to Dewald and Bowen (2010). The survey instrument was pre-tested with three industry experts to assure questions’ relevance for the particular context (time and industry), and with two experts from academia to assure theoretical relevance. All variables were based on self-report measures, using Likert-type 5-point scales with neutral central point. Multiple-item variables were calculated by averaging nonnormalized individual items, dropping occasional missing values. As the table in Appendix demonstrates, the reliability of all composite scales, as measured using Cronbach’s α statistics, was well above the acceptable threshold of .7 (Nunnally, 1978). Dependent variable, intention to innovate, captured the respondent’s intention to embrace the radical product innovation requiring major change in the business model through introducing a sort of discounted brokerage services. In the context of real estate brokerage (populated by owners-managers of the small businesses), the respondent’s individual intention reflects the intended strategy of the firm. The four-question measurement instrument used in the current study was developed on the basis of study of Dewald and Bowen (2010), where originally a single-question scale for measurement of !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! 8

!

!The!sum!exceeds!100,!since!respondents!were!able!to!choose!more!than!one!answer!

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intention to adopt the disruptive innovation was used. It is essential to stress that for real estate brokers introducing the discounted services (such as direct access to MLS, or unbundling the services) requires a major alteration of the existing business model, rather than mere change in pricing strategy (Roberts, 2011; Ladurantaye, 2011a,b). Introducing discounted offerings means shift from 1-2 deals a month giving 5-7% of the property value as commission, to hundreds of monthly deals for $50-$500 each. Indeed, introducing discounted offerings is not a minor change in pricing strategy (such as 5% discount), but rather a tectonic shift with discount of 90%-95%. Such change demands rethinking the whole business’s value proposition, as well as organization of transactions and systems that support them (new IT system, different approach to human resource management, etc.). Not surprisingly, the industry incumbents are not enthusiastic to change: At the time of data collecting, only 10 percent of the respondents in the sample have already introduced discounted offering, and only 5 percent introduced the service of simple posting on the local MLS list. This demonstrates that adopting the discounted model is obviously a radical innovation for the industry, and intention to engage in such innovation is still perceived as a major risky step, not as simple copying of the standard industry practice. The keystone independent variable, critical threat perception, was operationalized using the enhanced version of the Dewald & Bowen (2010) scale, capturing the threat to survival of the business because of the disruptive business model innovation – discounted brokerage – gaining momentum in the real estate brokerage industry. The survey questions captured both threat to the firm itself, and to the conventional real estate brokerage model as a whole. The perceived urgency was measured using a single item

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scale capturing the respondent’s evaluation of time pressure9. Finally, the predictability perception was operationalized using a 3-item scale intended to capture the elements of state uncertainty (Milliken,1987) – the respondent’s assessment of the ability to foresee the future change of the real estate brokerage industry in response to emerging technologies and gaining momentum disruptive business models. Controls. To account for alternative explanations of intention to innovate, we simultaneously tested the impact of four control variables. The homogenous sample (single industry and country, one period in time) made sure that the respondents are similar to each other in most essential features; ergo, there was no need to include numerous control variables. The first control variable is size, represented by number of employees. This control variable is intended to capture the possible impediments or inducements to innovation caused by organizational contextual factors correlated with its size (Donaldson, 1996), such as resource availability and organizational slack10. The second control variable, decision maker’s experience of making risky decisions in the past and perception of success of these decisions, is operationalized using the scale of Pablo (1997) and Dewald & Bowen (2010). The study of Pablo (1997) demonstrated that the history of prior risk outcomes determines individual decision maker’s risk propensity, as the positive risk experience reinforces risk propensity during the future decision making. Since innovating is a high-stake risky decision, accumulated high levels of risk propensity facilitates in making this decision. Empirically, the association between !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! 9

!In!the!replication!study!(see!Appendix!2!in!the!electronic!supplement!to!this!paper),!we!employed!the!3Uitem!scale.!The!results! remain!quantitatively!similar.! 10 !In!the!replication!study!(see!Appendix!2!in!the!electronic!supplement!to!this!paper),!we!employed!more!fineUgrained!measures!of! slack!and!size.!The!results!remain!quantitatively!similar.! !

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prior positive risk experience and intentions to innovate – manifested through adopting a new business model – was empirically supported in Dewald and Bowen (2010). Moreover, prior experience of making risky decisions was an important factor in organizational studies of actions in times of adversity, such as Shimizu (2007) and Miller and Chen (2004). Finally, the two remaining control variables are the decision maker’s experience within the industry, and substantive experience in other industries. Both of these variables stem from the research in top management team demography, and are aimed at capturing the influence of a manager’s professional experience on her strategic decision making. Some studies found that tenure (years in the industry) can lead to cognitive rigidity, commitment to status quo and reluctance to make strategic changes (Boeker, 1997; Smith et al., 1992; Wiersema & Bantel, 1992; Finkelstein & Hambrick, 1996), while the diversity of experience (e.g., obtained in other industries) improves cognitive flexibility (Harris & Helfat, 1997; Wiersema & Bantel, 1992). RESULTS Measurement Model Testing. To validate the measurement model for the multiple-item scales employed in the study, the confirmatory factor analysis was conducted in LISREL 8.80. The results of factor analysis revealed appropriateness of used construct measures: Overall factor model fitted the data reasonably well (χ2 = 157.57, df = 84, p < .01; χ2/ df = 1.88; RMSEA = .06, NFI = .93, Non-Normed Fit Index (NNFI) = .96, CFI = .97, RMR = .07, GFI=.92, AGFI=.88).

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Convergent Validity. We used three basic tests to assure the convergent validity of the employed measures (Hair et al., 2010). First, for latent constructs with multiple indicators, all hypothesized factor loadings were significant at .05 level, and all standardized loading values highly exceeded the recommended threshold of .5, all being higher than .6 and with majority being in >.75 range. Second, all average variance extracted (AVE) indicators for composite scales were above the recommended threshold of .5, suggesting an adequate convergence (see Appendix). Third, all Composite Reliability (CR) indices turned out to be well above the stipulated cut-off point of .7 (see Appendix), suggesting good reliability of measured constructs. Discriminant Validity. We used two tests to assure the satisfactory discriminant validity of the employed constructs (Hair et al., 2010). First, all AVE indicators (see Appendix) are substantively less than the estimated squared correlation between each variable (see Table 1), suggesting that the factor loadings are higher than the correlations between the factors, i.e., that the constructs are empirically discriminated. Second, we made sure that fixing the correlation between each latent construct (one pair at a time) to one yielded the constrained model with much worse fit (significantly higher χ2) comparing to the unrestricted base model, rejecting by this means the hypothesis of lack of mutual distinction of any of the used constructs. Common method bias. The mechanisms for mitigating or controlling the common method bias were employed in our study through design procedures and post-hoc statistical control. First, to minimize the consistency artifacts and order effect, the questions capturing dependent variables followed the ones for independent variables, and

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the sequence of questions in each variables block (dependent, independent) was randomized using electronic survey software. To improve respondents’ attentiveness, we used reverse scales and different answer anchors. Second, to assure that spurious variance resulting from social desirability or negative affectivity influencing simultaneously dependent and independent variables (Kline, Sulsky, & Rever-Moriyama, 2000) do not distort the findings, we added the direct measures of these two constructs to the questionnaire. The short versions of the scales of Greenwald and Satow (1970) and Watson, Clark and Tellegen (1988) were used for assessing social desirability and negative affectivity, respectively. The post-hoc correlations analysis (Table 1) revealed that neither of these variables was correlated with the dependent variable. Third, the posthoc Harman’s statistical test did not reveal a single factor simultaneously affecting the studied constructs. Maximum Likelihood Exploratory Factor Analysis demonstrated that the single-factor solution produced significantly different correlation matrix (χ2(df=20) = 52.036, p
Urgency perception

Anchors

Cronbach α

Very Unlikely(1) – Very Likely (5) – Have Already Done This (6) Very Unlikely (1) – Very Likely (5) – Have Already Done This (6) Very Unlikely (1) – Very Likely (5) – Have Already Done This (6)

0.717

On-line education will replace conventional education programs On-line education will eliminate traditional universities On-line technologies in higher education will eliminate the role of conventional universities

Very Unlikely (1) – Very Likely (5)

Changes caused by on-line education require immediate response from your university * How urgent is the need to act with regards to online education? Growing acceptance of on-line education requires immediately response from your university

Definitely True (1) – Definitely False (5) Absolutely not Urgent (1) – Very Urgent (5) Strongly Disagree (1) – Strongly Agree (5)

Predictability You feel confident in being able to predict how the perception higher education industry will look like in 10 years * It is difficult to anticipate how technology (Internet, social media, on-line course delivery) will change the higher education You can foresee how new offerings (on-line courses, programs) will change the higher education industry * * resulting construct employs the reversed item

Very Unlikely (1) – Very Likely (5) 0.755 Very Unlikely (1) – Very Likely (5)

0.848

Definitely True (1) – Definitely False (5) Definitely True (1) – Definitely False (5)

0.613

Definitely True (1) – Definitely False (5)

The reliability statistics of each cognitive construct is also presented in Table A1. The only concern is caused by the reliability of the predictability perception construct, with Cronbach α=0.613. Yet, since the reliability statistics for this construct is close to ! !

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the stipulated threshold of 0.7, and taking into account that this study is a replication (rather than the main one), we decided to keep the predictability perception construct as measured. Controls. The higher education institutions in our sample might be substantively different from each other. These inherent differences might cofound the results of further analyses. To account for potential differences among the organizations (some of which might drive the response to perceived crisis), we gathered a set of control variables. Not to rely on self-report measures, all control variables were obtained from public sources, such as official websites of the universities or their profiles in public databases (Chronicle of Higher Education). More specifically, we controlled for: 1)

Age of the university. It is conceivable that age is the determinant of rigidity (e.g., due to higher mission institutionalization of older organizations - Mone et al., 1998), with older universities less likely to innovate.

2)

Country effects. The differences in the education systems of different countries might determine the response (innovativeness/rigidity) to the threat from on-line education models. The country effects also control for the level of institutionalization of the environment, which influences the response to adversity and determines the type of adaptation. For instance, highly institutionalized environments buffer organizations that are aligned with the rules from the external turbulence; moreover, the same

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environments impose major costs of legitimacy to innovators (Meyer & Rowan, 1977). The country effects were modeled using dummy variables. 3)

Number of campuses worldwide. Some anecdotal evidence (obtained through initial interviews) suggests that universities with higher number of campuses might be more inclined towards implementing on-line courses or programs to be able to provide students in all locations the access to best courses and best instructors.

4)

Public/private ownership. The public institutions are more dependent on external stakeholders (most important, government), which might be willing to promote on-line delivery to enhance the accessibility of education. Variable is dummy coded, Public=1.

5)

Number of students, number of faculty. These control variables reflect the size of the organization, accounting for all covariates of scale that might lead to rigidity (Donaldson, 1996).

6)

Endowment funds. This control variable (converted to million U.S. dollars) reflects the availability of slack resources, which provide the support buffer essential for innovation efforts (e.g., Cyert & March, 1963).

To remediate the skewness of the distributions, we applied logarithmic transformation to the variables of age of the university, number of students, number of faculty, and endowment funds. ANALYSIS AND RESULTS

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The descriptive statistics and correlations between the variables in the replication study are reported in Table A2. Table A2. Replication Study 1: Descriptive Statistics and Correlations between Constructs Variable 1.

Intention to innovate

Mean (SD)

1.

2.

3.

4.

5.

6.

7.

8.

9.

4.217 (1.170)

2.

Critical threat perception

1.809 (0.574)

3.

Urgency perception

3.677 (0.789)

4.

Predictability perception

2.979 (0.717)

5.

Organization age (log)

4.303 (0.925)

6.

Number of campuses

1.202 (1.177)

7.

Public (dummy)

0.746 (0.437)

8.

Number of students (log)

9.323 (0.928)

9.

Number of faculty (log)

6.644 (0.843)

10. Endowment funds (log)

4.375 (1.636)

.139* .437**

.234**

.268**

.127*

.083

-.086

-.105

.016

-.138*

-.049

.042

.039

-.064

.004

-.024

.022

-.122

.038

-.235**

-.058

.161*

.129*

.043

.011

-.058

.087

.510**

.201**

.103

.054

.014

.002

.107

.312**

.684**

.126*

-.035

-.020

-.128*

.379**

.032

-.015

.273**

.337**

Note. SD = standard deviation. N=173. * p (two-tailed) < .05, ** p (two-tailed) < .01

The OLS regression modeling is summarized in Table A3. Similarly to the main analysis in this paper (see Table 2), in the replication analysis we added the predictors to the model in three steps: control variables (Model 1), main effects (Model 2), and interactions (Model 3). Comparison of the results of both studies (see Table 2 of the main study and Table A3 of the current replication appendix) reveals that the proposed theoretical model finds strong empirical support in both settings – strategic decision making by real estate brokers and management of the universities. ! !

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Table A3. Replication Study 1: Intention to Innovate, Standardized (β) coefficients Intention to Innovate Model 1

Model 2

Model 3

Organization age (log)

-.283**

-.236**

-.264**

Number of campuses

-.079

-.073

-.071

Public (dummy)

-.070

-.001

-.003

Number of students (log)

.187†

.117

.093

Number of faculty (log)

.135

.105

.108

Endowment funds (log)

.046

.123

.118

Country dummies

yes

yes

yes

.001

.008

Urgency perception

.402**

.352**

Predictability perception

.180**

.170*

Control Variables

Main Effects Critical threat perception

Interactions Critical threat x Urgency

-.170*

Critical threat x Predictability

.134*

R-squared

0.166

0.352

0.386

F

3.614

7.232

7.101

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