Printer Friendly

Storm clouds and silver linings: responding to disruptive innovations through cognitive resilience.

Incumbent firms facing disruptive business model innovations must decide whether to respond through inaction, resistance, adoption, or resilience. We focus on resilient responses to simultaneous perceived threat and opportunity by managers of small incumbent firms. Using cognitive framing arguments, we argue that risk experience moderates perceptions of opportunity, whereas perceived urgency moderates situation threat. We test our framework in the real estate brokerage context, where small incumbents face considerable challenges from disruptive business model innovations, such as discount brokers. Analysis of data from 126 real estate brokers broadly confirms our framework. We conclude with implications of our research for small business incumbents.

Introduction

When digital photography was initially developed, it represented an exciting and innovative opportunity for aspiring entrepreneurs. At the same time, digital photography was a disruptive technology that posed tremendous challenges for the incumbent film icon, Kodak. Digital photography struck at the very heart of the Kodak business model of producing, selling, and processing film--a model that could become redundant through digital substitution. For over a decade, Kodak has struggled to find ways to respond to this significant disruption, with little or no success. Kodak's experience suggests that new business model adoption is confronted with multiple barriers, none more significant than managers' cognitive barriers to change (Kim & Mauborgne, 2005; Voelpel, Leibold, Tekie, & Von Krogh, 2005).

Recently, the cognitive perspective has been emphasized as an important explanation for entrepreneurial opportunity generation (Baron & Ward, 2004; Mitchell et al., 2007). We turn this research on its head and present a cognitive perspective of managers' responses to disruptive business model innovation. From the manager' s point of view, the key question is how to respond to new entrepreneurial business models: whether through inaction (Charitou & Markides, 2003), proactive resistance (Markides, 2006), adoption (Christensen & Raynor, 2003), or resilience (Sutcliffe & Vogus, 2003). This body of literature focuses on the responses of large incumbents, such as hub-and-spoke airlines' difficulty in effectively responding to the low-cost carrier model, or traditional steel manufacturers' loss of market dominance to mini-mills. We extend this to consider how managers of small incumbent firms choose to respond to new business models.

The Kodak story is representative of an increasingly common situation where the introduction of disruptive business model innovations contributes to rapidly changing business environments for incumbents (Charitou & Markides, 2003; Christensen & Raynor, 2003; Markides, 2006). The challenge for managers is to find ways to adopt disruptive business model innovations in order to prosper within, or even survive, the pending environmental change. This is referred to as developing organizational resilience (Sutcliffe & Vogus, 2003). This challenge is particularly difficult for small incumbent firms, which are more resource constrained, and so less able to absorb environmental shocks than larger firms (Dewald, Hall, Chrisman, & Kellermanns, 2007; Jarillo, 1989; Klaas, McClendon, & Gainey, 2000). On the other hand, while resource limitations constrain managers of small incumbent firms, they may be able to develop organizational resilience more easily than corporate decision makers as they are less bound by corporate roles and contexts that reward caution and asset protection (Corbett & Hmieleski, 2007; Markides & Geroski, 2004). Hence, small business incumbents are generally more responsive to opportunity identification (Shepherd & DeTienne, 2005) and the cognitive linkages associated with exploiting opportunities, such as business model innovations (Baron, 2006).

Whether small or large, it is difficult for any organization invested in "old ways" to abandon those known ways in favor of unproven new technologies or business models. Charitou and Markides's (2003) study of 98 companies that had faced disruptive business model innovations demonstrated that a firm's motivation to respond was a key determinant of firm response. However, they fail to explain where the firm's motivation is drawn from, leaving a gap between individual managers' cognitive resilience and motivation. Hirschman (1970) argues that firms respond to their customer's actions. This is at odds with more recent findings by Christensen (1997), who argues that customers and many existing stakeholders, including employees, are embedded in the inertia of reliable but old ways. Either way, the influences of various stakeholders, whether positive or negative, are incorporated within managerial cognition.

Drilling further down from organizational resilience to the managerial decisionmaking unit of analysis, we note that Sutcliffe and Vogus (2003) argue that the resilient manager has a rare ability to simultaneously manage organization change and stability. This is consistent with Tushman and O'Reilly's (1996) description of the "ambidextrous manager." However, despite the importance of resilience in the face of environmental change (cf. Gittell, Cameron, Lira, & Rivas, 2006), specific attributes or indicators of organizational resilience have not been clearly delineated. This is particularly the case for those attributes associated with a manager's cognitive intentions. Hence, in understanding resilience to environmental shifts, we are faced with two primary questions: which factors determine a small incumbent firm's response to disruptive innovations? And, as organizational actions are an outcome of managerial actions, which factors influence the cognitive intentions of managers of small incumbent firms facing a pending disruption? In this paper, we derive and test a framework that focuses primarily on the second question. Thus, our research expands Charitou and Markides's (2003) approach by addressing individual-level attributes and perceptions that influence managerial cognition and underpins managerial motivation (Atkinson, 1957).

We begin with a review of the relevant literature in organizational resilience and related fields. From this review, we develop a framework that describes the specific attributes of cognitive resilience. These attributes are then incorporated into hypotheses and tested using primary data collected from 126 managers in small incumbents in the real estate brokerage industry. Our results are discussed, and conclusions are provided, including suggestions for managers of small incumbent firms and future research in this area.

A Cognitive Resilience Framework

Organizational resilience is a relatively new field of research (Lengnick-Hall & Beck, 2005). Unfortunately, the boundaries of organizational resilience have been ill defined and wide ranging. This field has included studies that range from a stubborn maintenance of previous routines in defiance of pending environmental change (Edmondson, 1999), maintenance of positive changes under challenging conditions (Weick, Sutcliffe, & Obstfeld, 1999), prosperity in the face of targeted industry threats (Gittell et al., 2006), and a capacity to adjust organizational routines to adapt to untoward events (Lengnick-Hall & Beck; Sutcliffe & Vogus, 2003). These perspectives are consistent with psychological studies of resilience, which focus on the ability of individuals to adapt and grow in the face of adversity (Masten & Reed, 2002; Richardson, 2002). Here, we focus on organizational resilience as an organizational capacity to adopt new organizational routines and processes to address the threats and opportunities arising from disruptive business model innovation.

Organizational resilience is manifested through both cognitive and behavioral resilience. Cognitive resilience is a decision-making intention based on decision makers' ability to "notice, interpret, analyze, and formulate responses" to pending environmental change (Gittell et al., 2006). Behavioral resilience represents the action of implementing the formulated response or intentions developed through cognitive resilience.

Disruptive business model innovations represent a specific form of environmental change described by Markides (2006) as representing a redefinition of product or service attributes in a manner that is generally perceived as inferior to incumbent product or service providers (Charitou & Markides, 2003; Christensen & Raynor, 2003; Kim & Mauborgne, 2005). While disruptive business models often incorporate disruptive technologies, adopters need not rely on the discovery of new products or services (Markides). The challenge for incumbents is that in adopting the business model of their new entrepreneurial competitors, they might run the risk of damaging their existing business and undermining their existing business model (Charitou & Markides). Furthermore, adopters need to recognize an opportunity to capitalize on the innovation, raising the question of what cognitive factors enable some decision makers to notice and respond to such changes in their environment, but not others (Mitchell et al., 2007). This is particularly challenging when the new business model might be perceived as both a threat and an opportunity to the incumbent.

Recognizing recombinations as a form of innovation is nothing new (cf. Schumpeter, 1934), and adding the dimension of perceived inferiority links to a depth of literature on disruptive technologies initiated by Christensen and Bower (1996). The study of disruptive technologies has evolved into a consideration of potential disruptions in a myriad of fields, including medical procedures (Christensen, Bohmer, & Kenagy, 2000), global competitiveness (Hart & Christensen, 2002), and newspaper advertising (Gilbert, 2001). Christensen and Raynor (2003) provided a comprehensive list of 75 historic disruptions, including business model innovations in airlines (Southwest), customer relationship management (Salesforce.com), fast food (McDonald's), car manufacturing (Ford, Toyota), retailing (Wal-Mart, Staples, Amazon), stock brokerage (Charles Schwab), computer manufacturing (Dell), and education (University of Phoenix). In each of these situations, available technologies were applied to business model innovations. The Kodak story is an example of how the widespread use of a well-known technology, digital photography, provided the entrepreneurial opportunity for the development of a new business model.

Combining the research on organizational resilience with research on disruptive innovations, and more specifically, disruptive business model innovations, we have developed a framework of cognitive resilience (see Figure 1). In complex and highly uncertain environments, managers of small incumbent firms are more likely to use heuristic-based rather than a systematic rational process to help them navigate change (Mitchell et al., 2007). The decision context is an important source of cognitive schemas aiding the framing of cognitive heuristics (Corbett & Hmieleski, 2007). Managers' intentions are particularly driven by the extent to which a given competitive situation is perceived as a threat or an opportunity for the firm.

Situation threat represents the manager's perception of an exogenous external threat, such as the introduction of a disruptive business model innovation. Firm opportunity represents the manager's inward assessment of the opportunity presented to the firm if it were to adopt a disruptive business model innovation.

To describe our framework, we considered the context of real estate brokerage. Disruptive business model innovations are taking hold in the real estate brokerage industries in many areas (Miceli, Pancak, & Sirmans, 2007; Rowley, 2005). Information sharing of real estate property listings has shifted the value network from information control and organization to service (NAR, 2003). This has provided ample entrepreneurial opportunities for both new firms and incumbents. There are three basic categories of new business models in the real estate brokerage industry. The most drastic is complete disintermediation of the brokerage industry through for sale by owner models ("FSBO"), which has gained momentum through online advertising. Second, the discount brokerage model offers targeted services for a reduced fee. This model transfers some of the work to consumers, including initial home search using electronic data sources. Third, the corporate model bundles additional services, such as utility connections and legal fees, so as to deliberately provide more customer value than traditional brokerage services.

[FIGURE 1 OMITTED]

The FSBO and discount models clearly fit the definition of disruptive business model innovations. They involve the recombination of existing activities and provide a service inferior to the traditional business model, albeit at a reduced fee. While the FSBO and discount models are significantly different value propositions, they both represent threats and opportunities to small incumbents. Since both of these disruptive business model innovations can spur a similar range of cognitive reactions in managers, we treat them together in this paper.

If a broker-manager perceives little or no threat from the introduction of disruptive business model innovations, and further anticipates little or no opportunity from adopting a new business model for their own brokerage, then we expect that they will not take any action (see quadrant 1 in Figure 1). As Charitou and Markides (2003) suggest, ignoring the innovation is a legitimate response by incumbents, particularly when the new business model targets different customers, offers different value propositions, and requires different skills and competences. With respect to the real estate brokerage industry, legally, only licensed realtors can facilitate a sale unless the sale is facilitated directly by the owner. This exclusion is used by homebuilders to allow for unlicensed employees to act as sales representatives, thereby cutting the brokerage industry out of these transactions. In response, many brokerage firms recognize that they do not have the builder relationships, knowledge of the building process, and ability to follow through on warranty concerns, and so by and large they have ignored this specific opportunity.

In the next section, we use theories of cognitive framing to describe, explain and illustrate the other quadrants of our framework in the real estate context, and propose formal hypotheses on managers' responses to disruptive business model innovations in a small incumbent context.

Cognitive Framing, Risk Experience, and Urgency

Responses to Perceived Threat: Proactive Resistance (Quadrant 2). Two contrasting schools of situation framing permeate the management literature: prospect theory (Kahneman & Tversky, 1979) and issues interpretation (Dutton & Jackson, 1987). In both schools, framing is malleable and subject to individual or organizational perceptions. For instance, an identical organizational situation can be viewed as being negative (i.e., the need to avoid the possible loss of an existing competitive advantage), or positive (i.e., the need to pursue an opportunity in order to gain a new competitive advantage). Paradoxically, each school predicts an opposite outcome from negatively framed situations. Prospect theory predicts a risk-seeking response (the "certainty effect"), while issues interpretation predicts a risk-adverse response ("threat rigidity").

Prospect theory was developed to challenge expected utility theory (Friedman & Savage, 1948). Empirical tests of prospect theory confirm the "certainty effect," wherein negative framing results in risk-seeking behaviors, while positive framing yields risk-adverse behaviors (Casey, 1994; Kuhberger, 1998; Mittal & Ross, 1998; Mukherji & Wright, 2002; Puto, 1987; Qualls & Puto, 1989; Sanders, 2001; Wang, 2004; Wang, Simons, & Bredart, 2001; Wiseman & Gomez-Mejia, 1998). Cognitive-based framing is at the center of explaining prospect theory predictions, and a recent meta-analysis concluded that framing is a "reliable phenomenon" (Kuhberger, p. 23). Further, individual cognition is explicitly incorporated into prospect theory as the cognitive-based "manipulation of the reference point is clearly effective in framing" (p. 36).

Like prospect theory, issues interpretation (Dutton & Jackson, 1987) relies on framing losses and gains around a cognitively constructed reference point. While the certainty effect predicts that negative framing will lead to risk-seeking behavior, issues interpretation research indicates the opposite. A "threat rigid" response to negative framing is predicted to lead to risk-adverse behavior. We argue that there are two specific differences between prospect theory and issues interpretation that explain this contradiction: the origin of framing and the defining nature of risk.

In issues interpretation, framing is socially constructed. The perception process is dynamic, involving either a central decision maker, a highly trusted individual within the strategic decision-making team, or a consensus among the members of the strategic decision-making team (Dutton & Jackson, 1987, p. 77). On the other hand, in prospect theory literature and testing, framing is embedded in the wording of the question. Hence, the origins and subsequent development of issues interpretation is distinctly different from prospect theory. In the issues interpretation process, a chain of events starts with the decision maker(s) through the categorization (labeling) of strategic issues as either opportunities or threats, which "affects the subsequent cognitions and motivations of key decision makers, these, in turn, systematically affect the process and content of organizational actions" (p. 77). Opportunity labeling implies a positive situation with expected gains and control, while threat labeling implies a negative situation with expected losses and little control. Due to the central influence of the decision maker(s) in labeling strategic issues based on their understanding of developments in the industry, we argue that issues interpretation more appropriately fits decision making in small incumbents than prospect theory.

The second distinction between prospect theory and issues interpretation responses relates to the definition and use of "risk." Prospect theory is grounded in pure risk, or knowing the available outcomes and the probability of those outcomes occurring without knowing the actual outcome (Knight, 1921). This is similar to the risk of rolling dice or flipping a coin. On the other hand, issues interpretation, and, more specifically, the concept of threat rigidity, addresses uncertain or ambiguous environments (McCrimmon & Wehrung, 1986). In organizational settings, decision making is mired in uncertainty, and managers are unable to decouple an uncertain future from deterministic calculations of risk probability. Thus, issues interpretation provides a better framework for understanding strategic decision making wherein managers of small incumbent firms must interpret uncertain or ambiguous changes without knowing the full range of outcomes and probabilities.

Prospect theory and issues interpretation predict opposite responses to negative framing. Usually, these two opposing theories are set in juxtaposition to each other, though some theorists have attempted to integrate them. For example, George, Chattopadhyay, Sitkin, and Barden (2006) argue that both theories apply. They propose that prospect theory explains a potential gain or loss of resources, while issues interpretation explains a potential gain or loss in control. They do, however, acknowledge the reality that both resources and control often travel together, and in any event, it is difficult to distinguish whether resource or control risk are central to the situation. We take a different approach and argue that issues interpretation relies on cognitive formulation of framing, which is consistent with organizational settings. Further, issues interpretation incorporates uncertainty within the determination of risk-adverse behavior, which is consistent with risk-oriented strategic decision making. Hence, we argue that negative framing on its own will encourage risk-adverse responses, such as proactive resistance (quadrant 2 in Figure 1) to disruptive business model innovations (Charitou & Markides, 2003).

Examples of proactive resistance in the heavily regulated real estate brokerage industry include lobbying regulators and making proactive efforts to amend legislation to protect incumbent business models. Proactive resistance is a sanctioned and encouraged action by the National Association of Realtors (NAR). In a 2005 memorandum to state affiliates, NAR urged its members to pressure for "state laws that are designated to replace competition with regulation." They also stated, "[r]ealtors have the right to lobby for legislative and regulatory action--even if the effect of such action would be anticompetitive" (Wall Street Journal, 2005, p. A8). Several states, including Missouri, Texas, Illinois, Oklahoma, Iowa, Utah, Florida, and Alabama (Wall Street Journal, 2005), have instituted minimum service standards. The minimum standards include requirements to receive and present offers, which are aimed specifically at squeezing out the discount brokerage models that provide a limited service for a relatively small flat fee.

We therefore propose the following hypothesis, which is consistent with quadrant 2 of our framework (Figure 1):

Hypothesis 1: Small incumbent managers' increased perception of situation threat will be positively related to their intention to proactively resist a disruptive business model innovation.

Responses to Opportunity: Proactive Adoption (Quadrant 3). There is both intuitive and theoretical support for the capability-based perspective that opportunity framing is consistent with a willingness to adopt disruptive business model innovations (Charitou & Markides, 2003; Christensen & Raynor, 2003; Markides, 2006). Researchers have considered many theories of how managers recognize the value in new opportunities, including financial potential (Schumpeter, 1934; Shepherd & DeTienne, 2005), prior knowledge (Shane, 2000), alertness (Ardichvili, Cardozo, & Ray, 2003), and managerial cognition (Baron, 2006). For our research, the "how" is less important than understanding why. We are interested in identifying which factors motivate managers to formulate cognitive-based intentions to adopt disruptive business models.

Hypothesis 1 suggests that threat framing will result in proactive resistance to disruptive business model innovations. The contrary view is that resistance is myopic (Levinthal & Warglien, 1999), particularly if the business model innovation is inevitable due to external forces, such as new customer demands (Christensen & Raynor, 2003). A manager of a small incumbent firm who expects the inevitable changeover may perceive benefits, including being an early adopter of a disruptive business model innovation, even though it requires significant resource reconfiguration (Lavie, 2006). Some managers will distinguish between the threat posed by external factors and the opportunity available through adoption of new innovative ways (Gilbert, 2003; Lavie). We expect that organizations that have the necessary skills, resources, or capabilities that may yield competitive advantage will select strategic options that facilitate the exploitation of the perceived opportunity (Barney, 1991).

In quadrant 3 of Figure 1, managers primarily perceive an opportunity for the firm. This stimulates an interest in pursuing the disruptive business model innovation. The combination of high firm opportunity and low situation threat is likely to reside with "early adopters" who sense the benefits of the disruption in advance of others in the industry. In the real estate brokerage industry, there are a few early adopter firms, some of which are new to the industry. However, many of the new ventures are led by entrepreneurial broker-managers who moved from managing within incumbent firms to starting their own entrepreneurial ventures. In Canada, Realty Sellers, which was among the first discount realtors, is led by a well-established realtor Stephen Moranis, who was previously president of the nation' s largest real estate board. As Charitou and Markides (2003, p. 62) point out, adoption can take at least two forms, depending on whether the firm is "playing two games at once" by spinning out a new venture internally, or embracing the new model completely and scaling it up. Both of these forms of adoption occur when managers perceive more opportunity than threat.

Hence, our second hypothesis, consistent with quadrant 3 of our framework, is as follows:

Hypothesis 2: Small incumbent managers' increased perception of firm opportunity will be positively related to their intention to adopt a disruptive business model innovation.

Cognitive Resilience: Simultaneous Threat and Opportunity (Quadrant 4). Finally, we introduce the paradox of high situation threat and high firm opportunity (quadrant 4 in Figure 1). If both hypotheses 1 and 2 are supported, then a contradiction exists between the threat response (to resist) and the opportunity response (to adopt). Charitou and Markides (2003) do not provide a specific response for this situation, and we argue that managers solve this paradox through cognitive resilience. In other words, while the high threat would normally cause incumbents to proactively resist disruptive business model innovations, a high sense of firm opportunity encourages the manager to consider the benefits of adoption. Adoption may occur through acquisition of a disruptive competitor or direct adoption of disruptive business model practices. The core contribution of our paper is to examine why managers in small incumbents might choose different resilient responses in this high threat and high opportunity situation. We use literature on cognitive framing to show the importance of risk experience and urgency as moderators in managers' intentions to adopt disruptive business models.

Gilbert and Bower (2002) explored this contradiction in earnest, applying the issues interpretation principles to their study of the newspaper industry facing disruptive business model innovations. They developed a matrix of responses to disruptive changes, anchored by an independent framing of (1) the resource allocation process, and (2) the venture management process. Resource allocation process framing occurs in advance of venture management framing (Gilbert, 2003). They identified a response paradox where threat framing at the resource allocation process attracts resources, but opportunity framing provides the control, gains, and positive situation for effective response to disruptive shocks. By decoupling the response matrix into two time periods, Gilbert and Bower argued that it is possible to isolate the decision making into two independent actions--one associated with threat framing of the resource allocation intentions, and the other based on opportunity framing associated with venture management. In other words, the firm justifies the resource allocation by recognizing the inherent threat posed by the disruption, and then spins off a new venture mandated to pursue the disruption as an opportunity (Christensen, 1997; Christensen & Bower, 1996; Markides, 2006). This two-staged approach would appear to necessitate a complex and unlikely combination of manipulated framing and ideological flip-flopping, and indeed the results are at best mixed (Charitou & Markides, 2003).

We contend that cognitive resilience provides a more reasonable response to the high threat and high opportunity paradox. The threat of disruptions is exogenous to the firm, and hence quite independent of firm framing of opportunity associated with firm resources. Threat rigidity arguments emphasize the human nature to resist risky change, but resilient managers are able to bridge the dominant threat reaction to consider a reasoned evaluation of the opportunity available based on firm capabilities. Hence, through a resilient response, it is possible to resolve the disruptive "dilemma" described by Markides (2006) as the conflict between new and existing ways. We draw on previous research that indicates that critical developmental experiences (Krueger, 2007), such as risk-based experience, and perceptions of urgency, will further moderate the intentions of a resilient manager (Vlaar, De Vries, & Willenborg, 2005).

The secondary matrix we incorporated within Figure 1 examines the variety of resilient responses in a high threat and high opportunity context. The right-hand side of the figure focuses on the moderating impact of risk experience and urgency on resilience. If the manager has a negative risk experience and perceives low urgency of the disruption (quadrant 4a), we expect them to defer a decision, attending to more pressing priorities while keeping an eye out for ways to gain the necessary experience. When managers perceive negative risk experience, coupled with a high sense of urgency (remembering that the threat perception is high), they will set a priority to acquire the necessary experience (quadrant 4b). In the real estate brokerage industry, managers might hire an experienced manager from Internet-based business models. If urgency is low but risk experience is positive, the manager will monitor the situation, keeping a watchful eye toward selecting the most effective time to adopt the disruption (quadrant 4c).

Our primary interest is in quadrant 4d, where risk experience is positive and urgency is high, and where we expect that the manager will formulate intentions to adopt the disruption. Sitkin and Pablo (1992) developed a risk propensity model that integrates both individual and situational factors, finding that: (1) risk behavior is a reflection of risk propensity interacting with risk perception (an individual indicator); (2) risk propensity is derived from three individual factors (risk preference, inertia, and outcome history); and (3) risk perception is determined by five situational factors (problem framing, problem domain familiarity, top management team heterogeneity, social influence, organizational control systems). Risk propensity is driven largely by risk outcome history (Pablo, 1997) or what we term risk experience, supporting the intuitive expectation that favorable experience in making risky decisions will enhance the small incumbent manager's risk propensity.

Notwithstanding the relative differences in characteristics of risky decisions, Pablo (1997) found that positive experiences realized through previous risky decision making will reinforce future risk propensity. Although the manager may not have faced a decision as risky or significant as adopting a new business model, positive past experience is expected to increase propensity to take on larger risks, an intuitive and empirically supported notion (Pablo). Adopting a new business model might involve the reallocation of critical resources and reconfiguration of capabilities, and can impact the very survival of the business. Managers will draw on their experience when facing unfamiliar risky situations, and we therefore expect that risk experience will moderate the response of resilient strategic decision makers facing disruptive business model innovations.

Hypothesis 3: The relationship between a small incumbent manager's increased perception of firm opportunity and intention to adopt a disruptive business model innovation is moderated by positive risk experience, such that positive risk experience increases the likelihood of intention to adopt.

Comparative studies indicate that innovative-induced industry change is idiosyncratic (cf. Cooper & Schendel, 1976). Managers often face gestation periods that are unpredictable ex ante and beyond the control of the incumbent. This uncertainty is further intensified with complex change, such as a business model adoption, which requires process evolution, and possibly the acquisition, integration, and elimination of certain firm capabilities (Eisenhardt & Martin, 2000; Lavie, 2006). Ainslie and Haslam (1992) argue that managers will put off addressing major decisions in favor of less important initiatives until there is an imminent cost to avoidance. While gestation periods cannot be predicted with any degree of certainty, managers have industry-specific knowledge of technologies and markets, and will therefore tend to formulate their own estimates of urgency. Even where an industry is experiencing an ongoing exogenous shock that presents a high situational threat, managers may not perceive this threat as immediately threatening. For instance, alternative energy sources pose a significant, but less than urgent threat to the energy industry. Wireless technologies pose a significant but less than urgent threat to cable companies. Hence, we expect that a manager's intentions to adopt disruptive business model innovations will be moderated by their perception of the urgency associated with the need to respond.

Hypothesis 4: The relationship between a small incumbent manager's increased perception of situation threat and intention to adopt a disruptive business model innovation is moderated by an increased perception of urgency, such that high urgency increases the likelihood of intention to adopt.

Research Method

The most critical criterion in our selection of an appropriate field of study was timing. Charitou and Markides (2003) emphasize the importance of timing, noting that there is a stage in the evolution of disruptive business model innovations when incumbents recognize that "the new ways of playing the game are in conflict with the established ways" (p. 57). With respect to the real estate brokerage industry at the time of our data collection in 2005, the National Association of Realtors (NAR) had already issued many reports indicating to its almost 10 million real estate brokerage members that the old ways would not suffice in the future (NAR, 2003). Technological advances had already taken hold and opened the path for new brokerage business models that offer either fewer services for reduced fees, or increased services for fees comparable with current rates. Disruptive business model innovations had already gained legitimacy as evidenced by NAR statements such as "... in the next three to five years, consolidation of firms and the shift in power from the independent contractor agent to the real estate firm will reinforce each other to alter the landscape of the real estate brokerage industry" (NAR, 2003, p. 48). It was clear to real estate brokers at the time of our study that the "new ways of playing the game" were surely in conflict with the established ways.

Real estate brokers are usually either independent owners of their firms or franchisees of large real estate firms, such as Remax International. Independent owners have the freedom to set their own business model, choosing their own variant of the traditional full service, new reduced fees, or enhanced service models. In a franchise relationship, there are term limits on franchises, and by statute, each franchise operation requires a broker as manager. Brokers in a franchise relationship may not have much control over the business model of their overall franchise parent, but have the freedom to sell their current franchise and step out on their own or franchise with another real estate firm. Thus, the field of study is both relevant and appropriate, as residential real estate brokers, by provincial statute, are the key decision makers in small incumbent firms facing imminent environmental changes from disruptive FSBO or discount business models.

A mail-in survey was sent to approximately 1,100 members (exact numbers were not provided by the administrators) of a real estate brokerage regulatory association in the Canadian province of Alberta. The survey was targeted at residential brokers, which represents the largest contingent association membership. Unfortunately, the association did not have a segregated list of residential brokers, and was only able to provide rough estimates of the proportion of members who primarily act as residential real estate brokers, which was estimated at 85-90% of the membership list.

To assist in the survey design, two industry representatives and a senior administrator of the association were asked to complete the instrument and comment on any language or structure concerns. Specifically, they were asked to provide their opinion as to whether the questions "made sense" in the context of the residential real estate brokerage industry. The surveys were then delivered in sealed envelopes to the association, and mailed by the association in order to preserve membership confidentiality. Due to confidentiality concerns of the association, reminders could not be sent, which limited the number of responses. Survey responses were treated as being anonymous, and thus no specific geographical or identifying statistics could be captured other than those requested on the questionnaire. Responses were received from 140 participants, representing approximately 15% of the population. Questionnaires from 14 respondents were not included in our analysis due to substantially incomplete questionnaires, leaving a sample of 126.

Although the responses were anonymous, some general information was captured to assess the extent of potential nonresponse bias within the sample. Comparing the data with other research data (AREA, 2004) indicates that nonresponse bias based on realtor gender or type of brokerage is not a serious threat to our study. The split between urban and rural clientele was 90/10 in our sample versus 91/9 for the AREA study, and respondents in both studies were predominantly male (83% in this study versus 69% in the AREA study). Our sample consisted of independent brokers (70%), franchise operators (28%), and corporate brokers (11%). (1)

Variables

Our empirical analysis involves nine variables, each measured through self-report questionnaire items. The measures are mostly based on a 5-point scale, with both anchor and mid-point references. To enhance reliability, most variables combine two or more measured items, with the total combined scores divided by the number of items measured, resulting in a composite score between 1 and 5. In substantially complete questionnaires, occasional missing fields were replaced by mean values unless noted otherwise. The variables are described in detail below. Cronbach's alpha values were determined to measure the reliability of all multi-item variables.

Dependent Variables. We measured two separate dependent variables to assess the extent to which managers intended to resist or adopt discounted fees, an important disruptive business model innovation in this industry. As Table 1 indicates, respondents were asked how likely they would be to lobby the authorities to protect the industry (resist), and whether they would be likely to include discounted fees in their service offering (adopt). Responses were scored on a 5-point Likert scale. In our study, adoption and resistance were not significantly correlated at the 0.05 level, indicating that these measures are capturing distinct intentions.

Independent Variables. We measured firm opportunity by three indicators designed for this study, asking questions on the extent to which discount brokerage was a new opportunity for the firm, and the extent to which the public and customers were encouraging the firm to adopt new business models ([alpha] = 0.64). We measured situation threat using three indicators of the extent to which the discount model is a threat to the brokerage industry, and the extent to which alternative models such as FSBO would threaten incumbents' profits ([alpha] = 0.61).

Moderating Variables. We measured urgency with a single open-ended question. The responses to the question on how long, if ever, it will be before commission rates are reduced in order to meet customer demands were coded to reflect the relative urgency of the pending disruption. Imminent adoption would reflect a gestation period that would clearly indicate a time period shorter than the period required by the incumbent to adopt the disruptive business model. The adoption period can vary depending on the capability reconfiguration needs of the incumbent (Lavie, 2006), from a few months to a few years. Discussions with industry representatives indicated that a comfortable period to adjust a business model would be 2-4 years. Hence, we classified responses under 4 years as a relatively "short" gestation period, and 5+ years as a relatively "long" gestation period. Missing items were coded at the mid-point of 3 out of 5 in order to position nonrespondents between the polar extremes of a perceived imminent adoption and long or nonexistent gestation period.

We used measures suggested by Pablo (1997) to measure risk experience. In that study the reliability of the measures was 0.87 compared to 0.94 found here.

Control Variables. Three control variables were included to address rival theories. Age of respondents was measured and regressed against the dependent variable as a test of the theory that younger managers would be less rigid in their cognitive frames due to a lack of institutionalized belief in the old business model. Gender was included as a control variable to determine if there was any distinction between the risk preferences of male and female small business managers (Bird & Brush, 2002; Langowitz & Minniti, 2007). Finally, firm performance was included to control for the learning from performance feedback effect, where managers in firms performing below the aspiration level are more likely to adopt strategic changes or innovations than those exceeding expectations (Cyert & March, 1963; Greve, 2003). We measured firm performance using three items with a very high degree of internal consistency (Cronbach's alpha = 0.91). The measures are all set in the current period, inquiring as to volume, profit, and profit per transaction measures in relation to expectations. Industry representatives indicated that these three measures accurately reflect brokerage performance.

We also included direct measures of items for use in testing the influence of social desirability (Greenwald & Satow, 1970) and negative affectivity (Watson, Clark, & Tellegen, 1988). Specific tests were undertaken with these variables and described below. The descriptive statistics and correlations for all variables are indicated in Table 2. All variables were tested and determined to follow a normal distribution, with the exception of negative affectivity. The impact of a non-normal distribution for this variable is discussed in the next section.

Analysis

One of the challenges of empirical studies employing cognitive-based decision-making models is that, by definition, cognition can only be measured by direct inquiry. Unfortunately, the use of self-report measures for both independent and dependent variables introduces concerns with respect to common method bias (Armitage & Conner, 2001). We addressed this concern through two primary techniques: (1) design procedures, and (2) statistical controls (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003). Various instrument and process design procedures were incorporated into the research design, including guaranteeing anonymity (to address social desirability), use of industry-specific language (to reduce item ambiguity), reversing pole anchors on item indicators (to improve attentiveness), separating predictor and criterion variables within the instrument design (to reduce response bias), and directly measuring both social desirability and negative affectivity. Although an independent source for the dependent variable would be desirable, self-report is unavoidable, as intentions can only be measured by asking the manager. With respect to statistical analysis, the social desirability and negative affectivity variables are included in the correlation matrix. The matrix shows that there were no unexpected relationships between our focal variables and either social desirability or negative affectivity (Kline, Sulsky, & Rever-Moriyama, 2000).

We applied ordinary least squares (OLS) regression using SPSS version 14.0 to test the models. Multiple regression is preferred for statistical modeling where independent variables are expected to have a direct effect on the dependent variable. In addition, hierarchical regression models allowed us to observe incremental effects of adding variables to the model.

Table 3 shows the standardized OLS multiple regression results of the control, independent, and moderating variables regressed on the alternative strategies of proactive resistance and adoption. Our results show support for all four hypotheses. Situation threat is positively and significantly related with proactive resistance (supporting hypothesis 1 in model 2), while opportunity is positively and significantly associated with adoption (supporting hypothesis 2 in model 4). Model 5 shows support for the moderated relationships hypothesized in hypotheses 3 and 4. Focusing on the external dimension, decision makers are even more likely to intend to adopt when the disruption is seen as urgent for a given level of situation threat (supporting hypothesis 3). Alternatively, and based on an internal focus, decision makers are more likely to intend to adopt when they have had a positive experience with risky situations in the past, given a particular perception of firm opportunity (supporting hypothesis 4).

The demographic control variables were nonsignificant, indicating that the decision maker's intention to adopt was not related to either age or gender. Interestingly, the firm performance coefficient was positive and significant in the unmoderated models of adoption (models 3 and 4). For small incumbents, the learning from performance feedback effect seems to be outweighed by the availability of slack resources, which might encourage experimentation with new business models (Cyert & March, 1963).

Implications and Conclusions

Disruptive business models introduce threats to existing ways, but also opportunities for new sources of competitive advantage (Markides, 2006). Managers of small incumbent firms show cognitive resilience when they form intentions based on their ability to notice, interpret, analyze, and formulate responses to simultaneously high threat and high opportunity situations. Cognitive resilience enables managers to look past the storm clouds of disruptive change to see the opportunities in silver linings.

Incumbent real estate brokers face considerable challenges from the threat of discount service providers (Miceli et al., 2007). Discount models are taking hold quickly in the United States. Examples include entrepreneurial firms, such as Iggy's House, which offers free listing services, and Buyside, which acts only as a buyer agent rebating 70% of any conventional split commission to the buyer. Both of these start-ups have shown impressive growth in listings and revenue (Cook, 2007). The challenge for small incumbents is to know when to proactively resist or adopt the new discounted business models, given their limited resources, current capabilities, and the danger that adoption might undermine their traditional full-service model (Charitou & Markides, 2003).

In our study, we found support for cognitive framing explanations of the likelihood of resistance and adoption. Specifically, we found support for the issues interpretation or threat rigid response that predicts increased likelihood of resistance when managers perceive business model innovation as a threat, and increased likelihood of adoption when the innovation is perceived as an opportunity. We also found evidence that urgency moderates situation threat, and that risk experience moderates firm opportunity in predicting intentions to adopt. Thus, real estate brokers respond based on whether they predominantly perceive discount models as a threat or a business opportunity.

Our findings suggest that once the moderating effects of risk experience and urgency are included, the main effects of firm opportunity and situation threat on intention to adopt are nonsignificant. This may indicate that managers separately align their internal and external perceptions. Looking internally, the real estate brokers evaluate their firm's capabilities, and hence opportunities through the lens of their own risk experience. Looking externally, they evaluate the threat of discount models through their perception of urgency. In this way, our research indicates that cognitive resilience grows out of simultaneous rather than a two-step threat-opportunity assessment as in Gilbert and Bower's (2002) response paradox. We suggest that cognitive resilience depends on a simultaneous internal and external evaluation of the situation and not a staged process.

Our study of the real estate brokerage industry serves as a reminder that not all incumbents are large, established firms. Examples of large incumbents, such as Kodak's failure to respond to the digital photography revolution, illustrate the importance of cognitive inertia in large established firms, and how managers in corporate contexts are more conditioned to consolidate or exploit existing business models rather than create new markets (Kim & Mauborgne, 2005; Markides & Geroski, 2004). Our findings suggest that managers in small incumbent firms are also likely to proactively resist adoption if they see the new model as a threat. One explanation for this is that smaller incumbents are generally operating closer to the survival level than large firms, and so managers are more likely to refer to the survival level in assessing their risk preferences than in larger firms (March & Shapira, 1992). Entrepreneurship researchers emphasize that cognitive framing may differ between entrepreneurial and corporate settings (Corbett & Hmieleski, 2007). We would encourage further investigation of cognitive framing in an intermediate context, that of small incumbent firms.

Implications for Small Incumbents

Our research holds at least three practical implications for small incumbents. First, standard issues interpretation threat rigidity arguments indicate that small incumbents will demonstrate the "deer in the headlights" response to disruptive business model innovations. We found evidence that this leads to proactive resistance by small incumbents in the short term. While the new business model is not a direct competitor providing a comparable service or aiming at the same market, urgency remains low, and small incumbents can defer a decision or monitor the market. However, if the disruptive business model becomes well established in the marketplace, it can lead to shifting customer expectations. For example, small boutique clothing retailers have relied on customers' desire to try on clothing to proactively resist changes to pricing and distribution of designer clothing introduced by online retailers. Initially, the online model was aimed at a more price-conscious market segment, and so deferral or monitoring by incumbents was successful. Over time, shifting customer expectations about wide stock availability, flexible return policies, transparent pricing, and even price flexibility through online auctions are increasing the urgency of this online retailing threat. As urgency increases, ultimately, small incumbent firms either face the need to adopt (by, for example, launching their own online store), acquire (by partnering to gain online experience), or accept a reduced market position.

Second, by framing situations as both high threat and high opportunity, resilient managers in small incumbents are able to position their organizations for adoption when the time is right. Even with negative risk experience and low urgency, the decision to defer (quadrant 4a) is much more resilient than rigid proactive resistance (quadrant 2). With positive risk experience or high urgency, the strategic repertoire available is wider, leading to more creative responses. An implication for managers of small incumbents is that they should look for ways to gain positive risk experience by, for example, sharing positive learning experiences within their strategic team or business partner networks. They should also try to find ways to keep urgency low. One way to do this is to clearly delineate the small incumbent's offering from the new business model, as independent bookstores have done by emphasizing the social experience of their stores contrasted with the remote delivery of online retailers, such as Amazon.com.

Third, small incumbents might find reassurance that once risk experience and urgency are taken into account, the availability of resources is not a significant predictor of likelihood of adoption. Thus, small incumbents should not fear limited resources as a barrier to adopting new models. Instead, they should focus on gaining positive risk experience and minimizing urgency as outlined above.

Directions for Future Research

We extended Charitou and Markides's (2003) study of responses to disruptive innovation by using cognitive framing to explain the origins of firm motivation. By focusing on small incumbents, we were able to make more direct connections between the cognitive perceptions of individual decision makers and motivation to respond at the firm level than is realistic in the large incumbent context. Further, we used cognitive framing to predict when small incumbents would exhibit each of the five responses to disruptive innovation. While our research focused on the intention to adopt, we would encourage others to test the other outcomes in our framework. Specifically, we posit that acquisition of capabilities (quadrant 4b) and monitoring disruptive innovations (quadrant 4c) are alternative forms of resilience, based on combinations of risk experience and urgency. These outcomes remain to be empirically tested, and would be a valuable extension to our work.

Mitchell et al. (2007), in their introduction to a special issue of this journal on cognition, focused on the central question of entrepreneurial cognition research: "How do entrepreneurs think?" Our research contributes directly to this central question by exploring the influences of situational, organizational, and individual factors that combine to generate cognitive response. We encourage researchers to extend beyond the isolated considerations of individual motivation to develop more comprehensive models that incorporate direct environmental influences on entrepreneurial cognition.

Our findings also contrast with George et al. (2006), who focused on responses to potential gains and losses of resources as opposed to control. Rather than separate resources from control, we incorporated both in our general questions on perceived threat and opportunity. We focused instead on risk experience and urgency. Integrating our study with George et al.'s raises the question as to whether risk experience and urgency might relate with gains or losses of resources as opposed to control. We expect that risk experience might relate more closely with control, whereas urgency might relate with resources, but did not test these conjectures in this study. Future studies might also juxtapose George et al.'s explanation for adoption and ours, and ask which factors dominate adoption decisions: urgency and risk experience, or the different dimensions of gains and losses.

Finally, we would encourage research assessing whether our findings are due to idiosyncrasies in our research context. More specifically, several of our measures were created to address the real estate context, and the reliability of some variables was less than desired. These measures should be further refined in future studies. We selected the real estate brokerage industry because of the imminent threat posed by discount models, and because independent or franchisee brokers are considered the primary managers and strategic decision makers in small incumbent real estate brokerages. Since our survey, pressures from discount models have intensified, as the success of firms such as Iggy's House and Buyside attests. While we expect our findings to be robust across contexts where the potentially disruptive business model does become a success and ones where the new business model ultimately fails, we would be interested to see this conjecture empirically tested. We would also encourage our cognitive resilience framework to be tested in the large incumbent context, or in small business contexts where there are no franchisees.

Our paper contributes to our understanding of innovation, specifically by identifying effective incumbent strategic responses to disruptive business model innovations. Our study of intention to adopt business model innovations in the real estate brokerage industry helps to answer how incumbents can both accept the risk of new ways and abandon their old ways. We found that managers' cognitive resilience is vital in meeting this crucial challenge.

DOI: 10.1111/j.1540-6520.2009.00312.x

REFERENCES

Ainslie, G. & Haslam, N. (1992). Hyperbolic discounting. In G. Loewenstein & J. Elster (Eds.), Choice over time (pp. 57-92). New York: Russell Sage Foundation.

Ardichvili, A., Cardozo, R., & Ray, A. (2003). A theory of entrepreneurial opportunity identification and development. Journal of Business Venturing, 18, 105-123.

AREA. (2004). Industry viability study--Final report. Prepared by the Alberta Real Estate Association and Overview Business Consulting Inc.

Armitage, C.J. & Conner, M. (2001). Efficacy of the theory of planned behavior: A meta analytic review. British Journal of Social Psychology, 40, 471-499.

Atkinson, J.W. (1957). Motivational determinants of risk-taking behavior. Psychological Review, 64, 359-372.

Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99-120.

Baron, R.A. (2006). Opportunity recognition as pattern recognition: How entrepreneurs "connect the dots" to identify new business opportunities. Academy of Management Perspectives, 20(1), 104-119.

Baron, R.A. & Ward, T.B. (2004). Expanding entrepreneurial cognition's toolbox: Potential contributions from the field of cognitive science. Entrepreneurship Theory and Practice, 28(6), 553-573.

Bird, B. & Brush, C. (2002). A gendered perspective on organizational creation. Entrepreneurship Theory and Practice, 26(3), 41-65.

Casey, J.T. (1994). Buyers' pricing behavior for risky alternatives: Encoding processes and preference reversals. Management Science, 40(6), 730-749.

Charitou, C.D. & Markides, C.C. (2003). Responses to disruptive strategic innovation. MIT Sloan Management Review, 44(2), 55-63.

Christensen, C.M. (1997). The innovator's dilemma: When new technologies cause great firms to fail. Boston: Harvard Business Press.

Christensen, C.M., Bohmer, R., & Kenagy, J. (2000). Will disruptive innovations cure health care? Harvard Business Review, 78(5), 102-105.

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.

Cook, J. (2007, June 15). New online real estate firm moves into Washington. Seattle Post Intelligencer. Available at http://seattlepi.nwsource.com/business/320108_buyside18.html, accessed 17 August 2007.

Cooper, A. & Schendel, D. (1976). Strategic responses to technological threats. Business Horizons, 19(1), 61-69.

Corbett, A.C. & Hmieleski, K.M. (2007). The conflicting cognitions of corporate entrepreneurs. Entrepreneurship Theory and Practice, 31(1), 103-121.

Cyert, R.M. & March, J.G. (1963). A behavioral theory of the firm. Englewood Cliffs, CO: Prentice-Hall.

Dewald, J.R., Hall, J., Chrisman, J.J., & Kellermanns, F.W. (2007). The governance paradox: Preferences of small vulnerable firms in the homebuilding industry. Entrepreneurship Theory and Practice, 31(2), 279-297.

Dutton, J.E. & Jackson, S.E. (1987). Categorizing strategic issues: Links to organizational action. Academy of Management Journal, 12(1), 76-90.

Edmondson, A. (1999). Psychological safety and learning behavior in work teams. Administrative Science Quarterly, 44, 350-383.

Eisenhardt, K.M. & Martin, J.A. (2000). Dynamic capabilities: What are they? Strategic Management Journal, 21, 1105-1121.

Friedman, M. & Savage, L.J. (1948). The utility analysis of choices involving risk. The Journal of Political Economy, 56(4), 279-304.

George, E., Chattopadhyay, P., Sitkin, S.B., & Barden, J. (2006). Cognitive underpinnings of institutional persistence and change: A framing perspective. Academy of Management Review, 31(2), 347-385.

Gilbert, C. (2003). The disruptive opportunity. MIT Sloan Management Review, 44(4), 27-32.

Gilbert, C. & Bower, J.L. (2002). Disruptive change: When trying harder is part of the problem. Harvard Business Review, 80(5), 95-101.

Gilbert, C.G. (2001). A dilemma response: Examining the newspaper industry's response to the Internet. Doctoral dissertation, Harvard University.

Gittell, J.H., Cameron, K., Lim, S., & Rivas, V. (2006). Relationships, layoffs, and organizational resilience: Airline industry responses to September 11. The Journal of Applied Behavioral Science, 42(3), 300-329.

Greenwald, H.J. & Satow, Y. (1970). A short desirability scale. Psychology Reports, 27, 131-135.

Greve, H.R. (2003). Organisational learning from performance feedback: A behavioral perspective on innovation and change. Cambridge: Cambridge University Press.

Hart, S.L. & Christensen, C.M. (2002). The great leap: Driving innovation from the base of the pyramid. MIT Sloan Management Review, 44(1), 51-56.

Hirschman, A.O. (1970). Exit, voice, and loyalty: Responses to decline in firms, organizations and states. Cambridge, MA: Harvard University Press.

Jarillo, J.C. (1989). Entrepreneurship and growth: The strategic use of external resources. Journal of Business Venturing, 4, 133-147.

Kahneman, D. & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263-292.

Kim, W.C. & Mauborgne, R. (2005). Blue ocean strategy: How to create uncontested market space and make the competition irrelevant. Boston: Harvard Business School Press.

Klaas, B.S., McClendon, J., & Gainey, T.W. (2000). Managing HR in small and medium enterprise: The impact of professional employer organizations. Entrepreneurship Theory and Practice, 25, 107-124.

Kline, T.J.B., Sulsky, L.M., & Rever-Moriyama, S.D. (2000). Common method variance and specification errors: A practical approach to detection. The Journal of Psychology, 134(4), 401-421.

Knight, F.H. (1921). Risk, uncertainty and profit. Boston: Houghton Mifflin.

Krueger, N.F. (2007). What lies beneath? The experiential essence of entrepreneurial thinking. Entrepreneurship Theory and Practice, 31(1), 123-138.

Kuhberger, A. (1998). The influence of flaming on risky decisions: A meta-analysis. Organizational Behavior and Human Decision Processes, 75(1), 23-55.

Langowitz, N. & Minniti, M. (2007). The entrepreneurial propensity of women. Entrepreneurship Theory and Practice, 31(3), 341-364.

Lavie, D. (2006). Capability reconfiguration: An analysis of incumbent responses to technological change. Academy of Management Review, 31(1), 153-174.

Lengnick-Hall, C.A. & Beck, T.E. (2005). Adaptive fit versus robust transformation: How organizations respond to environmental change. Journal of Management, 31(5), 738-757.

Levinthal, D.A. & Warglien, M. (1999). Landscape design: Designing for local action in complex worlds. Organization Science, 10(3), 342-357.

March, J.G. & Shapira, Z. (1992). Variable risk preferences and the focus of attention. Psychological Review, 99(1), 172-183.

Markides, C. (2006). Disruptive innovation: In need of a better theory. Product Innovation Management, 23, 19-25.

Markides, C.C. & Geroski, P.A. (2004). Fast second: How smart companies bypass radical innovation to enter and dominate new markets. San Francisco: Jossey-Bass.

Masten, A.S. & Reed, M.G.J. (2002). Resilience in development. In C.R. Snyder & S.J. Lopez (Eds.), Handbook of positive psychology (pp. 74-88). Oxford: Oxford University Press.

McCrimmon, K.R. & Wehrung, D.A. (1986). Taking risks: The management of uncertainty. New York: The Free Press.

Miceli, T.J., Pancak, K.A., & Sirmans, C.F. (2007). Is the compensation model for real estate brokers broken? Journal of Real Estate Finance Economics, 35, 7-22.

Mitchell, R.K., Busenitz, L.W., Bird, B., Gaglio, C.M., McMullen, J.S., Morse, E.A., et al. (2007). The central question in entrepreneurial cognition research 2007. Entrepreneurship Theory and Practice, 31(1), 1-27.

Mittal, V., Ross, & W.T. (1998). The impact of positive and negative affect and issue framing on issue interpretation and risk taking. Organizational Behavior and Human Decision Processes, 76(3), 298-324.

Mukherji, A. & Wright, P. (2002). Reexamining the relationship between action preference and managerial risk behaviors. Journal of Managerial Issues, 14(3), 314-330.

NAR. (2003). The future of real estate brokerage: Challenges and opportunities for Realtors. Chicago: National Association of Realtors.

Pablo, A.L. (1997). Reconciling predictions of decision making under risk: Insights from a reconceptualized model of risk behavior. Journal of Managerial Psychology, 12(1), 4-20.

Podsakoff, P., MacKenzie, S.B., Lee, J.Y., & Podsakoff, N.P. (2003). Common method bias in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879-903.

Puto, C.P. (1987). The framing of buying decisions. Journal of Consumer Research, 14, 301-315.

Quails, W.J. & Puto, C.P. (1989). Organizational climate and decision framing: An integrated approach to analyzing industrial buying decisions. Journal of Marketing Research, 26, 179-192.

Richardson, G.E. (2002). The metatheory of resilience and resiliency. Journal of Clinical Psychology, 55, 307-321.

Rowley, J. (2005). The evolution of internet business strategy: The case of UK estate agency. Property Management, 23, 217-226.

Sanders, W.G. (2001). Behavioral responses of CEOs to stock ownership and stock option pay. Academy of Management Journal, 44(3), 477-492.

Schumpeter, J. (1934). The theory of economic development: An inquiry into profits, capital, credit, interest, and the business cycle. Cambridge, MA: President of the Fellows of Harvard College.

Shane, S. (2000). Prior knowledge and the discovery of entrepreneurial opportunities. Organization Science, 11(4), 448-469.

Shepherd, D.A. & DeTienne, D.R. (2005). Prior knowledge, potential financial reward, and opportunity identification. Entrepreneurship Theory and Practice, 29(1), 91-112.

Sitkin, S.B. & Pablo, A.L. (1992). Reconceptualizing the determinants of risk behavior. Academy of Management Review, 17(1), 9-38.

Sutcliffe, K.M. & Vogus, T.J. (2003). Organizing for resilience. In K.S. Cameron, J.E. Dutton, & R.E. Quinn (Eds.), Positive organizational scholarship: Foundations of a new discipline (pp. 94-110). San Francisco: Berrett-Khoeler.

Tushman, M.L. & O'Reilly, C.A. (1996). Ambidextrous organizations: Managing evolutionary and revolutionary change. California Management Review, 38(4), 8-30.

Vlaar, P., De Vries, P., & Willenborg, M. (2005). Why incumbents struggle to extract value from new strategic options: Case of the European airline industry. European Management Journal, 23(2), 154-169.

Voelpel, S., Leibold, M., Tekie, E., & Von Krogh, G. (2005). Escaping the red queen effect in competitive strategy: Sense-testing business models. European Management Journal, 23(1), 37-49.

Wall Street Journal. (2005, August 12). The realtor racket [Editorial]. Wall Street Journal (Eastern Edition), p. A8.

Wang, X.T. (2004). Self-framing of risky choice. Journal of Behavioral Decision Making, 17, 1-16.

Wang, X.T., Simons, E, & Bredart, S. (2001). Social cues and verbal framing in risky choice. Journal of Behavioral Decision Making, 14(1), 1-15.

Watson, D., Clark, L.A., & Tellegen, A. (1988). Development and validation of brief measures of positive and negative affect: The PANAS scales. Journal of Personality and Social Psychology, 54(6), 1063-1070.

Weick, K.E., Sutcliffe, K.M., & Obstfeld, D. (1999). Organizing and the process of sensemaking. Organization Science, 16(4), 409-424.

Wiseman, R.M. & Gomez-Mejia, L.R. (1998). A behavioral model of managerial risk taking. Academy of Management Review, 23(1), 133-153.

(1.) The total is more than 100%, due to some respondents answering both franchise and independent (7%), and respondents checking all three (2%).

Jim Dewald is an Assistant Professor in the Strategy and Global Management Area of the Haskayne School of Business, University of Calgary, Calgary.

Frances Bowen is Director of IRIS: International Resource Industries and Sustainability Centre and an Associate Professor in the Strategy and Global Management Area of the Haskayne School of Business, University of Calgary, Calgary.

Please send correspondence to: Jim Dewald, tel.: 403-220-7845; e-mail: jim.dewald@haskayne.ucalgary.ca.
Table 1
Description of Variables

Variable name          Measured items        Anchors         [alpha]

Dependent variables
  Resist change        You will lobby the    Not at all
                         authorities to        likely-
                         ensure that the       very
                         industry is           likely
                         protected from
                         discounted fees.

  Adopt change         You will adopt a      Not at all
                         new business model    likely-
                         that includes the     very
                         option of             likely
                         discounted fees.
Independent variables
  Firm opportunity     The discount          Strongly        0.64
                         brokerage model is    disagree-
                         a new opportunity     strongly
                         for you.              agree

                       The public wants      Not we
                         realtors to limit     at all-
                         their role/service    absolutely
                         offering.             true

                       Your customers want
                         to play a more
                         direct role in the
                         real estate
                         process.

  Situation threat     The discount          Strongly        0.61
                         brokerage model is    disagree-
                         a threat to the       strongly
                         real estate           agree
                         brokerage
                         industry.

                       In the coming
                         years, FSBO (for
                         sale by owner)
                         will grow to
                         represent a
                         larger share of
                         the market.

                       In the next five
                         years, it is
                         likely that
                         profits will
                         shrink
Moderating variables
  Urgency              In your opinion,      Open ended
                         how long, if ever,
                         will it be before
                         commission rates
                         are reduced in
                         order to meet
                         customer demands?

  Experience           Think back to a
                         significant
                         business situation
                         in the past when
                         you took the more
                         risky alternative:

                         How pleased were    Not at all-     0.94
                           you with the        totally
                           outcome?

                         Overall how would   Very negative-
                           you rate the        very
                           outcome?            positive

                         How would you       Complete
                           classify the        failure
                           result?             complete
                                               success
Control variables
  Age                  Please indicate       Open-ended
                         your age.

  Gender               Please indicate       Female or male
                         your gender.

  Firm performance     Your transaction      Well above      0.91
                         volume in the         projections-
                         current year will     well
                         be.                   below
                                               projections

                       Your profit in the    Well above
                         current year will     projections-
                         be.                   well
                                               below
                                               projections

                       Your profit per       Well above
                         transaction in the    projections-
                         current year will     well
                         be.                   below
                                               projections

Table 2
Descriptive Statistics and Pearson Correlation Values

Variable                   Mean   SD      1         2         3

 1. Resist change           2.2   1.3
 2. Adopt change            2.2   1.2   -.08
 3. Age                    54.4   8.2   -.11     -.05
 4. Gender                  1.8    .4    .04     -.09       .24 **
 5. Firm performance        7.7   2.6   -.05      .20 *     .13
 6. Firm opportunity        6.9   2.5   -.14      .44 **    .04
 7. Situation threat        8.7   2.7    .19 *    .13       .00
 8. Urgency                 2.8   1.4   -.06      .45 **   -.04
 9. Risk experience        10.8   2.8   -.04      .17      -.04
10. Social desirability     2.1    .7   -.09      .07      -.04
11. Negative affectivity    1.3    .4    .11      .02      -.01

Variable                    4        5         6         7

 1. Resist change
 2. Adopt change
 3. Age
 4. Gender
 5. Firm performance        .03
 6. Firm opportunity       -.01    .08
 7. Situation threat       -.10    .43 **   -.02
 8. Urgency                -.08    .19 *     .42 **    .27 **
 9. Risk experience         .08   -.22 *     .20 *    -.23 **
10. Social desirability     .02   -.04       .14      -.13
11. Negative affectivity    .03    .29 **    .01       .44 **

Variable                      8        9      10

 1. Resist change
 2. Adopt change
 3. Age
 4. Gender
 5. Firm performance
 6. Firm opportunity
 7. Situation threat
 8. Urgency
 9. Risk experience        -.03
10. Social desirability     .08       .19 *
11. Negative affectivity    .24 **   -.05     .10

N = 126.

* p<.05, ** p<.02

SD, standard deviation.

Table 3
Standardized Regression Tests

                                   Proactive resistance Adoption

Dependent variable              Model 1   Model 2   Model 3   Model 4

Age                             -.12      -.12      -.06      -.08
Gender                           .06       .10      -.08      -.05
Firm performance                -.03      -.16       .22 *     .18 *
Situation threat                           .28 **
Firm opportunity                                               .42 **
Situation threat x urgency
Firm opportunity x experience
Adjusted [R.sup.2]              -.007      .050      .030      .203

                                Proactive resistance
                                     Adoption

Dependent variable                    Model 5

Age                                   -.07
Gender                                -.04
Firm performance                       .17
Situation threat                      -.11
Firm opportunity                       .01
Situation threat x urgency             .33 **
Firm opportunity x experience          .37 *
Adjusted [R.sup.2]                     .273

N= 126.

* p <.05, ** p <.01
COPYRIGHT 2010 Sage Publications, Inc.
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2010 Gale, Cengage Learning. All rights reserved.

Article Details
Printer friendly Cite/link Email Feedback
Author:Dewald, Jim; Bowen, Frances
Publication:Entrepreneurship: Theory and Practice
Geographic Code:1USA
Date:Jan 1, 2010
Words:10694
Previous Article:Business networks and economic development in rural communities in the United States.
Next Article:Entrepreneurial orientation and the performance of religious congregations as predicted by rational choice theory.
Topics:

Terms of use | Privacy policy | Copyright © 2019 Farlex, Inc. | Feedback | For webmasters