Adaptation to Temporal Shocks: Influences of Strategic Interpretation and Spatial Distance

INTRODUCTION

With the football games . . . it’s just going to be a myriad of people coming to this area and we’re already talking about maybe buying some vans and putting product things on there and helping move people with whatever. We will be having countries come in with their teams . . . So it’s going to be not only a national thing, but it’s an international opportunity . . . It’s been plastered on the news . . . this is the place to be. (Arlington business owner)

Address for reprints: Liliana Pérez-Nordtvedt, Department of Management, The University of Texas at Arlington, UTA Box 19467, Arlington, TX 76019, USA (lnordtvedt@uta.edu).

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I’ve experienced . . . the impact of the [Rangers] Ballpark in North Arlington . . . When the new stadium was built it became a black hole for sales. My actual location, which was right by the old ballpark, declined significantly the first two years even when the traffic went up. The reason it declined is because . . . [the Ballpark] became self-contained. And I believe the same thing is going to happen with the Cowboys Stadium. (Arlington business owner)

Both quotes come from in-depth interviews with business owners talking about their perceptions of a major and enduring disruption in their local environment. Yet, as the comments illustrate, prior to the disruption, business owners were interpreting its effect on their firms differently, even though its occurrence was certain. That disruption is not

a direct competitor’s entry ( Jia, 2008), a gradual increase/decline of ambiguous market forces (Milliken, 1990), a revolutionary technology’s introduction (Tushman et al., 1985), or an unanticipated ‘jolt’ (Meyer, 1982). Instead, the phenomenon is clear, known, and uncontested, but it involves an interwoven set of spatial and temporal disruptions in the incumbent firms’ environment.

The specific shock addressed by the business owners quoted above is the physical relocation of the Dallas Cowboys football franchise to a mammoth, multi-billion dollar entertainment complex in Arlington, Texas, USA. Specifically, the construction and completion of the Stadium along with surrounding infrastructure development (e.g. highway interchanges) marked a shift in the spatial and temporal environment within which local businesses would operate. When disruptions of this and similar scale occur, they spark multiple and potentially critical new market forces. Whether business owners interpret disruptions as either opportunities, threats, or both affects their intended responses, including those which are temporal in nature.

To date, however, research on intended responses of firms to environmental disrup- tions has focused on the ‘what’ and ‘how’ of strategic adaptation but rarely on the ‘when’. This is an important omission in the adaptation literature because the temporal lens ‘offers its own set of variables and relationships, its own view of specific phenomena, and its own set of parameters to guide managerial action’ (Ancona et al., 2001, p. 645). Our distinct contribution is to show how temporal adaptation (TA) serves as a viable response to disruptions in an incumbent firm’s environment, when such disruptions shift dominant cycles of activity. We build and test a model that examines how the combination of strategic cognition and spatial distance (i.e. the geographic distance between the incum- bent firm and the locus of the disruption) influence the degree to which business owners decide to embrace or shun newly created business rhythms. We argue that when the temporal environment of a firm is primed to change, TA is a useful tool in the repertoire of adaptive responses. Moreover, TA is viable not just when the entrant is a direct competitor, but also under conditions of uncertainty about who is and who is not a competitor.

Within this broader thrust, we make several specific contributions to the literature. First, we bring TA to the forefront of the strategy literature. In contrast to content adaptation, TA has received scant attention as a means of responding to environmental change (Meyer, 1982; Meyer et al., 1990). We propose that, when the environmental disruption generates new rhythms, organizations can temporally align their activities to

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be especially useful to entrepreneurial firms.

THEORY

How organizations cope with the changing environment and its impact on strategies, structures, processes, and performance has been the subject of considerable theoretical and empirical investigation (e.g. Goll and Rasheed, 2011; Hambrick and D’Aveni, 1988; Zuniga-Vicente and Vicente-Lorente, 2006), but remains contentious (Hrebiniak and Joyce, 1985). On the one hand, population ecologists assert that natural selection, rather than managerial choice, shapes organizational characteristics (Hannan and Freeman, 1984). Firms with characteristics that best fit the environment survive, prosper, and replicate. Organizational inertia makes it difficult, if not impossible, for managers to implement adjustments. By contrast, strategic choice theory emphasizes rational adap- tion to environmental changes (Child, 1972). Successful adaptation depends on decisions of managers who identify opportunities and threats, develop strategies, assemble resources, and demonstrate initiative. Adaptation follows from conscious, explicit deci- sions managers make to position their organizations in the (soon-to-be) altered environ- ment. This perspective is relevant to understanding the interplay between environmental events and subsequent organizational responses. Here, managerial interpretation of disruptive events influences action (Chattopadhyay et al., 2001).

In this paper, we take the strategic choice approach. Considerable research has examined adaptive responses of strategic decision makers to environmental change (e.g. Haveman et al., 2001; Zúñiga-Vicente and Vicente-Lorente, 2006). For example, railroad (Smith and Grimm, 1987), telecom (Eunni et al., 2005), and airline (Goll and

Adaptation to Temporal Shocks

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Figure 1. Model of effects of spatial distance, perceived opportunity, and perceived threat on temporal adaptation intentions

Rasheed, 2011) companies all became more competitively aggressive after their indus- tries were deregulated. Similarly, hospitals (Meyer et al., 1990), restaurants ( Julian et al., 2008), and small hardware stores (Litz and Stewart, 2000) all adapted to environmental changes in their industries.

For the most part, scholars have studied firms’ changes to the content (e.g. how to compete and what processes to adopt) or the structure (e.g. what design and organization to use) to realign themselves to environmental shifts (Ginsberg and Venkatraman, 1985). Modification to the timing of activities as a strategic response has received less attention (for an exception, see Litz and Stewart, 2000). The limited literature addressing temporal responses to environmental changes largely focused on micro-level, internal activities (e.g. changes in work rhythms within organizations) or how time itself acts as a stimulus for change (Staudenmayer et al., 2002). Therefore, important macro-level research questions remain. Specifically, when environmental disruptions change the temporal environment, are business owners likely to consider temporal adaptation (TA) as a means to respond? If so, what determines such a decision? Are firms in the environment likely to engage in such TA? And, can TA be effective?

To answer these questions, we propose and test a model of TA intentions of incumbent firms in response to a temporal disruption created by a new entrant (see Figure 1). Our specific context, the physical relocation of the Dallas Cowboys Stadium complex to Arlington, Texas, involved a major change in the incumbent firms’ ecosystem (e.g. increased traffic congestion, greater notoriety of Arlington, influx of new customers, etc.). Certainly, incumbent firms could have and likely did respond to the disruption with content changes (e.g. adding new services/products, providing promotions linked to the Stadium, selling parking spots, relocating).

However, relocating the Stadium and imposing its schedule of events on Arlington, Texas initiated a disruption to the temporal environment of incumbent firms as well. The

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873 timing and cycles of the Stadium’s events (primarily fall/winter weekends and evenings

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throughout the year) were out of phase with concurrent business cycles in Arlington (late spring through summer daily). Consequently, the Stadium had the power to impose a new clock on the environment it was joining. In light of this, we argue that when the disruption involves a major, dominant, new entrant, whether a competitor or not, who brings a new calendar, schedule, or rhythm to the ecosystem, TA is a salient response. Choosing TA to synchronize with the Stadium’s activity schedule (e.g., changing or extending opening hours) can allow firms to capture value from the disruption. More- over, TA can be less expensive and disruptive than content-type adaptations, making it an attractive option for small business owners. As Figure 1 shows, we first suggest that business owners’ perceptions of the expected consequences from a disruption drive their TA intentions. We theorize and test the effect of spatial distance as it ripples outward from the epicentre of the temporal disruption. We conjecture that, together, time and space create complexly determined reactions from firms. Lastly, we evaluate over time the linkage of TA with firm performance, and explore whether TA is indicative of organizational entrainment (OE).

HYPOTHESES Strategic Interpretation and TA Intentions

At the outset, we propose that interpretation will specifically affect whether business owners intend to temporally adapt to their environment when it has been disrupted (Daft and Weick, 1984). Interpretation is a sense-making process that frames the implications of environmental conditions as opportunities, threats, or both for an organization (Chattopadhyay et al., 2001; Thomas et al., 1993). In this paper, we see opportunity and threat as theoretically distinct and not as opposing ends of the same continuum (for a view of these as opposing cognitions, see Thomas et al., 1993). Opportunities are envi- ronmental events that offer significant prospects for an organization to improve while threats bring the prospect for its undermining (Dutton and Jackson, 1987). Both cogni- tive tendencies are associated with ‘urgency, difficulty, and high stakes’ (Chattopadhyay et al., 2001, p. 939); and, they can coexist in the minds of those affected (Thomas et al., 1993). When business owners tag an environmental disruption as a threat, opportunity, or both, they have ‘forcibly carved out’ a label that brackets the flow of experiences and simplifies the world (Chia, 2000, p. 517). This interpretation allows business owners to construct an answer to the question ‘what’s the story here?’ (Weick et al., 2005, p. 410). Threat and opportunity-tinged answers then open a range of options for deciding ‘what do I do next’, from which to engage in TA. Framing aids the sense-making processes and informs whether managers choose to react to shocks in the environment. For some business owners, it may seem more reasonable to take no action, ‘hunker down’, and weather the change, rather than alter their plans (Perry, 2001). Even for those business owners with strong and salient interpretations of an environmental disruption, it is conceivable that they may not seek TA to match the temporal change (Pérez-Nordtvedt et al., 2008). Because business owners can only attend to some stimuli in the environ- ment, as their time and attention are limited (Ocasio, 1997), we argue that opportunity

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and threat interpretations can have differential impact on business owners’ decisions to seek TA.

Perceived opportunity and TA intentions. Opportunities are situations with likely positive outcomes (Dutton and Jackson, 1987). Framing an event as an opportunity directs business owners to new possibilities the event presents. Such framing opens search processes (Dutton, 1993), encourages consideration of a wider range and a more flexible set of possible actions (Sharma, 2000), prompts the crafting of new alternatives (Hambrick and Mason, 1984; Sharma, 2000), and leads to more extensive implemen- tation of organizational change (Kennedy and Fiss, 2009). As a consequence, firms that see change as an opportunity will decide on reactions that improve their competitive position relative to rivals. Indeed, when managers frame an event as an exploitable opportunity, they deploy more resources (Mullins and Walker, 1996; White et al., 2003) and take more forceful strategic actions (Sharma, 2000). Moreover, extensive implemen- tation of organizational change is significantly more strongly associated with the desire to achieve gains than the desire to avoid losses (Kennedy and Fiss, 2009). Thus, business owners who perceive a disruption that creates new rhythms as an opportunity would likely decide on TA.

Hypothesis 1: Perceived opportunity is positively related to TA intentions. Perceived threat and TA intentions: the threat-rigidity hypothesis. Threats represent situations with

likely negative outcomes (Dutton and Jackson, 1987). Theory addressing how managers interpret and respond to threats has dual emphases. One thesis suggests that framing external events negatively can induce a ‘threat-rigidity’ response (Staw et al., 1981), a situation in which decision-makers exhibit risk-averse behaviour and become unwilling to change behaviour in response to the threat (Bettis and Prahalad, 1995). [1] The threat- rigidity perspective predicts that framing an environmental event negatively may focus decision-makers’ attention on the potential losses, make them less receptive to potential solutions, inflexible, and unwilling to respond or adapt (D’Aunno and Sutton, 1992). As

a consequence, managers facing a perceived threat may simplify their decision-making, reduce the alternatives they consider, centralize authority, formalize procedures, and standardize processes (Chattopadhyay et al., 2001; Staw et al., 1981). To avoid losses, decision-makers fall back on familiar routines and resist making changes to their business practices (Kennedy and Fiss, 2009). This phenomenon may be particularly prevalent in small firms that often demonstrate the ‘deer in the headlights’ response to environmental threats (Dewald and Bowen, 2010). Thus:

Hypothesis 2a: Perceived threat is negatively related to TA intentions. Perceived threat and TA intentions: the behavioural theory of the firm. The behavioural theory of

the firm (Cyert and March, 1963) suggests the opposite connection between perceived threat and TA intentions. [2] Instead of inaction and paralysis, the possibility of not meeting aspiration levels spurs decision-makers to go on a problemistic search for alternatives (Greve, 2010). Thus, perceiving an environmental disruption as a threat will

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875 drive the firm to implement new activities that challenge the status quo (Ketchen and

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Palmer, 1999). Under this perspective, higher levels of perceived threat are likely to lead to a search for solutions and adaptation (Argote and Greve, 2007). Furthermore, because the business owner is likely to select unfamiliar options to address anticipated setbacks in meeting performance expectations (Ketchen and Palmer, 1999), the behavioural theory of the firm suggests that changes in the timing of organizational activities may become a unique part of the repertoire of actions. Indeed, Staudenmayer et al. (2002, p. 583) found that unusual events ‘alter entrenched organizational rhythms’. Therefore, a competing hypothesis can be forwarded:

Hypothesis 2b: Perceived threat is positively related to TA intentions.

Strategic Interpretation and TA Intentions: The Moderating Role of Spatial Distance

We further predict that considerations of time depend on space. Strategic interpretation has a complex relationship with TA intentions. Specifically, that relationship depends – in different ways according to the valence of the interpretation – on the spatial distance an incumbent firm has from the locus of the environmental disruption. Economics and psychology both suggest the moderating role of spatial distance. Economists argue that distance is the underlying surrogate of three different sets of relevant issues. First, distance increases the transaction, coordination, and information costs for economic activity and resource exchange between actors (Krugman, 1991; Rosenthal and Strange, 2001). Second, proximity to the disruption is likely to increase the interaction between it and the firm (Fellmann et al., 1997), increasing the amount and accuracy of informa- tion [3] about the new temporal environment and reducing the costs of synchronization. For instance, nearby firms are more likely to get information through informal contacts who can be employees or customers at the Stadium (or who know them). Lastly, when the source of the disruption occupies a prominent location in the flows of economic activity, not only will customers be drawn into its orbit, but they will also engage in economic activity not far from it (Horton and Reynolds, 1971). Firms close to the locus of change are likely to recognize that, once there, customers are unlikely to venture far and stray from established routes of trade, and are likely to stay within the boundaries of their own prior patterns of movement (Gonzalez et al., 2008). Thus, we expect that the transaction, coordination, and information costs of TA to the new rhythms created by the disruption will be higher, while information accuracy and residual resource flows will

be lower for more distal firms than for more proximal ones, tempering the positive effects of perceived opportunity on TA intentions. Psychologists also argue that distance can matter as a moderator, particularly through the mechanisms of construal level theory (Liberman and Trope, 2008). One main idea from that theory is that spatial distance from an object or event – such as the disruptive entry of a major player into one’s market – is likely the strongest input to a sense of psychological distance (Trope and Liberman, 2010). Another idea is that, when some object or event is perceived as psychologically close, it is associated with stronger affective experiences (Trope et al., 2007). Perceived opportunity connotes positive affect, and

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even more so as the opportunity is seen as psychologically near (Ledgerwood et al., 2010; Trope et al., 2007). Importantly, positive affect broadens one’s awareness and builds a wide variety of exploratory thoughts and actions (Fredrickson, 2001; Fredrickson and Branigan, 2005), such as novel ways to adapt to a (positively viewed) environmental shock. In contrast, construal level theory suggests that the negative emotions associated with negatively valenced objects prompts narrower, ‘tunnel vision’ thinking and responses, particularly when that threat is psychologically near. Asymmetric to perceived opportunity, when threats are experienced at greater distances, they are evaluated at a higher level of abstraction (construal) and cognition. This higher level of abstraction allows a perceiver to engage in more expansive, rational, and structured processing about that object, which in turn allows more and longer-term, goal-relevant thinking and flexible responses.

Perceived opportunity and TA intentions: spatial distance as moderator. With economics and psychology in mind, how do the variability in information (its cost and accuracy),

customer flows, and the level of abstraction (construal) of the temporal disruptor affect the relationship between interpretation and TA intentions? Let us start with opportunity perceptions. Suppose there are two hypothetical business owners who perceive an environmental shift as an opportunity for their firms. However, one of the firms is close to the shock and the other one is further away. As argued earlier, because of their opportunity framing, both business owners will be more likely to actively search for information and be more open to processing its meaning (Dutton, 1993). Hence, they will seek out alternatives and be more receptive to pursue TA (Hambrick and Mason, 1984; Kennedy and Fiss, 2009; Sharma, 2000). However, the business owner whose firm is spatially nearer to the new entrant creating the temporal shift would obtain clearer, more actionable, and finer-grained information about the disruption. Because of the a priori positive interpretation of the disruption, its construal would expand his/her way of thinking about the disruption itself (Fredrickson, 2001), broadening and building the consideration of more (novel) actions that might be adaptive (Trope and Liberman, 2003). As this proximal business owner’s firm is interacting more with the disruptor, compared to more distant counterparts, the business owner would be more likely to understand the way to achieve temporal fit to seize the opportunity that the disruption represents. Thus, with the same level of perceived opportunity, the nearby firm will decide to engage in greater levels of TA.

Hypothesis 3: Perceived opportunity has a weaker effect on TA intentions at longer spatial distances from the source of the disruption than at shorter spatial distances from it.

Perceived threat and TA intentions: spatial distance as moderator. Consider what would happen if the same two business owners discussed in the preceding example perceive the disruption to be a threat instead of an opportunity. The business owner whose firm is closer to the disruption would likely receive stronger and more accurate signals about the major change in timing taking place. However, if he or she interprets the disruption as a threat, the tunnel vision that accompanies perceived threat would mean that some of the

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877 information inherent in the signals would be ignored (Bozeman and Slusher, 1979).

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Furthermore, according to construal level theory (Trope and Liberman, 2010), perceived threats that are seen as – perhaps even felt as – close by, will generate more negative affect and behavioural rigidity, with managers ploughing forward to execute existing strategies rather than pausing to more carefully consider all their adaptive options. The threat response will be to maintain their dominant behaviour, pursue the status quo, and avoid changes when the firm is spatially near to the disruption (Bettis and Prahalad, 1995). In other words, spatial closeness to the threat would prompt the business owner to ‘freeze’ more, focus internally on issues such as efficiency, and not adapt externally (Dewald and Bowen, 2010).

On the other hand, the business owner who perceives the disruption as a threat, but whose firm is distant from its pull, could have a different reaction. The perceived threat of the shock coupled with the higher level of construal would be evaluated in more abstract and strategic terms by business owners who are distant from the locus of the disruption (Trope and Liberman, 2010). That is, responses to them should be more carefully considered and aligned with strategic goals. At greater distances, with less physically looming changes in the environment, the business owner might feel less constrained (Staw et al., 1981) and consider a wider set of goal-relevant options. Thus, we expect that an increasing spatial distance neutralizes the paralysing effect of the perceived (and seemingly more imminent) threat posed by an environmental shift.

In other words, we propose that spatial distance works as the boundary determining under which conditions the threat-rigidity hypothesis and the behavioural theory of the firm coexist. Construal level theory would suggest that the threat-rigidity hypothesis applies to nearby firms and that the behavioural theory of the firm applies to distant firms. Hence, spatial distance ‘unfreezes’ the rigidity-type effect of threat and drives the business owners to engage in problemistic search, deciding on TA as a course of action.

Hypothesis 4: Perceived threat has a positive effect on TA intentions at longer spatial distances from the source of the disruption and a negative effect at shorter spatial distances.

Strategic Interpretation and TA Intentions: The Indirect Effect Role of Spatial Distance

As might be implicit in the arguments above, distance should shape interpretation, and affect TA intentions indirectly. Many questions have been raised about what decision makers notice and how accurately they interpret and process information in their environments (Ocasio, 1997). Individuals are continuously engaged in the creation and adjustment of personal schemata about their environments (McNamara, 1986; Tversky, 1993, 2000). They construct such schemata with landmarks as reference points, which are then connected through routes and consolidated into coherent mental representa- tions or cognitive maps (Sadalla et al., 1980). Those maps reflect a number of biases and distortions, including overemphasis on near, large, distinct structures and underemphasis on those that are smaller, common, and far away (Holyok and Mah, 1982; LeBoeuf and Shafir, 2009; Lee and Tversky, 2005).

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When a new landmark of great visual size and reputation appears in the vicinity of a business owner’s firm, it is likely to be perceived as having an impact – negative, positive, or both – on the firm and thus it is likely to instigate some form of information processing. We expect that as spatial distance increases, it would mute signals from the environment – reducing their strength and the probability that they would be noticed and perceived as relevant, decreasing the likelihood of deliberate interpretation. Spatial closeness heightens ‘perceptual readiness and information-processing proclivities’ of strategic deci- sion makers (Ozcan and Overby, 2008, p. 440). We expect that the closer the focal business is to the locus of disruption, the more it will alert the business owner to its potential as an opportunity, a threat, or both.

Once such opportunity and threat interpretation takes place, then decisions are made about how to adapt – in this case, temporally. In terms of TA, because firms have many environmental rhythms or pacers that they can adapt to, those that are close are likely to

be interpreted as dominant and drive TA intention. In line with previous work on the organizational interpretation process (e.g. Daft and Weick, 1984; Thomas et al., 1993), we formally hypothesize the mediating role of interpretation between spatial distance and TA intentions. Specifically, business owners whose firms are spatially closer to the disruption will focus their attention on it, view it as an opportunity, threat, or both, and decide or not on TA. Therefore:

Hypotheses 5–6: Perceived opportunity (H5) and perceived threat (H6) mediate the relationship between spatial distance and TA intentions.

METHODS Empirical Context

We tested our hypotheses in a specific context. The Dallas Cowboys, a professional American football team, is one of the most well-known and highly valued sports fran- chises in the world (Associated Press, 2008). Today, their stadium is located in the city of Arlington, Texas, which sits roughly in the geographic centre of what is commonly referred to as the Dallas/Fort Worth Metroplex (these two cities are about 32 miles apart). While Arlington’s population is less than 380,000 residents and its land area is less than 96 square miles (US Census Bureau, 2013b), the greater metropolitan area is over 9286 square miles and home to over 6.5 million residents (US Census Bureau, 2013a). To induce the franchise to move from Irving, Texas, another smaller suburban commu- nity within the Dallas/Fort Worth Metroplex, to Arlington, Arlington city leaders fast- tracked a series of financial and infrastructure improvement initiatives (e.g. tourism tax increases, rezoning, interstate highway interchanges; City of Arlington, Texas, 2004). The Stadium complex joined other ‘Fun Central’ venues in Arlington such as Six Flags over Texas amusement park, and the Texas Rangers Ballpark. At 104 million cubic feet, the Stadium is the largest enclosed sports facility in the world (Cowboys Stadium, 2013). It is surrounded by 200 acres of nearly 12,000 parking spots. It dominates the Arlington skyline. It has been the subject of media attention since construction began.

Interviews revealed a rich mix of perceptions about this change to the Arlington ecosystem. Some business owners’ perceptions echoed what one interviewee said, which

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879 was that Jerry Jones (the Cowboys owner) got a ‘sweetheart deal’ from [Arlington] to

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‘vacuum the wallets’ of event-goers by offering a full day’s activities, food, shopping, and other entertainment. In addition, the City of Arlington narrowly passed (54 per cent to

46 per cent) a sales tax referendum to finance US$325 million of the Stadium cost, which raised the relative prices of offerings purchased in Arlington (City of Arlington, Texas, 2006). Arlington officials and supporters also had to persuade state and county politicians to help fund US$1 billion in egress and access improvements on local highways (Dickson, 2004). Key to this paper, the Stadium served as a temporal disruptor of the ecosystem. The traditional seasonal cycle of the ‘Fun Central’ area mainly began around Memorial Day (late May) and mainly ended after Labor Day (beginning of September), when customers from the region attended theme parks and baseball games. However, football is played on Sundays or Monday nights in fall and early winter. Also, numerous concerts, sporting events (NBA All Star Game, Super Bowl), and college football rivalry games were hosted in the Stadium during fall and early winter. If sold out, such events would have meant upwards of 90,000 people coming to the area nearly every weekend from September through January.

Data

We tested our hypotheses using survey and archival data sources. Data were collected in phases. The first phase began in the Summer of 2008 before the Fall 2009 opening of the Stadium. We conducted 30-minute, on-site interviews involving 26 Arlington businesses, deliberately sampled to maximize variance in firm characteristics and distance from the Stadium, and thus to capture the broadest possible range of reactions. At the same time, lengthier (30–60-minute) interviews were conducted with proponents/opponents of the Stadium (e.g. representatives of the Cowboys, Six Flags over Texas, Arlington Chamber of Commerce, City of Arlington). These preliminary interviews dealt with overall per- ceptions, specific benefits and costs, and possible actions that firms might take in antici- pation of the Cowboys Stadium opening and the completion of surrounding infrastructure developments. They allowed us to populate and iteratively refine a survey protocol grounded in the language of the environmental shock.

In the Fall of 2008, the investigators and previously trained graduate students called and scheduled face-to-face interviews with 200 business owners within zip codes in the areas surrounding the Stadium. The geographic sampling area was defined as a 15-mile radius from the Stadium, which comprises the Arlington metropolitan area. Thirty-one potential respondents declined to participate. One was later found to be outside the 15-mile radius. Response rate was 84 per cent. Our sample was representative of Arlington businesses, which chiefly were in the retail, entertainment, and service indus- tries. Most sampled firms were small (median size 14 employees before log transforma- tion), independently owned (77 per cent), owner operated (67 per cent), and incumbents (median of 7 years at present location). Respondents were owners (86) or strategic decision makers. (We ensured that this was the case because we directly asked respond- ents how they made strategic decisions for their respective businesses.) Finally, the median travel distance from the firms in our sample to the Stadium was 3.4 miles (before

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a square root transformation for normalization; inter-quartile range was 1.7 to 5.5 miles), suggesting that the 168 sampled firms are those for whom the Stadium was not a theoretic abstraction but a concrete reality.

We administered the survey carefully. To avoid demand cues, we started by asking a series of open-ended questions about the local region, and segued to questions about how the Stadium and planned events could affect the respondent’s firm. At the end of the interview, business owners were asked to fill out a survey, which served as the data for our quantitative analyses.

We also collected follow-up data 1.5 and 4 years after we completed the initial survey data collection. In an effort to assess the impact that temporal changes had on firm performance, 1.5 years later, we requested follow-up interviews from our 168 sample firms. Sixty-three firms (or 38 per cent of the original sample) located near the Stadium agreed to participate. We created a brief interview with performance-related questions that we administered either over the phone or face-to-face. In addition, 4 years after our initial data collection, we reached out to 48 firms from our original sample to investigate whether or not TA intentions had translated into TA actions, and to evaluate the nature of the TA actions that had been taken. We used multiple interview questions that asked respondents about the Stadium’s impact on their firms, as well as questions regarding firms’ actual temporal changes in response to Stadium events. We obtained usable responses from 36, and richer, finer-grained data, which captured the form that TA actions took.

Hypotheses Testing Measures

To test our hypotheses, we used the following measures. TA intentions. To measure TA intentions, we relied on our pilot interviews to identify

possible temporal actions that business owners might make to adapt to the disruption in question. We asked respondents to indicate how much they would decrease/ increase the following time-related activities in response to the disruption: (1) ‘how long my business stays open each day’; (2) ‘how many days my business operates during the week’; (3) ‘how long my business’s busy or peak season will be’ [4] ; and (4) ‘the number of hours employees will work’. The response format was 1 = major decrease,

2 = decrease, 3 = minor decrease, 4 = no change, 5 = minor increase, 6 = increase, and

7 = major increase. We asked about each of these four TA intentions with respect to (1) the Stadium and (2) the surrounding infrastructure development, recognizing these could be considered two different potential sources of disruption. However, there were no dependable, empirical distinctions (mean, variance, or covariance) between the two. Business owners saw them as part of the same environmental shock. A confirmatory factor analysis (CFA) showed them as the same factor (see below). Thus, we combined the responses about these four TA intentions into a single, 8-item scale (α = 0.81). The distribution of this measure was unimodal around the central tendency. It had a mean of 4.84, median of 4.72, and mode of 4.50, and a slight positive skew of 1.08. There was a steeper decline on the lower rather than the upper side of the distribution. For example, few firms (<5 per cent) reported TA intentions in the lower portions of this

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881 distribution. Although we hesitate to equate the sum to the scale anchors of an indi-

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vidual item, that means only 5 per cent would correspond to an average of ‘decrease’ (1.55–2.5) or ‘major decrease’ (≤1.50) across all items. Nineteen per cent of firms were in the upper portion, with 5 per cent corresponding to a ‘major increase’ ≥6.50 aver- aged all items, and 14 per cent to an ‘increase’: 5.5–6.45. The bulk of the firms were distributed between 2.5 and 5.5.

To further support construct validity, we note that this measure correlated (r = 0.31, p < 0.01) with a no/yes interview question about the business owners’ plans to change

the timing of their business activities. [5] Although we did not explicitly map the temporal disruption to each of the local firms’ timing changes, our stipulation about how to

measure TA intentions is reasonable. No local major market player has its main season in the fall, nor attracts visitors to evening events as the Stadium would and has. As such,

a desire to change the timing of the businesses’ activities in response to the Stadium is an effort at aligning or adapting to the temporal shock.

Perceived opportunity and perceived threat. Items for perceived opportunity and perceived threat were based on Thomas et al. (1993). Response options ranged from ‘none at all’ (1) to ‘a great extent’ (7). The items for perceived opportunity were: ‘. . . (1) mainly good for my business, (2) creating positive opportunities for my firm, and (3) providing benefits to my firm overall’. The items for perceived threat were: ‘. . . (1) is primarily a threat to my firm’s interests, (2) will make my business worse off in the future, and (3) puts competitive pressure on my firm’s goods/services’. Once more, respondents perceived the Stadium and the infrastructure development equivalently. Hence, we combined the responses into two 6-item scales for perceived opportunity (α = 0.83) and perceived threat (α = 0.75).

To assess the perceptual distinction between the two stimuli – Stadium v. infrastruc- ture – we compared CFA models combining them (for each of the three main constructs: opportunity, threat, and TA intentions). Fit statistics for that measurement model were

χ 2 = 285.82 (df = 206), RMSEA = 0.08, CFI = 0.95, and TLI = 0.93. If business owners were making notable perceptual distinctions between the two stimuli, it would create six factors. We fit a 6-factor model that mapped to the three constructs by the two ‘disruptor’

stimuli. The 6-factor model fit the data no better than the 3-factor model: χ 2 = 280.97 (df = 194), RMSEA = 0.07, CFI = 0.95, and TLI = 0.81. The latter index penalizes for lack of parsimony. The change in fit was small and non-significant (χ 2 = 4.85, df = 12, ns). Indeed the model had difficulty fitting, occasionally generating Heywood cases of correlations greater than 1.00 between the split (Stadium v. infrastructure) factors. Factor correlations for each (Stadium v. infrastructure) pair were consistently greater than r = 0.85.

To assess common method variance (CMV), we conducted nested CFAs with our three main constructs. Our 3-factor model fit the data (see above). Other models involving more aggregated combinations of these constructs into two factors each

created substantially worse fit: all χ 2 s > 333.00 (df = 208; improvement in fit χ 2 s all > 48.00 on 2 df for comparison to the 3-factor model; all CFIs < 0.89 and all

TLIs < 0.88). A 1-factor model representing only CMV fit the data poorly: χ 2 = 384.22 (df = 209), with RMSEA = 0.12, CFI = 0.84, and TLI = 0.83. Also, CFA fit indices

© 2013 John Wiley & Sons Ltd and Society for the Advancement of Management Studies

L. Pérez-Nordtvedt et al.

involving the three main constructs, proactiveness and controllability (see control variables below) showed discriminant validity. There were no strong cross-loadings. No factor correlations exceeded r = 0.50, meaning there was no more than 25 per cent overlap in shared variance between these constructs.

Spatial distance. We operationalized spatial distance in terms of travel distance and com- puted it jointly from the latitude and longitude of the Stadium, and Reference USA provided the geocodes of the sampled firms. [6] We used Google TM Maps [7] to record the mileage that someone would travel by car between our sampled firms and the Stadium (Boscoe et al., 2012; Wang and Xu, 2011). For a robustness check, we calculated travel time which correlated with distance at r = 0.89. As a form of cross-validation, we also calculated distance using MapQuest and Windows Live Local on a random 25 busi- nesses. Correlations between these, GIS, and Google TM Maps exceeded r = 0.99, so we relied solely on the latter.

Control variables. Our analyses also included a number of controls. We controlled for firm age and size. We square root-transformed age, as it had a moderate skew. Firm size was log transformed, as it had a more severe skew. We used a dummy variable to distinguish between firms in the entertainment industry (coded 1) and those in other retail and service industries (coded 0). We controlled for firm proactiveness by using the three like-named items from Covin and Slevin’s (1989) entrepreneurial orientation scale. Proactiveness also served as a marker variable to control for, and even eliminate, CMV, as it is uncorrelated with the focal variables and likely suffers from social desirability (Siemsen et al., 2010). We also included a two-item covariate to account for perceived controllability, as it likely affects business owners’ interpretation of the environmental change (Thomas et al., 1993). Finally, we controlled for whether the respondent voted for the previous sales tax referendum to support part of the Stadium’s cost (coded 1) or not (coded 0), and for whether or not the respondent was the current business owner (coded 1, 0).

RESULTS Quantitative Results with the Full Sample

Table I shows means, standard deviations, and correlations. Although controllability is negatively correlated with perceived threat as expected (Thomas et al., 1993), correla- tions of controllability and proactiveness with all of the other main study variables are non-significant. Also, perceived opportunity and perceived threat, both measured at the same time and serving as potentially opposing concepts, were uncorrelated (r = −0.04, ns). This further shows that there is little evidence of any consistent or pernicious effect of CMV, acquiescence, or socially desirable responding on our survey of business owners.

We used OLS regression to test our hypotheses. Main effects explain 21 per cent more variance in TA intentions than controls alone. Results for Hypotheses 1, 2a, and 2b are shown in Table II, Model 2. We found strong support for Hypothesis 1; perceived

© 2013 John Wiley & Sons Ltd and Society for the Advancement of Management Studies

Table I. Correlations, means, standard deviations and estimated reliabilities John

(1) Firm age

& Sons

(2) Firm size

(3) Firm industry

to

Ltd (4) Owner

and (5) Proactiveness

Society (6) Supported tax referendum

(8) Spatial distance

(9) Perceived opportunity

(10) Perceived threat

(11) TA intentions

Notes: Coefficient alphas are on the diagonal. N = 168; † p < 0.10; * p < 0.05; ** p < 0.01. of

Management

Studies

L. Pérez-Nordtvedt et al.

Table II. Effects of spatial distance and strategic interpretation in predicting TA intentions

Model 1

Model 2

Model 3 Model 4 Model 5

β B Firm age

−0.00 −0.02 Firm size

−0.09 −0.08 Firm industry

0.01 0.03 0.04 0.05 0.05 Supported tax referendum

0.11 0.09 0.08 0.09 0.09 Spatial distance

−0.80** −0.34 Perceived opportunity

0.83** 0.45** 0.72** Perceived threat

0.02 −0.51** −0.56* Spatial distance × perceived opportunity

−0.55* −0.73* Spatial distance × perceived threat

0.83** 0.78** Adjusted R 2 0.05 0.26 0.28 0.30 0.31

6.80** 7.53** 7.23** d.f.

Change in R 2 0.21**

0.02* 0.04** 0.05** Notes: N = 168. Betas are standardized; † p < 0.10; * p < 0.05; ** p < 0.01; R 2 change in Model 2 is based on the

comparison with Model 1 and in Model 5 with Model 2.

opportunity positively predicts TA intentions (β = 0.51, p < 0.01). Competing Hypoth- eses 2a and 2b are not supported (β = −0.01, p > 0.05); perceived threat does not uniquely predict TA intentions. Thus, we do not find overarching support for either the threat-rigidity or the behavioural theory of the firm theses.

Table II, Model 5 shows results for Hypotheses 3 and 4. As Hypothesis 3 predicted, greater spatial distance weakened the positive effect of perceived opportunity on TA intentions (β = −0.73, p < 0.05; see Figure 2a). In support of Hypothesis 4, business owners who perceived the arrival of the Stadium as a threat, but whose firms were located further away from the Stadium, were more likely to decide on TA than those business owners who perceived the same level of threat but whose firms were nearby (β = 0.78, p < 0.01; see Figure 2b). Each interaction explained an extra 2 per cent and 4 per cent of the variance in TA intentions (Table II, Models 3 and 4, respectively). Our fully specified regression model explained 31 per cent of the variance in TA intentions (Table II, Model 5). Equivalent results and support were obtained when we substituted travel time for mileage.

To assess the fit of our data to Hypotheses 5 and 6, we conducted Sobel mediation tests (MacKinnon et al., 2007). Although Table I shows that spatial distance does have

a negative correlation with both perceived opportunity and perceived threat as expected, the pathway through which spatial distance affected how business owners decided to pursue TA, involved perceived opportunity (Sobel t = −2.12, p < 0.05) but not perceived threat (Sobel t = 0.08, n.s.). Thus, Hypothesis 5 is supported, but Hypoth- esis 6 is not.

© 2013 John Wiley & Sons Ltd and Society for the Advancement of Management Studies

Adaptation to Temporal Shocks

Figure 2. Interaction graphs. (a) Spatial distance × perceived opportunity on TA intentions. (b) Spatial distance × perceived threat on TA intentions

Because we were interested in determining the point at which the effect of spatial distance from the disruption begins to diminish, [8] we did supplemental analyses (that were not connected, a priori, to the hypotheses, because there is no existing theory to guide what the effects would be within the radius we sampled). We created a series of transitive dummy variables representing increasing distance from the Stadium, allowing for a number of different shapes of effects: <1 mile, <2 miles, <3 miles, <4 miles, <5 miles, <7 miles, and <10 miles. Negative correlations of these dummies with perceived oppor- tunity and threat, and TA intentions gradually increase in magnitude, peaking in the 3–5

© 2013 John Wiley & Sons Ltd and Society for the Advancement of Management Studies

L. Pérez-Nordtvedt et al.

mile range, and then dropping. That is, the overall effect of distance appears to wane after 5 miles. Despite its consistency, the trend of the magnitude of the correlations is not significant in our survey data except for perceived opportunity, where at miles 4–5 the effect rises to r = −0.17 (p < 0.05). The correlations for TA actions using our 4-year follow-up sample of 36 firms follow exactly the same pattern but are stronger, peaking again at the 3–5 mile range where they reach r = −0.42 to −0.47 (p < 0.01). Still, we have to note these results are only suggestive, because of a methodological caveat. The middle ranges of distance in our sample were also where there was the most optimal (nearest to

50 per cent) base rate of the dummy variables. Lower and higher base rates suppress correlation sizes (Bobko, 2001).

Quantitative Results from Two Sub-Samples

Using the data collected 18 months after the initial data collection and in an effort to provide some nomological validity to our dependent variable (Cronbach and Meehl, 1955), we correlated TA intentions with performance. Because we could not residualize current performance (profits or revenue/month) using prior data, we instead asked business owners directly about the change in their firms’ performance over those 18 months, using two questions about sales growth and profitability (α = 0.84). The corre- lation between TA intentions and this reported performance change was r = 0.28 (p < 0.05). Our data collected 4 years later revealed similar results. The correlation between TA intentions and performance for these 36 firms was significant (r = 0.31, p < 0.05). Together, these results suggest then that over time TA intentions are associ- ated with (perceived) improvements in firm performance.