Entrepreneurial Cognition
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Strategy researchers have dedicated a great deal of energy toward investigating how top-down processing decreases managers’ ability to detect discontinuous change. For instance, the top managers of Liz Claiborne effectively used top-down attention-allocation processes to respond to changes that matched their prevalent knowledge structures (i.e., incremental changes). However, these processes also blinded him to discontinuous changes:Environmental changes had decreased the value of a part of Liz Claiborne’s set of choices (in particular, those concerning production and distribution). Small, incremental changes—exploring the local neighborhood of the current position—no longer sufficed. At the same time, larger, systematic changes lay outside the mental maps of existing management. Different mental maps of the changed performance landscape were required to move Liz Claiborne to a new performance peak. (Siggelkow 2001: 853)
Low Levels of Top-Down Attention Allocation (More Bottom-Up Processing) and Recognizing Environmental Change
When top managers engage in bottom-up processing , they enable the environment to capture their attention. Specifically, attention capture refers to how aspects inherent in a particular situation draw attention to themselves in case people do not search for them actively (Pashler et al. 2001). In their study on the Challenger disaster, Starbuck and Milliken (1988) highlighted how individuals are more likely to pay attention to novel information than information that is less novel . Similarly, Rindova et al. (2010) showed that the most prominent elements of a situation are also the elements that will most likely capture managers’ attention seemingly due to the particularly distinct nature of the signals. Thus, by allowing environmental changes to grab their attention, decision makers are more open to possible surprises (Wyble et al. 2013). Since the most prominent features of a situation (either alone or in relation to other environmental features) are those most likely to capture managers’ attention (Shepherd et al. 2007), bottom-up processing can help managers pay more attention to unexpected indicators of changes in their environment.
On the other hand, bottom-up processing can also cause prominent environmental features to arouse and attract managers’ attention even when those features are only marginally related to the firm’s technologies, products, and markets (see Franconeri et al. 2005; Franconeri and Simons 2003). Researchers have shown that prominent environmental changes sometimes take people down the wrong path (Kruglanski and Boyatzi 2012) and can disrupt cognitive processing (Frey and Eagly 1993). In addition, bottom-up processing lessens people’s use of trial-and-error knowledge from their previous experiences . In this case, they might “reinvent the wheel” and repeat past mistakes, leading them to allocate attention to environmental features that have already been established as not being strategically crucial or not matching the organization’s range of actions (Katila and Ahuja 2002; Levinthal and Rerup 2006).
Thus, compared to bottom-up attention allocation, a high top-down process enables managers to detect incremental changes. At the same time, it obstructs the detection of discontinuous changes. This idea is in line with Eggers and Kaplan’s (2009) discovery that firms grow slower in a market that is radically new when managers focus on current technologies (high top-down attention allocation) as compared to focusing on emerging technologies (bottom-up attention allocation). Similarly, my (Dean) colleagues and I (Shepherd et al. 2017) recently proposed that top managers’ likelihood of detecting incremental change is greater when their attentional processing is more top down compared to when it is more bottom up. However, their probability of detecting discontinuous change is greater for attentional processing that is more bottom up compared to attentional processing that is more top down.
Managers’ Task Demands and Top-Down Attention Allocation
The necessity to reach a specific level of performance is called task demands. Task demands grow as individuals take on greater task challenges (Hambrick et al. 2005: 476), which frequently arise from inside the firm. For instance, “large firms with technologically interdependent units that are geographically far-flung, with complex matrix structures, require significant co-ordination and integration ” (Hambrick et al. 2005: 476), which in turn generates numerous challenges requiring top managers’ attention. The external environment can also contribute task challenges for an organization. Hostile external environments, for example, can cause a variety of managerial challenges that necessitate attention. These challenges include ensuring resource conservation, understanding threat characteristics, and developing successful strategies in a competitive marketplace (Miller and Friesen 1983). Additionally, more complex environments can also pose challenges as managers must take into account many fluctuating parameters and potential contingencies (Aldrich 1979; Eisenhardt 1989), including competitors’ actions and responses (Hambrick et al. 1996; McMullen et al. 2009). The task challenges arising from both of these environments constitute conflicting demands for managers’ information processing.
There is also heterogeneity in the performance demands that owners and stakeholders from different organizations place on top managers. For instance, an attentive board of directors is likely to implement high managerial task demands. More specifically, a board of directors monitors the performance of top management. With increasing attention of the board’s members, there is an increasing need for top managers to defend strategic decisions and moves through proposals to the board (Castaner and Kavadis 2013). Indeed, the vigilance of a board tends to increase when there is a higher percentage of external directors (Lim 2015), the CEO does not chair the board (Finkelstein and D’Aveni 1994; Kesner and Johnson 1990), the CEO does not appoint board members (Zajac and Westphal 1994), and ownership is very concentrated (Castaner and Kavadis 2013). In addition, top decision makers’ task demands tend to increase when they are facing activist shareholders (Walls et al. 2012).
Because top managers’ attentional capability has its limits (Ocasio 1997; Simon 2013), high levels of demands for one task make it necessary that they dedicate more attention to detecting environmental signals central to that task (e.g., collecting information regarding the efficiency of the firm). These types of tasks may compete for attention with the task of detecting signals of change in the external environment. In the face of competing multiple tasks and limited attention, managers will utilize their experience to determine how they should allocate their attention (Hambrick and Mason 1984). This experience may stem from their education (Carpenter 2002; Wiersema and Bantel 1992), functional backgrounds (Finkelstein and Hambrick 1990), and/or prior jobs (Beyer et al. 1997). As the demands that are competing between tasks—including the observation of the environment—increase, managers’ attention is more likely to be divided (e.g., Han and Humphreys 2002; Rodriguez et al. 2002). They are likely to direct available transient attention toward central concepts of the task-related knowledge structure and away from concepts that are only peripheral. In turn, these peripheral concepts do not receive managers’ transient attention, making it difficult for top managers to recognize changes in the environment that are novel or unfamiliar.
On the other hand, top managers with fewer task demands are less likely to depend on top-down attention-allocation processes. Such managers still focus on concepts that are at the core of their knowledge structures. However, these managers have higher levels of transient attention they can allocate to peripheral concepts and thus have a higher chance of noticing unanticipated environmental changes that signal opportunities. Based on this reasoning, my (Dean) colleagues and I (Shepherd et al. 2017) contended that higher levels of competing task demands cause decision makers to draw more heavily on top-down processing of attention to recognize changes in their environment.
Knowledge Structure Complexity and Recognizing Environmental Change
Unlike technology and market changes that are incremental and discontinuous (and thus consistent and inconsistent with a firm’s current trajectory, respectively), architectural changes represent opportunities because they alter how product or service componen
ts are combined and connected to form a coherent whole (Henderson and Clark 1990). In the case of architectural changes, design features that are at the core and thus the primary components of the product are unaltered (Henderson and Clark 1990). People frequently have more difficulties recognizing architectural changes than they have difficulties recognizing incremental changes because the former are concealed in the interactions and connections between components, thus leading to minimal observable surface change. To recognize architectural changes , individuals must have a complex knowledge structure (which entails connections that are rich and deep) that serves as the foundation for understanding the nature of such changes and how components are integrated and connected, although the components themselves are not modified. For instance, in the 1970s, Xerox—the plain-paper copiers pioneer—began seeing other firms pop up selling new copiers that were smaller in size and were more dependable than the existing products Xerox offered. Even though the new copiers did not incorporate significantly novel engineering or scientific knowledge, and although Xerox had come up with the core underlying technologies and had vast industry experience , the firm made mistakes and false starts for almost eight years before they had a viable product ready for entry (Henderson and Clark 1990).
Architectural modifications are frequently harder to detect since they are concealed in the exchanges and interconnections between components. Thus, managers need rich and deep knowledge structures. Nadkarni and Narayanan (2007) stressed that knowledge structures differ in complexity—namely, the scope and diversity of the concepts embedded in individuals’ cognitive structures—and in the number, richness, and depth of these concepts’ interconnections (Kiss and Barr 2015; Nadkarni and Narayanan 2007).1 The complexity of knowledge structures may increase flexibility in strategic decision making (Nadkarni and Narayanan 2007) because it enhances managers’ ability to detect more signals in their environment (Sutcliffe 1994; Walsh 1995). Therefore, managers who possess knowledge structures with greater complexity tend to be better at detecting incremental changes in the environment and then utilizing the knowledge they gain to make strategic decisions (Kiss and Barr 2015). Managers with knowledge structures that are more simple, on the other hand, not only have a smaller number of core concepts but also less rich and more shallow linkages between the concepts they possess, thus making them less able to detect architectural environmental changes. As such, my (Dean) colleagues and I (Shepherd et al. 2017) argued that managers’ likelihood of detecting architectural change increases with the complexity of their knowledge structures.
Attention Toward Early-Stage Exploration and Opportunity Evaluation Speed
Decision speed is frequently conceived of as “how quickly organizations execute all aspects of the decision making process” (Forbes 2005: 355). High decision speed has been linked to exceptional performance (Bourgeois and Eisenhardt 1988; Bingham and Eisenhardt 2011; Eisenhardt 1989; for an exception, see Perlow et al. 2002). Managers who make quick decisions enable their firms to act on opportunities before they vanish (Baum and Wally 2003; Stevenson and Gumpert 1985). In addition, quick decisions associated with opportunity exploitation demonstrate to stakeholders that the firm is flexible and proactive (Langley 1995). Further, quick decision making improves organizational learning because it enables the firm to make more decisions in a limited period of time and therefore provides more experiences and a higher number of interactions that expose information that is salient for learning (Baum and Wally 2003; Eisenhardt 1989; Forbes 2005). Quick strategic decisions can also lead to a first-mover advantage (Lieberman and Montgomery 1988) or a set of transient advantages (McGrath 2013). Researchers have also shown that decision speed is particularly important as a response to environmental dynamism (Baum and Wally 2003; Eisenhardt and Martin 2000; Judge and Miller 1991). However, quick decisions in dynamic environments are rather difficult to make because dynamism makes it more difficult for firms to understand the market and then inform how to make decisions (Priem et al. 1995).2 As such, a “central debate in the strategy, organization, and entrepreneurship literature surrounds how leaders effectively manage their organization and strategies in dynamic environments” (Eisenhardt et al. 2010: 1263).
Individuals may improve the speed of their decisions by using information that is real-time , developing and considering a greater number of alternatives, relying on intuition that is based on their experiences , and using techniques that actively resolve potential conflicts (Eisenhardt 1989). Moreover, the speed of making decisions increases when decision makers are younger (Forbes 2005), employ heuristics for opportunity recognition (Bingham and Eisenhardt 2011), utilize routines to guide their decision making (Helfat and Peteraf 2003), trust in their own intuition (Miller and Ireland 2005; Wally and Baum 1994), and rely on past experiences (Forbes 2005).
Extant studies thus shed light on how important it is to make decisions quickly to recognize transient opportunities and to achieve high firm performance. These studies have also explored the antecedents to organizations’ decision speed. Yet, research in this area has generally considered the speed of a firm’s decision making to be rather universal as opposed to being heterogeneous within a firm depending on the decisions at hand (e.g., Baum and Wally 2003; Eisenhardt 1989; Forbes 2005; Judge and Miller 1991). Therefore, this research stream does not yet provide a deep understanding of decision -making speed for different assessment decisions in the different stages of the opportunity progression process.
To begin to overcome this lack of understanding, my (Dean) colleague and I (Bakker and Shepherd 2017) explored the vital role of attention in this context (Ocasio 1997). As discussed earlier, when faced with large and complex option sets, individuals are unable to dedicate full attention to all matters simultaneously; rather, they are likely to focus their attention on a restricted set of issues (Lavie et al. 2010; Ocasio 2011). However, firms can develop methods to enhance their decision -making speed in areas of particular interest. My (Dean) colleague and I (Bakker and Shepherd 2017) built on Cho and Hambrick’s (2006) notion of attentional orientation (which in turn drew on Ocasio’s work on attention (1997, 2011)) to theorize on a firm’s attention level toward specific opportunity-advancement stages. Attention ranged from higher attention levels focused on earlier-stage exploration activities and related assessment decisions to higher attention levels focused on later-stage exploitation activities and related assessment decisions . The study found that firms that focus their attention on earlier-stage exploration activities tend to confront different issues than firms that pay more attention to the exploitation of potential opportunities. Exploration focuses the attention of individuals on seeking something novel by constantly scanning the environment for indications of wealth-generating opportunities (Brown and Eisenhardt 1997; McGrath 1999). In contrast, exploitation focuses the attention of individuals on current opportunities and on the capabilities required to take advantage of them (Rothaermel and Deeds 2004). The degree of attention a manager focuses on specific opportunity-advancement stages affects the relative speed of decision making for a particular potential opportunity based on three characteristics: experience (Levitt and March 1988; Ocasio 1997), standard operating procedures (Cyert and March 1963; Gavetti et al. 2007; Ocasio 1997), and confidence (Levitt and March 1988; March and Shapira 1987).
Experience and Managers’ Attention
Firms gain experience and learn by repeatedly executing certain tasks and activating routines (Levitt and March 1988). Because of differences in important activities, firms that focus more attention on earlier-stage exploration tend to have different experiences than those that focus their attention on later-stage development or exploitation . Early-stage exploration entails search, discovery, and experimentation ; in contrast, exploitation entails refinement, implementation, and execution (March 1991). These domain-specific activities and the resulting experience are likely to affect decision -making speed. More specifically, managers with domain-specific experience will allocate less time c
ollecting information; these managers already possess a strong knowledge base to draw from (Forbes 2005). Moreover, such managers are also likely to analyze information more quickly since they possess an organizing framework that “facilitates the storage, recall, and interpretation of data” (Forbes 2005: 358).
Standard Operating Procedures and Managers’ Attention
Firms generally develop standard operating programs, practices, and routines over time (Cyert and March 1963; Gavetti et al. 2007), which can be viewed as a set of behavioral rules learned as the firm tries to adjust to operating conditions (Cyert and March 1963). Not only do we contend that different attentional orientations guide individuals toward diverse experiences , but we also argue these different orientations result in the development of different kinds of operating procedures. For example, practices and routines associated with prospecting deal with how to allot slack resources to explore possible opportunities (George 2005), how to normalize and learn from minor failures (Sitkin 1992), and how to effectively redistribute resources from one firm to another (Brown and Eisenhardt 1997). Routines and practices related to exploitation , on the other hand, entail the management of risk and preservation of strategic congruence (Greve 2007; March 1991), the refinement of current technologies and attainment of efficiency (Csaszar 2013; March 1991), and the ramping up of operations to reach economies of scale and scope (Lavie et al. 2010). As these examples illustrate, standard operating procedures influence and direct the decisions firms make (Cyert and March 1963) as well as affect their speed. These practices also enable the transmission of past learning , which can then be applied again in new situations (Cyert and March 1963), and they can set rules for collecting, filtering, and processing information (Cyert and March 1963).