Reconceptualization of Entrepreneurial Expertise: A Multi-Dimensional Model

AbstractKnowledge about entrepreneurial expertise, how experts think and make decisions have progressed in the last decade. Despite this advancement, studies have difficulties in providing a general conceptual model of expertise that is empirically validated. We attribute these limitations to the lack of existing conceptualizations of entrepreneurial expertise to take into consideration the entrepreneur’s dual systems of information processing. Therefore, in this study, we advance a default-interventionist perspective of entrepreneurial expertise consisting of fast and slow-thinking expertise. We suggest that the two types of expertise coexist and interact during the decision-making process except in the decisions characterized by true uncertainty. Furthermore, taking into consideration that expertise is domain-specific, we suggest three broad sub-domains of entrepreneurial knowledge that are malleable to the specific sector in which an entrepreneur operates.

1 Introduction

Within the field of entrepreneurship, there is a general agreement that opportunity is the distinctive element of the scholarly domain (Shane & Venkataraman, 2000; Short, Ketchen, Combs, & Ireland, 2010). A prevalent view regarding the ontology of opportunities suggests opportunities as endogenous endpoints of path-dependent processes (Dimov, 2016). Viewing opportunities as socially constructed entities require scholars to apply a lens of research where one must go into the mind of the entrepreneur to understand with what effects decisions and actions are taken (Dimov, 2016). For example, effectuation (Sarasvathy, 2008; Sarasvathy, 2001) that is based on the study of expert entrepreneurs (Dew, Read, Sarasvathy, & Wiltbank, 2009).

The entrepreneurial expertise has received considerable attention within the field of entrepreneurship and is suggested as a fruitful area to explicate the mindset of successful entrepreneurs (Baron, 2004). Over the last decade, the subfield has advanced, and a plurality in conceptualizations of entrepreneurial expertise have been developed. For instance, business opportunity prototypes (Baron, 2006; Baron & Ensley, 2006), expert scripts (Mitchell, Mitchell, & Mitchell, 2017; Smith, Mitchell, & Mitchell, 2009), and effectuation (Dew, Read, et al., 2009; Sarasvathy, 2008). Unifying the multiplicity of conceptualizations of entrepreneurial expertise is the consensus that expertise is cognition that is specific to the entrepreneurial domain (Mitchell, Smith, Gustafsson, Davidsson, & Mitchell, 2005). A case can be made that scholars view entrepreneurial expertise as a one-dimensional concept (Lord & Maher, 1990); either an intuitive (cf. Dreyfus & Dreyfus, 2005) or a deliberate process (cf. Unger, Keith, Hilling, Gielnik, & Frese, 2009). This narrow view of expertise leads to confusion regarding how expert entrepreneurs are different compared to novices. For instance, Dew, Read, Sarasvathy, and Wiltbank (2009) found that expert entrepreneurs predominantly use heuristic-based logic, whereas Alsos, Mauer, Clausen, and Solvoll (2017) found that expert entrepreneurs predominantly use analysis-based logic. We attribute these contradictions to the lack of existing conceptualization of entrepreneurial expertise to take into consideration the interaction between individuals’ two systems of information processing and the conditions that lead to the interaction.

Towards that end, we strive for a more precise conceptualization of entrepreneurial expertise that enables the investigation of how expert entrepreneurs think different than novice

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entrepreneurs. In particular, this study aims to present a reconceptualization of the cognition of expert entrepreneurs to advance a reinterpretation of the underlying cognitive mechanisms used by experts and the conditions under which these mechanisms are applicable. We suggest a default-interventionist perspective consisting of dual processes of fast and slow-thinking expertise. Fast and slow-thinking (Kahneman, 2011; Sadler-Smith, 2004) relates to the individuals’ information processing, i.e., the former being automatic, effortless and unconscious, and the latter being deliberate, effortful and conscious. The model also proposes three broad categories of entrepreneurial knowledge sub-domains that are malleable to the specific sector in which an entrepreneur operates. The knowledge sub-domains bound entrepreneurial expertise, i.e., having expertise in one sub-domain does not automatically means having expertise in the other sub-domains. This model serves as a lens to guide future research of the heuristics and knowledge structures of expert entrepreneurs.

We seek to provide two contributions to the literature on entrepreneurial expertise. First, scholars in entrepreneurial expertise have had difficulties in developing a general conceptual model of expertise that is empirically validated. Research finds contradictory results regarding entrepreneurial expertise (Alsos et al., 2017). We propose a default-interventionist perspective of expertise consisting of both fast- and slow-thinking processes that are useful in different conditions of risk and true uncertainty. We suggest that there is an interaction between the fast and slow-thinking expertise, and the complexity of the opportunity moderates the relationship between the risk/true uncertainty and the fast and slow-thinking expertise. We hope that our model of entrepreneurial expertise serves to reconcile the inconsistent empirical findings by showing that different types of entrepreneurial expertise can co-exist. Second, the acquisition of expertise is intensively debated among entrepreneurship scholars. On the one hand, the acquisition of expertise is viewed as an innate talent determined by genetics (Shane & Nicolaou, 2013). On the other hand, the acquisition of expertise is viewed as a deliberate process (Unger et al., 2009). We seek to reconcile the debate of nature versus nurture regarding the acquisition of entrepreneurial expertise by advancing the thought that innate talent is necessary to acquire expertise.

2 Entrepreneurial expertise

2.1 The ontology and epistemology of expertiseExpertise has been traditionally studied from the psychology discipline (e.g., Ericsson, Krampe, & Tesch-Römer, 1993) and subdisciplines of cognition (e.g., Chase & Simon, 1973) and information processing (e.g., Smith et al., 2009). Expertise is derived from experience and experiment (Ericsson, 2014). Experience is necessary to acquire new knowledge (Reuber, Dyke, & Fischer, 1990). However, experience only does not result in expertise. There is rather a curvilinear relationship between experience and expertise (Shepherd, Zacharakis, & Baron, 2003; Ucbasaran, Westhead, & Wright, 2009). An expert is someone who shows to have great knowledge in a domain (Lord & Maher, 1990). Experts can perform at any time with limited preparation (Ericsson, 2008). Expertise is high performance in relation to others and not absolute levels. In addition, it is about decision-making (Ericsson, 2006) that comes with specialization in a certain domain (Baron & Ensley, 2006).

The performance of experts are cognitively mediated (Ericsson, 2008). There are several superior cognitive aspects of experts compared to novices (Baron & Henry, 2010; Mitchell, 2005). Experts can discriminate incoming information more finely than novices, and recognize

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more complex patterns. Experts develop perceptual abilities that aid in identifying cues for the anticipation of events (Ericsson, 2014; Ericsson et al., 1993). These abilities are drawn on previously stored knowledge (Cohen & Levinthal, 2000). Experts have superior working memory (Baron & Henry, 2010; Mitchell, 2005). They encode new information differently than novices which lead to quicker retrieval of stored information into the working memory and have larger memory capacity. Furthermore, experts also have greater developed intuition and have lower overconfidence. Experts especially excel in the speed to make decisions, but they can take longer to cognitively elaborate the decisions (Martin de Holan et al., 2013). In other words, experts are quicker in perceiving new stimuli but can be slower in processing the information due to their knowledge structures that are more complex and advance. Experts also have higher decision-making efficiency (i.e., payoff divided by response time) (Laureiro-Martínez et al., 2014).

Experts have enhanced metacognition. Metacognition is defined as “the ability to reflect upon, understand, and control one’s learning” (Schraw & Dennison, 1994, p.460). The metacognition of experts plays a vital role in their enhanced performances. Metacognition facilitates learning, which is a crucial component in developing expertise. Expertise is developed by learning to solve problems and developing better methods for performing the tasks (Ericsson, 2008). The learning occurs as experts reflect on their scripts and exchange these scripts for more advanced ones (Glaser, 1984). Scripts are sequential structures of knowledge of a content domain, and norms which serves to guide the actions of experts (Mitchell, Mitchell, & Mitchell, 2009).

In sum, the human cognition is essential for the development of expertise (Ericsson, 2008). In psychology, cognition is traditionally viewed as two distinct processes, fast-thinking versus slow-thinking (Kahneman, 2011; Sadler-Smith, 2004). Entrepreneurship studies suggest that there are multiple types of expertise (Johnson & Mervis, 1997; Krueger, 2007), but these types are viewed as either automatic and fast-thinking processes (cf. Dreyfus & Dreyfus, 2005) or as effortful, cognitively laden and slow-thinking processes (cf. Mitchell and et al., 2017). We turn to the discussion of these types of entrepreneurial expertise.

2.2 Fast-thinking expertiseA widely accepted view of entrepreneurial expertise suggests it as intuition (cf. Dreyfus & Dreyfus, 2005) and heuristics (Mitchell et al., 2007; Stanovich & West, 2003). Intuition finds its roots in the Latin word ‘intuir, ’ which means ‘looking, regarding or knowing from within’ (Hodgkinson, Langan-Fox, & Sadler-Smith, 2008). Intuition is affect-laden and often expressed as a ‘gut feeling’ (Hogarth, 2001). Heuristics are simplifying strategies (Kahneman & Tversky, 1979), are determined by the individual and the situation (Busenitz & Barney, 1997), and are based on informal processes and experience (Busenitz & Lau, 1996; Simon & Houghton, 2002). We refer to the intuition and heuristics-based expertise as fast-thinking. These are automatic and processed in the right brain (Gazzaniga, Ivry, & Mangun, 1998; Parsons & Osherson, 2001). We identified two prominent fast-thinking expertise in the entrepreneurship literature; exemplar prototypes (Baron, 2006) and effectuation (Sarasvathy, 2001; Sarasvathy, 2008).

Exemplar prototyping is an intuitive process (Baron, 2006) where experts use specific and unique knowledge. It is based on the prototype theory (Whittlesea, 1997). Experts create knowledge structures (i.e., prototypes) through experience and use these structures to recognize patterns by comparing identified information with stored concepts (Baron, 2006). Prototypes are defined as “idealized representations of the most member of a category (a class of objects or

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events that seem to belong together)” (Baron, 2006, p.109). Pattern recognition is “the process through which individuals identify meaningful patterns in complex arrays of events or trends” (Baron & Ensley, 2006, p.1332).

Effectuation (Sarasvathy, 2001; Sarasvathy, 2008) are heuristics (Dew, Read, et al., 2009) that expert entrepreneurs use under conditions of true uncertainty (Knight, 1921) where probabilistic decisions are impossible (Dew, Read, et al., 2009). Under the conditions of true uncertainty, individuals experience cognitive inability (Simon, 1955), and experts oscillate to fast-thinking (Dew, Read, Sarasvathy, & Wiltbank, 2015), and apply control rationality (Dew et al., 2015; Wiltbank, Read, Dew, & Sarasvathy, 2009). The heuristics allow experts to assimilate and make sense of fragmented information to make decisions quickly (Busenitz, 1999). The effectuation proposes five heuristics: 1) affordable loss (i.e., investing what one is able and willing to lose), 2) crazy quilt (i.e., co-creation partnerships), 3) lemonade (i.e., leveraging contingencies), 4) bird-in-hand (i.e., using means under control as the basis for taking action), and 5) worldview (i.e., striving to control the future instead of striving for prediction of an unpredictable future) (Sarasvathy, 2001; Sarasvathy, 2008).

2.3 Slow-thinking expertiseNext to fast-thinking expertise, we also identified two slow-thinking expertise; expert scripts (Mitchell, Mitchell, & Mitchell, 2017; Smith, Mitchell, & Mitchell, 2009), and business prototypes (Baron, 2006). Slow-thinking expertise is a left brain activity where experts generate lots of alternatives before making a decision (Simon & Simon, 1962).

Scripts are memory knowledge structures that specify a sequence of behavior or events in specific situations (Gioia & Poole, 1984). Scripts consist of knowledge structures (Lord & Maher, 1991) which are organized knowledge that gives meaning to stimuli from an environment (Fiske & Taylor, 1984). The knowledge structures are stored in chunks and allow experts to see causal relationships between events and determine appropriate current and future tasks (Abelson & Black, 1986). The scripts are seen as dependent and firmly established, and so defined as “the development and enactment of entrepreneurial expert scripts in response to a changing environment” (Mitchell et al., 2017, p.179). The dynamic process of developing and enacting scripts is also called entrepreneurial scripting.

Business prototyping (Baron, 2006), similar to exemplar prototyping, suggest that experts compare new stimuli with their stored idealized representations. However, business prototypes do not serve to identify highly unique events, neither when true uncertainty is present. Business prototyping is effortful (Baron, 2006) and processed in the left brain (Gazzaniga et al., 1998; Parsons & Osherson, 2001).

In sum, entrepreneurial expertise is viewed as either a fast-thinking (i.e., examplar prototyping and effectuation) or slow-thinking (i.e., scripts and business prototyping) processes relating to human cognition. However, studies in cognition suggest that fast and slow-thinking processes interact, thus suggesting that entrepreneurship expertise should also be viewed as an interaction between examplar prototyping and effectuation, and scripts and business prototyping. We turn to this discussion next.

3 Dual systems of information processing

Research shows that individuals have dual systems to process information and come to judgment and decisions. There are several theories of dual systems of information processing, for example

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the experimental vs. the rational (Epstein, 1994; Epstein & Pacini, 1999), the heuristic vs. the analytic (Evans, 1989), the System 1 (TASS) vs. the System 2 (Stanovich & West, 2000; Stanovich, 1999), the Type 1 vs. Type 2 (Evans & Stanovich, 2013) and the intuition vs. the analytical (Hammond, 1996) (see Evans (2008) for a comprehensive review of the dual processes theories). The commonality among these theories is that processing is viewed as both, on the one hand, an intuition (i.e., automatic, fast, unconscious and a low effort activity), and on the other hand, an analytical activity (i.e., deliberate, slow, conscious and effortful). The intuitive and the analytical systems can be distinguished based on four categories (Evans, 2008); 1) consciousness (implicit, automatic, low effort and fast vs. explicit, deliberate, high effort and slow), 2) evolution (evolutionary old and non-verbal vs. evolutionary new and linked to language), 3) functional characteristics (associative and contextualized vs. rule-based and abstract), and 4) individual differences (universal vs. heritable) (see Table 1 for a categorization of the types of expertise based on the intuitive and analytical systems of information processing).

Table 1: The types of entrepreneurial expertise

 Examplar prototyping Effectuation Scripts Business Prototyping

Consciousness implicit implicit explicit explicitautomatic automatic deliberate deliberatelow effort low effort high effort high effort fast fast slow slow

Evolution evolutionary old evolutionary old evolutionary new evolutionary new non-verbal non-verbal linked to language linked to language

Functional characteristics associative rule-based rule-based rule-based contextualized abstract abstract abstract

Individual differences   universal universal heritable heritable

Although intuition is considered to be evolutionary old, meaning that intuition is the first system that the human species have learned on which to rely for processing of information, frequent repetition of analytical processes also become automatic and part of the intuition (Evans, 2008). This process is also known as analysis frozen into habit (Simon, 1987).

Though the theories of dual processes agree that there are two distinct systems to process information, there are two views regarding the relationship between the two systems (Wang, Highhouse, Lake, Petersen, & Rada, 2017). On the one hand, the parallel-competitive theories suggest that the systems are bipolar, two opposites extremes on a continuum (Hodgkinson & Sadler-Smith, 2018). According to these theories, intuition is evolutionary old and is replaced by the newer analytic system. On the other hand, the default-interventionist theories suggest that the dual system is orthogonal, i.e., independent from each other that operate differently and have different uses (Epstein, Pacini, Denes-Raj, & Heier, 1996; Hodgkinson & Sadler-Smith, 2003; Wang et al., 2017). Intuition aids in processing information and guide decisions where time is a constraint, there is uncertainty (Khatri & Ng, 2000) and lack of information (Volz & von

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Cramon, 2006), and the individual is cognitively limited (Simon, 1956). Whereas, the analytical system facilitates abstract and hypothetical thinking (Evans, 2003). The default-interventionist views suggest that “although participants attempt to reason logically in accord with the instructions, the influence of prior beliefs is extremely difficult to suppress and effectively competes for control of the responses made” (Evans, 2007). Depending on the task and on the demand on the processing ability of the individual, intuition, and analysis also support each other in decision-making (Hodgkinson & Sadler-Smith, 2016). The analytical system is deliberate, conscious and under control and is supported with the percept and memories from intuition (Evans, 2007).

In this study, we side with the default-interventionist theories of dual systems of information processing. A meta-study to test the relationships between intuition and the analytical systems found no relationship between the two (Wang et al., 2017), supporting the default-interventionist views. These views are also supported by studies in neuroscience (cf. Greene, Nystrom, Engell, Darley, & Cohen, 2004) showing that different brain regions are activated when individuals make intuitive versus analytical decisions.

4 Reconceptualization of entrepreneurial expertise

4.1 The default-interventionist perspective of entrepreneurial expertiseThe review of the entrepreneurial expertise literature shows that expertise is commonly viewed as either fast or slow-thinking information processes. However, the accumulated evidence of cognition and neuroscience research suggest a default-interventionist perspective (Epstein, Pacini, Denes-Raj, & Heier, 1996; Hodgkinson & Sadler-Smith, 2003; Wang et al., 2017). Adhering to this view, we propose a multi-dimensional model of entrepreneurial expertise (see Figure 1 for the model).

Figure 1: A default-interventionist perspective of entrepreneurial expertise

4.1.2 Interaction between fast and slow-thinking expertise

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The fundamental premise of the default-interventionist perspective of entrepreneurial expertise is that expert entrepreneurs experience both fast and slow-thinking expertise. They use both their intuitions and analytical systems when processing information and making decisions. The two systems interact with each other (Finucane, Peters, & Slovic, 2003; Hayton & Cholakova, 2012). For example, in an experimental study, Welpe, Spörrle, Grichnik, Michl, & Audretsch (2012) found that emotions interact with cognitive evaluations in determining if entrepreneurs exploit entrepreneurial opportunities. The study shows that entrepreneurs judge the objective characteristics of an opportunity using their analytic system interactively with their emotions. In addition, intuition draws stored knowledge into the working memory which is considered in the analysis (Evans, 2007). Furthermore, the affect1 directs individuals towards new information that needs to be considered in the decision making (Peters, Lipkus, & Diefenbach, 2006). The consideration of new information is a characteristic of the analytical system. This system has been found to be correlated with openness to experience (Wang et al., 2017). Considering that intuition is affect-laden (Hogarth, 2001), we can suggest that the fast and slow-thinking expertise interacts in entrepreneurial decision making. For example, an expert entrepreneur that experiences a bad ‘gut-feeling’ relating to a complex investment opportunity. If before making the final decision, the entrepreneur takes additional efforts to analyze the consequences of the investment, the fast and slow-thinking expertise are interacting.

The fast and slow thinking expertise can also compete (Evans, 2007). For example, when judging risks, this is not only an analytic process but also emotion-laden (Finucane, Alhakami, & Slovic, 2000; Slovic, Finucane, Peters, & MacGregor, 2004; Slovic & Peters, 2006). For instance, in the bargaining process, emotions like fear, anger, and embarrassment can influence the entrepreneur to treat the opposing party contrary to his/her own interests (Loewenstein, 2000). Therefore, we propose:

Proposition 1: fast and slow-thinking expertise interact during the information processing, judgment, and decision-making.

4.1.3 Risk, uncertainty and expertiseThe interaction between the fast- and slow-thinking expertise, and whether intuition and the analytical system interact sequentially or simultaneously is also depended on the decision that has to be made (Epstein & Pacini, 1999). Research shows that the type of uncertainty of the decision influences entrepreneurial decision making (cf. McKelvie, Haynie, & Gustavsson, 2011). We make a distinction between risks where individuals can estimate the probabilities of the outcomes of their decisions versus uncertainty where individuals cannot make the estimation (Knight, 1921; Walker et al., 2003). On the one hand, a risk emerges both where there are incomplete information and excessive information that is conflicting (Lipshitz & Strauss, 1997). In risky situations, entrepreneurs can rely both on their intuition and analytical systems. Expert entrepreneurs use their knowledge structures to recognize patterns and enact scripts and use their intuition to guide the search process for new information. This process is simultaneous use of fast and slow-thinking expertise. For example, consider the case of an entrepreneur encountering new technology during a business trip to a foreign country. The entrepreneur comes in contact with new technology and experiences a ‘hunch’ regarding the application of the technology as a solution to a problem in his/ her home country. The entrepreneur carefully contemplates if he/she did not hear about the technology through professional associations (e.g., a community for

1 We use the term affect interchangeable with emotions (Cardon, Foo, Shepherd, & Wiklund, 2012)

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software developers) of which he/she is a member. Such a case is an example of a risky opportunity, i.e., a technology being applied to a new market (Sarasvathy, Dew, Velamuri, & Venkataraman, 2010), leading to fast and slow-thinking expertise. The fast-thinking expertise relates to the ‘hunch’ experiences, and the slow-thinking expertise relates to the cognitive process to compare the information regarding the technology with previously stored information.

Empirical studies show that risks lead to both fast-and slow-thinking expertise. For example, Dew et al., (2015) found that experts use effectuation in risky situations, and Baron and Ensley (2006) found that experts use business opportunity prototyping. Therefore, we propose:

Proposition 2: Perceived risk positively influences fast-thinking expertise.

Proposition 3: Perceived risk positively influences slow-thinking expertise.

On the other hand, true uncertainty leads to fast-thinking expertise (Khatri & Ng, 2000; Volz & von Cramon, 2006). In these situations, entrepreneurs cannot rely on slow-thinking expertise for the reason that entrepreneurs do not have stored knowledge structures to compare the information that has been perceived. For instance, take the case of the entrepreneur in our previous example. Let us consider that the entrepreneur is exploring new potential uses of materials under possession when he/she comes in contact with the new technology. The entrepreneur might experience a eureka moment regarding how the combination of the new technology and his/her materials can lead to new uses. However, the entrepreneur is uncertain regarding the technical and operational feasibility of the idea, and to which customer problems this idea can provide a solution. In these cases, the entrepreneur relies on fast-thinking expertise since the entrepreneur has no information on which to base a decision to pursue his/her idea or not.

Empirical studies also support the notion that uncertainty leads to fast-thinking expertise. For example, Dew, Read, et al. (2009) compared the decision-making of experts with non-experts (i.e., MBA students) in a think-aloud experiment. The participants were given a case called Venturing that described a business opportunity, and the participants were asked to report their decision-making processes. The study found that the expert entrepreneurs used effectuation when perceiving uncertainty. Chandler, DeTienne, McKelvie, and Mumford (2011) also found that perceived uncertainty leads to effectuation. Therefore, we suggest the following:

Proposition 4: Perceived Knightian uncertainty positively influences fast-thinking expertise.

4.1.4 The complexity of the opportunity and expertiseTheories of expertise state that in order to acquire it, associated tasks should be performed. Doing a task requires a combination of willingness, arrangement and the necessary abilities (Mitchell et al., 2009). Although all three cognitions are essential for performing a task, we take a special interest in the abilities cognition. The abilities cognition is defined as “the knowledge structures or scripts that individuals have about the capabilities, skills, knowledge, norms, and attitudes required to create a venture” (Mitchell, Smith, Seawright, & Morse, 2000, p.978). Experts need to match their abilities to the opportunity, in order to perform adequately. If the abilities used depend on the opportunity, the complexity of the opportunity (Baron & Ensley, 2006) also determines if individuals can exercise expertise. For example, solving a simple

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mathematics equation such as ‘y = 1 + 1’ does not require special expertise. Although mathematics experts have a more content enriched mental model (Mitchell et al., 2000), and are better capable of recognizing and solving complex mathematical problems, the expertise is not required to perform this simple equation. The use of expertise depends on the complexity of the decision that should be made, and experts know in which situations they need to deploy expertise (Krueger, 2007). Therefore, we propose:

Proposition 5: The complexity of the opportunity positively moderates the relationships between perceived Knightian uncertainty and fast-thinking expertise, and between risk and fast and slow-thinking expertise.

4.1.5 The metacognition context and expertiseMetacognition also forms part of the entrepreneurial expertise. It is defined as “the ability and willingness of individuals to rapidly sense, act, and mobilize in response to judgmental decision under uncertainty about a possible opportunity for gain” (Shepherd, Patzelt, & Haynie, 2010, p.62). In other words, through metacognition experts are able to develop entrepreneurial mindsets. Metacognition is also vital for the development of cognitive adaptability, which is defined as “the ability to be dynamic, flexible, and self-regulating in one's cognitions given dynamic and uncertain task environments” (Haynie, Shepherd, Mosakowski, & Earley, 2010, p.218). In other words, experts can define current and future tasks required in complex and uncertain situations (Baron & Henry, 2010). Haynie et al., (2010) suggest that experts have metacognitive knowledge (i.e., awareness and understanding of people, tasks, and strategy), experience (i.e., allows affective sensemaking of experienced stimuli), resources (i.e., facilitates to development of metacognitive strategies), and monitoring (i.e., evaluation of chosen metacognitive strategies). Together the metacognition enables experts to develop the ability to be dynamic, flexible, and steer one’s cognition towards the characteristics of a decision. It allows experts to identify the adequate cognitive strategy related to a situation. Especially, the metacognitive experience allows experts to self-regulate the emotions from experienced stimuli (Haynie et al., 2010). Thus, experts can adapt their cognitive behavior to the cognitive nature of the task (Gallouj, 1998). Hence, we propose:

Proposition 6: The metacognition knowledge, resources, experience and monitoring moderate the relationship between fast- and slow-thinking expertise.

4.2 The entrepreneurial domain

4.2.1 The entrepreneurial processSince expertise is domain-specific and it requires the knowledge of the domain to understand the expertise (Mitchell et al., 2009), an overview of what entrepreneurship entails is necessary. Entrepreneurship has commonly been conceptualized as a process. For instance, Bhave (1994) proposes a process where entrepreneurs first filtrate information, subsequently refine the ideas and finally create the physical product. Shane and Venkataraman (2000) suggest that entrepreneurs advance through the stages of opportunity identification, evaluation, and exploitation. Ardichvili and Cardozo (2000) also propose a discovery approach where an interaction between the entrepreneurial awareness and prior knowledge of customer demands, and access to social networks causes opportunity recognition. Engel, Kaandorp, and Elfring

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(2017) apply a creation lens to the entrepreneurial process. These authors suggest that entrepreneurs start with the process with a reflection on their means, knowledge, skill and social network. Subsequently, entrepreneurs share their ideas with their peers who have the opportunity to self-select to the network. This cyclical process expands in terms of means available to the network, but constraints itself and results in a created opportunity. Bakker and Shepherd (2017) propose an opportunity evaluation process where entrepreneurs progress through the stages of prospecting, developing and exploiting. Entrepreneurs simultaneously evaluate multiple opportunities, each at its stage of evaluation, and attention determines the speed at which opportunities advance along the stages or is terminated. Davidsson (2015) suggest that the entrepreneurial process should be analyzed from an integrated multi-level perspective; the Opportunity Confidence of the individual, the New Venture Idea (NVI) at the venture level, and the External Enablers at the aggregate level. Finally, based on Davidsson's (2015) concept of NVI, Martina and Hu-A-Ng (2018) posit that the entrepreneurial process is an evolving interaction between individuals and objects that start with and ends in co-created opportunities, products, and values. Furthermore, the NVI contains two dimensions, the enabler (i.e., external-internal) and the originality (i.e., existing-non-existing) that influences how logic is applied by entrepreneurs in the process.

As the various views of the entrepreneurial process reveal, entrepreneurship is a multi-sided phenomenon (Ács, Autio, & Szerb, 2014; Autio, Kenney, Mustar, Siegel, & Wright, 2014; Welter, 2011). It is “how, by whom, and with what effects opportunities to create future goods and services are discovered, evaluated, and exploited” (Shane & Venkataraman, 2000, p. 218). However, what is the entrepreneurial domain and what expertise is particular to this domain? What do entrepreneurs do? Entrepreneurs should master a wide variety of skills. Bacigalupo, Kampylis, Punie, & Van den Brande (2016) suggest that entrepreneurs possess cognitive abilities, be social and action-oriented. Mitchelmore and Rowley (2010) propose three areas of expertise: ideas and opportunities, resources, and action. Baron and Henry (2010), and Baron (2008) propose that entrepreneurs should be experts in recognizing and evaluating opportunities, build effective social networks, identify and acquire essential resources, make effective decisions, have superior meta-cognition and self-regulation. In this study we suggest three knowledge sub-domains of entrepreneurship (see Figure 2): 1) The discovery and creation of opportunities, 2) The identification, acquisition, and investment of resources, and 2) the social networking.

Figure 2: Entrepreneurial knowledge sub-domains

4.2.2 The discovery and creation of opportunitiesThe entrepreneurial opportunity is essential to the scholarly entrepreneurship domain (Short et

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al., 2010). It is also the cause of severe tension within the field (Dimov, 2011)2. The first views conceptualized opportunities as existing entities that should be discovered by alert entrepreneurs (cf. Shane, 2000). Opportunities are also seen as emerging and socially constructed (cf. Alvarez & Barney, 2007). A third view suggests that opportunities are discovered and simultaneously created entities (cf. Hansen, Monllor, & Shrader, 2016). In accordance to the latter view, Martina and Hu-A-Ng (2017, p. 22) define opportunity as “a set of factors that lead to the introduction of one or more new products in the marketplace by a group of individuals.” This definition suggests that entrepreneurs are simultaneously discovering and creating the necessary factors during the entrepreneurial process. Entrepreneurs concurrently apply discovery and creation logic for the reason that at the initiation of the entrepreneurial process an existing opportunity resides. Entrepreneurs discover a few aspects of the opportunity on which they act (Baron, 2006). However, conditions to introduce a product in a market are never ideal, and thus entrepreneurs also enact their environments to create additional necessary conditions. In this line of thought, since actions to exploit an opportunity results in the creation of complementary opportunities (Holcombe, 2003), the opportunity is also seen as an outcome of the interaction between entrepreneurs and other actors (Sarasvathy, 2001).

These processes suggest that in order to exercise expertise regarding the discovery and creation of opportunities, entrepreneurs should have the knowledge of the factors that exist and also can be created to exploit opportunities, for example regulatory institutions (Battilana, Leca, & Boxenbaum, 2009; Young, Welter, & Conger, 2017), under which conditions they are present and can be created, and the corresponding decision and actions.

4.2.3 The identification, acquisition, and investment of resourcesThere are two main views regarding how entrepreneurs identify, acquire and invest resources. One the hand, entrepreneurs implement elements of strategic planning (Ansoff, 1979). This view is rooted in the neoclassic economic principle that entrepreneurs are rational and utility maximizing individuals. Entrepreneurs make plans and subsequently determine the steps necessary to implement the plans (Knight, 1921), and thus are goal-oriented (Mintzberg, Ahlstrand, & Lampel, 1998). The identified opportunity determines the necessary resources and the acquisition strategies. Entrepreneurs engage in systematic searches to find the required resources (Porter, 1980). The investments made are based on expected returns. The investment decision is modeled as an evaluation of expected losses and gains (compared to a reference point) (Kahneman & Tversky, 1979) and entrepreneurs invest when the expected gains exceed the individual thresholds (McCann & Folta, 2012).

An alternative to the goal-oriented entrepreneurial investing logic is the affordable loss (Dew, Sarasathy, Read, & Wiltbank, 2009). Under conditions of uncertainty, entrepreneurs restrict from determining the required investment capital to realize expected returns (Martina, forthcoming) but evaluate their abilities and willingness regarding the entrepreneurial opportunity (Sarasvathy, 2015) suggesting these entrepreneurs are resource-driven (Barberis & Huang, 2001; Benartzi & Thaler, 1995). The ability represents the size of what entrepreneurs can risk, and the willingness is what entrepreneurs are risking to loose (Dew, Sarasathy, et al., 2009). The starting point is a self-reflection of personal means (Sarasvathy & Dew, 2005). Entrepreneurs construct their abilities and continue to determining their willingness, and the

2 For example, see the discussions between Davidsson (2017), Ramoglou and Tsang (2017a), Alvarez, Barney, McBride, and Wuebker (2017), Berglund and Korsgaard (2017), Foss and Klein (2017), and Ramoglou and Tsang (2017b), and between Wood (2017a, 2017b) and Davidsson (2017a, 2017b).

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transition from abilities to willingness is moderated by loss aversion (Kahneman & Tversky, 1979). However, in this view entrepreneurs do not have expectations regarding returns from their investments because these are unknowable. Under these circumstances, entrepreneurs chose to control their potential losses. When the willingness is lower than the abilities, entrepreneurs invest. The investments are limited by the abilities. Therefore, entrepreneurs engage in resourceful behaviors such as bootstrapping (Bhide, 1992) and entrepreneurial bricolage (Baker & Nelson, 2005) through effectual networks (Sarasvathy & Dew, 2005) to increase their abilities (Martina, forthcoming).

These views suggest that in order to exercise expertise regarding the identification, acquisition, and investment of resources, entrepreneurs should have the knowledge regarding the investment processes that are more suitable to the opportunity, and the corresponding decision and actions.

4.2.4 The social networkingThe third and final sub-domain of entrepreneurship is social networking. Entrepreneurs use their network for several ends. For example, entrepreneurs identify opportunities, mobilize resources, learn and create legitimacy. Consistent with the previous sub-domains, there are two main views regarding why and how entrepreneurs build their social networks. These views respond to the discovery and creation views of the entrepreneurial opportunities.

On the one hand, social networking is viewed as a “strategic (i.e., rationally motivated) behavioral effort that involves the dyadic exchange of interpersonal resources, which are directed towards building and maintaining network relationships with specific network contacts and motivated by whether they have access to specific interpersonal resources” (Porter & Woo, 2015, p.1481). Rooted in expectancy theory (Vroom, 1964), networking is seen as an instrument to access the pre-identified desired resources. Porter and Woo (2015) propose three stages of social networking. In the first stage, the initiation, entrepreneurs exchange universal economic goods. As the networking progresses to the growth and maintenance stages, the networking acquires a more social character, and both universal and particularistic resources are exchanged. This view of social networking suggests that entrepreneurs are analytic and can identify which resources are required to exploit their entrepreneurial opportunities.

On the other hand, Engel et al., (2017) suggest that much of entrepreneurial social networking takes place in uncertain conditions and with ambiguous goals (Alvarez & Barney, 2013; Alvarez & Barney, 2007). In response to the strategic and goal-oriented views of social networking, Engel et al., (2017, p.42) suggest that networking becomes almost the very first thing they [entrepreneurs] do.” In this model rooted in the effectuation theory (Sarasvathy, 2001), networking is seen as an instrument to expand one’s identities, preferences, and resources, i.e., who we are, what we know, and whom we know (Sarasvathy & Dew, 2005). Early in the networking process entrepreneurs make social exchanges, and as the networking matures, exchanges are also economical.

These views suggest that in order for expert entrepreneurs to exercise expertise regarding social networking, entrepreneurs should have the knowledge regarding the social networking processes that are more suitable to the opportunity, and the corresponding decision and actions. Bearing in mind the three sub-domains of entrepreneurship, the discovery and creation of opportunities, the identification, acquisition, and investment of resources, and the social networking, we suggest that:

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Proposition 7: The use of fast and slow-thinking expertise is subjected to the entrepreneurial knowledge sub-domains.

4.2.5 The entrepreneurial sectorWe discussed so far that entrepreneurship is viewed as a process and can be categorized into three knowledge sub-domains: 1) The discovery and creation of opportunities, 2) The identification, acquisition, and investment of resources, and 3) The social networking. Knowledge about these sub-domains can be considered general knowledge that individuals need to develop expertise in the entrepreneurial domain. However, the environment can influence which expertise entrepreneurs’ use the most (Wyer & Srull, 1989). Entrepreneurship is a social phenomenon and methods for discovering and creating opportunities, and other entrepreneurial activities change with context. For example, Baumol (1990) discussed how (unproductive) entrepreneurship differs through the times, from the ancient Rome to early China, the Middle Ages and Renaissance in Europe. The conclusion is that as the rules of the game change so do the allocation of entrepreneurial activities. Baumol's (1990) study speaks specifically to the spatial, temporal, social and institutional context (Zahra & Wright, 2011). However, we take particular interest in the sectoral context (Autio, Kenney, Mustar, Siegel, & Wright, 2014), since changes across sectors, ceteris paribus, will have profound implications for the sub-domains of entrepreneurship. In other words, methods for discovering and creating opportunities, and other entrepreneurial activities are likely to be different across sectors. 3 Take for example the Sectoral Systems of Innovation approach (Malerba, 2002, 2005). Breschi and Malerba (1997) identified five types of sectors: 1) Traditional (e.g., textiles), 2) Mechanical, 3) Automobiles, 4) Computer mainframe, and 5) Software and modern microelectronics.4 Malerba and Orsenigo (1997) observed differences in patterns of innovation across these sectors. For example, the automotive industry is characterized by few innovators, geographically concentrated with local knowledge boundaries. In this sector, scalability is accompanied by intensive capital investments making investing by venture capital firms in these entrepreneurs not attractive (Hargadon & Kenney, 2012). On the other hand, software and modern microelectronics are characterized by many innovators, geographically concentrated with both local and global knowledge boundaries (Breschi & Malerba, 1997). Scalability is less capital intensive, which makes startups in this sector attractive for venture capital firms (Hargadon & Kenney, 2012). The differences between the sectors are due to the nature of the technological environments (Nelson & Winter, 1982). Technological environments are composed of among others the level of appropriability of the technology and the type of entrepreneurial opportunity (Malerba & Orsenigo, 1997). Differences in these components induce entrepreneurs to use different mechanisms to organize (Alvarez & Barney, 2005). For example, well-enforced property rights incentivize entrepreneurs to act quickly to capture first-mover advantages (Lieberman & Montgomery, 1988). However, in an uncertain context, poorly enforced property rights is less of a concern due to the inherent ambiguities in the sector (Alvarez & Barney, 2014). Uncertain opportunities induce entrepreneurs to co-create with other stakeholders (Alvarez & Barney, 2007). We propose that:

Proposition 8: The entrepreneurial sub-domains are influenced by the sector.

3 We do recognize that institutions, seen as the rule of the game, also have profound implications on the entrepreneurial cognition and behavior. However, institutions are an integral component of the Sectoral Systems of Innovation (Malerba, 2002).4 Malerba (2005) presents an update to the types of sectors: 1) Pharmaceuticals and Biotechnology, 2) Chemicals, 3) Telecommunication Equipment and Services, 4) Software, and 5) Machine Tools.

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5 Discussion and implications

In this study, we sought to advance a default-interventionist perspective of entrepreneurial expertise. We suggest that expertise is a dual system of information processing consisting of both fast (i.e., effectuation (Sarasvathy, 2001; Sarasvathy, 2008) and exemplar prototypes (Baron, 2006)) and slow-thinking expertise (i.e., expert scripts (Mitchell, Mitchell, & Mitchell, 2017; Smith, Mitchell, & Mitchell, 2009) and business prototypes (Baron, 2006)). The fast and slow-thinking expertise interact in decision-making. The use of the expertise and the interaction between these is influenced by the availability of information in the decision-making. Risky decisions lead to both fast and slow-thinking expertise. However, true uncertainty leads to only fast-thinking expertise.

We seek to advance two contributions to the literature of entrepreneurial expertise. First, studies on entrepreneurial expertise have struggled to provide a general conceptual model of expertise that is empirically validated. For instance, Dew, Read, Sarasvathy, and Wiltbank (2009) found that experts entrepreneurs predominantly use fast-thinking expertise, whereas Alsos, Mauer, Clausen, and Solvoll (2017) found that expert entrepreneurs do not use effectuation. Our multi-dimensional model of expertise shows that fast- and slow-thinking expertise can co-exist but under different conditions of uncertainty. In the study of Dew, Read, et al. (2009) a general description of the product and market is given. However, there is uncertainty regarding the specific requirements of the product in the research instrument. It states that the educators indicated that they “want several additions and modifications made before they would be willing to pay a price of over $150 for it” (Dew, Read, et al., 2009, p.305). There is no information regarding what additions and modifications are required by the educators. Thus this information is unknowable. Under this condition, fast-think expertise is a viable option. As the authors have found, the uncertainty embedded in their research instruments suggests that entrepreneurs should use heuristics to make decisions and co-create the gaming experience with the educators.

On the other hand, Alsos, Mauer, Clausen, and Solvoll (2017) found that with increased expertise, levels of uncertainty are reduced. In these cases, experts use slow-thinking expertise. The uncertainty is not due to unknowable information (Knight, 1921) such as the study of Dew, Read, et al. (2009) but due to the inherent variability of the phenomenon that is perceived (Walker et al., 2003). Therefore, the levels of uncertainty are reduced because experts have superior perceptual abilities and can identify cues for the anticipation of events (Ericsson, 2014; Ericsson et al., 1993). Experts draw on previously stored knowledge (Cohen & Levinthal, 2000) and use slow-thinking expertise. In other words, the findings of Alsos and co-authors suggest that causation can also be viewed as expertise, but in the form of slow-thinking. Causation is expertise that is useful in conditions of risk.

In sum, fast- and slow-thinking expertise are not mutually exclusive but can co-exist. The expertise is useful in different conditions, fast-thinking under conditions of uncertainty due to unknowable information, and slow-thinking under conditions of risk due to a complex phenomenon.

Second, we would like to contribute to the discussion about the acquisition of entrepreneurial expertise. The acquisition of expertise and the effects of nature versus nurture on the development of expertise are intensely debated. On the one hand, expertise is seen as acquired through learning (Baron & Ensley, 2006), especially through deliberate practice

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(Ericsson et al., 1993). For example, Mitchell et al. (2007, p.14) comment that “mounting evidence in recent entrepreneurship literature suggests that the path to becoming an entrepreneur is not special, but is in fact general— rooted in the cognitive systems created by deliberate practice.” Deliberate practice is defined as “engaging in practice activities assigned by a teacher with a clear, specific goal of improvement, and where the practice activities provide immediate feedback and opportunities for repetitions to attain gradual improvements” (Ericsson, 2014, p.510). In entrepreneurship, deliberate practice is modeled as a goal improvement of competence through the regular execution of tasks (Sonnentag & Kleine, 2000). Unger, Keith, Hilling, Gielnik, and Frese (2009) found that entrepreneurs execute several activities5 which increased their entrepreneurial knowledge and subsequently lead to higher performance.

On the other hand, several studies point to the limitations of deliberate practice to predict expertise (cf. Drake & Winner, 2017; Macnamara, Moreau, & Hambrick, 2016). Genetic factors are seen as explaining expertise. For example, Plomin et al., (2013) found that DNA markers account for 66% of heritable cognitive abilities, and in a study of expertise in reading in children. Plomin, Shakeshaft, McMillan, & Trzaskowski (2014) found that over half of the variance in performance is determined by genetics. Hambrick and Tucker-Drob (2015) found in a study of 800 pairs of twins that genetic factor explained over 75% of the musical abilities. Mosing, Madison, Pedersen, Kuja-Halkola, & Ullén (2014) found in a study of 10,500 Swedish twins that genetic factors influenced both ability and inclination to practice. Regarding specifically the entrepreneurial domain, Nicolaou, Cherkas, and Spector (2009) found that genetic factors influenced the propensity to recognize opportunities. Mayer-Haug, Read, Brinckmann, Dew, and Grichnik (2013) found that entrepreneurial talent is related to venture performance, and Shane and Nicolaou (2013) found that genetics positively influenced entrepreneurial performance measured by income generated through self-employment.

We advance the thought that innate talent is necessary for the acquisition of expertise. The default-interventionist perspective of entrepreneurial expertise that we advance in this study suggests that entrepreneurs use both fast and slow-thinking expertise interactively. The former is universal and independent of general intelligence, whereas the latter is positively related to general intelligence and is heritable Evans (2008). In other words, slow-thinking expertise is at least in part heritable and determined by genetics. Consider for instance an expert’s ability to perceive and make sense of complex situations (Ericsson, 2014; Ericsson et al., 1993). The expert should be able to retain large chunks of information in his/her working memory and also process the retained information. The working memory is important for expertise and can be trained to increase its capacity (Mitchell, 2005). However, studies have found that the working memory capacity is only correlated to the processing of information (Conway, Kane, & Engle, 2003). Studies show that the processing of information, also called the g factor, is the basis of intelligence (Jensen, 1998) and the g factor is heritable (Haworth et al., 2010). Therefore, the slow-thinking expertise is also determined by genetics. Taking into consideration that in decision-making the fast and slow-expertise interact, the overall exercised entrepreneurial expertise is determined by nature.

6 Limitations and future research

5 These activities are 1) Mental simulation, 2) Exploring new strategies, 3) Consulting colleagues or experts, 4) Asking customers for feedback, 5) Firm meetings, 6) Private conversations, 7) Professional reading, 8) Workshops/ training, 9) Observing others, and 10) Controlling/ checking.

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6.1 LimitationsLike any other research, also this study contains a few limitations. We identified three limitations. First, we treat effectuation as heuristics and as part of the fast-thinking expertise. We do not consider that effectuation can also be slow-thinking expertise. Though effectuation is positioned as heuristics that unconsciously are formed by entrepreneurs when facing true uncertainty (Sarasvathy, 2001), in later works Sarasvathy, Dew, Read, and Wiltbank (2003) position effectuation as rationality where “all possible worlds lie within the actual” (p.17). In this case, heuristics are seen as a form of rationality (Basel & Brühl, 2013).

The second limitation of this study concerns the dynamics of entrepreneurial expertise. We viewed expertise as static meaning that a form of expertise can only form part of one of the systems to process information. For example, relating to the first limitation, we viewed effectuation as only fast-thinking expertise. However, heuristics can be learned over time and become part of the analytical system (Kahneman & Frederick, 2002). For example, effectuation can be related to fast-thinking expertise, but through training, one can learn to reason through the formed rules of thumb consciously. Therefore, entrepreneurial expertise can also be viewed as a dynamic shifting between fast and slow-thinking kinds.

Finally, in this study, we view fast-thinking processes as intuition and heuristics. Although we name the two processes differently in relation to the literature, e.g., Dreyfus and Dreyfus (2005) for the former, and Read and Sarasvathy (2005) for the latter, we treat intuition and heuristics as containing similar characteristics regarding its consciousness, evolution, functions and individual differences. However, research also suggests that fast-thinking processes consist of several sub-systems that operate independently (cf. Stanovich & West, 2003), implying that intuition and heuristics are different. In this study, we do not make distinctions between the sub-systems of fast-thinking expertise.

6.2 Future researchWe propose four areas that are fruitful for future research on entrepreneurial expertise. First, we advanced the thought that innate talent influences the acquisition of expertise, especially the slow-thinking expertise. However, the default-interventionist perspective of entrepreneurial expertise also consists of fast-thinking expertise. The intuitive system is not heritable (Evans, 2008). An interesting area of future research is the processes that lead to the acquisition of the fast-thinking expertise. For example, under which conditions does learning through emotions (Turnbull, Evans, Bunce, Carzolio, & O’Connor, 2005) aid in the acquisition of entrepreneurial expertise?

Second, as we discussed in the limitations of our study, research shows that human cognition is not static but can change with time. For instance, frequent repetition of analytical processes can become part of the intuition (Evans, 2008). Vice versa, heuristics and be learned thus become part of the analytical system (Kahneman & Frederick, 2002). Future research can address this dynamic and study how one form of expertise induces another form of expertise.

Third, we discussed that the intuitive system consists of two sub-systems. The analytical system can also be divided into several sub-systems of rationality, instrumental (i.e., analysis on how to achieve goals) and epistemic (i.e., analyses that forms beliefs of the world) (Stanovich & West, 2000). Future research can address the subs-subsystems in how these systems relate to the entrepreneurial expertise. For instance, Krueger (2007) suggest that the beliefs (i.e., epistemic rationality), what he refers to as deep beliefs, influence expertise. Deep beliefs are defined as “deeply held strong assumptions that underpin our sensemaking and our decision making”

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(Krueger, 2007, p.124) that is pertinent to a certain topic (Gerring, 1997). Deep beliefs determine attention to stimuli, how information is processed, stored, retrieved, and the learning and development of expertise. Important deep beliefs are among others antecedents of successful entrepreneurs such as self-efficacy, self-control and regulatory mechanisms (Baron & Henry, 2010). These deep beliefs relate to the entrepreneurial behavior of experts. What are the other deep beliefs that relate to the knowledge sub-domains of entrepreneurship?

Finally, expertise is also viewed as socially situated cognition that pertains to a particular domain (Posner, 1998; Posner & Rothbart, 2007). Socially situated cognition (Smith & Semin, 2004) explains how social objects reside in and influence the cognitive processes. Socially situated cognition suggests that cognition is action-oriented (i.e., cognition is positive or negative feelings about an object or a concept that directs future action); embodied (i.e., cognition resides in the brain and the human body, and is influenced by the body); situated (i.e., cognition action resides in three levels of an individual’s of communicative, relational, and group contexts); and distributed (i.e., cognition resides in social processes and the environment). Entrepreneurial expertise as socially situated cognition means that the development and use of expertise are adapted based on a changing environment, over time, and influenced by other social actors (Mitchell et al., 2017; Operario & Fiske, 1999). Future research can shed light on these topics. For example, how do social actors with whom an entrepreneur engages influence his/her development of expertise?

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References

Abelson, R. P., & Black, J. B. (1986). Introduction. In J. A. Galambos, R. P. Abelson, & J. B. Black (Eds.), Knowledge Structures (pp. 1–18). Hillsdale, NJ: Lawrence Erlbaum Associates, Inc.

Alsos, G. A., Mauer, R., Clausen, T., & Solvoll, S. (2017). Disentangling the Role of Experience and Expertise in the Context of Effectuation and Causation. Academy of Management Proceedings, 2017(1), 16448. http://doi.org/10.5465/AMBPP.2017.16448abstract

Alvarez, S. A., & Barney, J. B. (2005). How Do Entrepreneurs Organize Firms Under Conditions of Uncertainty? Journal of Management, 31(5), 776–793. http://doi.org/10.1177/0149206305279486

Alvarez, S. A., & Barney, J. B. (2013). Epistemology, opportunities, and entrepreneurship: Comments on Venkataraman et al. (2012) and Shane (2012). Academy of Management Review. http://doi.org/10.5465/amr.2012.0069

Alvarez, S. A., Barney, J. B., McBride, R., & Wuebker, R. (2017). On Opportunities: Philosophical and Empirical Implications. Academy of Management Review, 42(4), 726–730. http://doi.org/10.5465/amr.2016.0035

Alvarez, S., & Barney, J. (2007). Discovery and creation: Alternative theories of entrepreneurial action. Strategic Entrepreneurship Journal. Retrieved from http://onlinelibrary.wiley.com/doi/10.1002/sej.4/abstract

Alvarez, S., & Barney, J. (2014). Entrepreneurial opportunities and poverty alleviation. Entrepreneurship Theory and Practice. Retrieved from http://onlinelibrary.wiley.com/doi/10.1111/etap.12078/full

Ansoff, H. I. (1979). Strategic management. London, U.K.: Macmillan.Ardichvili, A., & Cardozo, R. N. (2000). A MODEL OF THE ENTREPRENEURIAL

OPPORTUNITY RECOGNITION PROCESS. Journal of Enterprising Culture, 08(02), 103–119. http://doi.org/10.1142/S0218495800000073

Autio, E., Kenney, M., Mustar, P., Siegel, D., & Wright, M. (2014). Entrepreneurial innovation: The importance of context. Research Policy, 43(7), 1097–1108. http://doi.org/10.1016/j.respol.2014.01.015

Bacigalupo, M., Kampylis, P., Punie, Y., & Van den Brande, G. (2016). EntreComp: The entrepreneurship competence framework. Publication Office of the European Union. Luxembourg. Retrieved from https://www.researchgate.net/profile/Margherita_Bacigalupo2/publication/303947159_EntreComp_The_Entrepreneurship_Competence_Framework/links/57612c0508aeeada5bc4d3b5.pdf

Baker, T., & Nelson, R. E. (2005). Creating Something from Nothing: Resource Construction through Entrepreneurial Bricolage. Administrative Science Quarterly, 50(3), 329–366. http://doi.org/10.2189/asqu.2005.50.3.329

Barberis, N., & Huang, M. (2001). Mental Accounting, Loss Aversion, and Individual Stock Returns. The Journal of Finance, 56(4), 1247–1292. http://doi.org/10.1111/0022-1082.00367

Baron, R. A. (2004). The cognitive perspective: a valuable tool for answering entrepreneurship’s basic “why” questions. Journal of Business Venturing, 19(2), 221–239. http://doi.org/10.1016/S0883-9026(03)00008-9

Baron, R. A. (2006). Opportunity Recognition as Pattern Recognition: How Entrepreneurs

18

"Connect the Dots" to Identify New Business Opportunities. Academy of Management Perspectives, 20(1), 104–119. http://doi.org/10.5465/AMP.2006.19873412

Baron, R. A., & Ensley, M. D. (2006). Opportunity Recognition as the Detection of Meaningful Patterns: Evidence from Comparisons of Novice and Experienced Entrepreneurs. Management Science, 52(9), 1331–1344. http://doi.org/10.1287/mnsc.1060.0538

Baron, R. A., & Henry, R. A. (2010). How entrepreneurs acquire the capacity to excel: insights from research on expert performance. Strategic Entrepreneurship Journal, 4(1), 49–65. http://doi.org/10.1002/sej.82

Basel, J. S., & Brühl, R. (2013). Rationality and dual process models of reasoning in managerial cognition and decision making. European Management Journal, 31(6), 745–754. http://doi.org/10.1016/J.EMJ.2013.07.004

Battilana, J., Leca, B., & Boxenbaum, E. (2009). 2 How Actors Change Institutions: Towards a Theory of Institutional Entrepreneurship. The Academy of Management Annals, 3(1), 65–107. http://doi.org/10.1080/19416520903053598

Baumol, W. J. (1990). Entrepreneurship: Productive, unproductive, and destructive. The Journal of Political Economy, 98(5), 893–921.

Benartzi, S., & Thaler, R. H. (1995). Myopic Loss Aversion and the Equity Premium Puzzle. The Quarterly Journal of Economics, 110(1), 73–92. http://doi.org/10.2307/2118511

Berglund, H., & Korsgaard, S. (2017). Opportunities, Time, and Mechanisms in Entrepreneurship: On the Practical Irrelevance of Propensities. Academy of Management Review, 42(4), 730–733. http://doi.org/10.5465/amr.2016.0168

Bhide, A. (1992). Bootstrap Finance: The Art of Start-ups. Harvard Business Review, November-D. Retrieved from https://hbr.org/1992/11/bootstrap-finance-the-art-of-start-ups

Breschi, S., & Malerba, F. (1997). Sectoral Innovation Systems: Technological Regimes, Schumpeterian Dynamics, and Spatial Boundaries. In Edqui (Ed.), Systems of innovation: Technologies, institutions and organizations (pp. 130–156). London and New York: Routledge.

Cardon, M. S., Foo, M.-D., Shepherd, D., & Wiklund, J. (2012). Exploring the Heart: Entrepreneurial Emotion Is a Hot Topic. Entrepreneurship Theory and Practice, 36(1), 1–10. http://doi.org/10.1111/j.1540-6520.2011.00501.x

Chandler, G. N., DeTienne, D. R., McKelvie, A., & Mumford, T. V. (2011). Causation and effectuation processes: A validation study. Journal of Business Venturing, 26(3), 375–390. http://doi.org/10.1016/j.jbusvent.2009.10.006

Chase, W., & Simon, H. (1973). Skill in chess. American Scientist.Cohen, W., & Levinthal, D. A. (2000). Absorptive Capacity: A New Perspective on Learning

and Innovation. In Strategic Learning in a Knowledge Economy (pp. 39–67). Elsevier. http://doi.org/10.1016/B978-0-7506-7223-8.50005-8

Conway, A. R. A., Kane, M. J., & Engle, R. W. (2003). Working memory capacity and its relation to general intelligence. Trends in Cognitive Sciences, 7(12), 547–552. http://doi.org/10.1016/J.TICS.2003.10.005

Davidsson, P. (2015). Entrepreneurial opportunities and the entrepreneurship nexus: A re-conceptualization. Journal of Business Venturing, 30(5), 674–695. http://doi.org/10.1016/j.jbusvent.2015.01.002

Davidsson, P. (2017a). Entrepreneurial opportunities as propensities: Do Ramoglou & Tsang move the field forward? Journal of Business Venturing Insights, 7, 82–85. http://doi.org/10.1016/J.JBVI.2016.02.002

19

Davidsson, P. (2017b). Opportunities, propensities, and misgivings: Some closing comments. Journal of Business Venturing Insights, 8, 123–124. http://doi.org/10.1016/J.JBVI.2017.09.002

Davidsson, P. (2017c). Reflections on misgivings about “dismantling” the opportunity construct. Journal of Business Venturing Insights (Vol. 7). Retrieved from http://www.sciencedirect.com/science/article/pii/S2352673417300240

Dew, N., Read, S., Sarasvathy, S. D., & Wiltbank, R. (2009). Effectual versus predictive logics in entrepreneurial decision-making: Differences between experts and novices. Journal of Business Venturing, 24(4), 287–309. http://doi.org/10.1016/j.jbusvent.2008.02.002

Dew, N., Read, S., Sarasvathy, S. D., & Wiltbank, R. (2015). Entrepreneurial expertise and the use of control. Journal of Business Venturing Insights, 4, 30–37. http://doi.org/10.1016/J.JBVI.2015.09.001

Dew, N., Sarasathy, S., Read, S., & Wiltbank, R. (2009). Affordable loss: behavioral economic aspects of the plunge decision. Strategic Entrepreneurship Journal, 3(2), 105–126. http://doi.org/10.1002/sej.66

Dimov, D. (2011). Grappling with the unbearable elusiveness of entrepreneurial opportunities. Entrepreneurship Theory and Practice. Retrieved from http://onlinelibrary.wiley.com/doi/10.1111/j.1540-6520.2010.00423.x/full

Dimov, D. (2016). Toward a Design Science of Entrepreneurship (pp. 1–31). http://doi.org/10.1108/S1074-754020160000018001

Drake, J. E., & Winner, E. (2017). Why Deliberate Practice Is Not Enough: Evidence of Talent in Drawing. In D. Z. Hambrick, G. Campitelli, & B. N. Macnamara (Eds.), The Science of Expertise: Behavioral, Neural, and Genetic Approaches to Complex Skill (1st ed., p. 101-). Routledge.

Dreyfus, H. L., & Dreyfus, S. E. (2005). Peripheral Vision. Organization Studies, 26(5), 779–792. http://doi.org/10.1177/0170840605053102

Engel, Y., Kaandorp, M., & Elfring, T. (2017). Toward a dynamic process model of entrepreneurial networking under uncertainty. Journal of Business Venturing, 32(1), 35–51. http://doi.org/10.1016/j.jbusvent.2016.10.001

Epstein, S. (1994). Integration of the cognitive and the psychodynamic unconscious. American Psychologist, 49(8), 709–724. Retrieved from http://psycnet.apa.org/buy/1994-45153-001

Epstein, S., & Pacini, R. (1999). Some basic issues regarding dualprocess theories from the perspective of cognitive–experiential self-theory. In S. Chaiken & Y. Trope (Eds.), Dual-process theories in social psychology (pp. 462–482). New York, NY: Guilford Press.

Epstein, S., Pacini, R., Denes-Raj, V., & Heier, H. (1996). Individual differences in intuitive–experiential and analytical–rational thinking styles. Journal of Personality and Social Psychology, 71(2), 390–405. Retrieved from http://psycnet.apa.org/buy/1996-06400-015

Ericsson, K. A. (2006). An introduction to the Cambridge handbook of expertise and expert performance. In K. A. Ericsson, N. Charness, P. J. Feltovich, & R. R. Hoffman (Eds.), The Cambridge handbook of expertise and expert performance (pp. 3–20). Cambridge, UK: Cambridge University Press.

Ericsson, K. A. (2008). Deliberate Practice and Acquisition of Expert Performance: A General Overview. Academic Emergency Medicine, 15(11), 988–994. http://doi.org/10.1111/j.1553-2712.2008.00227.x

Ericsson, K. A. (2014). Expertise. Current Biology, 24(11), R508–R510. http://doi.org/10.1016/j.cub.2014.04.013

20

Ericsson, K. A., Krampe, R. T., & Tesch-Römer, C. (1993). The role of deliberate practice in the acquisition of expert performance. Psychological Review, 100(3), 363–406. http://doi.org/10.1037/0033-295X.100.3.363

Evans, J. S. B. T. (1989). Bias in Human Reasoning: Causes and Consequences. Erlbaum.Evans, J. S. B. T. (2003). In two minds: dual-process accounts of reasoning. Trends in Cognitive

Sciences, 7(10), 454–459. http://doi.org/10.1016/J.TICS.2003.08.012Evans, J. S. B. T. (2008). Dual-Processing Accounts of Reasoning, Judgment, and Social

Cognition. Annual Review of Psychology, 59(1), 255–278. http://doi.org/10.1146/annurev.psych.59.103006.093629

Evans, J. S. B. T., & Stanovich, K. E. (2013). Dual-Process Theories of Higher Cognition. Perspectives on Psychological Science, 8(3), 223–241. http://doi.org/10.1177/1745691612460685

Finucane, M., Alhakami, A., & Slovic, P. (2000). The affect heuristic in judgments of risks and benefits. Journal of Behavioral Decision Making, 12(1), 1–17. Retrieved from http://search.proquest.com/openview/de890147071c90cf2af39f5017f29265/1?pq-origsite=gscholar&cbl=36333

Finucane, M. L., Peters, E., & Slovic, P. (2003). Judgment and Decision Making: The Dance of Affect and Reason. In S. L. Schneider & J. Shanteau (Eds.), Emerging perspectives on judgment and decision research (Cambridge, pp. 327–364). Cambridge, UK: Cambridge University Press.

Fiske, S. T., & Taylor, S. E. (1984). Social cognition. Reading, MA: Addison-Wesley.Foss, N. J., & Klein, P. G. (2017). Entrepreneurial Discovery or Creation? In Search of the

Middle Ground. Academy of Management Review, 42(4), 733–736. http://doi.org/10.5465/amr.2016.0046

Gallouj, F. (1998). Innovating in reverse: services and the reverse product cycle. European Journal of Innovation Management. Retrieved from http://www.emeraldinsight.com/doi/pdf/10.1108/14601069810230207

Gazzaniga, M. S., Ivry, R. B., & Mangun, G. R. (1998). Cognitive neuroscience: The biology of the mind. New York: Norten.

Gerring, J. (1997). Ideology: A Definitional Analysis. Political Research Quarterly, 50(4), 957–994. http://doi.org/10.1177/106591299705000412

Gioia, D. A., & Poole, P. P. (1984). Scripts in Organizational Behavior. Academy of Management Review, 9(3), 449–459. http://doi.org/10.5465/AMR.1984.4279675

Glaser, R. (1984). Education and thinking: The role of knowledge. American Psychologist, 39(2), 93–104. http://doi.org/10.1037/0003-066X.39.2.93

Hambrick, D. Z., & Tucker-Drob, E. M. (2015). The genetics of music accomplishment: Evidence for gene–environment correlation and interaction. Psychonomic Bulletin & Review, 22(1), 112–120. http://doi.org/10.3758/s13423-014-0671-9

Hammond, K. (1996). Human Judgment and Social Policy. New York: Oxford University Press.Hansen, D. J., Monllor, J., & Shrader, R. C. (2016). Identifying the elements of entrepreneurial

opportunity constructs. The International Journal of Entrepreneurship and Innovation, 17(4), 240–255. http://doi.org/10.1177/1465750316671471

Hargadon, A. B., & Kenney, M. (2012). Misguided Policy? Following Venture Capital into Clean Technology. California Management Review, 54(2), 118–139. http://doi.org/10.1525/cmr.2012.54.2.118

Haworth, C. M. A., Wright, M. J., Luciano, M., Martin, N. G., de Geus, E. J. C., van

21

Beijsterveldt, C. E. M., … Plomin, R. (2010). The heritability of general cognitive ability increases linearly from childhood to young adulthood. Molecular Psychiatry, 15(11), 1112–1120. http://doi.org/10.1038/mp.2009.55

Haynie, J. M., Shepherd, D., Mosakowski, E., & Earley, P. C. (2010). A situated metacognitive model of the entrepreneurial mindset. Journal of Business Venturing, 25(2), 217–229. http://doi.org/10.1016/J.JBUSVENT.2008.10.001

Hayton, J. C., & Cholakova, M. (2012). The role of affect in the creation and intentional pursuit of entrepreneurial ideas. Entrepreneurship: Theory and Practice. http://doi.org/10.1111/j.1540-6520.2011.00458.x

Hodgkinson, G. P., Langan-Fox, J., & Sadler-Smith, E. (2008). Intuition: A fundamental bridging construct in the behavioural sciences. British Journal of Psychology, 99(1), 1–27. http://doi.org/10.1348/000712607X216666

Hodgkinson, G. P., & Sadler-Smith, E. (2003). Reflections on reflections … on the nature of intuition, analysis and the construct validity of the Cognitive Style Index. Journal of Occupational and Organizational Psychology, 76(2), 279–281. http://doi.org/10.1348/096317903765913740

Hodgkinson, G., & Sadler-Smith, E. (2018). The Dynamics of Intuition and Analysis in Managerial and Organizational Decision Making. Academy of Management Perspectives, amp.2016.0140. http://doi.org/10.5465/amp.2016.0140

Hogarth, R. M. (2001). Educating intuition. Chicago/London: University of Chicago Press.Holcombe, R. G. (2003). The Origins of Entrepreneurial Opportunities. The Review of Austrian

Economics, 16(1), 25–43. http://doi.org/10.1023/A:1022953123111Jensen, A. R. (1998). The g factor: The science of mental ability. Westport, CT: Praeger

Publishers.Johnson, K. E., & Mervis, C. B. (1997). Effects of varying levels of expertise on the basic level

of categorization. Journal of Experimental Psychology: General, 126(3), 248–277. http://doi.org/10.1037/0096-3445.126.3.248

Kahneman, D. (2011). Thinking, fast and slow. Farrar, Straus and Giroux.Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk.

Econometrica: Journal of the Econometric Society, 47(2), 263–292. Retrieved from http://www.jstor.org/discover/10.2307/1914185?sid=21106338748563&uid=2&uid=4

Khatri, N., & Ng, H. A. (2000). The Role of Intuition in Strategic Decision Making. Human Relations, 53(1), 57–86. http://doi.org/10.1177/0018726700531004

Knight, F. (1921). Risk, uncertainty and profit. Boston, MA: Hart, Schaffner & Marx.Knight, F. (2012). Risk, uncertainty and profit. Retrieved from https://books.google.com/books?

hl=en&lr=&id=LFsVFS3OG_gC&oi=fnd&pg=PP1&dq=Risk,+uncertainty+and+profit&ots=pM4Wtka9ZS&sig=Zhxkqaj1nnb5L2ViwspbVtxT_Jc

Krueger, N. F. (2007). What Lies Beneath? The Experiential Essence of Entrepreneurial Thinking. Entrepreneurship Theory and Practice, 31(1), 123–138. http://doi.org/10.1111/j.1540-6520.2007.00166.x

Laureiro-Martínez, D., Canessa, N., Brusoni, S., Zollo, M., Hare, T., Alemanno, F., & Cappa, S. F. (2014). Frontopolar cortex and decision-making efficiency: comparing brain activity of experts with different professional background during an exploration-exploitation task. Frontiers in Human Neuroscience, 7, 927. http://doi.org/10.3389/fnhum.2013.00927

Lieberman, M. B., & Montgomery, D. B. (1988). First-mover advantages. Strategic Management Journal, 9(S1), 41–58. http://doi.org/10.1002/smj.4250090706

22

Lipshitz, R., & Strauss, O. (1997). Coping with Uncertainty: A Naturalistic Decision-Making Analysis. Organizational Behavior and Human Decision Processes, 69(2), 149–163. http://doi.org/10.1006/obhd.1997.2679

Loewenstein, G. (2000). Emotions in Economic Theory and Economic Behavior on JSTOR. The American Economic Review, 90(2), 426–432. Retrieved from http://www.jstor.org/stable/117263?seq=1#page_scan_tab_contents

Lord, R. G., & Maher, K. J. (1990). Alternative Information-Processing Models and Their Implications for Theory, Research, and Practice. Academy of Management Review, 15(1), 9–28. http://doi.org/10.5465/AMR.1990.4308219

Lord, R. G., & Maher, K. J. (1991). Cognitive theory in industrial and organizational psychology. In M. D. Dunette & L. M. Hough (Eds.), Handbook of Industrial and Organizational Psychology (pp. 1–62). Palo Alto, CA: Consulting Psychologist Press.

Macnamara, B. N., Moreau, D., & Hambrick, D. Z. (2016). The Relationship Between Deliberate Practice and Performance in Sports. Perspectives on Psychological Science, 11(3), 333–350. http://doi.org/10.1177/1745691616635591

Malerba, F. (2002). Sectoral systems of innovation and production. Research Policy, 31(2), 247–264. http://doi.org/10.1016/S0048-7333(01)00139-1

Malerba, F. (2005). Sectoral systems of innovation: a framework for linking innovation to the knowledge base, structure and dynamics of sectors. Economics of Innovation and New Technology, 14(1–2), 63–82. http://doi.org/10.1080/1043859042000228688

Malerba, F., & Orsenigo, L. (1997). Technological Regimes and Sectoral Patterns of Innovative Activities. Industrial and Corporate Change, 6(1), 83–118. http://doi.org/10.1093/icc/6.1.83

Martin de Holan, P., Ortiz-Terán, E., Turrero, A., Ortiz Alonso, T., de Holan, M., & Ortiz, T. (2013). Towards Neuroentrepreneurship? Early Evidence From a Neuroscience Study (SUMMARY). Frontiers of Entrepreneurship Research, 33(5). Retrieved from http://digitalknowledge.babson.edu/fer/vol33/iss5/12

Martina, R. A. (n.d.). Towards A Theory of Affordable Loss. Small Business Economics.Martina, R. A., & Hu-A-ng, Y. (2018). Human and non-human actors in the gestation process of

entrepreneurial opportunities.Mayer-Haug, K., Read, S., Brinckmann, J., Dew, N., & Grichnik, D. (2013). Entrepreneurial

talent and venture performance: A meta-analytic investigation of SMEs. Research Policy, 42(6–7), 1251–1273. http://doi.org/10.1016/J.RESPOL.2013.03.001

McCann, B. T., & Folta, T. B. (2012). Entrepreneurial entry thresholds. Journal of Economic Behavior & Organization, 84(3), 782–800. http://doi.org/10.1016/j.jebo.2012.09.020

McKelvie, A., Haynie, J. M., & Gustavsson, V. (2011). Unpacking the uncertainty construct: Implications for entrepreneurial action. Journal of Business Venturing, 26(3), 273–292. http://doi.org/10.1016/J.JBUSVENT.2009.10.004

Mintzberg, H., Ahlstrand, B., & Lampel, J. (1998). Strategy Safari. Prentice Hall.Mitchell, B. T., Mitchell, J. R., & Mitchell, R. K. (2017). Situated Scripting and Entrepreneurial

Expertise: A Socially Situated View of the Information-Processing Perspective (pp. 175–181). Springer, Cham. http://doi.org/10.1007/978-3-319-45544-0_12

Mitchell, J. R., Smith, J. B., Gustafsson, V., Davidsson, P., & Mitchell, R. K. (2005). Thinking about thinking about thinking: Exploring how entrepreneurial metacognition affects entrepreneurial expertise. In The Babson Research Conference.

Mitchell, R. K. (2005). Tuning up the Global Value Creation Engine: The Road to Excellence in

23

International Entrepreneurship Education. In J. A. Katz & A. C. Corbett (Eds.), Book Series: Advances in Entrepreneurship, Firm Emergence and Growth (pp. 185–248). Emerald Group Publishing Limited. http://doi.org/10.1016/S1074-7540(05)08008-6

Mitchell, R. K., Busenitz, L. W., Bird, B., Marie Gaglio, C., McMullen, J. S., Morse, E. A., & Smith, J. B. (2007). The Central Question in Entrepreneurial Cognition Research 2007. Entrepreneurship Theory and Practice, 31(1), 1–27. http://doi.org/10.1111/j.1540-6520.2007.00161.x

Mitchell, R. K., Mitchell, B. T., & Mitchell, J. R. (2009). Entrepreneurial Scripts and Entrepreneurial Expertise: The Information Processing Perspective. In A. L. Carsrud & M. Brännback (Eds.), Understanding the Entrepreneurial Mind: Opening the Black Box (pp. 97–137). Springer.

Mitchell, R. K., Smith, B., Seawright, K. W., & Morse, E. A. (2000). Cross-Cultural Cognitions And The Venture Creation Decision. Academy of Management Journal, 43(5), 974–993. http://doi.org/10.2307/1556422

Mitchelmore, S., & Rowley, J. (2010). Entrepreneurial competencies: a literature review and development agenda. International Journal of Entrepreneurial Behavior & Research, 16(2), 92–111. http://doi.org/10.1108/13552551011026995

Mosing, M. A., Madison, G., Pedersen, N. L., Kuja-Halkola, R., & Ullén, F. (2014). Practice Does Not Make Perfect. Psychological Science, 25(9), 1795–1803. http://doi.org/10.1177/0956797614541990

Nelson, R. R., & Winter, S. G. (1982). An evolutionary theory of economic change. Cambridge, MA and London, England: The Belknap Press of Harvard University Press.

Nicolaou, N., Cherkas, L., Shane, S., & Spector, T. D. (2009). Opportunity recognition and the tendency to be an entrepreneur: A bivariate genetics perspective. Organizational Behavior and Human Decision Processes, 110(2), 108–117. http://doi.org/10.1016/J.OBHDP.2009.08.005

Operario, D., & Fiske, S. T. (1999). Social Cognition Permeates Social Psychology: Motivated Mental Processes Guide the Study of Human Social Behavior. Asian Journal of Social Psychology, 2(1), 63–78. http://doi.org/10.1111/1467-839X.00026

Parsons, L. M., & Osherson, D. (2001). New Evidence for Distinct Right and Left Brain Systems for Deductive versus Probabilistic Reasoning. Cerebral Cortex, 11(10), 954–965. http://doi.org/10.1093/cercor/11.10.954

Peters, E., Lipkus, I., & Diefenbach, M. A. (2006). The Functions of Affect in Health Communications and in the Construction of Health Preferences. Journal of Communication, 56(s1), S140–S162. http://doi.org/10.1111/j.1460-2466.2006.00287.x

Plomin, R., Haworth, C. M. A., Meaburn, E. L., Price, T. S., Davis, O. S. P., & Davis, O. S. P. (2013). Common DNA Markers Can Account for More Than Half of the Genetic Influence on Cognitive Abilities. Psychological Science, 24(4), 562–568. http://doi.org/10.1177/0956797612457952

Plomin, R., Shakeshaft, N. G., McMillan, A., & Trzaskowski, M. (2014). Nature, nurture, and expertise. Intelligence, 45, 46–59. http://doi.org/10.1016/J.INTELL.2013.06.008

Porter, C. M., & Woo, S. E. (2015). Untangling the Networking Phenomenon. Journal of Management, 41(5), 1477–1500. http://doi.org/10.1177/0149206315582247

Porter, M. E. (1980). Competitive strategy: Techniques for analyzing industries and competition. New York.

Posner, M. I. (1998). Introduction: What Is It to Ben an Expert? In M. T. H. Chi, R. Glaser, & M.

24

J. Farr (Eds.), The Nature of Expertise. Hillsdale, NJ: Lawrence Erlbaum Associates.Posner, M., & Rothbart, M. (2007). Expertise. In M. I. Posner & M. K. Rothbart (Eds.),

Educating the human brain (pp. 189–208). Washington, D.C.: American Psychological Association.

Ramoglou, S., & Tsang, E. W. K. (2017a). Accepting the unknowables of entrepreneurship and overcoming philosophical obstacles to scientific progress. Journal of Business Venturing Insights, 8, 71–77. http://doi.org/10.1016/J.JBVI.2017.07.001

Ramoglou, S., & Tsang, E. W. K. (2017b). In Defense of Common Sense in Entrepreneurship Theory: Beyond Philosophical Extremities and Linguistic Abuses. Academy of Management Review, 42(4), 736–744. http://doi.org/10.5465/amr.2017.0169

Read, S., & Sarasvathy, S. D. (2005). Knowing What to Do and Doing What You Know. The Journal of Private Equity, 9(1), 45–62. http://doi.org/10.3905/jpe.2005.605370

Reuber, A. R., Dyke, L. S., & Fischer, E. M. (1990). Experientially Acquired Knowledge And Entrepreneurial Venture Success. Academy of Management Proceedings, 1990(1), 69–73. http://doi.org/10.5465/AMBPP.1990.4978176

Sadler-Smith, E. (2004). Cognitive Style and the Management of Small and Medium-Sized Enterprises. Organization Studies, 25(2), 155–181. http://doi.org/10.1177/0170840604036914

Sarasvathy, S. (2008). Effectuation: Elements of Entrepreneurial Expertise. Retrieved from http://level3.dit.ie/html/issue11/alshibani/Book Review FINAL.pdf

Sarasvathy, S. D. (2001). Causation and Effectuation: Toward A Theoretical Shift From Economic Inevitability To Entrepreneurial Contingency. Academy of Management Review, 26(2), 243–263. http://doi.org/10.5465/AMR.2001.4378020

Sarasvathy, S. D. (2015). The Downside of Entrepreneurial Opportunities. M@n@gement, Vol. 17(4), 305–315. Retrieved from http://www.cairn.info/resume.php?ID_ARTICLE=MANA_174_0305

Sarasvathy, S. D., & Dew, N. (2005). New market creation through transformation. Journal of Evolutionary Economics, 15(5), 533–565. http://doi.org/10.1007/s00191-005-0264-x

Sarasvathy, S. D., Dew, N., Read, S., & Wiltbank, R. E. (2003). Accounting for the future: Psychological aspects of effectual entrepreneurship.

Sarasvathy, S. D., Dew, N., Velamuri, S. R., & Venkataraman, S. (2010). Three views of entrepreneurial opportunity. In Z. J. Acs & D. B. Audretsch (Eds.), Handbook of entrepreneurship research (pp. 77–96). New York.: Springer.

Schraw, G., & Dennison, R. S. (1994). Assessing Metacognitive Awareness. Contemporary Educational Psychology, 19(4), 460–475. http://doi.org/10.1006/CEPS.1994.1033

Shane, S. (2000). Prior Knowledge and the Discovery of Entrepreneurial Opportunities. Organization Science, 11(4), 448–469. http://doi.org/10.1287/orsc.11.4.448.14602

Shane, S., & Nicolaou, N. (2013). The genetics of entrepreneurial performance. International Small Business Journal, 31(5), 473–495. http://doi.org/10.1177/0266242613485767

Shane, S., & Venkataraman, S. (2000). The promise of entrepreneurship as a field of research. Academy of Management Review, 25(1), 217–226. http://doi.org/10.5465/AMR.2000.2791611

Shepherd, D. A., Patzelt, H., & Haynie, J. M. (2010). Entrepreneurial Spirals: Deviation-Amplifying Loops of an Entrepreneurial Mindset and Organizational Culture. Entrepreneurship Theory and Practice, 34(1), 59–82. http://doi.org/10.1111/j.1540-6520.2009.00313.x

25

Shepherd, D. A., Zacharakis, A., & Baron, R. A. (2003). VCs’ decision processes: Evidence suggesting more experience may not always be better. Journal of Business Venturing, 18(3), 381–401. http://doi.org/10.1016/S0883-9026(02)00099-X

Short, J. C., Ketchen, D. J., Combs, J. G., & Ireland, R. D. (2010). Research Methods in Entrepreneurship. Organizational Research Methods, 13(1), 6–15. http://doi.org/10.1177/1094428109342448

Simon, H. A. (1955). A behavioral model of rational choice. The Quarterly Journal of Economics, 69(1), 99–118.

Simon, H. A. (1956). Rational choice and the structure of the environment. Psychological Review, 63(2), 129–138.

Simon, H. A. (1987). Making Management Decisions: the Role of Intuition and Emotion. Academy of Management Perspectives, 1(1), 57–64. http://doi.org/10.5465/ame.1987.4275905

Simon, H. A., & Simon, P. A. (1962). Trial and error search in solving difficult problems: Evidence from the game of chess. Behavioral Science, 7(4), 425–429. http://doi.org/10.1002/bs.3830070402

Slovic, P., Finucane, M. L., Peters, E., & MacGregor, D. G. (2004). Risk as Analysis and Risk as Feelings: Some Thoughts about Affect, Reason, Risk, and Rationality. Risk Analysis, 24(2), 311–322. http://doi.org/10.1111/j.0272-4332.2004.00433.x

Slovic, P., & Peters, E. (2006). Risk Perception and Affect. Current Directions in Psychological Science, 15(6), 322–325. http://doi.org/10.1111/j.1467-8721.2006.00461.x

Smith, E. R., & Semin, G. R. (2004). Socially situated cognition: Cognition in its social context. Advances in Experimental Social Psychology, 36, 57–121.

Smith, J. B., Mitchell, J. R., & Mitchell, R. K. (2009). Entrepreneurial Scripts and the New Transaction Commitment Mindset: Extending the Expert Information Processing Theory Approach to Entrepreneurial Cognition Research. Entrepreneurship Theory and Practice, 33(4), 815–844. http://doi.org/10.1111/j.1540-6520.2009.00328.x

Sonnentag, S., & Kleine, B. M. (2000). Deliberate practice at work: A study with insurance agents. Journal of Occupational and Organizational Psychology, 73(1), 87–102. http://doi.org/10.1348/096317900166895

Stanovich, K. E. (1999). Who is Rational? Studies of Individual Differences in Reasoning. Elrbaum.

Stanovich, K. E., & West, R. F. (2000). Individual differences in reasoning: Implications for the rationality debate? Behavioral and Brain Sciences, 23(5), 645–665. Retrieved from https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/article/individual-differences-in-reasoning-implications-for-the-rationality-debate/2906AEF620B36C10018DD291F790BE97

Stanovich, K. E., & West, R. F. (2003). Evolutionary versus instrumental goals: How evolutionary psychology misconceives human rationality. In D. E. Over (Ed.), Evolution and the Psychology of Thinking: The Debate. Psychology (pp. 171–230). Psychology Press. Retrieved from https://philpapers.org/rec/STAEVI

Turnbull, O. H., Evans, C. E. Y., Bunce, A., Carzolio, B., & O’Connor, J. (2005). Emotion-based learning and central executive resources: An investigation of intuition and the Iowa Gambling Task. Brain and Cognition, 57(3), 244–247. http://doi.org/10.1016/J.BANDC.2004.08.053

Ucbasaran, D., Westhead, P., & Wright, M. (2009). The extent and nature of opportunity

26

identification by experienced entrepreneurs. Journal of Business Venturing, 24(2), 99–115. http://doi.org/10.1016/j.jbusvent.2008.01.008

Unger, J. M., Keith, N., Hilling, C., Gielnik, M. M., & Frese, M. (2009). Deliberate practice among South African small business owners: Relationships with education, cognitive ability, knowledge, and success. Journal of Occupational and Organizational Psychology, 82(1), 21–44. http://doi.org/10.1348/096317908X304361

Vogel, P. (2017). From Venture Idea to Venture Opportunity. Entrepreneurship Theory and Practice, 41(6), 943–971. http://doi.org/10.1111/etap.12234

Volz, K. G., & von Cramon, D. Y. (2006). What Neuroscience Can Tell about Intuitive Processes in the Context of Perceptual Discovery. Journal of Cognitive Neuroscience, 18(12), 2077–2087. http://doi.org/10.1162/jocn.2006.18.12.2077

Vroom, V. H. (1964). Work and motivation. John Wiley & Sons, Inc.Walker, W. E., Harremoës, P., Rotmans, J., van der Sluijs, J. P., van Asselt, M. B. A., Janssen,

P., & Krayer von Krauss, M. P. (2003). Defining Uncertainty: A Conceptual Basis for Uncertainty Management in Model-Based Decision Support. Integrated Assessment, 4(1), 5–17. http://doi.org/10.1076/iaij.4.1.5.16466

Wang, Y., Highhouse, S., Lake, C. J., Petersen, N. L., & Rada, T. B. (2017). Meta-analytic Investigations of the Relation Between Intuition and Analysis. Journal of Behavioral Decision Making, 30(1), 15–25. http://doi.org/10.1002/bdm.1903

Welpe, I. M., Spörrle, M., Grichnik, D., Michl, T., & Audretsch, D. B. (2012). Emotions and Opportunities: The Interplay of Opportunity Evaluation, Fear, Joy, and Anger as Antecedent of Entrepreneurial Exploitation. Entrepreneurship Theory and Practice, 36(1), 69–96. http://doi.org/10.1111/j.1540-6520.2011.00481.x

Whittlesea, B. W. A. (1997). The representation of general and particular knowledge. In K. Lamberts & D. Shanks (Eds.), Knowledge, Concepts, and Categories (pp. 211–264). Cambridge, MA: MIT Press.

Wiltbank, R., Read, S., Dew, N., & Sarasvathy, S. D. (2009). Prediction and control under uncertainty: Outcomes in angel investing. Journal of Business Venturing, 24(2), 116–133. http://doi.org/10.1016/j.jbusvent.2007.11.004

Wood, M. S. (2017a). Continued misgivings: A response to Davidsson on dismantling the opportunity construct. Journal of Business Venturing Insights (Vol. 7). Retrieved from http://www.sciencedirect.com/science/article/pii/S235267341730046X

Wood, M. S. (2017b). Misgivings about dismantling the opportunity construct. Journal of Business Venturing Insights, 7, 21–25. http://doi.org/10.1016/j.jbvi.2017.01.001

Wyer, R., & Srull, T. (1989). Memory and cognition in its social context. Hillsdale, NJ, England: Lawrence Erlbaum Associates, Inc.

Young, S. L., Welter, C., & Conger, M. (2017). Stability vs. flexibility: The effect of regulatory institutions on opportunity type. Journal of International Business Studies, 1–35. http://doi.org/10.1057/s41267-017-0095-7

Zahra, S. A., & Wright, M. (2011). Entrepreneurship’s Next Act. Academy of Management Perspectives, 25(4), 67–83. http://doi.org/10.5465/amp.2010.0149

27