TEK is defined as experiential learning from past experiences and insights that results in tacit knowledge regarding entrepreneurship that is implicit, personal, and uncodified (Kogut and Zander, 1992; Grant, 1996b). We foresee that individuals with a high level of TEK, based on their own idiosyncratic experiences and insights related to entrepreneurship, will enable a venture to generate competitive advantages (Reed and Defillippi, 1990; Spender and Grant, 1996). It is through the combination of personal experiences that TEK potentially becomes valuable, rare, non-imitable, and non-replaceable, and therefore a crucial resource for both the individual entrepreneur and the entrepreneurial firm (Barney, 1991). TEK thus represents an individual’s experiences, insights, and learning moments regarding entrepreneurship. Therefore, the construct transcends sectors or industries since it emphasizes the individual entrepreneurs’ experiences, not their ventures. Entrepreneurs often recognize opportunities due to the knowledge they previously acquired (Shane, 2000). More specifically, people often have different beliefs based upon an inkling, instinct, or exclusive information, which is critical in discovering entrepreneurial opportunities (Shane and Venkataraman, 2000). This type of information, which can lead to a competitive advantage, is gained through knowledge incurred throughout personal experiences. TEK has a latent presence within any entrepreneur and is instrumental in deploying resources according to their objectives. Entrepreneurs facing identical situations, receiving identical information, and pursuing identical objectives will interpret the situation differently and make different decisions, drawing on their tacit knowledge as entrepreneurs (Foss and Klein, 2012). Nascent entrepreneurs tend to accrue a certain amount of TEK, even with limited entrepreneurial experience. Exposure to entrepreneurial role models, participation in entrepreneurship education, and past work experience in entrepreneurial firms shape each individual’s understanding of entrepreneurship (Peterman and Kennedy, 2003; Zapkau et al., 2017). In sum, as every entrepreneur has a different background (i.e., experience and education), the amount of TEK within the individual entrepreneur can be very heterogeneous since tacit knowledge varies between experts and novices (Wagner and Sternberg, 1985).

Through the conceptualization of TEK as a separate construct, it is clear that if we continue to make progress in this research domain, a measurement instrument for TEK is imperative.

In order to distinguish TEK from other related constructs and position it in the research context, we present a framework based on the essential characteristics of the concept to delineate a nomological network for TEK, in which it occurs (Cronbach and Meehl, 1955). Our framework consists of three dimensions: knowledge, experience, and time (see Figure 1).

The first dimension of the framework relates to the distinction between tacit and explicit knowledge. The distinction between tacit and explicit knowledge offers more insight into how experiences are transformed in an entrepreneur’s memory. Since tacit knowledge is implicitly present in everyone, yet only accessible by “engaging in or running through the skills in which the knowledge is embedded” (Squire, 1986, p. 188). It is a fragment of procedural memory, acquired through personal experience, therefore directly influential for behavior and not readily articulable (Sternberg et al., 2000). In contrast, explicit knowledge is accumulated through formal training or teaching and feeds an individual’s semantic memory. Semantic memory refers to the capacity to store and recollect factual information and is especially necessary for the use of language (Tulving, 1972; Ribas Fernandes and Holroyd, 2017). It is seen as context-free memory concerning facts and is also called the general knowledge about the world (Vargha-Khadem et al., 1997; Squire and Zola, 1998). While TEK is related to Baron and Ensley’s (2006) cognitive frameworks, the concepts differ in terms of tacit and explicit knowledge. The cognitive frameworks are also acquired through experience and play a critical role in the recognition of entrepreneurial patterns (Baron and Ensley’s, 2006). But they rely upon a combination of both explicit and tacit knowledge, whereas TEK relies solely on the latter.

The second dimension outlines the perspective of experiences. As Reuber and Fischer (1999) exemplified, experiences can be considered from two perspectives: a continuous stream or a constant stock (Reuber and Fischer, 1999). Since TEK is dynamic and the culmination of event-based experiences to capture an entrepreneur’s ongoing learning process concerning tacit knowledge, TEK can be seen as a continuous stream of experience, upon which past and future experiences are impacted, as opposed to a consistent stock of experience (Reuber and Fischer, 1999; Cope, 2005). In contrast, experiences scrutinized as stocks indicate a steady state at any given time, including depth and breadth of experience, and emphasize an exogenous learning process (Reuber and Fischer, 1999; Westhead et al., 2005). Measures such as age, specific experience (e.g., management, industry, and start-up), and education can be regarded as stocks of knowledge, considering these stocks can be summed together to produce an aggregate.

The final dimension outlines the aspect of time in the TEK construct. Experiential learning in entrepreneurship proceeds in separate dynamic temporal phases (Reuber and Fischer, 1999; Cope, 2005), and research on experiential learning tends to favor experiences and insights that occurred throughout the firm’s lifecycle (Naffziger et al., 1994; Cope, 2005). As opposed to mainstream concepts, TEK is not confined by temporal phases. On the contrary, it considers all insights gained from the entrepreneur’s experiences, whether before, during, or after dissolving the venture. TEK is not limited to a specific temporal phase but instead places the focal point on the life of the individual entrepreneur, even before the start of the entrepreneurial journey. Adjacent constructs, such as learning-by-doing, are reduced to learning elements that occur during the firm’s lifecycle (Cope and Watts, 2000).

The previous sections clarified the need for a new construct for the entrepreneurship domain and conceptualized TEK. In the following section, we will present the instrument development process, followed by the validation of the instrument.

Since tacit knowledge is characterized as context-specific, embedded, and subjective, it has for many years been considered extremely difficult to measure with standard measurement methods (Gertler, 2003; Tsoukas, 2003). Therefore, we are turning to an alternative approach, the situational judgment test (SJT). This approach is well-known for quantifying tacit knowledge (Lievens et al., 2008; Motowidlo et al., 2018), because SJTs employ work-related scenarios, followed by a set of possible responses. In so doing, the context is taken into account, and a measurement instrument is created in which reverberations between the entrepreneur’s cognition and social interactions are quantified. For this study, we opted for an SJT developmental approach consistent with the current literature on tacit knowledge (e.g., Wagner and Sternberg, 1985, 1987; Cianciolo et al., 2006; Sternberg, 2006). Developing an SJT is usually characterized by three steps (see Figure 2 for a full description of the development approach): first, the scenarios are collected through interviews, followed by formulating the responses to the scenarios, and finally, the effectiveness of the responses for each scenario is evaluated (Motowidlo et al., 1990; Lievens et al., 2008). As is standard procedure for SJTs, we engage with subject matter experts to develop our instrument. We recruited a panel of 14 subject matter experts based on the following three criteria: first, each entrepreneur needed to be the current and founding CEO of a high-growth firm in order to ensure that the entrepreneur has played a significant role in starting up a business venture (Stuart and Abetti, 1990). The first criterion ensured that the expert entrepreneur had gone through an evolution with the company and excluded inexperienced entrepreneurs from the panel. Second, the venture should apply to the high-growth firm definition, as defined by the European Commission (Eurostat - OECD, 2007), demonstrating an average annualized growth greater than 20% per annum in terms of employees or turnover, over a 3-year period. This criterion is not so much important for the stage in which the organization finds itself (Levie and Lichtenstein, 2010), but its importance lies in the distinguishing capacity of growth-oriented entrepreneurs. Assuming that entrepreneurs have succeeded in growing their new venture into a high-growth firm, we substantiated entrepreneurial success. Finally, there should be no more than two CEOs from corresponding sectors represented in the expert panel. By purposefully eliciting TEK from entrepreneurs active in different sectors, the findings will be allowed to be replicated within divergent sectors (Eisenhardt, 1989).

The main objective of this study lies in the development of the TEK construct and its measurement instrument. Due to its implicit and unobservable nature, no objective measurements of the criterion variable are available to date. Therefore, we decided to use the rational scoring approach suggested by Lievens et al. (2008) by relying on the judgments of subject matter experts for the TEK instrument. As opposed to an empirical scoring approach typically conducted on a large pilot sample (Lievens et al., 2008), a rational scoring approach is a commonly accepted scoring method for SJTs and consists of deriving the TEK-score for each scenario by comparing the results to a control group (Bergman et al., 2006). Since an SJT is an auspicious tool for overcoming cross-cultural validity issues (Peng et al., 1997), future researchers can relatively easily compile their own expert responses and subsequent benchmark.

The comparison between participants and the control group is performed by calculating the Mahalanobis distance (MD). The MD standardizes the data and adjusts for correlations among variables and is therefore particularly suitable for TEK since it measures similarity between groups using a multidimensional method across cases and variables (Hair et al., 2014). Typically, the MD is used to detect outliers in multivariate data (Penny, 1996). The standard approach pools all data to determine the centroid (i.e., the average discriminant z-score for all group members) and subsequent outliers from that centroid. In this study, we adopt an alternative approach, where the control group’s centroid is constructed, and the respondent’s distance is measured from that centroid. According to this approach, the control group’s centroid is used as the benchmark to calculate the respondents’ scores. This application is burgeoning in several research fields, such as archaeometry, and has merit over the standard approach (Karacic et al., 2016; Overholtzer et al., 2020). The main advantage is that by omitting the respondents from the control group’s centroid, the respondents are prevented from influencing the control group’s characteristics, leading to an unbiased comparison, especially important in smaller samples (Leese and Main, 1994).

The final scores are derived using a series of calculations² based on 17 scenarios (j = 17) where each scenario has five potential reactions (i = 5) that can each be scored on a seven-point Likert scale. First, the distance between the respondent’s rating for each item (x_ij) and the control group (μ¯) is calculated.

Second, for each scenario, the inverse of the covariance matrix (C⁻¹) of the control group’s responses is created.

Third, the transpose of the distance between the participant’s rating for each item (x_ij) and the expert group (μ¯) is calculated.

Fourth, a D² per scenario per respondent is calculated by multiplying the three preceding computations.

Fifth, the MD is characterized by a chi-square distribution, and the number of variables per scenario equals the degrees of freedom (i.e., df = 5) (Hahs-Vaughn and Lomax, 2020). In order to calculate a TEK-score per scenario, each D² is then transformed based on the respective chi-squared value for 5 degrees of freedom at a 0.001 confidence level, which, according to Hair et al. (2014), equals a critical value of 20.515. The transformation involves adjusting any outcome compared to the respective critical value. On the one hand, if the calculated D² is greater than the critical value of 20.515, the TEK-score is transformed to zero. Since the MD is typically used for outlier detection, values greater than the critical value are treated as an outlier and are not assigned a score (Hair et al., 2014). On the other hand, if D² is smaller than or equal to the critical value of 20.515, the TEK-score is transformed by subtracting the outcome by the critical value.

Finally, to calculate the final TEK-score, the TEK-scores per scenario are added up. To facilitate interpretation, all scores are converted to 100 by multiplying the final scores by 100 and dividing them by 348.755.³

The final two steps in the score calculation process ensure that TEK represents a score ranging between 0 and 100. The closer a respondent’s score approaches 100, the more significant that individual’s TEK is.

TEK is accumulated as a result of past experiences, insights, and learnings regarding entrepreneurship, which makes it self-evident that TEK is influenced by factors that enable the individual to develop a specific image of entrepreneurship, identify opportunities, and allocate resources to achieve their entrepreneurial objectives. As possible antecedents of TEK, the primary focus in this study lies in an individual’s learning path and how it might be instrumental in the accumulation of TEK.

This ability is reflected in a person’s learning orientation, which is the tendency to cultivate competence by obtaining new skills and comprehending new situations (Dweck, 1986; VandeWalle, 1997; De Clercq et al., 2013). Empirical evidence demonstrates that learning-oriented individuals are consistently open to new experiences (VandeWalle et al., 2001), prefer to engage in more challenging tasks (Elliott and Dweck, 1988), and appreciate activities that might enhance personal development (VandeWalle and Cummings, 1997). As it is a fact that tacit knowledge is best transmitted through social interactions and experiences (Cook and Brown, 1999; Haldin-Herrgard, 2000), an individual’s willingness or openness to such valuable learning opportunities governs his exposure to TEK.

The same holds for prior entrepreneurial exposure (PEX). PEX is understood as an individual’s personal history related to entrepreneurship, such as entrepreneurial parents or prior work experience in entrepreneurial firms (Krueger, 1993; Zapkau et al., 2017). While the individual might not be an entrepreneur (yet), entrepreneurial insights are already being gathered through exposure from within the family or beyond, forming a breeding ground for possible future entrepreneurial development. This is how even nascent entrepreneurs tend to amass a certain amount of TEK. It has been argued that PEX facilitates the transfer of tacit knowledge, yet evidence remains sparse (Dohse and Walter, 2012). The introduction of TEK into the PEX-literature has the potential to shed light on this subject.

An individual who has amassed TEK has a distinct perspective on the entrepreneurial process and guides his venture based on personal insights and experiences (Hambrick, 2007). The idiosyncratic combination of experiences makes this knowledge valuable, rare, non-replaceable, and non-imitable (Barney, 1991), and therefore an asset to both the entrepreneur and the organization (Hambrick and Mason, 1984). This is reflected in a dichotomous disaggregation of the consequences of TEK. On the one hand, the consequences of TEK for the individual entrepreneur and, on the other hand, the consequences of TEK for the organization.

At the individual level, TEK can be considered an explanatory factor for nascent entrepreneurship. The impact of TEK on entrepreneurial intentions would be a fruitful research avenue to investigate since these entrepreneurial intentions are partly based on intuition or hunches (Bird, 1988), and the cognitive processes behind intentions are not yet fully understood (Krueger et al., 2000). As Ajzen’s (1991) theory of planned behavior is often used to study the entrepreneurial intentions of (nascent) entrepreneurs, it would be interesting to investigate the extent to which TEK influences entrepreneurial intent drivers. For example, TEK may serve as the antecedent of personal attitude, subjective norms, and perceived behavioral control in the decision-making process of becoming an entrepreneur.

Research on passion proliferates in entrepreneurship literature, mainly because of its positive impact on several vital outcomes (Murnieks et al., 2020). Yet to date, there is little understanding of the determinants of passion (Shepherd, 2015). Prior research has already identified several important stimuli for entrepreneurial passion, such as an individual’s social capital (Hoang and Gimeno, 2010), making way for TEK as an additional explanatory factor. TEK can be a possible impetus for this research, for instance, by investigating the relationship between an individual’s TEK and entrepreneurial passion (i.e., passion for inventing, founding, and developing) (Cardon et al., 2013). We contend that a high TEK-score, which equates to being proficient in entrepreneurship, results in a higher intense positive feeling associated with the different dimensions of entrepreneurial passion.

Finally, the prospect of inspiring and motivating followers is an essential prerequisite for many entrepreneurial founders. Although research on leadership in entrepreneurship is burgeoning, cognitive processes influencing a founder’s leadership behavior are sparse (Leitch and Volery, 2017). Jensen and Luthans (2006) emphasize the importance of prior experience in exhibiting leadership behavior. Consequently, TEK may also be applied as a potential explanatory factor for various leadership behaviors, such as transactional and transformational leadership.

At the organizational level, a crucial factor in the longevity of a venture’s success is its dynamic capabilities (Teece et al., 1997). A firm’s dynamic capabilities are defined as “the capacity (1) to sense and shape opportunities and threats, (2) to seize opportunities, and (3) to maintain competitiveness through enhancing, combining, protecting, and, when necessary, reconfiguring the business enterprise’s intangible and tangible assets” (Teece, 2007, p. 1,319). Organizational dynamic capabilities are closely intertwined with its founder, particularly in the context of a new business venture (Adner and Helfat, 2003; Teece, 2012). Both Zahra et al. (2006) and Teece (2007) have argued that entrepreneurial activities play a crucial role in the theory of dynamic capabilities since “the invisible hand must have fingers that can work in a coordinated fashion” (Augier and Teece, 2009, p. 410). Experiential learning lies at the heart of developing dynamic capabilities (Zollo and Winter, 2002), and this type of learning results from the tacit accumulation of experiences. Therefore, we believe TEK to be a driver for organizational dynamic capabilities. Moreover, we suspect founders who have amassed high TEK levels to influence their venture’s dynamic capabilities positively.

An additional role for TEK at the organizational level is evident in new ventures. New ventures are often initiated and led by a collective (Beckman, 2006; Schjoedt et al., 2013; Klotz et al., 2014), resulting in the convergence of people with different backgrounds, experiences, and mental models (Beckman, 2006; Leung et al., 2013). These emerging ventures depend on and utilize their new venture team members’ knowledge due to the absence of capital, organizational structures, and formalized rules (Gilbert et al., 2006; Dai et al., 2016). It is the new venture team’s collective perspective that steers organizational decisions (Mitchell et al., 2007; West, 2007). Many scholars find consensus for the fact that collective knowledge exceeds an individual’s cognitive capabilities (Knockaert et al., 2011; Bryant, 2014), but what knowledge is addressed or exchanged to elevate the collective remains unclear (de Mol et al., 2015). Moreover, how is valuable information regarding opportunities distributed among the collective? TEK represents a possible explanatory factor for the distribution of collective knowledge, considering that antecedents have received little attention in scientific research (Seyb et al., 2019).

The relationship between internal characteristics and organizational effectiveness is highly influenced by contextual factors (Donaldson, 2001). We posit that this is also the case for an individual’s TEK. Certain contingencies can potentially influence the main effect between TEK and its antecedents or consequences. Since the individual is at the center of our model, several important contextual factors can be considered, such as firm size, industry, and institutional conditions.

Small and large companies are equipped with different resources and capabilities to interact with their business environment (Dean et al., 1998). The size differences are also apparent in the firm’s tacit knowledge resources (Antonelli and Scellato, 2015). Examples include a nascent entrepreneur who relies only on his own knowledge stock (Chrisman, 1999) or his new venture team (Schjoedt et al., 2013) to pursue his entrepreneurial endeavors. Large firms rely more on highly qualified personnel to innovate through formal R&D activities leading to intellectual property and patents (Antonelli and Scellato, 2015). Therefore, we expect a variance of firm size (either in prior or present work experience) to affect the central relation between TEK and its antecedents or consequences.

Much of the current literature on intra-firm knowledge transfer pays particular attention to the role of the industry. However, there are conflicting rationales for this role. On the one hand, some industries require a particular shared team background and experience to facilitate tacit knowledge transfer (Knockaert et al., 2011). On the other hand, there are industries where team heterogeneity is necessary to innovate and respond appropriately to external events or opportunities (Beckman, 2006). Given the differences in knowledge intensity, an exploration of the sector heterogeneity as a contingency variable on the process of accumulation and distribution of an individual’s TEK, in line with Park et al. (2015), would be an exciting research avenue.

The macro-economic perspective of entrepreneurship mainly explores a growth relationship, making way for a more deliberate focus on the individual’s unique sets of resources (Terjesen et al., 2016). Therefore, we believe that the introduction of TEK into the macro-economic perspective would unlock a broader perspective for the field. Scientific evidence suggests that institutional conditions, such as a nation’s start-up procedures, unemployment rate, income disparity, FDI inflows, and labor market conditions, can potentially impact a country’s total level of entrepreneurial activity (Casson and Wadeson, 2007; Chowdhury et al., 2015). Therefore, we posit that beneficial institutional conditions can enhance the relationships between TEK and its antecedent and consequences, respectively.

This research has implications for different stakeholders in the entrepreneurial ecosystem for a variety of reasons. The TEK measure developed in this paper can be used by different stakeholders to reduce the information asymmetry related to the tacit knowledge of an entrepreneur. For instance, TEK can be a valuable instrument for banks to make an initial, preliminary assessment of entrepreneurs who turn up for a loan. Consistent with this argument, business angels and venture capitalists can make a better ex-ante assessment of new ventures and their entrepreneurs. In line with this reasoning, accelerators can integrate TEK into their selection processes. Moreover, the TEK measure developed in this paper can be used by entrepreneurs themselves in their search for new employees or cofounders. Namely, research suggests that companies need entrepreneurs within their organization (i.e., intrapreneurs) to drive innovation and explore new opportunities (Kraus et al., 2019). The TEK tool can help identify the tacit knowledge related to entrepreneurship of potential employees or cofounders during the recruitment process.