Entrepreneurial Action and Competencies: Exploring Pathways to Venturing Accomplishments

2.1 Entrepreneurial Action (EA)

Prior research relies on several varying conceptualizations or taxonomies of EA, such as the abstractness of EA (Chen et al. 2018), the level of volition required by the action (Delanoë‐Gueguen and Fayolle 2018), or action as exploration or exploitation (Mueller, Volery, and Von Siemens 2012). This paper, however, specifically focuses on EA using the typology of actions falling under opportunity discovery and exploitation. Hence, consistent with this process perspective, and, perhaps as the most theoretically well-grounded typology (which should facilitate knowledge accumulation) (Davidsson 2018; Farmer, Yao, and Kung–Mcintyre 2011), this research relies on the discovery/exploitation structure initially put forward by Shane and Venkataraman (2000). This structure views the discovery and exploitation of an opportunity as two necessary and mutually dependent stages, which lead to the emergence of new ventures and broadly characterize the vast range of smaller activities taken throughout this process of emergence (Shane and Venkataraman 2000).

Discovery results from early-stage venture idea generation and development activities, such as thinking about the idea, identifying market opportunities, and evaluating the feasibility and desirability of the venture concept (Vogel 2017). On the other hand, exploitation results from later-stage implementation or execution of the venture concept, such as acquiring the necessary funding, facilities and materials, as well as creating a legal entity and registering with the tax authorities (Mueller, Volery, and Von Siemens 2012; Shane and Venkataraman 2000).

2.2 Entrepreneurial Competencies (ECs)

For years, entrepreneurship scholars have sought to determine the key competencies for accomplishing desired outcomes in the entrepreneurial context (Bird 1995; Mamabolo, Kerrin, and Kele 2017), and the central antecedents of these competencies (Man, Lau, and Chan 2002). Similarly, scholars have relied on varying conceptualizations of ECs, such as competence as a minimum standard of performance used to evaluate a specific domain of activity (Bird, Schjoedt, and Baum 2012), or competencies as demonstrated through actions by an individual in a specific context (Mitchelmore and Rowley 2010). A specific focus is given on the latter conceptualization since ECs can, ultimately, only be demonstrated through an individual’s concrete behaviors (Man, Lau, and Chan 2002), which are seen as the most proximal outcomes of knowledge and abilities (Bird, Schjoedt, and Baum 2012). Consistent with other scholars, it is argued that the abilities to quickly recognize lucrative opportunities (Venkataraman et al. 2012; Vogel 2017), create value (Dyer, Gregersen, and Christensen 2008; Lepak, Smith, and Taylor 2007), and take risks (Knight 2012) are central to accomplishing the emergence of a venture which is both efficient and effective. Thus, this paper focuses on these three key ECs: opportunity recognition; value creation abilities, and calculated risk-taking.

Opportunity recognition is the ability to perceive conditions or potentialities in the environment that signify latent sources of income for a venture (Morris et al. 2013). Value creation is the ability to generate a new business offering which offers sufficient consumer benefits to produce revenues exceeding its costs (Lepak, Smith, and Taylor 2007). Calculated risk-taking is the ability to take risks when engaging in EA and to also act in such a way as to minimize the probability of negative consequences and the impact thereof (Morris et al. 2013). This paper, therefore, focuses on the role the three ECs play in explaining an entrepreneur’s venturing accomplishments.

2.3 Venturing Accomplishments: An Efficiency and Effectiveness Perspective

While significant attention has been given to the impact of the ECs on EA, less attention has been given to predicting specific venturing accomplishments emerging from ECs, such as reaching the breakeven point and achieving a certain level of turnover. This dearth of investigation is somewhat surprising, given that initial research into ECs was primarily driven by aspirations to accomplish superior venturing performance outcomes (Herron and Robinson 1993; Mitchelmore and Rowley 2010). Perhaps a major cause of this dearth is the high likelihood that ECs will differentially impact specific venturing accomplishments, depending on which EC and venturing accomplishment are considered (Politis 2005; Shepherd et al. 2019). Specifically, this paper focuses on the time taken for a venture to breakeven as well as the level of turnover achieved, as two key venturing accomplishments which are particularly appropriate indicators of efficiency and effectiveness respectively (Auh and Menguc 2005). Efficiency refers to the output achieved with a given input, while effectiveness refers to the degree to which a desired result is achieved, typically in venture terms, conceptualized as obtaining paying customers (Davis and Timothy 2002).

While breakeven refers to the point at which revenue covers expenses and zero profits or losses are made, turnover refers to the total revenue or sales generated (Scott and Bruce 1987; Yang and Danes 2015). The breakeven period is a critical metric which demonstrates an initial benchmark of efficiency in terms of the feasibility of the exploited opportunity and the potential to begin generating a profit and building a financially stable venture (Scott and Bruce 1987; Yang and Danes 2015). Similarly, turnover or revenue is a key metric, particularly for entrepreneurial ventures which, more than seeking profits, are interested in demonstrating demand for, or effectiveness of, their value proposition through sales and revenue (Clarysse, Bruneel, and Wright 2011; Longenecker et al. 2013). The next section will focus on these two venturing accomplishment metrics and hypothesize the effect of the discovery and exploitation of EAs on these metrics, through the opportunity recognition, value creation abilities, and calculated risk-taking ECs as mediators.

2.4 A ST Framework Connecting EA and ECs to the Efficiency and Effectiveness Venturing Accomplishments

Consistent with the work by Morris et al. (2013), ST or structuration theory is employed as a framework for this paper’s hypotheses. A core supposition of this paper is that ECs are developed over time through the EA undertaken by an individual. This notion is inherently different from previous EC work, which generally considers how entrepreneurship education interventions develop ECs which influence EA (Morris et al. 2013). Yet, in either case, the underlying premise of ST holds—ECs are a result of learned scripts developed through the interactions between an individual and their environment (Giddens 1984). Learned scripts refer to guidelines or a framework of how to behave within specific contexts (Morris et al. 2013).

2.5 Action Stage and ECs

ST suggests that scripts are formed through experience or feedback obtained in the environment which indicates whether a script works in a particular situation and should be accepted (Chiasson and Saunders 2005). It is thus posited that EA will play a pivotal role in this developmental process, acting as a key driver of feedback to further test one’s understanding of the entrepreneurial context and mold behaviors to be more efficient and effective. Indeed, scholars are beginning to recognize that the very decision to pursue an entrepreneurial opportunity (i.e., take EA), may influence the attitudes, aspirations, and abilities of the decision-maker, possibly through the feedback or information revealed by that decision (Shepherd, Williams, and Patzelt 2015).

The entrepreneurial context is characterized by uncertainty, with the potential outcomes of one’s actions being unknown and dependent on various actors which, in turn, are dependent on yet other actors (Townsend et al. 2018). Therefore, since entrepreneurial ventures have few established procedures or scripts of action (Stinchcombe 2000), with little initial information or feedback on which to base decisions (Huang and Pearce 2015), a logic of action may be more appropriate to gain the necessary feedback (Wiklund, Patzelt, and Dimov 2016), before developing efficient and effective scripts for behavior (i.e., ECs). Determining whether a script works depends on the particularities of the business context, in that the entrepreneur must understand and respond to this feedback from discovering and exploiting an opportunity, in order to recognize, produce, and modify appropriate scripts which, ultimately, form the crux of business advantage and performance outcomes (Chiasson and Saunders 2005). On the basis of this reasoning, the following is hypothesized:

In addition, given that opportunity discovery is more exploratory, involving venture idea development, experimentation and search for the most potentially lucrative venture concept (Mueller, Volery, and Von Siemens 2012; Vogel 2017), it is argued that opportunity discovery activities will be more strongly related to the key ECs being investigated. On the other hand, opportunity exploitation is more implementation oriented, involving administrative and resource orchestrating behaviors (Mueller, Volery, and Von Siemens 2012). Therefore, it would make sense that ECs related to recognizing opportunities, finding a way to create value from opportunities, and taking calculated risks to pursue opportunities, are more exploratory (March 1991), and hence more strongly developed by discovery activities. Thus, it is hypothesized that:

2.6 Indirect Effects of EA on Venturing Accomplishments Through the ECs

Competency research was initially motivated by the notion that ECs may be important antecedents to achieving superior performance outcomes (Mitchelmore and Rowley 2010). Consequently, using ST, scholars have theorized that entrepreneurs earn their business advantage and accomplish desired venturing outcomes through the recognition, production, and modification of appropriate scripts based on feedback from acting in a venturing context (Chiasson and Saunders 2005).

Consistent with this, it is proposed that EA influences venturing accomplishments through effects on the three ECs investigated. More specifically, it is contended that opportunity discovery and exploitation activities lead to EC development as the engagement in venture-related activities provides ample feedback for the development of appropriate behavioral scripts (Giddens 1984; Grégoire, Corbett, and McMullen 2011; Morris et al. 2013). The greater this engagement, the greater will be the feedback for EC development, which should enhance the potential for superior venturing accomplishments such as quicker breakeven periods and higher levels of turnover (Mitchelmore and Rowley 2010). Thus, it is hypothesized:

2.7 EA and Efficiency Versus Effectiveness Accomplishments

In addition, given that breakeven time and turnover appear to represent efficiency and effectiveness accomplishments respectively (Auh and Menguc 2005), it is also proposed that the impact of EA through the opportunity recognition, value creation, and calculated risk-taking ECs, will differ between breakeven and turnover. While the breakeven period indicates efficiency in terms of the initial feasibility of an opportunity and the potential to generate a profit (Scott and Bruce 1987; Yang and Danes 2015), turnover indicates effectiveness in terms of generating a value proposition for which there is a demand and which effectively solves a customer need (Clarysse, Bruneel, and Wright 2011; Longenecker et al. 2013). The ECs related to recognizing opportunities, finding a way to create value from them, and taking calculated risks to pursue them, are more exploratory in nature and aimed at searching, innovating and risk-taking to provide effective solutions to customer problems (March 1991; Mueller, Volery, and Von Siemens 2012). Therefore, the impact of EA through the opportunity recognition, value creation, and calculated risk-taking ECs will probably be stronger for venturing accomplishments indicating effectiveness (i.e., turnover), as opposed to efficiency (i.e., breakeven). On this basis, the following is hypothesized:

Given this efficiency/effectiveness distinction, it is further suggested that there are differential indirect effects between opportunity discovery and exploitation in impacting breakeven and turnover. In particular, since opportunity discovery is more exploratory, involving searching for the most (potentially) lucrative venture concept (Mueller, Volery, and Von Siemens 2012; Vogel 2017), while opportunity exploitation involves administrative and resource orchestrating behaviors (Mueller, Volery, and Von Siemens 2012), it is proposed that discovery will more strongly affect effectiveness indicators such as turnover, while exploitation will more strongly influence efficiency indicators such as breakeven. It is, therefore, hypothesized that:

Given the above discussion, the hypothesized relationships are depicted in Figure 1 below.

Figure 1:

Hypothesized model.

3.1 Sampling Procedure

The model proposed in this study was investigated on a realized sample of 1150 entrepreneurs, consisting of 354 start-up and 796 established entrepreneurs in South Africa. Consistent with prior research, an entrepreneur is defined as an owner-manager of a business, with those being owner-managers for up to 3.5 years termed start-up, and those over 3.5 years termed established, entrepreneurs (Singer, Herrington, and Menipaz 2018; Turton and Herrington 2012). Data were obtained from a random sampling frame of 20,000 entrepreneurs, which was acquired from a South African-based market research firm. An online, self-administered survey questionnaire was emailed to the potential respondents, yielding a total of 1150 responses, reflecting a 5.75% response rate. 30% of respondents were female, while 60% were male. Furthermore, the respondents predominately operated around the economic hubs of South Africa, with 49% of respondents operating in the Gauteng province, 23% in the Western Cape, and 12% in Kwazulu-Natal. The respondent profile in terms of gender and location reflected a reasonably representative profile of South African small business owners based on prior data (Herrington, Kew, and Mwanga 2017). The respondents participated in a wide array of industries, with 25% operating in the business and financial services sector, 10% in telecommunications, 15% in manufacturing and construction, and 5% offering real-estate services. Forty-eight percent of respondents offered tangible products, 15.9% offered services only, while 36% offered both. Furthermore, respondents were well educated, with 41% having a post-graduate degree, 38% having some form of tertiary education, and 19% having a high school certificate. Finally, respondents age ranged from 18 to 83, with an average age of 51.5 years (standard deviation (SD) = 10.9 years).

4.1 Entrepreneurial Action Survey Development

Given the array of different typologies used to conceptualize EA, this paper focused on a discovery/exploitation typology which, to date, has been suggested to be the most theoretically valuable (Davidsson 2018; Farmer, Yao, and Kung–Mcintyre 2011; Shane and Venkataraman 2000). To tap these constructs, the study used a 17-item scale capturing a range of gestational behaviors which have mostly been derived from the Panel Study of Entrepreneurial Dynamics (PSED) and the work of Carter, Gartner, and Reynolds (1996), as used by Farmer, Yao, and Kung–Mcintyre (2011). These items included, for example, identifying market opportunities, developing a business plan, and accessing funding assistance (refer to items for all substantive constructs in the Appendix). Building on the work of Farmer, Yao, and Kung–Mcintyre (2011) and Autio, Dahlander, and Frederiksen (2013), who assigned these various activities into the discovery or exploitation indices, this paper employed formative measures for these two constructs. For both constructs, respondents were asked the extent to which they had engaged in each of the particular activities in the past three years. Their responses were scored on a Likert scale from 1 (never) to 5 (very regularly), and responses were modeled as causal indicators of discovery or exploitation, using the formative approach to modeling within a CB-SEM framework as outlined by Diamantopoulos (2011).

Following Farmer, Yao, and Kung–Mcintyre (2011), discovery activities were defined as those which fall on “the conceptual side of new venture development”, ranging from early-stage venture idea generation and development activities such as thinking about the idea, identifying market opportunities, and evaluating the feasibility and desirability of the venture concept (Vogel 2017). Alternatively, exploitation activities were defined as the concrete behaviors undertaken to implement the venture idea. Such exploitation essentially deals with execution, structuring and resource orchestration behaviors (Mueller, Volery, and Von Siemens 2012).

4.2 Entrepreneurial Competencies Survey Development

In this paper ECs are represented by opportunity recognition, value creation, and calculated risk-taking. Consistent with other EC research (e.g., Kyndt and Baert 2015), since competencies can only be espoused through an individual’s concrete behaviors (Man, Lau, and Chan 2002), which are seen as the most proximal outcomes of knowledge and abilities (Bird, Schjoedt, and Baum 2012), the key three ECs are specifically operationalized through a behavioral lens using behavioral indicators. More specifically, the ECs were measured as separate latent constructs using the scales outlined by Morris et al. (2013). Each scale consisted of five, three, and five items respectively on a seven-point Likert-type scale ranging from 1 (strongly disagree) to 7 (strongly agree).

4.3 Venturing Accomplishments: Efficiency and Effectiveness Survey Development

Consistent with similar studies exploring entrepreneur characteristics (Delmar and Shane 2006; Yang and Danes 2015), efficiency and effectiveness venturing accomplishments were measured through the duration taken to achieve breakeven, and through the turnover generated. The sample of entrepreneurs provided the duration it took for their venture to reach the breakeven point, with a longer duration indicating less efficient operations (Yang and Danes 2015). They also provided the total revenue generated from their venture, with higher revenues indicating greater effectiveness. Similar measures have been used to explore venturing accomplishments in the past and have shown to have a close correlation to secondary sources of venture data (Murphy, Trailer, and Hill 1996; Podoynitsyna et al. 2013; Yang and Danes 2015).

4.4 Control Variables

Since the use of control variables is a contentious issue in entrepreneurship (Schjoedt and Bird 2014), and management research (Atinc, Simmering, and Kroll 2012; Spector and Brannick 2011), a prudent approach was taken and only controls which might have theoretical relevance were incorporated. In particular, the potentially confounding effects, as suggested by prior research, of self-efficacy (Baron, Mueller, and Wolfe 2016), prior business ownership experience (Delmar and Shane 2006), age (Jin et al. 2017), and industry (Eddleston and Kellermanns 2007) on venturing accomplishments were controlled for.

5.1 Common Method Bias (CMB)

CMB occurs when characteristics of the measurement instrument cause systematic variance, or bias, in the theorized model which is attributed to common method effects rather than to the effects of the theorized constructs (Richardson, Simmering, and Sturman 2009). Since all substantive variables (i.e., independent, mediator, and dependent variables) in this study were collected at one point in time from the same respondents, recommendations by Podsakoff et al. (2003) were followed to limit, as well as assess the likelihood of CMB. More specifically, two procedures were employed, one ex-ante, the other ex-post. First, the instrument was carefully constructed with mediators, then outcome variables and independent variables in random order (Podsakoff et al. 2003). In addition to these preventative procedures, the unmeasured latent method factor technique was employed as an ex-post test of the possible effect of CMB. Specifically, a common method factor was introduced into the measurement model, allowing all items to load onto this additional factor. Item loadings and model fit were all substantively similar between this model and the original model. Therefore, it can be concluded that CMB is not a major concern in this paper (Podsakoff et al. 2003; Richardson et al. 2009). Additionally, the nature of the variables in this paper (i.e., actual behavior and objective performance measures) should further limit the risk of CMB and lend further support to the robustness of the results.

5.2 Structural Model Results

A central hypothesis of this paper’s model is the notion that the ECs mediate the relationship between EA and venturing accomplishments in the form of efficiency and effectiveness of the venture. To test this model, the lavaan package (version 0.6-3) (Rosseel 2012) was used in R to conduct CB-SEM using the maximum likelihood estimation method and the approach outlined by MacKinnon et al. (2002). This approach accommodates multiple mediators and enables the comparison of nested models for theory testing (Zhao, Lynch, and Chen 2010).

As depicted in Table 2, the conceptual model in this paper was tested and the model fit compared with a series of three nested models. These three nested models were alternative explanations for the data that differ from what this paper hypothesized and were compared to the theorized model as a robustness test following best practice recommendations in CB-SEM (Anderson, Wennberg, and McMullen 2019). Several fit indices were employed to assess the fit of these models: CFI, IFI, TLI values close to and above 0.9; Normed chi-square χ 2   /(df) < 3.00; and RMSEA values between 0.08 and 0.05 indicated a reasonably good-fitting model, while RMSEA values below 0.05 represent an excellent fit (Weston and Gore 2006). Firstly, the hypothesized partial mediation model was tested and demonstrated good fit to the data ( χ 2 (739) = 1923.423, CFI = 0.918, TLI = 0.900, RMSEA = 0.037). Thereafter a variety of alternative models were tested. As alternative explanations, there is the possibility that the ECs fully mediate the EA-venturing accomplishments relationship, or that EA and the ECs independently effect venturing accomplishments. Therefore, Model 2 and 3 assess the fit of a full mediation model and direct effects model to the data respectively. In addition, there is the possibility that EA enhances performance outcomes which, as a form of feedback, in turn enhances EC development. Thus, Model 4 assessed the fit of an alternative causal path model to the data (i.e., switching mediators and dependent variables). However, based on the chi-square difference test, the hypothesized model fit the data significantly better than all alternative models. Therefore, initial support is found for the theorized model, allowing individual hypothesized paths to be assessed further.

Figure 2 summarizes the structural model results. Hypotheses 1 and 2 posited that opportunity discovery and exploitation behaviors are respectively positively related to EC development. Since all paths from these EA constructs to the ECs are positive and significant, support is found for these hypotheses. Hypothesis 3 argued that opportunity discovery is more strongly related to the development of opportunity recognition, value creation, and calculated risk-taking ECs than opportunity exploitation. Since all paths from opportunity discovery to the ECs are stronger than from opportunity exploitation to the ECs, support is found for this hypothesis.

Figure 2:

Summary of structural model results.

In the next step, the indirect-effect hypotheses (i.e., Hypotheses 4 and 5) were analyzed by computing the unique indirect effects (UIE) of EA on breakeven and turnover through the ECs, using the more rigorous, non-parametric, bootstrap technique (MacKinnon et al. 2002). Bias-corrected confidence intervals using 5000 bootstrap samples were calculated with the 95% confidence intervals presented in Table 3.

As predicted in hypothesis 4, there is an overall positive indirect relationship between opportunity discovery and venturing accomplishments, mediated through the (a) opportunity recognition (UIE_Breakeven, β = 0.042, p < 0.1; UIE_Turnover, β = 0.074, p < 0.05), and (b) value creation ECs (UIE_Breakeven, β = 0.156, p < 0.01; UIE_Turnover, β = 0.251, p < 0.005). However, there was no overall positive indirect relationship between opportunity discovery and the venturing performance outcomes, mediated through the (c) calculated risk-taking ECs (UIE_Breakeven, β = 0.003, p > 0.1; UIE_Turnover, β = 0.028, p > 0.1).

Similarly, as predicted in hypothesis 5, there is an overall positive indirect relationship between opportunity exploitation and the venturing accomplishments, mediated through (a) opportunity recognition (UIE_Breakeven, β = 0.022, p < 0.1; UIE_Turnover, β = 0.038, p < 0.05), and (b) value creation ECs (UIE_Breakeven, β = 0.054, p < 0.01; UIE_Turnover, β = 0.086, p < 0.005). However, there was no overall positive indirect relationship between opportunity discovery and the venturing accomplishments, mediated through the (c) calculated risk-taking ECs (UIE_Breakeven, β = 0.002, p > 0.1; UIE_Turnover, β = 0.016, p > 0.1).

With regard the efficiency versus effectiveness hypotheses, since all the indirect paths are stronger for the turnover performance dimension than the breakeven dimension, support is found for the predictions of hypothesis 6. Furthermore, since all the indirect paths from exploitation to the efficiency outcome of breakeven are stronger than they are for the indirect paths from discovery to breakeven, support is found for the predictions of hypothesis 7. Finally, support is found for hypothesis 8 because all the indirect paths from discovery to the effectiveness outcome of turnover are stronger than they are for the indirect paths from exploitation to turnover. Thus, overall, it seems that the effects of discovery and exploitation activities significantly differ, with the former being more important to EC development and achieving effective venturing accomplishment outcomes, while the latter is a stronger predictor of the achievement of efficient venturing accomplishment outcomes, a theme returned to in the discussion section.

7.1 Contributions to Theory

By adopting an ST perspective (Giddens 1984), this paper extends theorizing by clarifying how EA influences ECs and venturing accomplishments. ST suggests that scripts are formed through feedback obtained by acting in a particular environment (Chiasson and Saunders 2005). The model in this paper affirms this assertion and identifies the discovery and exploitation of entrepreneurial opportunities (Shane and Venkataraman 2000), as key drivers of competent entrepreneurial behavior (ECs) and firm accomplishments. As a potent driver of feedback, this paper shows that the very experience of engaging in EA may spark the recognition, production, and modification of appropriate scripts which, ultimately, form the crux of business advantage (i.e., effectiveness) and efficiency (Chiasson and Saunders 2005). This is an important contribution, since it begins to help in resolving one of the central conundrums in entrepreneurial characteristic research, namely whether entrepreneurs have particular competencies and characteristics that attract them to entrepreneurship and lead to venturing performance outcomes and accomplishments, or whether EA results in the development of these characteristics (Grégoire, Corbett, and McMullen 2011). Notwithstanding the value of interventions to enhance ECs and encourage EA, this paper demonstrates that EA does in fact contribute to the development of three key ECs and, ultimately, venturing accomplishments. In fact, in an environment characterized by extreme uncertainty, rapid change, and information and resource scarcity (Baum and Locke 2004; van Gelderen 2012; van Gelderen, Kautonen, and Fink 2015), entrepreneurs have few established procedures or scripts for action (Stinchcombe 2000), with little initial information or feedback on which to base their decisions (Huang and Pearce 2015). Therefore, this paper demonstrates that a logic of action is highly appropriate in entrepreneurship, which is facilitated by the generation of the necessary feedback for the development of efficient and effective scripts for behavior (i.e., ECs).

Furthermore, scholars are beginning to recognize that most antecedents of venturing accomplishments have dual outcomes (i.e., both good and bad) (Busse, Mahlendorf, and Bode 2016), and that it is inappropriate to consider venturing success as a unidimensional construct, given that antecedents may be beneficial to one success metric, while being harmful to another metric (Murphy, Trailer, and Hill 1996). This paper contributes by demonstrating the empirical exploration of specific venturing accomplishments within an efficiency versus effectiveness framework. While there are several variables which could be considered potentially important venturing accomplishments (Shepherd et al. 2019), all of which may be differently affected by EA and ECs (Politis 2005), this paper begins to develop nuanced insight within this domain upon which future research can build. This is a notable contribution, as this research provides important confirmation of EA’s role in influencing EC’s and specific venturing accomplishments while also, through an efficiency/effectiveness framework, allowing for a degree of theoretical extrapolation to other potential effectiveness/efficiency venturing accomplishments.

7.2 Implications for Practice

From a practice perspective, by demonstrating how engaging in the entrepreneurial process impacts on specific competencies and venturing outcomes, this paper has important implications for entrepreneurs as well as entrepreneurship education interventions in terms of the learning-by-doing and effectuation versus causation schools of thought (Chandler et al. 2011). Discovery and causation approaches assume that an individual must ex ante, possess the competencies, know-how and alertness to pursue a pre-defined business opportunity that exists in the market (Kirzner 1997; Wang and Chugh 2014). However, consistent with a more creationist stance (Alvarez and Barney 2007), this paper indicates that one need not possess key ECs to pursue an entrepreneurial career, as engagement in the process leads to the emergence of competencies and lucrative opportunities. That is, nascent and established entrepreneurs may be well advised to simply get started and act in the entrepreneurial context since, from this active approach, key competencies can be developed which lead to desired venturing accomplishments.

In addition, in contrast to the typical static views of ECs (Grégoire, Corbett, and McMullen 2011), by demonstrating how specific EAs lead to distinct outcomes, this paper provides a dynamic view of the ECs and venturing accomplishments and how they can be ameliorated. In particular, this paper shows that those seeking more effective, higher-turnover, venturing pursuits might be wise to regularly engage in the more exploratory opportunity discovery EAs, involving searching, planning, and conceptual development of one’s venture concept (Vogel 2017). On the other hand, those seeking more efficient venturing operations should engage more in the administrative and resource orchestrating behaviors associated with opportunity exploitation (Mueller, Volery, and Von Siemens 2012).

These research implications extend to entrepreneurship education interventions and can serve as the impetus to ameliorate these programs. While more experiential approaches are gaining popularity in these programs, many interventions often take a more textbook, as opposed to a learning-by-doing approach (c.f., Brush and Noyes 2012) and aim to develop the “competencies necessary for entrepreneurial action” (Morris et al. 2013, 352). This research proposes that highly action-oriented programs, where students engage with their target market, conduct market research and start their business could significantly improve learning outcomes, as appropriate scripts are formed through experience or feedback in the entrepreneurial context (Giddens 1984). Thus, this research lends further credence to the experiential and learning-by-doing schools which may support the advancement of pedagogical interventions which seek to enhance entrepreneurial outcomes.

7.3 Limitations and Future Research

There are important limitations of the present study that should be noted in weighing the results of this paper. First, the performance measures in this paper, while objective in nature, were self-reports and hence open to reporting bias. While the risk of such bias was minimized by seeking objective performance metrics and ensuring respondents’ anonymity, future research should seek to obtain measures of performance from alternative sources to the entrepreneur, such as company documents and colleagues in order to confirm the results. Second, future investigations would benefit from longitudinal and experimental designs, not only to clarify causal influence, but also to further rule out third-variable confounds (Anderson et al. 2019). While this study carefully explored the possibility of a variety of competing causal order models, the cross-sectional nature of the data does not allow us to fully rule out other model explanations (MacKinnon et al. 2002). In this regard, future research may particularly benefit from exploring ECs which predate EA, as well as those that proceed from EA, to more fully disentangle the drivers of EC development and differences in these ECs (Grégoire, Corbett, and McMullen 2011).

Finally, while this paper explores three ECs and two venturing accomplishments which are argued as central to the entrepreneurship domain, scholars have, nevertheless, noted a host of other ECs (e.g., Morris et al. 2013; Santos, Caetano, and Curral 2013), and venturing accomplishments (Shepherd et al. 2019), all of which cannot be examined within the scope of a single study and should rather be explored in future research. In particular, while it was found that opportunity discovery most strongly influences EC development, this is probably because this paper focused on ECs which are inherently tied to the development of the particular venture concept. Therefore, it may be the case that opportunity exploitation behaviors, which are more administrative in nature, rather than focused on the conceptual development of the idea, may be related to more administrative ECs, such as perseverance and the ability to structure and mobilize resources (Santos, Caetano, and Curral 2013).

Notwithstanding these limitations, this research provides a basis from which future research may explore these other ECs to come to a more holistic understanding of how EA influences EC development. Furthermore, many relevant questions could be pursued along these lines from a qualitative stance. From the standpoint of the research implications emerging in this paper, these questions could explore in more detail the lived experiences of entrepreneurs and how these experiences shaped their perceived ECs. Such investigations could fruitfully contribute towards greater insight into how interaction with the entrepreneurial context updates an entrepreneur’s learned scripts.