Abstract
While scholars agree that institutions are critical for enabling and constraining entrepreneurial action, the mechanisms by which institutions shape individual entrepreneurs’ actions remain underdeveloped. Whereas a prior work focuses on the direct and moderating effects of institutions on entrepreneurial action, we propose that institutions also indirectly influence entrepreneurial action through their influence on the mental models of actors. To that end, we theorize an underexplored role of institutions: shaping three socio-cognitive traits (SCT)—opportunity recognition, entrepreneurial self-efficacy, and fear of failure—that influence entrepreneurial action. Using GEM data from 735,244 individuals in 86 countries, we test and find evidence that SCTs mediate the relationship between institutions and opportunity entrepreneurship. The social legitimacy of entrepreneurship exerts a weaker direct effect on opportunity entrepreneurship but a stronger indirect effect through the SCT channels relative to pro-market institutions. Our study thus provides more nuanced findings concerning the ways formal and informal institutions, as well as the direct and indirect effects of institutions, enable and constrain entrepreneurial action.
Plain English Summary
Pro-market institutions and favorable societal attitudes do not only create beneficial market conditions for entrepreneurship; they also potentially shape the cognitive characteristics conducive to entrepreneurial action. In a sample of 735,244 individuals in 86 countries from 2002 to 2015, we find that formal and informal institutions increase the socio-cognitive traits that, in turn, increase the propensity for opportunity-motivated entrepreneurship. This reveals that the effects of institutions on individual engagement with entrepreneurship are both direct and indirect, suggesting the importance of policy and culture in shaping the cognitive foundations of the entrepreneurial process.
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Data Availability
The data that support the findings of this study are openly available at https://www.gemconsortium.org/data/keyaps.
Notes
We also note the dashed lines indicative of the “bidirectional” nature of the institution-entrepreneurship relationship (Elert & Henrekson, 2017). Recent scholarship in NIE has clarified how entrepreneurs can transcend institutional constraints (Bylund & McCaffrey, 2017; Lucas & Fuller, 2017), break formal rules (Elert & Henrekson, 2016; Lucas et al., 2018, 2022), and even elicit institutional change (Elert & Henrekson, 2021). This body of work suggests that entrepreneurial exchange informs institutional evolution through two main feedback channels. The first channel is the emergent result of the sum of entrepreneurial market activity (i.e., “the results of human action but not of human design,” Hayek, 1967). The second channel is the notable (but somewhat rarer) entrepreneur who lobbies for or otherwise engenders institutional change. While both important, neither mechanism is particularly salient to our research question and interest in “everyday” entrepreneurial action at the individual level (Welter et al., 2017). As such, we focus on elaborating the institution-entrepreneurship mechanisms and offer some considerations of bidirectionality in the discussion.
Our coding of OME places individuals pursuing necessity-motivate entrepreneurship (NME) as 0. To check the sensitivity of our results to this coding choice, we excluded NMEs from the sample in lieu of coding them as non-OMEs and re-estimated our main results. This reduced our sample size to 722,930, but the results were nearly identical to those reported in Table 3. Results were omitted here but available in a supplemental appendix. We thank an anonymous reviewer for suggesting this robustness check.
We report the results from the traditional fixed effects approach as an additional robustness check. These results are qualitatively similar to our CRE estimates. The results are available in the supplemental appendix in Table 6.
In the case of unbalanced panels, caution must be taken to calculate the group-level means on the final sample after any missing observations are deleted (Wooldridge, 2019).
Linear probability models (LPM) are an alternative estimation method that provide consistent estimation of average partial effects. LPM involves estimating a linear regression model with a binary dependent variable. Although average partial effects are consistent, predicted effects can lie outside of the (0, 1) interval (Angrist & Pischke, 2009).
Another alternative is a random intercepts model (i.e., mixed-effects). The major drawback in this case is that the orthogonality condition must be satisfied, which states that the model’s regressors should not be correlated with random intercepts. If violated, parameter estimates will also be inconsistent. Recent research has found that many papers violate this assumption in the management literature (Antonakis et al., 2019). Nonetheless, we re-estimated our main results with a random intercepts model and found very similar results. The one discernible difference is that pro-market institutions are now negatively and statistically significantly associated with fear of failure (\(\beta =-0.043, p=0.000\)), such that this SCT negatively mediates the positive effect of pro-market institutions on OME. These results are provided in a supplemental appendix.
Indirect effect of pro-market institutions via opportunity recognition: 0.028 = 0.201 × 0.137.
Indirect effect of pro-market institutions via entrepreneurial self-efficacy: 0.007 = 0.021 × 0.341.
Indirect effect of pro-market institutions via fear of failure: − .000 = 0.001 × − 0.098.
Indirect effect of social legitimacy of entrepreneurship via opportunity recognition: 0.017 = 0.125 × 0.137.
Indirect effect of social legitimacy of entrepreneurship via entrepreneurial self-efficacy: 0.041 = 0.119 × 0.341.
Indirect effect of social legitimacy of entrepreneurship via fear of failure: − 0.004 = 0.041 × − 0.098.
This is an absolute value.
The total effect combines the direct effect with the indirect effect through each SCT channel.
The total effect combines the direct effect with the indirect effect through each SCT channel.
15.70 = (12.56 + 3.14 + 0.10).
We note that fear of failure’s coefficient is not statistically significant, which is one of the criteria in traditional mediation analysis. However, we have included it here to reflect the total magnitude of the mediation effect per KHB.
98.69 = (28.36 + 70.34 + 9.59).
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Boudreaux, C.J., Bennett, D.L., Lucas, D.S. et al. Taking mental models seriously: institutions, entrepreneurship, and the mediating role of socio-cognitive traits. Small Bus Econ 61, 465–493 (2023). https://doi.org/10.1007/s11187-022-00712-8
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DOI: https://doi.org/10.1007/s11187-022-00712-8
Keywords
- Economic freedom
- Opportunity-motivated entrepreneurship
- Pro-market institutions
- Social legitimacy of entrepreneurship
- Socio-cognitive traits
- Shared mental models