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A configurational approach to understanding gender differences in entrepreneurial activity: a fuzzy set analysis of 40 countries

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Abstract

This paper explores entrepreneurial activity for men and women across 40 countries. Building on research grounded in social cognitive and institutional theories, we propose a configurational approach to explore how effects of different causal conditions are interdependent in explaining gender specific entrepreneurial activity. Using fuzzy-set qualitative comparative analysis, we find that no one causal element is sufficient for promoting entrepreneurial activity. Instead micro-level attributes of entrepreneurial self-efficacy and opportunity recognition in combination with macro-level business environment formal institutions and national culture create configurations of conditions that lead to high levels of entrepreneurial activity amongst men and women. Moreover, the causal factors configure in different ways for male and female entrepreneurs. This study demonstrates the value of using a configurational analytical technique to explore together the micro- and macro- complexities of what drives men and women around the world to engage in entrepreneurial activity.

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Notes

  1. Detailed explanations of the principles and methods of calibration are provided by Ragin (2008, pp. 71–105) and Schneider and Wagemann (2012, pp. 31–41)

  2. Consistency values reflect the degree to which the cases (countries) sharing a given condition or combination of conditions agree in the occurrence of a given outcome (EA) and is calculated as follows (Ragin 2008: 134):

    Consistency of condition or combination X as a subset of the outcome Y: (Xi ≤ Yi) = ∑[min(Xi,Yi)]/ ∑(Xi)

  3. Based on Boolean algebra, the software first creates a truth table of the 2k (k = number of conditions) possible combinations. Based on the frequency and consistency thresholds set, any combinations that do not meet those values are deleted. The fsQCA software employs the Quine-McCluskey algorithm to simplify the causal combinations. Ragin (2008) provides an in-depth discussion of the specific steps.

  4. Other recommended ways of selecting minimum consistency thresholds, include using values where there are gaps in the raw consistency values reported in the truth table (Ragin 2008). For our data, these occurred at 0.83 and 0.87 for men and women, on either side of our chosen value of 0.85. As we are interested in comparing the combinations between men and women we deemed it more appropriate to use the same threshold value for both. Subsequent analysis using 0.83 for men and 0.87 for women gave identical results as those we report

  5. Coverage values should only be calculated after confirming that consistency values are at an appropriate level. Coverage and consistency often move in opposing directions, such that high consistency leads to low coverage. Coverage is calculated as follows (Ragin 2008): Coverage of condition or combination X as a subset of the outcome Y: (Xi ≤ Yi) = ∑[min(Xi,Yi)]/ ∑(Yi)

  6. The UN agencies include the International Labor Organization, the Economic Commission on Latin America, the United Nations Development Program and the Food and Agriculture Organizations.

  7. The region comprising developed Asia Pacific countries had lower rates of EOR and ESE. The region that comprises European developing countries had lower EOR.

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Acknowledgments

We would like to thank participants at the 2014 Academy of Management Annual Meeting in Philadelphia, PA, USA for helpful comments on an earlier version of this paper. We are also grateful to the anonymous reviewers for constructive and insightful comments throughout the review process.

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  1. University of Wyoming, Management & Marketing Dept., 1000 E. University Ave, Laramie, WY, 82071-2000, USA

    Krista B. Lewellyn

  2. The Pennsylvania State University, York, Office: 216 ISTC Building, 1031 Edgecomb Ave, York, PA, 17403-3326, USA

    Maureen I. Muller-Kahle

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Lewellyn, K.B., Muller-Kahle, M.I. A configurational approach to understanding gender differences in entrepreneurial activity: a fuzzy set analysis of 40 countries. Int Entrep Manag J 12, 765–790 (2016). https://doi.org/10.1007/s11365-015-0366-3

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