Using fuzzy-set qualitative comparative analysis for a finer-grained understanding of entrepreneurship

https://doi.org/10.1016/j.jbusvent.2019.105970Get rights and content

Highlights

  • FsQCA allows incorporation of asymmetric data, outliers, asymmetric relationships, interdependent conjunctural causation, and equifinality.

  • FsQCA reveals patterns within the data that are left hidden by traditional methods, and it accommodates data that traditional methods cannot.

  • The fsQCA method is outlined in the entrepreneurship context to highlight the contribution this analytical tool can make to developing entrepreneurship theory.

  • Replication of a prior symmetric study using the fsQCA method demonstrates the finer-grained information that this analytical tool can reveal.

  • A detailed future research agenda for the application of fsQCA method to a wide range of micro and macro entrepreneurial topics.

Abstract

Entrepreneurship theory has largely been developed and tested using symmetrical correlational methods, effectively describing the sample-average respondent and subsuming individual differences. Such methods necessarily limit investigation of asymmetries that are evident in entrepreneurship, and provide only a single explanation that belies the multiple pathways to entrepreneurship observed in practice. This paper employs a case-based approach—fuzzy-set Qualitative Comparative Analysis (fsQCA)—to identify configurations of antecedent attributes of individuals in groups within samples, thereby revealing asymmetries and multiple entrepreneurial pathways that are otherwise hidden in the data. We explain the application of fsQCA to reveal these common issues in entrepreneurship; demonstrate how fsQCA complements correlational methods and offers finer-grained understanding of individual entrepreneurial behavior; and offer a comprehensive research agenda to build new entrepreneurship theory.

Section snippets

Executive summary

The great majority of entrepreneurship theory has been conceptualized to be tested using symmetric quantitative methods, such as multiple regression analysis and structural equation modeling. These traditional symmetric methods test relationships between proposed independent and dependent variables to explain entrepreneurial phenomena. Symmetric methods require the data to conform to restrictive assumptions, including normally distributed data, symmetric data relationships, and independence of

Fuzzy-set qualitative comparative analysis

It is not our purpose here to provide a fully-comprehensive “how-to” guide for fuzzy-set Qualitative Comparative Analysis (fsQCA) since such guides already exist in the management (e.g. Greckhamer et al., 2018) and entrepreneurship literatures (e.g. Leppänen et al., 2019). Rather, our purpose is to outline the method for the scholar who is relatively new to fsQCA and who may be considering the use of this method in the entrepreneurship context.

FsQCA is well-suited to the dealing with the

Re-examining the data of a prior entrepreneurship study

To illustrate the potential contribution that fsQCA can make to research into entrepreneurial phenomena, we now use this tool to re-analyze data from an earlier study that had utilized traditional symmetric analytical methods. We chose a “micro-entrepreneurship” investigation of the determinants of entrepreneurial intention by Douglas (2013), who used multiple regression analysis, consistent with what is commonly used in the “intentions” literature (see surveys by Liñán and Fayolle, 2015;

Future research opportunities

There appear to be many interesting research questions for which scholars can use fsQCA to investigate patterns between and among the antecedent conditions that associate with a focal outcome of interest in the entrepreneurial domain.15

Conclusion, limitations, and implications

We have argued that while research using traditional symmetric methods has contributed much to revealing the determinants of entrepreneurial phenomena, such methods may leave information largely undiscovered about individuals and sub-groups within the population. To make further progress in explaining and predicting entrepreneurial phenomena, researchers can use fsQCA to complement knowledge derived from traditional analytical methods. In this regard, the current paper makes three main

Acknowledgements

We wish to thank JBV editor Brian Anderson, and three anonymous reviewers, for their insightful and constructive comments and guidance on earlier versions of this paper. We would also like to thank Joanna Campbell for her incredible feedback on earlier versions of this manuscript.

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