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dc.contributor.authorDouglas, Evan J
dc.contributor.authorShepherd, Dean A
dc.contributor.authorPrentice, Catherine
dc.date.accessioned2019-11-20T05:37:06Z
dc.date.available2019-11-20T05:37:06Z
dc.date.issued2020
dc.identifier.issn0883-9026
dc.identifier.doi10.1016/j.jbusvent.2019.105970
dc.identifier.urihttp://hdl.handle.net/10072/389176
dc.description.abstractThe 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 the variables, and these restrictions limit the ability of these methods to deal with some of the complexity of entrepreneurial behavior that we observe. In effect, the complexity of entrepreneurial phenomena exceeds the capability of traditional methods to reflect important aspects of its heterogeneity. Five main issues occur in entrepreneurship that are problematic for traditional symmetric methods, such that these issues have been afforded less research attention. First, the majority of key entrepreneurship variables exhibit highly skewed distributions, rather than being normally distributed around their means, necessitating data manipulation and the elimination of outlier observations, yet the skew and the outliers may be important in understanding the entrepreneurship context. Second, the heterogeneity of entrepreneurial behavior is reflective of inter-case differences amongst entrepreneurs and their ventures, whereas traditional methods are designed to capture the commonalities across all the cases, and necessarily suppress inter-case differences that may be causal for the observed heterogeneity. Third, entrepreneurial phenomena are often characterized by relationship asymmetry, meaning that an antecedent variable may be both positively and negatively associated with the outcome, for different cases. Symmetric methods find the single “net-effects model” which highlights the dominant relationship found and ignores any minority relationships that lie within the data. Fourth, entrepreneurial outcomes, whether at the individual, firm or institutional level of analysis, tend to be pursued after taking into account the interdependencies between and among the antecedent variables, yet traditional methods explain entrepreneurship phenomenon as the linear additive impact of the antecedent variables considered discretely – i.e. independently of the effect of other antecedent variables. Finally, we observe entrepreneurs and organizations taking a variety of pathways to entrepreneurial outcomes, yet traditional methods offer a single dominant net-effects explanation. Thus, symmetric methods cannot reveal important aspects of entrepreneurial heterogeneity (because it is not the method’s purpose) that we observe in practice. Fuzzy-set qualitative comparative analysis (fsQCA) provides a method to dig deeper into the data to reveal finer-grained detail about the complexity of entrepreneurial phenomenon. The fsQCA method accommodates data asymmetry, recognizes the potential interdependence of antecedent variables, identifies asymmetric data relationships, and reveals multiple equally-effective pathways to the same outcome, if they exist. FsQCA examines the within-case relationships among the antecedent variables (referred to as conditions), and characterizes cases as having a particular combination of conditions (known as a configuration) that associates with the dependent variable (known as the outcome). It discovers the configuration common to multiple cases who take a particular pathway to a given outcome, as distinct from those who take other pathways to the same outcome. FsQCA is thus complementary to traditional symmetric methods, adding finer-grained detail about entrepreneurial phenomena and providing an empirical basis for abduction, i.e., it can reveal surprising empirical findings to provoke new theory building efforts. In this paper we outline the fsQCA method, demonstrate the additional information this method can provide from a given data set, and provide a comprehensive agenda for future entrepreneurship research where fsQCA can be used to complement traditional methods and thereby provide new information for future theory building. This paper provides motivation for entrepreneurial public policy on multiple fronts, rather than for “one-size-fits-all” policies. It similarly suggests that educators can go beyond the notion of the archetypical entrepreneur to encourage entrepreneurship by those who do not fit the classical mold, and that would-be entrepreneurs and investors should recognize that individuals exhibiting a variety of different configurations can act entrepreneurially and potentially achieve entrepreneurial success.
dc.languageEnglish
dc.publisherElsevier BV
dc.relation.ispartofpagefrom105970:1
dc.relation.ispartofpageto105970:17
dc.relation.ispartofissue1
dc.relation.ispartofjournalJournal of Business Venturing
dc.relation.ispartofvolume35
dc.subject.fieldofresearchMarketing
dc.subject.fieldofresearchBanking, Finance and Investment
dc.subject.fieldofresearchBusiness and Management
dc.subject.fieldofresearchcode1505
dc.subject.fieldofresearchcode1502
dc.subject.fieldofresearchcode1503
dc.titleUsing fuzzy-set qualitative comparative analysis for a finer-grained understanding of entrepreneurship
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationDouglas, EJ; Shepherd, DA; Prentice, C, Using fuzzy-set qualitative comparative analysis for a finer-grained understanding of entrepreneurship, Journal of Business Venturing, 2020, 35 (1), pp. 105970:1-105970:17
dcterms.licensehttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.date.updated2019-11-14T11:13:24Z
dc.description.versionPost-print
gro.rights.copyright© 2019 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence (http://creativecommons.org/licenses/by-nc-nd/4.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.
gro.hasfulltextFull Text
gro.griffith.authorPrentice, Catherine
gro.griffith.authorDouglas, Evan


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