2020
DOI: 10.1016/j.jbusvent.2019.105970
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Using fuzzy-set qualitative comparative analysis for a finer-grained understanding of entrepreneurship

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Cited by 344 publications
(324 citation statements)
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References 82 publications
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“…This is an important property of fsQCA, which make it an appropriate tool to study the relationships which causality is complex, and the traditional net effect approach is not able to accommodate such interdependencies and constrains theory development. Second, fsQCA also has the advantage over the traditional symmetric analysis methods as it is not restricted to any given probability distribution of the data (e.g., normal distribution) and accommodates asymmetric relationships across cases (Douglas et al 2020;Fiss 2011). While symmetric analysis methods provide only a net-effects model based on the majority of samples and subsume minority cases, fsQCA captures the asymmetry of data relationships and, hence, serves as a good method for identifying these differences (Douglas et al 2020).…”
Section: Statistical Methods and Analysismentioning
confidence: 99%
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“…This is an important property of fsQCA, which make it an appropriate tool to study the relationships which causality is complex, and the traditional net effect approach is not able to accommodate such interdependencies and constrains theory development. Second, fsQCA also has the advantage over the traditional symmetric analysis methods as it is not restricted to any given probability distribution of the data (e.g., normal distribution) and accommodates asymmetric relationships across cases (Douglas et al 2020;Fiss 2011). While symmetric analysis methods provide only a net-effects model based on the majority of samples and subsume minority cases, fsQCA captures the asymmetry of data relationships and, hence, serves as a good method for identifying these differences (Douglas et al 2020).…”
Section: Statistical Methods and Analysismentioning
confidence: 99%
“…Second, fsQCA also has the advantage over the traditional symmetric analysis methods as it is not restricted to any given probability distribution of the data (e.g., normal distribution) and accommodates asymmetric relationships across cases (Douglas et al 2020;Fiss 2011). While symmetric analysis methods provide only a net-effects model based on the majority of samples and subsume minority cases, fsQCA captures the asymmetry of data relationships and, hence, serves as a good method for identifying these differences (Douglas et al 2020). Third, fsQCA utilizes an inductive research method based on the principle of equifinality, assuming that multiple paths to a desired outcome can co-exist (i.e., more than one solution to the problem) (Fiss 2007).…”
Section: Statistical Methods and Analysismentioning
confidence: 99%
“…To uncover such complexities, we used nonlinear methods, specifically artificial neural networks. Our study builds on recent arguments that traditional symmetric data analysis methods are not enough to understand the complexity of entrepreneurship (Douglas et al, 2020) and resolves prior inconsistencies about the affective experiences during the early stage of entrepreneurship (e.g. García et al, 2015;Fodor and Pintea, 2017).…”
Section: Discussionmentioning
confidence: 79%
“…These methods assume symmetric central tendency and require data normally distributed around its mean, such that the relations between variables can be interpreted as linear dependencies (e.g. Douglas et al, 2020). Beyond the prospective linearity in some aspects, the entrepreneurial journey is about turbulence, uncertainty, change, and unstable leaps.…”
mentioning
confidence: 99%
“…Such comparative analysis is often informed by triangulating interview and archival data. Beyond established comparative case methods, we point to the potential of Qualitative Comparative Analysis (QCA) for analyzing sets of cases and comparing them along different dimensions (Douglas et al, 2020).…”
Section: Heterogeneitymentioning
confidence: 99%