Abstract
The increasing interest in fuzzy-set Qualitative Comparative Analysis (fsQCA) in Information Systems and marketing raises the need for a tutorial paper that discusses the basic concepts and principles of the method, provide answers to typical questions that editors, reviewers, and authors would have when dealing with a new tool of analysis, and practically guide researchers on how to employ fsQCA. This article helps the reader to gain richer information from their data and understand the importance of avoiding shallow information‐from‐data reporting. To this end, it proposes a different research paradigm that includes asymmetric, configurational‐focused case‐outcome theory construction and somewhat precise outcome testing. This article offers a detailed step-by-step guide on how to employ fsQCA by using as an example an already published study. We analyze the same dataset and present all the details in each step of the analysis to guide the reader onto how to employ fsQCA. The article discusses differences between fsQCA and variance-based approaches and compares fsQCA with those from structured equation modelling. Finally, the article offers a summary of thresholds and guidelines for practice, along with a discussion on how existing papers that employ variance-based methods are extendable and complemented through fsQCA.
Highlights
The increasing interest in fuzzy-set Qualitative Comparative Analysis in Information Systems and marketing raises the need for a tutorial paper that discusses the basic concepts and principles of the method, provide answers to typical questions that editors, reviewers, and authors would have when dealing with a new tool of analysis, and practically guide researchers on how to employ fsQCA
Additional details are provided while we offer suggestions on how existing works can be extended by employing fsQCA based on their findings (Appendix D)
Contrarian case analysis is performed outside fsQCA, but we present it here because it can serve as an easy and quick way to examine how many cases in our sample are not explained by main effects, and they would not be included in the outcome of a typical variance-based approach (Woodside, 2014, 2016a)
Summary
“Scientists’ tools are not neutral” (Gigerenzer, 1991, p. 264): both symmetric (e.g., correlation and multiple regression analysis) and asymmetric (i.e., individual case outcome forecasts) data analysis tools express theoretical stances as well as analytical procedures (Woodside, 2019). Qualitative comparative analysis (QCA) is an asymmetric data analysis technique that combines the logic and empirical intensity of qualitative approaches that are rich in contextual information, with quantitative methods that deal with large numbers of cases and are more generalizable (Ragin, 1987) than symmetric theory and tools. We use as an example a study available in the literature (i.e., (Pappas et al, 2016)) and we offer all the details on how to perform the analysis, that are not included in a typical research article
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