Fuzzy-set Qualitative Comparative Analysis (fsQCA): Guidelines for research practice in Information Systems and marketing

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.

Automated summary

This article discusses the basic concepts and principles of fuzzy-set Qualitative Comparative Analysis (fsQCA), provides detailed practical guidance, and helps researchers better utilize data to avoid shallow reporting. It compares the differences between fsQCA and variance-based approaches, and explores how existing papers using variance-based methods can be extended and complemented through fsQCA.