Publication | Open Access
Fuzzy-set Qualitative Comparative Analysis (fsQCA): Guidelines for research practice in Information Systems and marketing
1.8K
Citations
75
References
2021
Year
Customer SatisfactionInformation SystemsBusiness IntelligenceBusiness CaseQuality Function DeploymentFuzzy Multi-criteria Decision-makingInformation Technology ManagementManagementResearch PracticeBusiness Information SystemInformation System PlanningFuzzy LogicBusiness Information SystemsPrecise Outcome TestingDecision Support SystemsInformation ManagementStrategic ManagementOperations ManagementMarketingStructured Equation ModellingBusiness OperationsFuzzy MathematicsBusinessMarketing Insights
This tutorial guides Information Systems and marketing researchers through the fundamentals of fsQCA, addressing common questions and demonstrating how to extract richer insights while avoiding shallow data reporting. The authors present a step‑by‑step protocol that applies a configurational, asymmetric case‑outcome paradigm to a published dataset, illustrating how fsQCA differs from variance‑based methods and compares with structural equation modeling. The paper concludes with practical thresholds and guidelines, showing how existing variance‑based studies can be extended and complemented by fsQCA.
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.
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