Fuzzy-set qualitative comparative analysis (fsQCA) in business and management research: A contemporary overview

To make scientific inferences about business phenomena, it may not be sufficient to consider the real-world context of the business environment as statistically symmetrical (e.g., linearly, regular frequencies). This is why recently the use of asymmetrical techniques which draw on the reasoning of complexity theory - such as fuzzy-set qualitative comparative analysis (fsQCA) - to better predict and explain real-world business phenomena using a configurational approach is being increasingly promoted. This article aims to identify the key contributors and knowledge structure of business and management research involving the application of complexity theory and fsQCA. Using bibliographic data of 1,155 articles extracted from Scopus, our review conducts (1) a performance analysis to shed light on the field's key contributors based on the criteria of journal, article, author, institution, and country, and (2) a scientific mapping using keyword cooccurrence and PageRank to reveal three knowledge clusters and the prominent articles in each cluster. Taken collectively, this review is a useful resource to gain a comprehensive understanding of the state-of-the-art and promising avenues for future research involving the prediction of business phenomena using complexity theory and fsQCA.

Automated summary

This article aims to identify the key contributors and knowledge structure of business and management research involving the application of complexity theory and fsQCA, in order to provide insights into future research directions.