Fuzzy-set qualitative comparative analysis (fsQCA) in business and management research

A contemporary overview

Dados Bibliográficos

ID5844652
AutoresSatish Kumar (0000-0001-7648-2630, Swinburne University of Technology Sarawak Campus), Saumyaranjan Sahoo (0000-0003-3609-2448, Jaipuria Institute of Management), Weng Marc Lim (0000-0001-7196-1923, Swinburne University of Technology), Krau (0000-0003-4886-7482, University of Johannesburg, autor correspondente), Umesh Bamel (0000-0001-8722-3028, International Management Institute)
Ano2022
Volume178
Páginas121599-121599
Data de publicação2022-03-07
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoTechnological Forecasting and Social Change (JOURNAL)
Identificadores do periódicoISSN: 0040-1625 • E-ISSN: 1873-5509
EditoraElsevier BV (PUBLISHER)
DOI10.1016/j.techfore.2022.121599
OpenAlexW4220710261
IdiomaEN
Citações recebidas78
Referências citadas163

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

Context (archaeology) · Data science · Economics · Fuzzy logic · Fuzzy set · Knowledge management · Machine learning · Management science · Political science · Qualitative comparative analysis · Scopus · Set (abstract data type) · Artificial Intelligence · Computer Science · Innovation and Knowledge Management · Intellectual Property and Patents · Qualitative Comparative Analysis Research