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Integrating qualitative comparative analysis (QCA) and fuzzy cognitive maps (FCM) to enhance the selection of independent variables

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  • Ferreira, Fernando A.F.
  • Jalali, Marjan S.
  • Ferreira, João J.M.

Abstract

This study proposes the use of fuzzy cognitive maps (FCMs) in qualitative comparative analysis (QCA) applications to enhance the selection of independent variables in the QCA framework. QCA techniques hold great potential to identify the causal models that exist among different but comparable cases. Due to the complexity of causality issues, however, such techniques may not be able to uncover the “true” causal foundation of a given phenomenon. FCMs typically offer a fuller view of the cause-and-effect relationships between variables, thus allowing for a better understanding of their behavior; for instance, the manner in which variables relate to each other, or the measure of their intensity. This study thus proposes that such maps can be a useful support in the selection of independent variables for a QCA model, and provides specific guidelines and an illustrative example of how to integrate FCMs in QCA applications.

Suggested Citation

  • Ferreira, Fernando A.F. & Jalali, Marjan S. & Ferreira, João J.M., 2016. "Integrating qualitative comparative analysis (QCA) and fuzzy cognitive maps (FCM) to enhance the selection of independent variables," Journal of Business Research, Elsevier, vol. 69(4), pages 1471-1478.
    • Handle: RePEc:eee:jbrese:v:69:y:2016:i:4:p:1471-1478
    DOI: 10.1016/j.jbusres.2015.10.127
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    9. Alexander Tselykh & Vladislav Vasilev & Larisa Tselykh & Fernando A. F. Ferreira, 2022. "Influence control method on directed weighted signed graphs with deterministic causality," Annals of Operations Research, Springer, vol. 311(2), pages 1281-1305, April.
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