Systematic FsQCA Forecasting and its Performance

Abstract

There is a growing trend in utilizing fuzzy set/qualitative comparative analysis (fsQCA) as a research method for business and management studies, hence showing a need for a systematic procedure in fsQCA forecasting. This article presents a systematic forecasting procedure and introduces two methods (fixed and stepwise) to handle in-sample data. It offers three distinct forecasting approaches: 1) forecasting by solution, 2) forecasting by consistency, and 3) forecasting by ratio of sufficient condition. With data separation, predictive validity can be verified after adding a layer of rigor to the proposed forecasting procedure. The three approaches represent various perspectives for forecasting. Finally, this study suggests integrating the evaluation from all three approaches in order to have a holistic view of forecasting performance.