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PurposeIn this paper, the authors aim to present a novel methodological tool – necessary condition analysis (NCA) to aid managerial psychology researchers in properly testing necessity statements.Design/methodology/approachThe authors employ NCA to analyze whether three basic psychological needs for autonomy, competence and relatedness are necessary for work engagement.FindingsThe authors illustrate the value and application of NCA by revealing that basic psychological needs for autonomy, competence and relatedness are necessary for work engagement, as proposed by self-determination theory (SDT).Originality/valueThe authors illustrate the importance of the sufficiency-necessity distinction and the relevance of a necessity logic in managerial psychology. They also discuss NCA's methodological implications for managerial psychology research, theory and practice.

Based on the INUS theory of causality, the search target of qualitative comparative analysis (QCA) is to find all the minimally sufficient conditions for the outcome’s occurrence in a data set, where the condition’s sufficiency, the necessity of the condition’s components, and the completeness of the solution are three core requirements. However, QCA’s current top-down approach, which relies on a truth table and Boolean minimization, cannot meet the main objective of QCA. Conditions generated by the top-down approach can be insufficient for the outcome or contain unnecessary components that can be removed. We found evidence supporting our arguments by examining the correctness of top-down QCA in Study 1. Then, we show that QCA can also proceed with a “bottom-up” search strategy in sufficiency analysis, similar to coincidence analysis (CNA). We contrast solutions of the top-down and bottom-up QCA approaches by analyzing a simulated crisp-set data set in Study 2 and a real-world fuzzy-set data set in Study 3. Both results show that only the bottom-up approach can produce all the minimally sufficient conditions. We contribute to the ongoing debate pertain QCA solution types and QCA algorithms by critically evaluating the limitations of QCA’s top-down approach and introducing a bottom-up approach for QCA.

The traditional algorithm for Qualitative Comparative Analysis (QCA) relies on a truth table and Boolean minimization. Many problems arise when researchers analyze causal complexity by adopting this “top-down” approach. However, a truth table and Boolean minimization are not necessary procedures for causal inference in configurational data. This study shows that QCA can also proceed with a “bottom-up” search strategy in the analysis of sufficiency, similar to Coincidence analysis (CNA). Based on a data set with limited diversity, noise, and varying frequency, this study also uses an empirical demonstration to derive QCA solutions by the new algorithm. It shows that the bottom-up approach is superior to the traditional top-down approach in many respects: it avoids many problems rooted in the traditional top-down approach and enables scholars to get all the redundancy-free sufficient solutions. This article provides a bottom-up approach to deriving QCA solutions, contributing to the ongoing debate pertain QCA solution types and QCA algorithms.