Behavioral drivers of cryptocurrency investment intentions: an integrated PLS-SEM and NCA approach

Purpose This research examines how behavioral factors shape cryptocurrency investment intention, with particular emphasis on overconfidence, fear of missing out (FOMO), crypto decision bias (CID) and gamified trading. Design/methodology/approach A latent variable model was developed using exploratory factor analysis (EFA) and tested using partial least squares structural equation modeling (PLS-SEM) on survey data collected from 208 individual cryptocurrency investors in Croatia. Necessary condition analysis (NCA) was additionally applied to complement the sufficiency logic of PLS-SEM. Findings The results show that FOMO and gamified trading have significant positive effects on cryptocurrency investment intention, while overconfidence and crypto decision bias show nonsignificant effects. NCA findings further indicate that FOMO and gamified trading represent necessary conditions for achieving higher levels of investment intention. Originality/value This study contributes to behavioral finance and cryptocurrency literature by showing the differential roles of affective, cognitive and platform-related behavioral factors in cryptocurrency investment intention. It further combines sufficiency and necessity perspectives to distinguish factors associated with higher investment intention from necessary conditions for higher levels of intention.