The impact of algorithm-based personalization on repurchase intention on e-commerce platforms: the roles of perceived value, perceived intrusiveness, perceived control, and trust

This study examines the effects of algorithm-based personalization on repurchase intention in the context of general e-commerce platforms in Vietnam. It investigates how personalization quality and algorithmic transparency relate to perceived value, perceived intrusiveness, perceived control, trust, and subsequent repurchase intention. Using a quantitative approach and covariance-based structural equation modeling (CB-SEM), the findings show that personalization quality positively affects both perceived value and perceived intrusiveness, while perceived intrusiveness negatively affects trust. Algorithmic transparency positively affects perceived control, which in turn strengthens trust. The study contributes an integrated mechanism-based model that traces benefit, privacy-cost, and control-related pathways to repurchase intention in e-commerce. Privacy Calculus Theory is employed as an interpretive lens for understanding the coexistence of benefit and privacy-cost evaluations rather than as a directly operationalized benefit-cost comparison construct.