Personalized recommendations and consumer trust: The role of perceived control and locus of control

With the rapid growth of the platform economy, personalized recommendation has emerged as a key strategy for enhancing user experience and improving conversion efficiency. While such systems significantly improve consumer decision-making efficiency, they have also raised increasing concerns regarding the authenticity of choice and the potential for algorithmic control. Drawing on perceived control theory, this study develops a theoretical model in which perceived control serves as a mediating mechanism between platform design features and consumer evaluations. Specifically, two structural features of personalized recommendation—recommendation fit and choice visibility—are identified as key antecedents. A 2 × 2 scenario-based experiment is conducted to manipulate users' perceived recommendation environments and assess their psychological responses and attitudinal outcomes. The results show that both recommendation fit and choice visibility significantly enhance perceived control, which in turn increases recommendation satisfaction and subsequently strengthens platform trust. This study contributes to the literature by empirically validating the causal effects of platform design on consumer psychological mechanisms and attitudinal responses. It also extends the application of perceived control theory to the context of algorithmic recommendation and offers theoretical insights and practical implications for optimizing user experience through more transparent and controllable recommendation environments.