Regulatory Fit in Explainable AI-Based Recommendations: A Comparative Study of Personalized Explanation Strategies on User Experience and Persuasion

Explainable AI-based recommendations aim to enhance transparency and user trust by offering human-understandable justifications. While most explanation strategies rely on static templates, recent research highlights the value of personalized explanations that reflect users’ psychological characteristics. Drawing on regulatory fit theory, this study examines how aligning users’ regulatory focus with explanation strategies influences user experience (UX) and persuasion in explainable AI-based recommendations. Two online experiments compared a process-based approach using emotional versus rational appeals (Experiment 1) and an outcome-based approach using promotion- versus prevention-framed explanations (Experiment 2). Promotion-dominant users reported lower hedonic UX when exposed to promotion-framed explanations, indicating a reverse-fit effect. In contrast, rational appeals consistently enhanced satisfaction and adoption intention regardless of regulatory focus. These findings suggest that motivational alignment does not uniformly improve user responses. Through a controlled comparison of two regulatory fit-generation approaches, this study provides design insights for developing personalized, user-sensitive explanations in explainable AI-based recommendations.