Algorithmic Personalization and Impulse Buying in Social Commerce: The Mediating Role of Hedonic Value under the S-O-R Framework - Maria Helena Chandra; Conchita Junita Chandra (Politeknik Ganesha Medan, 2026)
The rapid expansion of social commerce, particularly TikTok Shop, has intensified the role of algorithmic personalization in shaping consumer behavior, yet its affective mechanisms remain insufficiently examined, especially in emerging markets. Prior studies largely focus on general digital stimuli, leaving a limited understanding of how algorithm-driven personalization specifically triggers impulse buying through emotional processes. Drawing on the Stimulus–Organism–Response (S-O-R) framework, this study investigates the effect of algorithmic personalization on impulse buying, with hedonic value as a mediating variable. This study contributes by extending the S-O-R framework into algorithm-driven social commerce by empirically validating the mediating role of hedonic value in AI-personalized environments. Using survey data from 150 active TikTok Shop users, the study employs regression-based mediation analysis with SPSS. The results indicate that algorithmic personalization significantly affects hedonic value (? = 0.62, p < 0.001) and impulse buying (? = 0.41, p < 0.001), while hedonic value also significantly influences impulse buying (? = 0.47, p < 0.001). The model explains 53% of the variance in impulse buying (R² = 0.53), with mediation analysis confirming a partial mediating effect of hedonic value (p < 0.001). These findings demonstrate that algorithmic personalization operates not only as a cognitive trigger but also as an affective driver that enhances hedonic experiences, thereby increasing impulsive purchasing behavior. This study extends the S-O-R framework in algorithm-driven commerce and provides actionable insights for optimizing personalization strategies in social commerce platforms.