BUILDING TRUST THROUGH AI: HOW INTERACTION FEATURES DRIVE IMPULSE BUYING IN LIVESTREAM COMMERCE

Abstract

This study examines how AI-driven interaction features shape consumer trust and impulse buying behavior in livestream commerce within the Stimulus-Organism-Response (S-O-R) framework. AI-driven interaction is conceptualized through three dimensions: hedonic interaction, personalization, and adaptive support. Data were collected from 480 consumers with prior livestream shopping experience in Vietnam and analyzed using Structural Equation Modeling (SEM). The results show that all three AI-driven interaction features significantly enhance consumer trust and directly influence impulse buying behavior. Adaptive support has the strongest effect on trust, whereas personalization exerts the strongest direct effect on impulse buying. Trust also has a significant positive effect on impulse buying behavior. These findings demonstrate that different AI-driven interaction features play distinct roles in shaping consumers’ cognitive evaluations and behavioral responses in livestream commerce. The study extends the S-O-R framework to AI-enabled livestream environments and highlights the importance of designing AI interactions that combine responsiveness, personalization, and engaging experiences to strengthen consumer trust and stimulate purchasing behavior.