Exploring the moderating effects of flow experience in strengthening artificial intelligence-based customer interactions on social commerce

Abstract

Purpose This study aims to investigate the effect of utilizing artificial intelligence (AI) in social commerce on increasing consumer engagement, specifically in terms of engagement and conversion rates. It also examines the role of flow experience in strengthening this relationship. Design/methodology/approach This study collected data from 354 Indonesian social commerce shoppers. Covariance based Structural Equation Modeling was employed to analyze the data following the two-stage procedure. First, the measurement model was assessed for validity and reliability. Second, the structural hypothesis model was evaluated to test the proposed hypotheses. Findings The utilization of AI significantly influences social commerce visitor interactions in terms of engagement and conversion. Both variables can enhance customer satisfaction, thereby impacting repurchase intention. Flow experience also strengthens the relationship between engagement and satisfaction, offering practical guidance for creating immersive and enjoyable user experiences. Originality/value This study extends the existing social commerce and AI literature by positioning flow experience as a moderator, explaining how and when engagement and conversion translate into satisfaction. Unlike previous models that primarily focus on direct effects, this research introduces psychological mechanisms that capture the conditional and experiential nature of AI-driven interactions, thereby offering a more comprehensive understanding of consumer behavior in social commerce.