Explaining Consumer Continuance Intention toward AI-Enabled E-Commerce - Angtyasti Jiwasiddi (Journal of Social Research, 2026)
The increasing integration of artificial intelligence (AI) into e-commerce platforms has transformed consumer-platform interactions. However, empirical understanding of how AI-enabled service experiences shape consumer continuance intention remains limited, particularly in emerging markets. This research aims to examine the effects of AI service experience dimensions AI responsiveness, AI reliability, and AI empathy on consumer continuance intention toward AI-enabled e-commerce platforms. It also investigates trust as a mediating mechanism, considering the roles of novelty seeking and parasocial interaction. A quantitative, cross-sectional survey was conducted using data from the Omnibus Survey on AI Usage in E-Commerce 2024 in Indonesia. A total of 725 valid responses were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that AI responsiveness, AI reliability, and AI empathy positively influence trust, with AI empathy showing the strongest effect. All three AI service dimensions also have direct positive effects on continuance intention. Trust significantly influences continuance intention and partially mediates the relationships between AI service dimensions and continuance intention. Additionally, novelty seeking and parasocial interaction both positively affect continuance intention. The findings suggest that continuance intention toward AI-enabled e-commerce platforms is shaped by a combination of experiential, cognitive, motivational, and relational factors. The study extends information systems continuance theory by demonstrating that experiential and relational evaluations of AI services complement trust-based mechanisms in explaining sustained consumer engagement. These insights are important for the design and management of AI-enabled e-commerce platforms in emerging digital markets.