Effect of Motivation to Use Generative AI on Service Experiences and Outcomes : Focusing on the Moderating Effect of Technostress

This study investigates how different usage motivations of generative artificial intelligence influence users’ perceived performance through the mediating role of service experience. Additionally, it examines the moderating effect of techno-stress in the relationship between service experience and outcomes. Grounded in the Uses and Gratifications Theory, the study employs a quantitative research design based on an online survey conducted in China and South Korea. Data were analyzed using structural equation modeling and moderation mediation analysis. The results reveal that most AIGC usage motivations, such as task efficiency, information seeking, and personalization motivation, enhance service experience. In turn, service experience mediates the effect of these motivations on user outcomes. Furthermore, techno-stress was found to moderate the relationship between service experience and performance, such that high levels of techno-stress weaken the positive impact of service experience. This study contributes to the growing literature on AIGC by offering a comprehensive model that links user motivation, experiential interaction, and performance outcomes. It provides both theoretical insight into the consumer-AI interaction mechanism and practical implications for AI product designers and marketers seeking to enhance service value in the era of intelligent automation.