Impact of generative AI service adoption intent on user attitudes: Focusing on the Unified Theory of Acceptance and Use of Technology

Abstract

 The popularization of generative AI has led to significant social and industrial changes globally. As generative AI technology rapidly evolves, its influence is expected to grow, increasing the need for research on its acceptance and use. This study empirically analyzes the relationship between user attitudes and the adoption intent of generative AI services, offering insights into their utilization. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT), four factors—Performance Expectancy, Effort Expectancy, Facilitating Conditions, and Hedonic Motivation—were identified as components of adoption intent. Additionally, this study analyzed the causal relationship between these factors and user attitudes, mediated by users' perceived emotional and functional values. A structural equation model was constructed with data from 356 users of generative AI services in South Korea. The analysis revealed that Performance Expectancy and Facilitating Conditions influence user attitudes through emotional and functional value mediation. Effort Expectancy significantly affected functional value, while Hedonic Motivation influenced emotional value, both exhibiting mediating effects. Emotional value had a greater impact on user attitudes than functional value. These findings suggest that emotional experiences are critical in the adoption of generative AI services, highlighting the need for strategies to enhance user engagement and satisfaction.