This study aims to investigate how user experiences with generative AI assistants influence consumers’ perceived usefulness, perceived ease of use, and usage intention in a fashion shopping context. Drawing on the technology acceptance model (TAM), user experience was categorized into three dimensions—cognitive, affective, and relational—and their effects on consumers’ behavioral intention were examined. Data were collected through an online survey of 220 adult consumers and analyzed using structural equation modeling (SEM) with AMOS. The results were as follows. First, cognitive and affective experiences had significant positive effects on both perceived usefulness and perceived ease of use, whereas relational experience did not. Second, both perceived usefulness and perceived ease of use had significant positive effects on usage intention, with perceived usefulness derived from cognitive experience having the strongest influence. These findings suggest that consumers place greater importance on accurate and useful information and positive emotional experiences than relational connections with AI assistants. This study contributes to the literature by extending the TAM through the incorporation of multidimensional user experiences in generative AI services within fashion shopping contexts. It also provides practical implications for fashion companies by suggesting strategic directions for effectively designing and implementing AI assistant services to enhance consumers’ user experience and increase usage intention.

Abstract
I. Introduction
Ⅱ. Theoretical Framework andHypothesis Development
    1. TAM and AI assistants
    2. Multidimensional user experience in generativeAI assistants
    3. Effects of user experience on perceived easeof use and perceived usefulness
    4. Perceived usefulness, perceived ease of use,and behavioral intention
Ⅲ. Research Method
    1. Research model
    2. Data collection and analysis method
Ⅳ. Research Results
    1. General characteristics of subjects
    2. Results of the measurement model
    3. Results of the structural model
Ⅴ. Conclusion
References

• Shinyoung Park(Visiting Professor, Dept. of Fashion Design & Marketing, Seoul Women’s University, Korea) | 박신영 (서울여자대학교 패션산업학과 초빙강의교수)
• Su-yun Shin(Professor, Dept. of Fashion Design & Marketing, Seoul Women’s University, Korea) | 신수연 (서울여자대학교 패션산업학과 교수) Corresponding author