A Comparative Study on the User Experience of Virtual Fitting: The Effects of Fitting Types on UX Factors and Purchase Intention in Fashion E-commerce

Objective: This study aims to compare the effects of three primary fitting types in fashion e-commerce (traditional fitting model images, avatar-based virtual fitting, and image-compositing-based virtual fitting) on the user experience (UX) and purchase intention. It also seeks to identify the core UX drivers of purchase intention for each type.<br/>Background: As the online fashion market has grown, providing a differentiated UX has become a key competitive advantage. However, online fashion shopping faces the inherent challenge of discrepancies between on-screen images and actual products, as consumers cannot directly check factors like fit and material. Virtual fitting technology has emerged as a promising solution to this problem, yet there is a lack of research directly comparing different types of virtual fitting with traditional methods.<br/>Method: An online survey was conducted, and 231 valid responses were collected and analyzed. The study used demonstration videos of three fitting types (model image, avatar fitting, and image-compositing) as experimental stimuli. Repeated measures ANOVA was performed to test the differences in key UX factors (usefulness, perceived realism, trust, satisfaction, psychological distance, and continuous use intention) across the fitting types. Stepwise multiple regression analysis was used to identify the main predictors of purchase intention for each type.<br/>Results: Avatar-based virtual fitting provided a superior overall UX, receiving the most positive evaluations across most UX metrics such as usefulness, realism, trust, and satisfaction. While consumer innovativeness was positively associated with purchase intention, satisfaction, and trust, it did not moderate the effects of the fitting types. The most critical UX factor predicting purchase intention varied by fitting type: satisfaction for model fitting images, trust for avatar fitting, and continuous use intention for image-compositing fitting, which was also negatively influenced by psychological distance.<br/>Conclusion: The type of fitting solution significantly impacts the consumer's UX. Avatar-based fitting is currently the most preferred method by consumers, largely due to the sense of control and engagement it offers. Furthermore, the key psychological drivers that lead to a purchase decision are different for each fitting technology.<br/>Application: The results can guide the strategic implementation and marketing of different virtual fitting technologies to enhance the user experience in fashion e-commerce.