The AI Revolution in Virtual Try-Ons: A Means–End Chain Model Perspective

Leading brands have begun to implement artificial intelligence-driven virtual try-on (AI VTO) technology, which helps reduce returns and increase conversion rates, repeat purchases, and customer loyalty. This research aimed to examine how retail user experience with specific perceived quality factors of AI VTOs influences users’ value equity and downstream loyalty and to investigate the moderating effects of clothing in relation to the self as structure and concern for physical appearance, based on a Means–End Chain model. Data were collected from 509 U.S. online apparel shoppers using a consumer panel. Structural equation modeling and multigroup analysis were used for data analysis. This study found that pragmatic quality, hedonic quality, and customization positively affected the value equity for AI VTOs. Value equity had a positive effect on satisfaction with AI VTOs, which had a positive impact on repurchase loyalty using AI VTOs and brand loyalty. The effect of value equity for AI VTOs on satisfaction was found to be stronger among users with high levels of clothing in relation to the self as structure than among those with low levels. This study confirmed the applicability of the Means–End Chain model to the quality–value–satisfaction–loyalty chain in the AI VTO context. This study helps identify which features of AI VTO systems most significantly affect cognitive and affective assessment and behavioral outcomes.