Emotional and Functional Values of Generative AI in Fashion: Effects on User Satisfaction and Continued Use

Generative artificial intelligence (AI) is reshaping fashion through personalized styling and interactive shopping. Yet the balance between its functional utility and emotional engagement remains underexplored. Drawing on expectation confirmation theory, this study examines how emotional and functional attributes affect satisfaction and continuance intention. Survey data from 297 consumers with experience using fashion AI assistants were analyzed via structural equation modeling. Findings show that personalization, emotional engagement, and information accuracy enhance expectation confirmation, while emotional engagement emerged as the strongest predictor of satisfaction. Satisfaction strongly predicts continuance intention, with gender partially moderating effects. The study extends expectation confirmation theory by incorporating emotional dynamics and provides implications for designing generative AI systems that foster satisfaction and long-term consumer engagement.