Decoding Customer Intentions Towards Adoption of Artificial Intelligence-powered Fashion Retail Applications: A Motivation–Inhibition Perspective

Artificial intelligence (AI) in fashion retail has transformed consumer interaction, yet limited research explains why customers simultaneously feel motivated and inhibited towards AI-powered applications. This study examines the factors shaping adoption intention in AI-based fashion retailing by applying the motivation–inhibition perspective. A qualitative phase involving 63 Indian users of AI-enabled retail applications identified three motivators, namely personalized recommendations, feature appeal and positive shopping experience, and three inhibitors, namely privacy concern, technology anxiety and complexity in use. These themes guided the development of a measurement scale, which was validated through a two-phase survey of 971 Indian consumers using exploratory factor analysis, confirmatory factor analysis and structural equation modelling. The results reveal that feature appeal ( β = 0.646, p < .05) significantly enhances adoption intention, while privacy concern significantly inhibits it ( β = –0.681, p < .05). The study concludes that adoption intention is best understood through the coexistence of both motivators and inhibitors. Managerially, the findings highlight the need to improve feature-rich experiences, strengthen personalization engines and reduce friction and privacy-related concerns in AI-powered fashion applications.