Artificial intelligence (AI) has become a transformative force in fashion e-commerce, reshaping how consumers interact with brands across design, marketing, and retail. However, a systematic synthesis of recent empirical research on consumer interactions with AI in fashion remains underexplored. This study utilizes both systematic and bibliometric approaches to review 50 empirical articles published between 2022 and 2025, retrieved from Scopus and Web of Science, to reveal dominant topics and intellectual structures. Guided by the Theme–Theory–Method (TTM) framework, the review employs systematic content analysis complemented by bibliometric mapping to identify key themes, trends, theories, and methods in existing AI-related consumer studies in fashion. AI-powered chatbots and general AI services are most frequently examined, with the Technology Acceptance Model and Stimulus-Organism-Response framework as leading theoretical lenses. Quantitative research designs prevail, though qualitative and mixed-method approaches are emerging. Based on the findings and identified gaps, a TTM-guided future research agenda is proposed to inform theoretically grounded and practically relevant investigations in the fashion domain, meanwhile contributing to the broader advancement of e-commerce scholarship and practice. This study offers the first integrated synthesis of AI-related empirical consumer research in the contemporary fashion context and the Generative AI era.
Zhang et al. (Tue,) studied this question.