Understanding AI visual search adoption in secondhand fashion retail

Purpose Secondhand fashion markets are characterised by high uncertainty, heterogeneous products and fragile trust, complicating consumers’ search and evaluation processes. This study employs the technology acceptance model while incorporating consumer trust to explain AI visual search adoption in a high-uncertainty secondhand C2C retail context. Design/methodology/approach Data were obtained from 299 Chinese consumers, and the model was tested using partial least squares structural equation modelling. Findings Perceived intelligence and perceived enjoyment enhance perceived ease of use, perceived usefulness and consumer trust, indicating their roles as core cognitive and affective drivers of adoption. Perceived risk negatively influences consumer trust, whereas perceived complexity reduces perceived ease of use. Perceived ease of use, perceived usefulness and consumer trust collectively shape adoption intention. Additionally, technology-related discomfort attenuates the effect of perceived ease of use on perceived usefulness. Practical implications The findings highlight the importance of accurate AI-powered visual search, trust-building interface design and user support in facilitating adoption of AI visual search under conditions of high information uncertainty. Originality/value This research extends the TAM by demonstrating that consumer trust plays a central role in AI visual search adoption under conditions of high information uncertainty, whereas technology-related discomfort constrains the translation of perceived ease of use into perceived usefulness.