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The progression of generative artificial intelligence (GenAI) has lowered the technical barriers for consumers to independently customize clothing. Its profit potential has driven fashion brands to adopt GenAI-based interactive customization services. However, the factors influencing consumers’ behavioral intention toward GenAI-supported clothing customization services (GAICCS) remain underexplored. In this study, an extended framework is constructed based on the Technology Acceptance Model (TAM), incorporating trust, perceived enjoyment, service quality, and social influence to examine their effects on behavioral intention across cognitive, affective, service, and social dimensions. A total of 692 valid responses were collected from Chinese consumers via convenience sampling. A multi-stage SEM-ANN analysis was conducted to test the model. The results show that perceived usefulness (β = 0.237, p < 0.001), perceived ease of use (β = 0.332, p < 0.001), social influence (β = 0.181, p < 0.001), and service quality (β = 0.093, p < 0.05) significantly enhance behavioral intention, with perceived ease of use being the most influential. Perceived ease of use and perceived usefulness also act as key mediators. Unlike previous studies, satisfaction showed no significant effect. This study underscores the importance of functional performance, social influence, and service quality in driving GAICCS adoption, providing theoretical insights and multi-level practical guidance for promoting GenAI in fashion contexts.
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Stored only when both papers are in the corpus — 4 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
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