AI-ENABLED ONLINE SHOPPING FEATURES AND THEIR EFFECTS ON BRAND LOYALTY AND PURCHASE INTENTION IN FASHION RETAIL
Abstract
The growing use of artificial intelligence (AI) in online fashion retail has shifted its role from a purely functional technology to a strategic tool for building consumer–brand relationships; however, limited empirical research explains how AI-enabled online shopping features influence brand loyalty and purchase intention. Addressing this gap, this study investigates the effects of AI-enabled online shopping features on purchase intention, with brand loyalty as a mediating variable in the UK fashion retail context. Drawing on the Technology Acceptance Model and brand loyalty theory, AI-enabled features are conceptualised in terms of perceived usefulness. Using a quantitative, cross-sectional survey of UK consumers with prior experience purchasing from H&M’s online platform, the proposed relationships were tested through regression-based mediation analysis. The findings indicate that AI-enabled online shopping features perceived as useful have a significant positive effect on both brand loyalty and purchase intention, and that brand loyalty partially mediates the relationship between AI-enabled features and purchase intention. These results suggest that AI-driven personalisation and usefulness enhance not only functional shopping efficiency but also emotional and behavioural attachment to brands. By integrating brand loyalty into a TAM-based framework, this study extends technology adoption research and highlights the strategic importance of AI as a long-term brand-building mechanism in digital fashion retail.
JEL: M15; M31; L81; D91
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DOI: http://dx.doi.org/10.46827/ejefr.v10i2.2144
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