Understanding customers’ intentions to use AI-enabled services in online fashion stores – a longitudinal study

Mustafeed Zaman, K. Mohamed Jasim, Rajibul Hasan, Shahriar Akter and Demetris Vrontis
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Mustafeed Zaman: Métis Lab EM Normandie - EM Normandie - École de Management de Normandie = EM Normandie Business School
K. Mohamed Jasim: Trinity College Dublin
Rajibul Hasan: Métis Lab EM Normandie - EM Normandie - École de Management de Normandie = EM Normandie Business School
Shahriar Akter: University of Wollongong [Australia]
Demetris Vrontis: University of Nicosia

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Abstract: Purpose Artificial intelligence (AI) services are vital in enhancing customer experience and purchase intentions in the international online fashion retail sector. This study explores customers' intentions to use AI-enabled services, focusing on transaction utility, trust and product uniqueness across the customer journey in the context of international online fashion stores. This study also assesses how privacy moderates customer intentions. Design/methodology/approach This study adopted a longitudinal research design and purposive sampling technique to collect a total of 566 participants. The final data were analyzed using IBM SPSS Amos version 21 software. Findings The study highlights the significance of transaction utility, trust and product uniqueness in AI integration across the customer journey (pre-purchase, during purchase and post-purchase stages). Most of the direct relationships are significant, except the relationship between the during purchase and post-purchase stages. With a few exceptions, AI integration commonly does not mediate the relationship between antecedents and intention to use AI-enabled services. Privacy moderates AI integration in post-purchase, during purchase and intention to use AI-enabled services, except in the pre-purchase stage. Originality/value This study bridges important gaps in the literature by integrating AI-enabled services and customer behavior, contributing to a broader knowledge of customer interactions in global e-commerce fashion stores. The study examines multiple attributes that impact intention, such as transaction utility, trust, product uniqueness, AI integration in three stages of purchases (pre-purchase, during purchase and post-purchase) and privacy, using three major theories: mental accounting theory, trust commitment theory and commodity theory.

Keywords: Online fashion stores; AI-enabled services; AI integration; Customer intention; Longitudinal study (search for similar items in EconPapers)
Date: 2025-03-05
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Published in International Marketing Review, 2025, ⟨10.1108/IMR-04-2024-0118⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04993474

DOI: 10.1108/IMR-04-2024-0118

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