Examining the role of consumer motivations to use voice assistants for fashion shopping: The mediating role of awe experience and eWOM

P. Kautish, S. Purohit, R. Filieri () and Y. Dwivedi ()
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R. Filieri: Audencia Business School

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Abstract: Artificial intelligence-enabled voice assistant services have received notable scholarly attention. Fashion retailers offer AI-based voice assistants to facilitate online shoppers. However, the consumer motivations to use digital voice assistants and their effect on the purchase intentions of online fashion shoppers are unexplored. To bridge this literature gap, this study presents a unique theoretical model grounded in the consumer innovativeness concept, broaden-and-built theory, and stimulus-organism-response model to explore the effect of motivated consumer innovativeness to use digital voice assistants on purchase intention and awe experience of online shoppers. The study used data collected from 538 users of digital voice assistants for online shopping of fashion products. Structural equation modeling analysis revealed that the functional, hedonic, social, and cognitive motivated consumer innovativeness for using voice assistants affects purchase intention and awe experience. Further, the awe experience mediates the relationship between motivated consumer innovativeness and purchase intention; and electronic word-of-mouth mediates the relationship between awe experience and purchase intention. The study theoretically contributes to the extant literature on consumer innovativeness, AI-based voice assistants, and fashion shopping. The findings offer insights to fashion retailers for improved use of voice assistants by online shoppers.

Keywords: AI-enabled services; Voice assistants; Motivated consumer innovativeness; Awe experience; WOM; Purchase intentions (search for similar items in EconPapers)
Date: 2023-05
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Published in Technological Forecasting and Social Change, 2023, ⟨doi.org/10.1016/j.techfore.2023.122407⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04713694

DOI: 10.1016/j.techfore.2023.122407

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