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  • Consumers acceptance of artificially intelligent (AI) device use in service delivery

    Author(s)
    Gursoy, Dogan
    Chi, Oscar Hengxuan
    Lu, Lu
    Nunkoo, Robin
    Griffith University Author(s)
    Nunkoo, Robin
    Year published
    2019
    Metadata
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    Abstract
    This study develops and empirically tests a theoretical model of artificially intelligent (AI) device use acceptance (AIDUA) that aims to explain customers’ willingness to accept AI device use in service encounters. The proposed model incorporates three acceptance generation stages (primary appraisal, secondary appraisal, and outcome stage) and six antecedents (social influence, hedonic motivation, anthropomorphism, performance expectancy, effort expectancy, and emotion). Utilizing data collected from potential customers, the proposed AIDUA model is tested. Findings suggest that customers go through a three-step acceptance ...
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    This study develops and empirically tests a theoretical model of artificially intelligent (AI) device use acceptance (AIDUA) that aims to explain customers’ willingness to accept AI device use in service encounters. The proposed model incorporates three acceptance generation stages (primary appraisal, secondary appraisal, and outcome stage) and six antecedents (social influence, hedonic motivation, anthropomorphism, performance expectancy, effort expectancy, and emotion). Utilizing data collected from potential customers, the proposed AIDUA model is tested. Findings suggest that customers go through a three-step acceptance generation process in determining whether to accept the use of AI devices during their service interactions. Findings indicate that social influence and hedonic motivation are positively related to performance expectancy while anthropomorphism is positively related to effort expectancy. Both performance and effort expectancy are significant antecedents of customer emotions, which determines customers’ acceptance of AI device use in service encounters. This study provides a conceptual AI device acceptance framework that can be used by other researchers to better investigate AI related topics in the service context.
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    Journal Title
    International Journal of Information Management
    Volume
    49
    DOI
    https://doi.org/10.1016/j.ijinfomgt.2019.03.008
    Subject
    Distributed computing and systems software
    Information systems
    Library and information studies
    Publication URI
    http://hdl.handle.net/10072/385138
    Collection
    • Journal articles

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