This study aims to examine customers’ willingness to engage in service interactions enabled by artificial intelligence (AI) controlled voice assistants (VA). Drawing on the tenets of dual-factor theory, this study measures the impact of both enablers and inhibitors – mediated by trust in Alexa – on customers’ intentions to transact through VAs.
Data from a survey of 290 users of VAs from Japan was collected through “Macromill”. The authors used a covariance-based path analysis technique for data analysis after establishing the validity and reliability of the measures.
The results of this study demonstrate that convenience and status-seeking act as enablers and positively influence trust in VAs, whereas risk barrier acts as an inhibitor and negatively influence trust in VAs. In turn, trust in VAs positively influences the intention to use VAs for transactional service interactions. This association is positively moderated by technology comfort.
This study applies dual-factor theory to the context of VAs – a context that scholars have, to date, examined solely from a technology adoption perspective. For the first time, the authors adopt a dual-factor approach to identify a new set of antecedents for customers’ intentions to use VAs for transactional service interactions.
The paper is an output of the project NFP313011BWN6 “The implementation framework and business model of the Internet of Things, Industry 4.0 and smart transport”.
Malodia, S., Ferraris, A., Sakashita, M., Dhir, A. and Gavurova, B. (2023), "Can Alexa serve customers better? AI-driven voice assistant service interactions", Journal of Services Marketing, Vol. 37 No. 1, pp. 25-39. https://doi.org/10.1108/JSM-12-2021-0488
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