Understanding consumers' acceptance of automated technologies in service encounters: Drivers of digital voice assistants adoption.

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Date: Jan. 2021
Publisher: Elsevier B.V.
Document Type: Report; Brief article
Length: 281 words

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Abstract :

Keywords Automated technologies; Artificial intelligence; Digital voice assistants; Service robot acceptance model; Drivers Highlights * Drivers of AI digital voice assistants' adoption in service encounters are examined. * The Service Robot Acceptance Model (sRAM) is validated and extended. * Functional, social and relational elements explain 88% of user acceptance variance. * Customer-robot rapport influence is validated, but humanness may not drive adoption. * User experience and preference for human interactions play a moderating role. Abstract Customers increasingly orchestrate their everyday activities with the support of technology, with services increasingly adopting AI-based applications. Yet, research is still in its infancy and has been largely conceptual. Therefore, based on data collected from 238 young consumers, analyzed using PLS-SEM, this study focuses on users' motivations to adopt intelligent digital voice assistants in service encounters. Findings show that functional, social and relational elements drive adoption, untangle crossover effects between them and reveal the moderating role of experience and need for human interaction. While empirically validating and extending the Service Robot Acceptance Model by Wirtz and colleagues, this study provides evidence that anthropomorphism is not universally positive and adds a new perspective regarding the underexplored role of customer-robot rapport building. The study contributes to a more holistic understanding of digital voice assistants' adoption and provides managerial guidance on how to successfully implement such technologies. Author Affiliation: School of Economics and Management, University of Porto, Rua Dr. Roberto Frias, s/n, 4200-464 Porto, Portugal * Corresponding author. Article History: Received 2 January 2020; Revised 25 August 2020; Accepted 28 August 2020 (footnote)[white star] This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Byline: Teresa Fernandes [tfernandes@fep.up.pt] (*), Elisabete Oliveira

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Gale Document Number: GALE|A648491656