Adoption of smart voice assistants technology among Airbnb guests: A revised self-efficacy-based value adoption model (SVAM)

Dongmei Cao, Yan Sun, Edmund Goh, Rachel Wang, Kate Kuiavska

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

Smart technology applications in hospitality can leverage user experience values only if they are motivated to adopt the provided technology. This study aimed to understand Airbnb guests’ intentions to adopt smart voice assistants (SVAs) like Amazon Alexa or Google Home. Underpinned by social cognitive theory (SCT), a revised self-efficacy-based value adoption model (SVAM) was developed for the study. A survey sample of 255 UK Airbnb guests was analysed using PLS-SEM statistical technique. The results indicate that perceived functional value, perceived emotional value and perceived privacy risk were the significant determinants for Airbnb guests’ intention to adopt SVAs, while the effect of perceived social value was insignificant. Self-efficacy directly influenced SVA adoption intention among Airbnb guests and indirectly via the perceived values. Our multiple group analysis suggests that self-efficacy on perceived functional value contrasted significantly between everyday users and occasional users. This study is one of the pioneering empirical studies investigating guests’ technology adoption behaviour in the Airbnb context. Specifically, the revised SVAM model advances SCT literature and contributes to understanding smart technology adoption associated with Airbnb guests. Also, this study provides practical implications for Airbnb stakeholders to enhance the Airbnb guest experience value by using Airbnb smart technology applications.
Original languageEnglish
Article number103124
JournalInternational Journal of Hospitality Management
Volume101
Early online date13 Dec 2021
DOIs
Publication statusPublished - Feb 2022

Keywords

  • self-efficacy
  • smart voice assistant
  • perceived value
  • privacy risk
  • technology adoption

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