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Enablers and Inhibitors of AI-Powered Voice Assistants: A Dual-Factor Approach by Integrating the Status Quo Bias and Technology Acceptance Model

Author

Listed:
  • Janarthanan Balakrishnan

    (National Institute of Technology – Tiruchirappalli)

  • Yogesh K. Dwivedi

    (Swansea University Bay Campus
    Pune & Symbiosis International (Deemed University))

  • Laurie Hughes

    (Swansea University Bay Campus)

  • Frederic Boy

    (Swansea University Bay Campus)

Abstract

This study investigates the factors that build resistance and attitude towards AI voice assistants (AIVA). A theoretical model is proposed using the dual-factor framework by integrating status quo bias factors (sunk cost, regret avoidance, inertia, perceived value, switching costs, and perceived threat) and Technology Acceptance Model (TAM; perceived ease of use and perceived usefulness) variables. The study model investigates the relationship between the status quo factors and resistance towards adoption of AIVA, and the relationship between TAM factors and attitudes towards AIVA. A sample of four hundred and twenty was analysed using structural equation modeling to investigate the proposed hypotheses. The results indicate an insignificant relationship between inertia and resistance to AIVA. Perceived value was found to have a negative but significant relationship with resistance to AIVA. Further, the study also found that inertia significantly differs across gender (male/female) and age groupings. The study's framework and results are posited as adding value to the extant literature and practice, directly related to status quo bias theory, dual-factor model and TAM.

Suggested Citation

  • Janarthanan Balakrishnan & Yogesh K. Dwivedi & Laurie Hughes & Frederic Boy, 2024. "Enablers and Inhibitors of AI-Powered Voice Assistants: A Dual-Factor Approach by Integrating the Status Quo Bias and Technology Acceptance Model," Information Systems Frontiers, Springer, vol. 26(3), pages 921-942, June.
    • Handle: RePEc:spr:infosf:v:26:y:2024:i:3:d:10.1007_s10796-021-10203-y
    DOI: 10.1007/s10796-021-10203-y
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    Cited by:

    1. Vincent Charles & Nripendra P. Rana & Ilias O. Pappas & Morten Kamphaug & Keng Siau & Kenth Engø-Monsen, 2024. "The Next ‘Deep’ Thing in X to Z Marketing: An Artificial Intelligence-Driven Approach," Information Systems Frontiers, Springer, vol. 26(3), pages 851-856, June.

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