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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 / Janarthanan Balakrishnan, Yogesh Dwivedi, Laurie Hughes, Frederic Boy

Information Systems Frontiers

Swansea University Authors: Yogesh Dwivedi, Laurie Hughes, Frederic Boy

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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 thr...

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Published in: Information Systems Frontiers
ISSN: 1387-3326 1572-9419
Published: Springer Science and Business Media LLC 2021
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URI: https://cronfa.swan.ac.uk/Record/cronfa57813
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spelling 2021-10-29T11:06:39.3793370 v2 57813 2021-09-08 Enablers and Inhibitors of AI-Powered Voice Assistants: A Dual-Factor Approach by Integrating the Status Quo Bias and Technology Acceptance Model d154596e71b99ad1285563c8fdd373d7 0000-0002-5547-9990 Yogesh Dwivedi Yogesh Dwivedi true false 7abaa0ecff88cdfd7a208d27a8b62173 0000-0002-0956-0608 Laurie Hughes Laurie Hughes true false 43e704698d5dbbac3734b7cd0fef60aa 0000-0003-1373-6634 Frederic Boy Frederic Boy true false 2021-09-08 BBU 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. Journal Article Information Systems Frontiers 0 Springer Science and Business Media LLC 1387-3326 1572-9419 Voice assistants; Artificial intelligence; Dual-factor model; Status quo bias theory; Resistance to change 15 10 2021 2021-10-15 10.1007/s10796-021-10203-y COLLEGE NANME Business COLLEGE CODE BBU Swansea University 2021-10-29T11:06:39.3793370 2021-09-08T12:44:47.5682115 School of Management Business Janarthanan Balakrishnan 1 Yogesh Dwivedi 0000-0002-5547-9990 2 Laurie Hughes 0000-0002-0956-0608 3 Frederic Boy 0000-0003-1373-6634 4 57813__21364__5539041b11704560ae08a5b6ea4b50d7.pdf 57813.pdf 2021-10-29T11:05:39.3014380 Output 903496 application/pdf Version of Record true © The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License true eng http://creativecommons.org/licenses/by/4.0/
title Enablers and Inhibitors of AI-Powered Voice Assistants: A Dual-Factor Approach by Integrating the Status Quo Bias and Technology Acceptance Model
spellingShingle Enablers and Inhibitors of AI-Powered Voice Assistants: A Dual-Factor Approach by Integrating the Status Quo Bias and Technology Acceptance Model
Yogesh, Dwivedi
Laurie, Hughes
Frederic, Boy
title_short Enablers and Inhibitors of AI-Powered Voice Assistants: A Dual-Factor Approach by Integrating the Status Quo Bias and Technology Acceptance Model
title_full Enablers and Inhibitors of AI-Powered Voice Assistants: A Dual-Factor Approach by Integrating the Status Quo Bias and Technology Acceptance Model
title_fullStr Enablers and Inhibitors of AI-Powered Voice Assistants: A Dual-Factor Approach by Integrating the Status Quo Bias and Technology Acceptance Model
title_full_unstemmed Enablers and Inhibitors of AI-Powered Voice Assistants: A Dual-Factor Approach by Integrating the Status Quo Bias and Technology Acceptance Model
title_sort Enablers and Inhibitors of AI-Powered Voice Assistants: A Dual-Factor Approach by Integrating the Status Quo Bias and Technology Acceptance Model
author_id_str_mv d154596e71b99ad1285563c8fdd373d7
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author_id_fullname_str_mv d154596e71b99ad1285563c8fdd373d7_***_Yogesh, Dwivedi
7abaa0ecff88cdfd7a208d27a8b62173_***_Laurie, Hughes
43e704698d5dbbac3734b7cd0fef60aa_***_Frederic, Boy
author Yogesh, Dwivedi
Laurie, Hughes
Frederic, Boy
author2 Janarthanan Balakrishnan
Yogesh Dwivedi
Laurie Hughes
Frederic Boy
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1572-9419
doi_str_mv 10.1007/s10796-021-10203-y
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description 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.
published_date 2021-10-15T04:18:11Z
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