No Cover Image

Journal article 966 views 163 downloads

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 Orcid Logo, Laurie Hughes, Frederic Boy, Laurie Hughes Orcid Logo, Frederic Boy Orcid Logo

Information Systems Frontiers

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

  • 57813.pdf

    PDF | Version of Record

    © The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License

    Download (882.32KB)

Check full text

DOI (Published version): 10.1007/s10796-021-10203-y

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...

Full description

Published in: Information Systems Frontiers
ISSN: 1387-3326 1572-9419
Published: Springer Science and Business Media LLC 2021
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa57813
Tags: Add Tag
No Tags, Be the first to tag this record!
first_indexed 2021-09-08T11:49:06Z
last_indexed 2023-01-11T14:37:58Z
id cronfa57813
recordtype SURis
fullrecord <?xml version="1.0" encoding="utf-8"?><rfc1807 xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:xsd="http://www.w3.org/2001/XMLSchema"><bib-version>v2</bib-version><id>57813</id><entry>2021-09-08</entry><title>Enablers and Inhibitors of AI-Powered Voice Assistants: A Dual-Factor Approach by Integrating the Status Quo Bias and Technology Acceptance Model</title><swanseaauthors><author><sid>d154596e71b99ad1285563c8fdd373d7</sid><ORCID>0000-0002-5547-9990</ORCID><firstname>Yogesh</firstname><surname>Dwivedi</surname><name>Yogesh Dwivedi</name><active>true</active><ethesisStudent>false</ethesisStudent></author><author><sid>7abaa0ecff88cdfd7a208d27a8b62173</sid><ORCID>0000-0002-0956-0608</ORCID><firstname>Laurie</firstname><surname>Hughes</surname><name>Laurie Hughes</name><active>true</active><ethesisStudent>false</ethesisStudent></author><author><sid>43e704698d5dbbac3734b7cd0fef60aa</sid><ORCID>0000-0003-1373-6634</ORCID><firstname>Frederic</firstname><surname>Boy</surname><name>Frederic Boy</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2021-09-08</date><deptcode>BBU</deptcode><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.</abstract><type>Journal Article</type><journal>Information Systems Frontiers</journal><volume>0</volume><journalNumber/><paginationStart/><paginationEnd/><publisher>Springer Science and Business Media LLC</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint>1387-3326</issnPrint><issnElectronic>1572-9419</issnElectronic><keywords>Voice assistants; Artificial intelligence; Dual-factor model; Status quo bias theory; Resistance to change</keywords><publishedDay>15</publishedDay><publishedMonth>10</publishedMonth><publishedYear>2021</publishedYear><publishedDate>2021-10-15</publishedDate><doi>10.1007/s10796-021-10203-y</doi><url>http://dx.doi.org/10.1007/s10796-021-10203-y</url><notes/><college>COLLEGE NANME</college><department>Business</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>BBU</DepartmentCode><institution>Swansea University</institution><apcterm>SU Library paid the OA fee (TA Institutional Deal)</apcterm><funders/><projectreference/><lastEdited>2023-09-20T10:54:15.4817615</lastEdited><Created>2021-09-08T12:44:47.5682115</Created><path><level id="1">Faculty of Humanities and Social Sciences</level><level id="2">School of Management - Business Management</level></path><authors><author><firstname>Janarthanan</firstname><surname>Balakrishnan</surname><order>1</order></author><author><firstname>Yogesh</firstname><surname>Dwivedi</surname><orcid>0000-0002-5547-9990</orcid><order>2</order></author><author><firstname>Laurie</firstname><surname>Hughes</surname><order>3</order></author><author><firstname>Frederic</firstname><surname>Boy</surname><order>4</order></author><author><firstname>Laurie</firstname><surname>Hughes</surname><orcid>0000-0002-0956-0608</orcid><order>5</order></author><author><firstname>Frederic</firstname><surname>Boy</surname><orcid>0000-0003-1373-6634</orcid><order>6</order></author></authors><documents><document><filename>57813__21364__5539041b11704560ae08a5b6ea4b50d7.pdf</filename><originalFilename>57813.pdf</originalFilename><uploaded>2021-10-29T11:05:39.3014380</uploaded><type>Output</type><contentLength>903496</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><documentNotes>© The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License</documentNotes><copyrightCorrect>true</copyrightCorrect><language>eng</language><licence>http://creativecommons.org/licenses/by/4.0/</licence></document></documents><OutputDurs/></rfc1807>
spelling 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 http://dx.doi.org/10.1007/s10796-021-10203-y COLLEGE NANME Business COLLEGE CODE BBU Swansea University SU Library paid the OA fee (TA Institutional Deal) 2023-09-20T10:54:15.4817615 2021-09-08T12:44:47.5682115 Faculty of Humanities and Social Sciences School of Management - Business Management Janarthanan Balakrishnan 1 Yogesh Dwivedi 0000-0002-5547-9990 2 Laurie Hughes 3 Frederic Boy 4 Laurie Hughes 0000-0002-0956-0608 5 Frederic Boy 0000-0003-1373-6634 6 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
7abaa0ecff88cdfd7a208d27a8b62173
43e704698d5dbbac3734b7cd0fef60aa
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
Laurie Hughes
Frederic Boy
format Journal article
container_title Information Systems Frontiers
container_volume 0
publishDate 2021
institution Swansea University
issn 1387-3326
1572-9419
doi_str_mv 10.1007/s10796-021-10203-y
publisher Springer Science and Business Media LLC
college_str Faculty of Humanities and Social Sciences
hierarchytype
hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str School of Management - Business Management{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Business Management
url http://dx.doi.org/10.1007/s10796-021-10203-y
document_store_str 1
active_str 0
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 2021-10-15T10:54:12Z
_version_ 1777549865485598720
score 11.012924

Similar Items