Cited by 195Technology Adoption and User BehaviourArtificial Intelligence in EducationPrivacy, Security, and Data Protection

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Assessing AI adoption in developing country academia: A trust and privacy-augmented UTAUT framework

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Abstract

The rapid evolution of Artificial Intelligence (AI) and its widespread adoption have given rise to a critical need for understanding the underlying factors that shape users' behavioral intentions. Therefore, the main objective of this study is to explain user perceived behavioral intentions and use behavior of AI technologies for academic purposes in a developing country. This study has adopted the unified theory of acceptance and use of technology (UTAUT) model and extended it with two dimensions: trust and privacy. Data have been collected from 310 AI users including teachers, researchers, and students. This study finds that users' behavioral intention is positively and significantly associated with trust, social influence, effort expectancy, and performance expectancy. Privacy, on the other hand, has a negative yet significant relationship with behavioral intention unveiling that concerns over privacy can deter users from intending to use AI technologies which is a valuable insight for developers and educators. In determining use behavior, facilitating condition, behavioral intention, and privacy have significant positive impact. This study hasn't found any significant relationship between trust and use behavior elucidating that service providers should have unwavering focus on security measures, credible endorsements, and transparency to build user confidence. In an era dominated by the fourth industrial revolution, this research underscores the pivotal roles of trust and privacy in technology adoption. In addition, this study sheds light on users' perspective to effectively align AI-based technologies with the education system of developing countries. The practical implications encompass insights for service providers, educational institutions, and policymakers, facilitating the smooth adoption of AI technologies in developing countries while emphasizing the importance of trust, privacy, and ongoing refinement.

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Crossref · 10.1016/j.heliyon.2024.e37569

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195 of 201 references verified0 citation errors · 0 not checkable

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Figures

Fig. 1 Conceptual framework.
Fig. 1 Conceptual framework.CC BY-NC-ND · PubMed Central
Fig. 2 Extended UTAUT measurement model.
Fig. 2 Extended UTAUT measurement model.CC BY-NC-ND · PubMed Central
Fig. 3 PLS-SEM results.
Fig. 3 PLS-SEM results.CC BY-NC-ND · PubMed Central
Fig. A1 UTAUT measurement model without privacy and trust.
Fig. A1 UTAUT measurement model without privacy and trust.CC BY-NC-ND · PubMed Central
Fig. A2 UTAUT structural model without privacy and trust.
Fig. A2 UTAUT structural model without privacy and trust.CC BY-NC-ND · PubMed Central

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Citations

Cites

Show all 197 references

Research works printed but not linked: 27

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Metadata

Authors
Rana, M. M., Siddiqee, M. S., Sakib, M. N., & Ahamed, M. R.
Affiliations
University of Dhaka (BD)
Corresponding author
Sakib, Md. Nazmus
Venue
Heliyon
Publisher
Elsevier BV
Published
2024-09
Volume
10
Issue
18
Pages
e37569
ISSN
2405-8440 (ISSN-L)
OpenAlex
W4402404634
Cited by
143
References
197
Citations by year
2026: 80 · 2025: 61 · 2024: 2
FWCI
53.36
Type
journal article
Language
en
Open access
Locations
  • Heliyon (publishedVersion, cc-by-nc-nd)
  • SSRN Electronic Journal (acceptedVersion)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
  • Social Sciences › Education › Artificial Intelligence in Education
  • Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
Keywords
artificial intelligence adoption, technology acceptance, UTAUT, trust, privacy concerns, behavioral intention, use behavior, developing countries, academia, social influence, effort expectancy, performance expectancy, facilitating conditions, artificial intelligence in education

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