Assessing AI adoption in developing country academia: A trust and privacy-augmented UTAUT framework
Open accessAbstract
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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Recent expansions of UTAUT have integrated trust, privacy concerns, and ethical considerations as critical moderators.
“Recent expansions of UTAUT have integrated trust, privacy concerns, and ethical considerations as critical moderators, which mirrors the distinctive challenges introduced by AI systems (Rana et al., 2024).”
From A Multi-Dimensional Decision Framework for Evaluating and Selecting AI-Enabled Instructional Technology in U.S. Higher Education (2025) · cites this paper as Rana et al., 2024
The Discussion’s Theoretical contributions section says the study extends UTAUT by integrating trust and privacy as key constructs for understanding AI adoption. It does not identify ethical considerations as an integrated construct or describe these factors as moderators; the paper discusses trust and privacy chiefly in relation to adoption intentions and use behavior. Page numbers are unavailable in our copy. (LLM, conf=0.90)
Experimental — accuracy not yet measuredTrust in the credibility of the AI system's output is consistently linked to higher adoption intentions.
“Trust in the credibility of the AI system's output, defined as the conviction that the tool will deliver accurate, reliable, and impartial information, was consistently linked to higher adoption intentions (Rana et al., 2024).”
From A Multi-Dimensional Decision Framework for Evaluating and Selecting AI-Enabled Instructional Technology in U.S. Higher Education (2025) · cites this paper as Rana et al., 2024
The Results (Structural model, page unavailable) report a positive association between trust and intention to adopt AI (estimate 0.192, p = 0.003). But the Methodology measures general trust in AI technologies, not specifically confidence that their output is accurate, reliable, and impartial. The paper also reports one study's finding, rather than establishing that the link holds consistently across studies. (LLM, conf=0.87)
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“Research consistently showed that AI tools viewed as easy to use, well-documented, and requiring little instruction were more apt to be adopted, especially among faculty with limited technical expertise (Rana et al. , 2024).”
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- Authors
- Rana, M. M., Siddiqee, M. S., Sakib, M. N., & Ahamed, M. R.
- Affiliations
- University of Dhaka (BD)
- Corresponding author
- Sakib, Md. Nazmus
- ORCID
- Sakib, Md. Nazmus
- Venue
- Heliyon
- Publisher
- Elsevier BV
- Published
- 2024-09
- Volume
- 10
- Issue
- 18
- Pages
- e37569
- ISSN
- 2405-8440 (ISSN-L)
- PMC
- PMC11417232
- 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
- gold
- doi.org
- Licence
- 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
- Physical Sciences › Computer Science › Information Systems › 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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