Extending the UTAUT Model for Artificial Intelligence Adoption: A Systematic Review of the Role of Perceived Intelligence and Trust

The Unified Theory of Acceptance and Use of Technology (UTAUT) has been widely used to explain technology adoption, although its suitability for complex, learning-oriented systems such as Artificial Intelligence (AI) is contested. This systematic literature review, following PRISMA 2020, reviews 71 empirical and review articles published from 2020 to 2025 that apply UTAUT or its extensions to AI adoption in a variety of domains such as education, healthcare, finance and banking, public administration, and retail and services. The review identifies three common limitations of traditional UTAUT in the AI context: little attention to the opaque “black box” decision-making of AI, little consideration of relational user AI interactions, and little consideration of ethical issues such as algorithmic bias and fairness. To address these shortcomings, it proposes an integrated framework where Perceived Intelligence captures users’ evaluation of an AI system’s learning ability, adaptability, and predictive accuracy. Perceived Intelligence is positioned as an intermediary between core UTAUT constructs, in particular Performance Expectancy and Effort Expectancy, and Behavioural Intention. Trust in AI is positioned as a critical antecedent of Perceived Intelligence and disaggregated into competence-based, transparency-based, and privacy-based dimensions. The review offers a theoretically grounded UTAUT extension for AI adoption, specifies hypothesized structural paths, and examines sectoral variation in ethical and competency concerns, providing a testable model for future structural equation modelling and practical guidance for trust-centred AI implementation.

Artificial Intelligence, AI Adoption, UTAUT, Perceived Intelligence

1. Almarwani, A. M. (2026). Adoption of AI in nursing education - A systematic review of factors influencing student intentions. Nurse Education in Practice. [Google Scholar] [Crossref]

2. Burton, J. W., Stein, M. K., & Jensen, T. B. (2020). A systematic review of algorithm aversion in augmented decision making. Journal of Behavioral Decision Making, 33(2), 220–239. https://doi.org/10.1002/bdm.2155 [Google Scholar] [Crossref]

3. Desouza, K. C., & Jacob, B. (2017). Big data in the public sector: Lessons for practitioners and scholars. Administration & Society, 49(7), 1043–1064. https://doi.org/10.1177/0095399714555751 [Google Scholar] [Crossref]

4. Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., Galanos, V., Ilavarasan, P. V., Janssen, M., Jones, P., Kar, A. K., Kizgin, H., Kronemann, B., Lal, B., Lucini, B., … Williams, M. D. (2021). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, Article 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002 [Google Scholar] [Crossref]

5. Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., … Wright, R. (2023). So what if ChatGPT wrote it? Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, Article 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642 [Google Scholar] [Crossref]

6. Grimmelikhuijsen, S. (2022). Explaining why the computer says no: Algorithmic transparency affects the perceived trustworthiness of automated decision-making. Public Administration Review, 83(2), 241–262. https://doi.org/10.1111/puar.13483 [Google Scholar] [Crossref]

7. Janowski, T. (2016). Implementing sustainable development goals with digital government–aspiration-capacity gap. Government Information Quarterly, 33(4), 603–613. https://doi.org/10.1016/j.giq.2016.12.001 [Google Scholar] [Crossref]

8. Jumaat, E. J., Mat Sania, A., & Mohamad, N. (2025). Adoption of AI marketing tools among SMEs: Insights from technology scale validation in Malaysia. Journal of Technology Management and Technopreneurship (JTMT), *13*(3). [Google Scholar] [Crossref]

9. Kauttonen, J., Rousi, R., & Alamäki, A. (2025). Trust and acceptance challenges in the adoption of AI applications in health care: Quantitative survey analysis. Journal of Medical Internet Research, 27, Article e65567. https://doi.org/10.2196/65567 [Google Scholar] [Crossref]

10. Kim, Y., Blazquez, V., & Oh, T. (2024). Determinants of generative AI system adoption and usage behaviour in Korean companies: Applying the UTAUT model. Behavioral Sciences, 14(11), 1035. https://doi.org/10.3390/bs14111035 [Google Scholar] [Crossref]

11. Klievink, B., Romijn, B. J., & Cunningham, S. (2018). The role of public sector entrepreneurship in achieving public sector innovation: A systematic literature review. Public Management Review, *20*(8), 1230–1258. [Google Scholar] [Crossref]

12. Lean, O. K., Ramayah, T., & Zainal, S. R. M. (2019). E-government implementation in Malaysia: A comprehensive review. International Journal of Public Administration, *42*(10), 802–815. [Google Scholar] [Crossref]

13. Malaysian Administrative Modernisation and Management Planning Unit (MAMPU). (2021). Digital Government Transformation Framework. Putrajaya, Malaysia: Prime Minister's Department. [Google Scholar] [Crossref]

14. Ministry of Science, Technology and Innovation (MOSTI). (2021). National Artificial Intelligence Roadmap 2021-2025. Putrajaya, Malaysia. [Google Scholar] [Crossref]

15. Molnár, L., Nagy, S., & Hajdú, N. (2025). Factors influencing the intention to use robo-advisors: A Hungarian perspective. Acta Polytechnica Hungarica, *22*(7), 263–283. [Google Scholar] [Crossref]

16. Muhammad Shafeeq, M. S., Thinaraj, B., Zawawi, A. A., & Isai Amutan, K. (2026). Voices from the trails: Hikers' perceptions of Malaysian forest guides. Malaysian Journal of Social Sciences and Humanities (MJSSH), *11*(1), Article e003763. https://doi.org/10.47405/mjssh.v11i1.3763 [Google Scholar] [Crossref]

17. Musa, H., Abdullah, A. R., Othman, M. N., Rashid, N., & Azmi, F. R. (2025). Rethinking the technology acceptance model: A structural analysis of sustainable technology adoption in Malaysian tourism. International Journal of Research and Innovation in Social Science (IJRISS), *9*(1), 6125–6139. [Google Scholar] [Crossref]

18. Musa, H., Ab. Rahman, A., & Mohd Shariff, M. N. (2016). Adoption of mobile marketing in SMEs: Extending the UTAUT model. European Proceedings of Social and Behavioural Sciences, *15*, 267–276. https://doi.org/10.15405/epsbs.2016.05.02.24 [Google Scholar] [Crossref]

19. Musa, H., Khadar, N. Z. A., & Azmi, F. R. (2026). Enhancing tourist awareness and preferences through eBECA: A digital empowerment approach. International Journal of Research and Innovation in Social Science (IJRISS), *10*(1), 772–783. [Google Scholar] [Crossref]

20. Nikolic, S., Wentworth, I., Sheridan, L., Moss, S., Duursma, E., Jones, R. A., … Middleton, R. (2024). A systematic literature review of attitudes, intentions and behaviours of teaching academics pertaining to AI and generative AI (GenAI) in higher education: An analysis of GenAI adoption using the UTAUT framework. Australasian Journal of Educational Technology, *40*(6), 56–75. https://doi.org/10.14742/ajet.9643 [Google Scholar] [Crossref]

21. Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, Article n71. https://doi.org/10.1136/bmj.n71 [Google Scholar] [Crossref]

22. Pasipamire, N., & Muroyiwa, A. (2024). Navigating algorithm bias in AI: Ensuring fairness and trust in Africa. Frontiers in Research Metrics and Analytics, 9, Article 1486600. https://doi.org/10.3389/frma.2024.1486600 [Google Scholar] [Crossref]

23. Rahim, F. A., Ahmad, K. Z., & Jambari, D. I. (2022). Digital transformation in Malaysian public sector: A systematic review of challenges and opportunities. Journal of Government and Development, *14*(1), 45–62. [Google Scholar] [Crossref]

24. Shin, D. (2021). The effects of explainability and causability on perception, trust, and acceptance: Implications for explainable AI. International Journal of Human-Computer Studies, 146, Article 102551. https://doi.org/10.1016/j.ijhcs.2020.102551 [Google Scholar] [Crossref]

25. Sun, T. Q., & Medaglia, R. (2019). Mapping the challenges of Artificial Intelligence in the public sector: Evidence from public healthcare. Government Information Quarterly, *36*(2), 368–383. https://doi.org/10.1016/j.giq.2018.09.008 [Google Scholar] [Crossref]

26. Taib, M. S. M., Musa, H., Ghani, M. G. A., Abdullah, A. N. H., & Taib, M. S. M. (2025). Trust and privacy concern in AI-powered chatbot: A conceptual framework for customer purchase intention in Malaysia apparel SMEs. International Journal of Research and Innovation in Social Science (IJRISS), *9*(28), 229–238. [Google Scholar] [Crossref]

27. Tamilmani, K., Rana, N. P., & Dwivedi, Y. K. (2021). Consumer acceptance and use of information technology: A meta-analytic evaluation of UTAUT2. Information Systems Frontiers, 23(4), 987–1005. https://doi.org/10.1007/s10796-020-10007-6 [Google Scholar] [Crossref]

28. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540 [Google Scholar] [Crossref]

29. Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178. https://doi.org/10.2307/41410412 [Google Scholar] [Crossref]

30. Wirtz, B. W., Weyerer, J. C., & Geyer, C. (2019). Artificial intelligence and the public sector—applications and challenges. International Journal of Public Administration, 42(7), 596–615. https://doi.org/10.1080/01900692.2018.1498103 [Google Scholar] [Crossref]

31. Xue, L. (2025). A systematic review of UTAUT and UTAUT2 for AI adoption in education. International Journal of Human-Computer Interaction, 42(8). https://doi.org/10.1080/10447318.2025.2552867 [Google Scholar] [Crossref]

32. Zuiderwijk, A., Chen, Y. C., & Salem, F. (2021). Implications of the use of artificial intelligence in public governance: A systematic literature review and a research agenda. Government Information Quarterly, 38(3), Article 101577. https://doi.org/10.1016/j.giq.2021.101577 [Google Scholar] [Crossref]

• The Role of Artificial Intelligence in Revolutionizing Library Services in Nairobi: Ethical Implications and Future Trends in User Interaction
• ESPYREAL: A Mobile Based Multi-Currency Identifier for Visually Impaired Individuals Using Convolutional Neural Network
• Comparative Analysis of AI-Driven IoT-Based Smart Agriculture Platforms with Blockchain-Enabled Marketplaces
• AI-Based Dish Recommender System for Reducing Fruit Waste through Spoilage Detection and Ripeness Assessment
• SEA-TALK: An AI-Powered Voice Translator and Southeast Asian Dialects Recognition