A Decade of UTAUT in Education: A PRISMA 2020 Systematic Review of Technology Acceptance Studies (2015–2025)

Agus Widayoko; Syunu Trihantoyo; Nunuk Hariyati

doi:10.35445/alishlah.v18i1.9160

A Decade of UTAUT in Education: A PRISMA 2020 Systematic Review of Technology Acceptance Studies (2015–2025)

Agus Widayoko, Syunu Trihantoyo, Nunuk Hariyati

Abstract

The increasing integration of digital technologies in education, particularly during and after the COVID-19 pandemic, has intensified interest in understanding technology acceptance. The Unified Theory of Acceptance and Use of Technology (UTAUT) has been widely applied in educational research; however, existing reviews remain fragmented and lack a longitudinal, model-focused synthesis.This study employed a PRISMA 2020–guided systematic literature review of UTAUT-based research in educational contexts published between 2015 and 2025. A structured search of the Scopus database identified 13,131 records. Following duplicate removal and multi-stage screening based on predefined inclusion criteria, 56 empirical journal articles were retained. Study quality was assessed using a combined framework integrating the Mixed Methods Appraisal Tool (MMAT) and Kitchenham’s checklist. Data were analysed using descriptive and thematic synthesis.Findings indicate a substantial increase in UTAUT-based studies during the COVID-19 period, with e-learning, mobile learning, and learning management systems as dominant technologies. Quantitative approaches, particularly Structural Equation Modelling, predominated. Performance Expectancy emerged as the most consistent predictor of behavioural intention, followed by Effort Expectancy, Social Influence, and Facilitating Conditions. Research remains geographically concentrated in Asia and largely focused on behavioural intention rather than actual use behaviour.This review highlights the sustained relevance of UTAUT in educational technology research while identifying methodological and conceptual gaps, including limited attention to learning outcomes, overreliance on cross-sectional designs, and emerging extensions related to AI-enabled learning. Future research should integrate longitudinal and mixed-method approaches and align technology acceptance with educational effectiveness.

Keywords

systematic review, educational technology adoption, UTAUT, learning management system

Full Text:

PDF

References

Al-Azawei, A., Parslow, P., & Lundqvist, K. (2017). Barriers and opportunities of e-learning implementation in Iraq. Smart Learning Environments, 4(12). https://doi.org/10.1186/s40561-017-0051-5

Al-Maroof, R. A., Alhumaid, K., & Salloum, S. A. (2021). Continuous intention to use e-learning: UTAUT2 perspective. Education and Information Technologies, 26. https://doi.org/10.1007/s10639-020-10335-x

Al Mulhim, E. N. (2021). Flipped learning acceptance among university students: UTAUT. Education and Information Technologies, 26. https://doi.org/10.1007/s10639-021-10572-3

Alqahtani, M. A., & Rajkhan, A. A. (2020). E-learning in higher education during COVID-19: UTAUT approach. Education and Information Technologies, 25. https://doi.org/10.1007/s10639-020-10221-z

Bansal, S., Garg, I., & Padariya, R. (2023). Adoption of AI tools in engineering education: UTAUT2 approach. Computers & Education: Artificial Intelligence, 4. https://doi.org/10.1016/j.caeai.2023.100129

Chang, C.-T., & Chen, C.-W. (2020). Augmented reality adoption in higher education: Extending UTAUT. Interactive Technology and Smart Education, 17(4). https://doi.org/10.1108/ITSE-06-2020-0090

Chao, C.-M. (2019). Factors determining the behavioral intention to use mobile learning. Journal of Advanced Nursing, 75(3). https://doi.org/10.1111/jan.13832

Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M., & Williams, M. D. (2019). Re-examining the Unified Theory of Acceptance and Use of Technology (UTAUT). Information Systems Frontiers, 21. https://doi.org/10.1007/s10796-017-9774-y

Granić, A. (2022). Educational technology adoption: A systematic review. Education and Information Technologies, 27(7). https://doi.org/10.1007/s10639-022-10951-7

Kittinger, L., & Law, V. (2024). A systematic review of the UTAUT and UTAUT2 among K–12 educators. Journal of Information Technology Education: Research, 23. https://doi.org/10.28945/5246

Muflih, S. M., Abuhamdah, M., & Karasneh, R. (2021). Exploring medical students’ intention to use mobile learning: A UTAUT2 study. BMC Medical Education, 21. https://doi.org/10.1186/s12909-021-02625-w

Nikolic, S., Wentworth, I., Sheridan, L., Moss, S., Duursma, E., & Middleton, R. (2024). Adoption of AI in higher education: A UTAUT analysis. Australasian Journal of Educational Technology, 40(6). https://doi.org/10.14742/ajet.9643

Nikou, S. A., & Economides, A. A. (2017). Mobile-based assessment acceptance in higher education. Computers & Education, 109. https://doi.org/10.1016/j.compedu.2017.02.014

Oke, A., & Fernandes, P. O. (2020). Students’ acceptance of mobile learning apps: UTAUT2. Education and Information Technologies, 25. https://doi.org/10.1007/s10639-020-10188-x

Raghu, T. S., Sinha, R. K., & Vinze, A. (2018). Understanding learning management systems adoption through UTAUT. Decision Support Systems, 112. https://doi.org/10.1016/j.dss.2018.06.007

Raman, A., & Don, Y. (2015). Acceptance of LMS in Malaysian universities: UTAUT. Education and Information Technologies, 20. https://doi.org/10.1007/s10639-013-9298-1

Sangeetha, N., & Sivaprakash, P. (2022). Faculty adoption of AI-based teaching assistants: UTAUT study. Journal of Computing in Higher Education, 34. https://doi.org/10.1007/s12528-022-09308-1

Sharma, S. K., & Chandel, J. K. (2020). UTAUT in educational technology: A systematic review. Education and Information Technologies, 25. https://doi.org/10.1007/s10639-019-09971-4

Sharma, S. K., & Sharma, H. (2019). Examining UTAUT in mobile learning adoption. Education and Information Technologies, 24. https://doi.org/10.1007/s10639-018-9773-3

Suki, N. M., & Suki, N. M. (2017). Determinants of mobile learning adoption: A UTAUT2 perspective. Journal of Educational Computing Research, 56(4). https://doi.org/10.1177/0735633117737356

Tan, P. J. B. (2021). Students’ adoption of AI-based education tools using UTAUT. Interactive Learning Environments, 29(5). https://doi.org/10.1080/10494820.2019.1709210

Teo, T. (2019). Comparing UTAUT and UTAUT2 in explaining teachers’ intention to use technology. Interactive Learning Environments, 27(4). https://doi.org/10.1080/10494820.2018.1546744

Tolba, E. G., & Youssef, N. H. (2022). High school science teachers’ acceptance of using distance education in light of UTAUT. Eurasia Journal of Mathematics, Science and Technology Education, 18(9). https://doi.org/10.29333/ejmste/12365

Yakubu, M. N., & Dasuki, S. (2019). Adoption of e-learning using UTAUT. Education and Information Technologies, 24. https://doi.org/10.1007/s10639-018-9798-7

Zapata-Cuadrado, M., & Rodríguez, M. A. (2021). University students’ acceptance of virtual learning environments based on UTAUT. Interactive Technology and Smart Education, 18(2). https://doi.org/10.1108/ITSE-05-2020-0074

Zhou, M. (2022). Understanding preservice teachers’ intention to use AI-powered teaching tools: UTAUT2. Computers & Education: Artificial Intelligence, 3. https://doi.org/10.1016/j.caeai.2022.100068

DOI: https://doi.org/10.35445/alishlah.v18i1.9160