Extending the UTAUT Model with Service Quality to Assess Moodle LMS Acceptance in Ghanaian Colleges of Education
Interdisciplinary Educational Technology, 2(1), 2026, e107, https://doi.org/10.71176/interedtech/17900
Publication date: Feb 12, 2026
ABSTRACT
This study aimed to increase understanding of how the Unified Theory of Acceptance and Use of Technology (UTAUT) constructs and service quality of Moodle Learning Management System (LMS) influence its acceptance among college of education students. The UTAUT model was extended to include service quality, capturing technical support, system reliability, and user satisfaction, which is crucial in Ghanaian Colleges of Education, where LMS effectiveness depends on institutional support and service delivery. The study was guided by three research questions examining students’ perceptions of key UTAUT constructs, service quality, behavioural intention, and use behaviour in relation to Moodle LMS in Ghanaian Colleges of Education. It also explored the influence of performance expectancy, effort expectancy, and social influence on behavioural intention, as well as the effects of facilitating conditions, service quality, and behavioural intention on actual use behaviour. A descriptive and confirmatory cross-sectional survey design was employed for the study. Multi-stage sampling approach was used to select a sample of 382 students for the research. A questionnaire was designed to assess students’ perceptions of performance expectancy, effort expectancy, social influence, facilitating conditions, service quality, behavioural intention, and use behaviour. Structural Equation Modelling (SEM) was used to analyse relationships of the effects of UTAUT constructs and service quality on behavioural intentions and use behaviour. The results indicated a positive but weak influence of performance expectancy and effort expectancy on behavioural intentions. Social influence, however, had a strong effect on behavioural intentions while service quality had a moderate effect on use behaviour of students to use Moodle LMS. The study suggests that colleges of education should prioritize strategies that leverage social influence, such as peer support and instructor endorsements as well as the service quality, to enhance students' intentions to use Moodle LMS.
KEYWORDS
CITATION (APA)
Korsah, D. P., Wireko-Ampem, J. K., & Forson, I. (2026). Extending the UTAUT Model with Service Quality to Assess Moodle LMS Acceptance in Ghanaian Colleges of Education. Interdisciplinary Educational Technology, 2(1), e107. https://doi.org/10.71176/interedtech/17900
Harvard
Korsah, D. P., Wireko-Ampem, J. K., and Forson, I. (2026). Extending the UTAUT Model with Service Quality to Assess Moodle LMS Acceptance in Ghanaian Colleges of Education. Interdisciplinary Educational Technology, 2(1), e107. https://doi.org/10.71176/interedtech/17900
Vancouver
Korsah DP, Wireko-Ampem JK, Forson I. Extending the UTAUT Model with Service Quality to Assess Moodle LMS Acceptance in Ghanaian Colleges of Education. Interdisciplinary Educational Technology. 2026;2(1):e107. https://doi.org/10.71176/interedtech/17900
AMA
Korsah DP, Wireko-Ampem JK, Forson I. Extending the UTAUT Model with Service Quality to Assess Moodle LMS Acceptance in Ghanaian Colleges of Education. Interdisciplinary Educational Technology. 2026;2(1), e107. https://doi.org/10.71176/interedtech/17900
Chicago
Korsah, Daniel Paa, Justice Kwame Wireko-Ampem, and Irene Forson. "Extending the UTAUT Model with Service Quality to Assess Moodle LMS Acceptance in Ghanaian Colleges of Education". Interdisciplinary Educational Technology 2026 2 no. 1 (2026): e107. https://doi.org/10.71176/interedtech/17900
MLA
Korsah, Daniel Paa et al. "Extending the UTAUT Model with Service Quality to Assess Moodle LMS Acceptance in Ghanaian Colleges of Education". Interdisciplinary Educational Technology, vol. 2, no. 1, 2026, e107. https://doi.org/10.71176/interedtech/17900
REFERENCES
- Aboagye, E., Yawson, J. A., & Appiah, K. N. (2020). COVID-19 and e-learning: The challenges of students in tertiary institutions. Social Education Research, 2(1), 1–8. https://doi.org/10.37256/ser.212021422
- Adeoye, I. A., Adanikin, A. F., & Adanikin, A. (2020). COVID-19 and e-learning: Nigeria tertiary education system experience. International Journal of Research and Innovation in Applied Science, 5(5), 28–31. https://rsisinternational.org/journals/ijrias/DigitalLibrary/Vol.5&Issue5/28-31.pdf
- Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
- Al-Busaidi, K. A., & Al-Shihi, H. (2012). Key factors to instructors’ satisfaction with learning management systems in blended learning. Journal of Computing in Higher Education, 24(1), 18–39. https://doi.org/10.1007/s12528-011-9051-x
- Al-Fraihat, D., Joy, M., Masa’deh, R., & Sinclair, J. (2020). Evaluating e-learning systems success: An empirical study. Computers in Human Behavior, 102, 67–86. https://doi.org/10.1016/j.chb.2019.08.004
- Almaiah, M. A., Alamri, M. M., & Al-Rahmi, W. (2019). Applying the UTAUT model to explain students’ acceptance of mobile learning systems in higher education. IEEE Access, 7, 174673–174686. https://doi.org/10.1109/ACCESS.2019.2957206
- Almaiah, M. A., Al-Khasawneh, A., & Althunibat, A. (2020). Exploring the critical challenges and factors influencing e-learning system usage during the COVID-19 pandemic. Education and Information Technologies, 25, 5261–5280. https://doi.org/10.1007/s10639-020-10219-y
- Almpanis, T. (2015). Staff development and institutional support for technology-enhanced learning in UK universities. The Electronic Journal of E-Learning, 13(5), 381–389. https://academic-publishing.org/index.php/ejel/article/view/1940
- Al-Rahmi, A. M., Shamsuddin, A., Wahab, E., Al-Rahmi, W. M., Alismaiel, O. A., & Crawford, J. (2022a). Social media usage and acceptance in higher education: A structural equation model. Frontiers in Education, 7, Article 964456. https://doi.org/10.3389/feduc.2022.964456
- Al-Rahmi, A. M., Shamsuddin, A., Wahab, E., Al-Rahmi, W. M., Alturki, U., Aldraiweesh, A., & Almutairy, S. (2022b). Integrating the role of UTAUT and TTF model to evaluate social media use for teaching and learning in higher education. Frontiers in Public Health, 10, Article 905968. https://doi.org/10.3389/fpubh.2022.905968
- Arkorful, V., & Abaidoo, N. (2015). The role of e-learning, advantages and disadvantages of its adoption in higher education. International Journal of Instructional Technology and Distance Learning, 12, 29–42. https://www.itdl.org/Journal/Jan_15/Jan15.pdf#page=33
- Babbie, E. (2016). The practice of social research (14th ed.). Cengage Learning.
- Boateng, B. O., Asare, C., Sekyere, K. N., Akude, D. N., & Walden, B. H. (2023). Understanding blockchain adoption in emerging markets: Integrating the technology acceptance model and innovation diffusion theory. International Journal of Entrepreneurship, 27(5), 1–23. https://www.abacademies.org/articles/understanding-blockchain-adoption-in-emerging-markets-integrating-the-technology-acceptance-model-and-innovation-diffusion-theory-16141.html
- Boateng, L. K., Xungang, Z., Ansah, S., & Kwarko, E. A. (2021). E-commerce adoption among small and medium enterprises in Ghana. International Journal of Sciences: Basic and Applied Research, 58(2), 182–205. https://www.gssrr.org/JournalOfBasicAndApplied/article/view/12702
- Bozan, K., Parker, K., & Davey, B. (2016). A closer look at the social influence construct in the UTAUT model: An institutional theory-based approach. Proceedings of the 49th Hawaii International Conference on System Sciences, 3105–3114. https://doi.org/10.1109/HICSS.2016.387
- Brata, A. H., & Amalia, F. (2018). Impact analysis of social influence factor on using free blogs as learning media for driving teaching motivational factor. Proceedings of the 4th International Conference on Frontiers of Educational Technologies, Russia, 4, 29-33. ACM. https://doi.org/10.1145/3233347.3233360
- Buabeng-Andoh, C. (2012). Factors influencing teachers’ adoption and integration of ICT into teaching: A review of the literature. International Journal of Education and Development using Information and Communication Technology, 8(1), 136–155. https://files.eric.ed.gov/fulltext/EJ1084227.pdf
- Chao, C. M. (2019). Factors determining the behavioral intention to use mobile learning: An application and extension of the UTAUT model. Frontiers in Psychology, 10, Article 1652. https://doi.org/10.3389/fpsyg.2019.01652
- Chiu, J.-C., Hsu, C.-Y., Chien, C.-Y., Fan, Y. Q., & Cheng, Y.-W. (2023). Applying the unified theory of acceptance and use of technology model on the behavior of home buyers using housing apps. Engineering Proceedings, 38(1), Article 85. https://doi.org/10.3390/engproc2023038085
- Cigdem, H., & Topcu, A. (2015). Predictors of instructors’ behavioral intention to use learning management system: A Turkish vocational college example. Computers in Human Behavior, 25, 22–28. https://doi.org/10.1016/j.chb.2015.05.049
- Creswell, J. W., & Creswell, J. D. (2017). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). Sage.
- Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://misq.umn.edu/misq/article-abstract/13/3/319/191/Perceived-Usefulness-Perceived-Ease-of-Use-and?redirectedFrom=fulltext
- 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. Information Systems Frontiers, 21(3), 719–734. https://doi.org/10.1007/s10796-017-9774-y
- Fadzil, F. H. (2017). A study on factors affecting the behavioral intention to use mobile apps in Malaysia. SSRN. https://ssrn.com/abstract=3090753
- Foltz, C. B., Newkirk, H. E., & Schwager, P. H. (2016). An empirical investigation of factors that influence individual behavior toward changing social networking security settings. Journal of Theoretical and Applied Electronic Commerce Research, 11(2), 1–15. https://doi.org/10.4067/S0718-18762016000200002
- George, D., & Mallery, P. (2019). IBM SPSS statistics 26 step by step (16th ed.). Routledge. https://doi.org/10.4324/9780429056765
- Giandi, O., Irawan, I., & Ambarwati, R. (2020). Determinants of Behavior Intention and Use Behavior among Bukalapak’s Consumers. Journal of Technology and Science, 31(2), 158–168. https://doi.org/10.12962/j20882033.v31i2.5585
- Hanson, M. (2025, March 17). College enrollment and student demographic statistics. EducationData.org. https://educationdata.org/college-enrollment-statistics
- Hu, J., Jiang, P., Zhou, Q., McKeand, A., & Choi, S.-K. (2020). Model validation methods for multiple correlated responses via covariance-overlap based distance. Journal of Mechanical Design, 142(4), Article 041401. https://doi.org/10.1115/1.4044330
- Lin, H. F. (2007). The impact of website quality dimensions on customer satisfaction in the B2C e-commerce context. Total Quality Management & Business Excellence, 18(4), 363–378. https://doi.org/10.1080/14783360701231302
- Lin, W. R., Lin, C. Y., & Ding, Y. H. (2020). Factors affecting the behavioral intention to adopt mobile payment: An empirical study in Taiwan. Mathematics, 8, Article 1851. https://doi.org/10.3390/math8101851
- Machado, M., & Tao, E. (2007). Blackboard vs. Moodle: Comparing user experience of learning management systems. In Proceedings of the 37th Annual Frontiers in Education Conference. https://doi.org/10.1109/FIE.2007.4417910
- Mtebe, J. (2015). Learning management system success: Increasing learning management system usage in higher education in sub-Saharan Africa. International Journal of Education and Development Using Information and Communication Technology, 11(2), 51–64. https://files.eric.ed.gov/fulltext/EJ1074158.pdf
- Mtebe, J. S., & Raisamo, R. (2014). Challenges and instructors’ intention to adopt OER. International Review of Research in Open and Distributed Learning, 15(1), 249–271. https://doi.org/10.19173/irrodl.v15i1.1687
- Musa, P. F., Meso, P., & Mbarika, V. W. (2005). Toward sustainable adoption of technologies for human development in Sub-Saharan Africa: Precursors, diagnostics, and prescriptions. Communications of the Association for Information Systems, 15(33), 1–31. https://doi.org/10.17705/1CAIS.01533
- Naveed, S., Khan, M. S., & Khan, G. A. (2020). Investigating the impact of mobile application on learning among teachers based on Technology Acceptance Model (TAM). Global Educational Studies Review, 5(2), 45–54. http://dx.doi.org/10.31703/gesr.2020(V-II).06
- Naveh, G., Tubin, D., & Pliskin, N. (2012). Student satisfaction with learning management systems: A lens of critical success factors. Technology, Pedagogy and Education, 21(3), 337–350. https://doi.org/10.1080/1475939X.2012.720413
- National Council for Tertiary Education. (2018). Statistical report on tertiary education for the 2017/2018 academic year. National Council for Tertiary Education. https://www.gtec.edu.gh
- Neslin, S. A., & Shankar, V. (2009). Key issues in multichannel customer management: Current knowledge and future directions. Journal of Interactive Marketing, 23(1), 70–81. http://dx.doi.org/10.1016/j.intmar.2008.10.005
- Rita, P., Oliveria, T., & Farisa, A. (2019). The impact of e-service quality and customer satisfaction on customer behavior in online shopping. Heliyon, 5(10), Article e02690. https://doi.org/10.1016/j.heliyon.2019.e02690
- Raza, S. A., Qazi, W., Khan, K. A., & Salam, J. (2020). Social isolation and acceptance of the learning management system (LMS) in the time of COVID-19 pandemic: An Expansion of the UTAUT Model. Journal of Educational Computing Research, 59(2), 183–208. https://doi.org/10.1177/0735633120960421
- Salloum, S. A., Al-Emran, M., Habes, M., Alghizzawi, M., Ghani, M. A., & Shaalan, K. (2019). Factors affecting e-learning acceptance: A case study from UAE. Education and Information Technologies, 24(1), 509–530. https://doi.org/10.1007/s10639-018-9786-3
- Tamilmani, K., Rana, N. P., Wamba, S. F., & Dwivedi, R. (2021). The extended Unified Theory of Acceptance and Use of Technology (UTAUT2): A systematic literature review and theory evaluation. International Journal of Information Management, 57, Article 102269. https://doi.org/10.1016/j.ijinfomgt.2020.102269
- Tarawneh, M. A., Nguyen, T. P., Yong, D. G., & Dorasamy, M. A. (2023). Determinant of mobile banking usage and adoption among Millennials. Sustainability, 15(10), Article 8216. https://doi.org/10.3390/su15108216
- Tarhini, A., Masa’deh R., Al-Busaidi K.A., Mohammed A.B., & Maqableh M. (2017). Factors influencing students' adoption of e-learning: a structural equation modeling approach. Journal of International Education in Business, 10(2), 164–182. https://doi.org/10.1108/JIEB-09-2016-0032
- Tarus, J. K., Gichoya, D., & Muumbo, A. (2015). Challenges of implementing e-learning in Kenya: A case of Kenyan public universities. International Review of Research in Open and Distributed Learning, 16(1), 120–141. https://doi.org/10.19173/irrodl.v16i1.1816
- Tondeur, J., Petko, D., Christensen, R., Drossel, K., Starkey, L., Knezek, G., & Schmidt-Crawford, D. A. (2021). Quality criteria for conceptual technology integration models in education: bridging research and practice. Educational Technology Research and Development, 69(4), 2187–2208. https://doi.org/10.1007/s11423-020-09911-0
- 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
- 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
- Wei, W., Sun, J., Miao, W., Chen, T., Sun, H., Lin, S., & Gu, C. (2024). Using the extended unified theory of acceptance and use of technology to explore how to increase users’ intention to take a robotaxi. Humanities and Social Sciences Communications, 11, Article 746. https://doi.org/10.1057/s41599-024-03271-3
- Yakubu, M. N., & Dasuki, S. I. (2018). Factors affecting the adoption of e-learning technologies among higher education students in Nigeria: A structural equation modelling approach. Information Development, 35(3), 492–502. https://doi.org/10.1177/0266666918765907
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