Factors affecting the intention of master's students in Ho Chi Minh City to utilize the Learning Management System (LMS)
Published: July 23, 2026
Abstract
This study examines the factors influencing the intention of master's students in Ho Chi Minh City to utilize the Learning Management System (LMS). Based on relevant background theories, previous studies and opinions from master's students, the study proposes a research model and surveys 313 master's students from different universities in Ho Chi Minh City. The study finds that there are six key factors affecting the intention of master's students to use the LMS. These factors are: self-efficacy, self-directed learning, learning motivation, subjective norm, financial considerations, and perceived usefulness. Based on the study’s findings, some managerial implications are presented in order to improve the training quality.
Keywords
the Learning Management System (LMS)self-efficacyself-directed learninglearning motivationsubjective normfinanceperceived usefulness
References
1.
1. Altawalbeh, M. A. (2023). Adoption of academic staff to use the Learning Management System (LMS): Applying Extended Technology Acceptance Model (TAM2) for Jordanian universities. International Journal on Studies in Education (IJonSE), 5(3), 288-300.
2.
2. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211.
3.
3. Bolliger, D. U., Supanakorn, S., & Boggs, C. (2010). Impact of podcasting on student motivation in the online learning environment. Computers & Education, 55(2), 714-722.
4.
4. Chyung, S. Y. Y. (2007). Age and gender differences in online behavior, self-efficacy, and academic performance. Quarterly Review of Distance Education, 8(3), 213-222.
5.
5. Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2), 189-211.
6.
6. Despotović-Zrakić, M., Marković, A., Bogdanović, Z., Barać, D., & Krčo, S. (2012). Providing adaptivity in Moodle LMS courses. Journal of Educational Technology & Society, 15(1), 326-338.
7.
7. Khoa, B. T., Ha, N. M., Nguyen, T. V. H., & Bich, N. H. (2020). Lecturers' adoption to use the online Learning Management System (LMS): Empirical evidence from TAM2 model for Vietnam. Ho Chi Minh City Open University Journal of Science - Economics and Business Administration, 10(1), 3-17.
8.
8. Lynch, R., & Dembo, M. (2004). The relationship between self-regulation and online learning in a blended learning context. International Review of Research in Open and Distributed Learning, 5(2), 1-16.
9.
9. Moore, M. G., & Kearsley, G. (2005). Distance education: A systems view (2nd ed.). Belmont, CA: Wadsworth Publishing Co.
10.
10. Ryan, R. M., & Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psychology, 25(1), 54-67.
11.
11. Salas-Pilco, S. Z., Yang, Y., & Zhang, Z. (2022). Student engagement in online learning in Latin American higher education during the COVID-19 pandemic: A systematic review. British Journal of Educational Technology, 53(3), 593-619.
12.
12. Taylor, S., & Todd, P. (1995). Assessing IT usage: The role of prior experience. MIS Quarterly, 19(4), 561-570.
13.
13. Tello, S. F. (2008). An analysis of student persistence in online education. In Information Communication Technologies: Concepts, Methodologies, Tools, and Applications (pp. 1163-1178). IGI Global.
14.
14. Veluvali, P., & Surisetti, J. (2022). Learning management system for greater learner engagement in higher education - A review. Higher Education for the Future, 9(1), 107-121.
