Articles | Open Access |

An Empirical Model for Evaluating Determinants Influencing the Effective Use of Learning Management Systems in TVET Environments

Dr. Arvind Kulkarni , Department of Data Science, Indian Institute of Advanced Analytics, Pune, India
Dr. Siti Rahmawati , Faculty of Computer Science, Universitas Teknologi Nusantara, Jakarta, Indonesia

Abstract

The integration of Learning Management Systems (LMS) within Technical and Vocational Education and Training (TVET) institutions has transformed instructional delivery, learner engagement, and institutional efficiency. However, disparities in effective usage persist due to varying technological, behavioral, and institutional determinants. This study develops an empirical model to evaluate the critical factors influencing LMS effectiveness in TVET environments. Grounded in the Technology Acceptance Model (TAM), Unified Theory of Acceptance and Use of Technology (UTAUT), and Information Systems Success Model, the study synthesizes behavioral, technical, and contextual determinants into a multidimensional analytical framework. Using a quantitative research design, the model incorporates constructs such as perceived usefulness, system quality, user satisfaction, technical support, and institutional readiness. The findings reveal that system interactivity, perceived ease of use, and organizational support significantly influence LMS effectiveness, while socio-cognitive factors moderate user engagement. The study contributes to theoretical advancement by integrating multiple acceptance frameworks and offers practical implications for optimizing LMS deployment in TVET institutions.



Keywords

Learning Management Systems, TVET, Technology Acceptance, System Quality

References

Ahmad, N. A., Elias, N. F., Sahari, N., & Mohamed, H. (2023). Learning management system acceptance factors for Technical and Vocational Education Training (TVET) institutions. TEM Journal, 12(2), 1156–1165.

Akmal Nizam Mohammed & Farzad Ismail (2013) Study of an entropy-consistent Navier-Stokes flux, International Journal of Computational Fluid Dynamics, 27(1), 1-14

Al-Bashayreh, M. (2022). An empirical investigation of reasons influencing student acceptance and rejection of mobile learning apps usage. Sustainability.

Alfalah, A. A. (2023). Factors influencing students' adoption and use of mobile learning management systems (m-LMSs): A quantitative study of Saudi Arabia. International Journal of Information Management Data Insights, 3(1), 100143.

Baleghi-Zadeh, S., Ayub, A. F. M., Mahmud, R., & Daud, S. M. (2017). The influence of system interactivity and technical support on learning management system utilisation. Knowledge Management and E-Learning, 9(1), 50–68.

Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Prentice-Hall.

Bhatiasevi, V., & Naglis, M. (2016). Investigating the structural relationship for the determinants of cloud computing adoption in education. Education and Information Technologies, 21(5), 1197–1223.

Chang, S. C., & Tung, F. C. (2008). An empirical investigation of students’ behavioural intentions to use the online learning course websites. British Journal of Educational Technology. 39(1), 71-83.

Cheng, Y. M. (2021). Can gamification and interface design aesthetics lead to MOOCs’ success? Education and Training, 63(9), 1346–1375.

Chua, Y. P. (2020). Mastering research methods. Mcgraw-Hill Education.

Cochran, W. G. (1977). Sampling techniques. John Wiley & Sons.

Coskuncay, D. F., & Ozkan, S. (2013). A model for instructors adoption of learning management systems: Empirical validation in higher education contextual. The Turkish Online Journal of Educational Technology-TOJET, 12(2), 13-25.

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319.

DeLone, W. H., & McLean, E. R. (1992). Information systems success: The quest for the dependent variable. Information Systems Research, 3(1), 60–95.

Edeh, N. I., Ugwoke, E. O., Abanyam, F. E., Madu, M. A., Augustine, N. O., & Pulife, M. C. (2021). Extending technology acceptance model in learning-management-systems in TVET institutions: The impact of vocational educators' gender, experience and perception. Journal of Technical Education and Training, 13(3), 93–107.

Fathema, N., Shannon, D., & Ross, M. (2015). Expanding The Technology Acceptance Model ( TAM ) to Examine Faculty Use of Learning Management Systems ( LMSs) In Higher Education Institutions. MERLOT Journal of Online Learning and Teaching, 11(2), 210–232.

Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. In Philosophy Rhetoric.

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39.

Ginaya, G., Sri Astuti, N. N., Mataram, I. G. A. B., & Nadra, N. M. (2020). English digital material development of information communication technology ICT in higher vocational education. Journal of Physics: Conference Series, 1569(2), 0–9.

Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). Prentice Hall.

Hall, R. J., Snell, A. F., & Foust, M. S. (1999). Item parceling strategies in sem: Investigating the subtle effects of un modeled secondary constructs. Organisational Research Methods, 2(3), 233–256.

Han, I., & Han, S. (2014). Adoption of the mobile campus in a cyber-university. International Review of Research in Open and Distance Learning,15(6), 237–256.

Huang, R.-T. (2018). What motivates people to continuously post selfies? The moderating role of perceived relative advantage. Comput. Hum. Behav., 80, 103–111.

Kline, R. B. (2011). Principles and practice of structural equation modelling. In theGuilford Press.

Mushi, R. (2022). Impact of normality pressure on acceptance of mobile phone technology. International Journal of ICT Research in Africa and the Middle East.

Nalathambi, D. K., Salleh, K. S. M., Noh, S. H. M., Solaiman, H. S., & Jayaraman, R. (2023). Effort of politeknik Malaysia as TVET institute in attaining Sustainable Development Goals (SDGs) through Twelfth Malaysia Plan. Borneo Engineering & Advanced Multidisciplinary International Journal, 2(01), 37–46.

Nookhao, S., & Kiattisin, S. (2023). Achieving a successful e-government: Determinants of behavioral intention from Thai citizens' perspective. Heliyon, 9(8), e18944.

Noorbhai, H., & Ojo, T. A. (2023). mHealth and e-Learning in health sciences curricula: a South African study of health sciences staff perspectives on utilisation, constraints and future possibilities. BMC Medical Education, 23(1), 1–18.

Nunnally, J. C. (1978). Phychometric theory. In McGraw Hill.

Nurchayati, N. (2023). Antecedents of user attitude towards e-commerce and future purchase intention. International Journal of Data and Network Science.

Pituch, K. A., & Lee, Y. K. (2006). The influence of system characteristics on e-learning use. Computers and Education, 47(2), 222–244.

Prihantoro, C. R. (2021). Examining the use of wheeler-model based curriculum development in a learning management system for vocational study program. International Journal of Education and Practice, 9(3), 507–519.

Roca, J. C., Chiu, C.M., & Martínez, F. J. (2006). Understanding e-learning continuance intention: An extension of the Technology Acceptance Model. International Journal of Human-Computer Studies,64(8), 683–696.

Rogers, E M. (2003). Diffusion of innovations. Free Press.

Rogers, Everett M. (1962). Diffusion of innovations (1st ed.). Free Press.

Saadati, F., Tarmizi, R. A., Ayub, A. F. M., & Bakar, K. A. (2015). Effect of internet-based cognitive apprenticeship model (i-CAM) on statistics learning among postgraduate students. PLoS ONE, 10(7), 1–16.

Sayaf, A. M. (2021). Information and communications technology used in higher education: An empirical study on digital learning as sustainability. Sustainability.

Shi, D., & Maydeu-Olivares, A. (2020). The effect of estimation methods on SEM fit indices. Educational and Psychological Measurement, 80(3), 421–445.

Şimşek, A. and Ateş, H. (2022). The extended technology acceptance model for web 2.0 technologies in teaching. Innoeduca International Journal of Technology and Educational Innovation, 8(2), 165-183.

Teo, T., Zhou, M., Fan, A. C. W., & Huang, F. (2019). Factors that influence university students' intention to use Moodle: a study in Macau. Educational Technology Research and Development, 67(3), 749–766.

Tezer, M., & Çimşir, B. T. (2018). The impact of using mobile-supported learning management systems in teaching web design on the academic success of students and their opinions on the course. Interactive Learning Environments, 26(3), 402–410.

Thompson, R. L., Higgins, C. A., & Howell, J. M. (1991). Personal computing: Toward a conceptual model of utilization. MIS Quarterly, 15(1), 124–143.

Tsai, C.-C. (2021). Impacts of AIOT implementation course on the learning outcomes of senior high school students. Education Sciences.

UNESCO. (2022). Transforming technical and vocational education and training for successful and just transitions: UNESCO strategy 2022-2029. United Nations Educational, Scientific and Cultural Organization, 26.

Utamachant, P., Anutariya, C., & Pongnumkul, S. (2023). i-Ntervene: Applying an evidence-based learning analytics intervention to support computer programming instruction. Smart Learning Environments, 10(1).

Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204.

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.

Virtue, D. C., & Pinter, H. H. (2023). A case study of responsive middle grades education in a laboratory middle school in the USA. Education Sciences, 13(6), 549.

Wu, J.H., & Wang, S.C. (2005). What drives mobile commerce?: An empirical evaluation of the revised technology acceptance model. Information & Management, 42(5), 719–729.

Xu, H. and Mahenthiran, S. (2021). Users’ perception of cybersecurity, trust and cloud computing providers’ performance. Information &Amp; Computer Security, 29(5), 816-835.

Zheng, H., Qian, Y., Wang, Z., & Wu, Y. (2023). Research on the Influence of E-Learning Quality on the Intention to Continue E-Learning: Evidence from SEM and fsQCA. Sustainability, 15(6), 5557.

Article Statistics

Copyright License

Download Citations