Determinants of Learning Management System (LMS) Adoption by University Students for Distance Learning

Yohane Soko, Mubanga Mpundu, Tryson Yangailo

Abstract


Gone are the days when face-to-face teaching was the only dominant way of delivering education to learners worldwide. The advent of ICT has enabled the provision of enriched online learning experiences. Since the beginning of 2020, the role of ICT in education has been highlighted globally and in Zambia due to the lockdown to counter the spread of the coronavirus. In response to the COVID-19 pandemic, public and private universities in Zambia quickly developed and expanded online learning to ensure continuous education for learners. In this context, a study of the determinants of learning management systems was designed and implemented. The study collected primary data from two public and five private universities in Zambia. The study tested twelve hypotheses using a novel structural equation modelling approach using SPSS Amos 24 and SPSS 26 software. The theoretical basis of the study was a modified unified theory of technology acceptance and use model. The results of the study indicated that performance expectancy and facilitating conditions had statistically insignificant influences on behavioural intentions to use learning management systems. Effort expectancy, social influence and hedonic motivation positively influence behaviour intentions. Facilitating conditions, behavioural intentions and course evaluation positively influence actual LMS use. However, instructor characteristics and course design negatively influence actual LMS use. Finally, course evaluation has a negative effect, while course design has a positive effect on performance expectancy. The study contributes to the literature by providing information on how to strengthen e-learning. It is recommended that the government of Zambia should provide an enabling environment for online learning to flourish. Universities should adopt convenient and easy-to-use learning management systems.

Keywords


Adoption; Distance education; Learning management system; Online learning; Structural equation modelling; University students

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References


Abbad, M. M. (2021). Using the UTAUT model to understand students’ usage of e-learning systems in developing countries. Education and Information Technologies, 26(6), 7205-7224.

Aldowah, H., Al-Samarraie, H., and Ghazal, S. (2019). How course, contextual, and technological challenges are associated with instructors’ individual challenges to successfully implement E-learning: A developing country perspective. IEEE Access, 7, 48792-48806.

Alhabeeb, A., and Rowley, J. (2018). E-learning success factors: Comparing perspectives from academic staff and students. Computers and Education, 127, 1-12.

Almaiah, M. A., and Alyoussef, I. Y. (2019). Analysis of the effect of course design, course content support, course assessment and instructor characteristics on the actual use of e-learning system. IEEE Access, 7, 171907-171922.

Al-Mamary, Y. H. S. (2022). Understanding the use of learning management systems by undergraduate university students using the UTAUT model: Credible evidence from Saudi Arabia. International Journal of Information Management Data Insights, 2(2), 100092.

Aloulou, M., and Grati, R. (2022). An e-learning system in a higher education institution in the UAE during the COVID-19 pandemic: A students’ perspective. Global Journal of Engineering Education, 24(1), 6-13.

Alshehri, A. J., Rutter, M., and Smith, S. (2020). The effects of UTAUT and usability qualities on students’ use of learning management systems in Saudi tertiary education. Journal of Information Technology Education: Research, 19, 891-930.

Bansal, R., Jain, R., and Seth, N. (2022). Digitalization in education: Application of Utaut to use learning management system. Journal of Content, Community and Communication, 15, 260-275.

Chatti, H., and Hadoussa, S. (2021). Factors affecting the adoption of E-learning technology by students during the COVID-19 quarantine period: The application of the UTAUT model. Engineering, Technology and Applied Science Research, 11(2), 6993-7000.

Eneizan, B., Mohammed, A. G., Alnoor, A., Alabboodi, A. S., and Enaizan, O. (2019). Customer acceptance of mobile marketing in Jordan: An extended UTAUT2 model with trust and risk factors. International Journal of Engineering Business Management, 11, 1-10.

Fan, Y., Chen, J., Shirkey, G., John, R., Wu, S. R., Park, H., and Shao, C. (2016). Applications of structural equation modeling (SEM) in ecological studies: An updated review. Ecological Processes, 5, 1-12.

Fornell, C., and Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50.

Gruzina, Y., Firsova, I., and Strielkowski, W. (2021). Dynamics of human capital development in economic development cycles. Economies, 9(2), 1-18.

Haron, H., Hussin, S., Yusof, A. R. M., Samad, H., Yusof, H., and Juanita, A. (2020). Level of technology acceptance and factors that influence the use of MOOC at public universities. International Journal of Psychosocial Rehabilitation, 24(6), 5412-5418.

Hartelina, H., Batu, R., and Hidayanti, A. (2021). What can hedonic motivation do on decisions to use online learning services?. International Journal of Data and Network Science, 5(2), 121-126.

Hunde, M. K., Demsash, A. W., and Walle, A. D. (2023). Behavioral intention to use e-learning and its associated factors among health science students in Mettu university, southwest Ethiopia: Using modified UTAUT model. Informatics in Medicine Unlocked, 36, 101154.

Ikhsan, R. B., Prabowo, H., and Yuniarty. (2021). Drivers of the mobile-learning management system’s actual usage: Applying the utaut model. ICIC Express Letters. Part B, Applications: An International Journal of Research and Surveys, 12(11), 1067-1074.

Kamalasena, B. D., and Sirisena, A. B. (2021). Factors influencing the adoption of E-learning by university students in Sri Lanka: application of UTAUT-3 model during COVID-19 pandemic. Wayamba Journal of Management, 12(2), 99-124.

Luarn, P., and Lin, H. H. (2005). Toward an understanding of the behavioral intention to use mobile banking. Computers in Human Behavior, 21(6), 873-891.

Mahande, R. D., and Malago, J. D. (2019). An e-learning acceptance evaluation through UTAUT model in a postgraduate program. Journal of Educators Online, 16(2), n2.

Masimba, F., and Zuva, T. (2021). Individual acceptable of technology: A critical review of technology adoption models and theories. Indiana Publications, 2(1), 37-48.

McGee, P., and Reis , A. (2012). Blended course design: A synthesis of best practices. Journal of Asynchronous Learning Networks, 16(4), 7-22.

Mtebe, J. S., and Raisamo, R. (2014). A model for assessing learning management system success in higher education in Sub‐Saharan countries. The Electronic Journal of Information Systems in Developing Countries, 61(1), 1-17.

Nguyen, T. D., Nguyen, T. M., Pham, Q. T., and Misra, S. (2014). Acceptance and use of e-learning based on cloud computing: The role of consumer innovativeness. Springer International Publishing, 2014, 159-174.

Nuankaew, W., and Nuankaew, P. (2021). Educational engineering for models of academic success in Thai universities during the COVID-19 pandemic: Learning strategies for lifelong learning. International Journal of Engineering Pedagogy, 11(4), 96-114.

Ozturk, I. (2001). The role of education in development: A theoretical perspective. Journal of Rural Development and Administration, XXXIII(1), 1-7.

Raman, A., and Don, Y. (2013). Preservice teachers’ acceptance of learning management software: An application of the UTAUT2 model. International Education Studies, 6(7), 157-164.

Raman, A., Don, Y., Khalid, Y., and Rizuan, M. (2014). Usage of learning management system (Moodle) among postgraduate students: UTAUT model. Asian Social Science, 10(14), 186-192.

Raza, S. A., Qazi, W., Khan, K. A., and Salam, J. (2021). 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.

Reyes-Mercado, P., Barajas-Portas, K., Kasuma, J., Almonacid-Duran, M., and Zamacona-Aboumrad, G. A. (2022). Adoption of digital learning environments during the COVID-19 pandemic: merging technology readiness index and UTAUT model. Journal of International Education in Business, 16(1), 91-114.

Sarfraz, M., Khawaja, K. F., and Ivascu, L. (2022). Factors affecting business school students’ performance during the COVID-19 pandemic: A moderated and mediated model. The International Journal of Management Education, 20(2), 100630.

Sitar‐Tăut, D. A. (2021). Mobile learning acceptance in social distancing during the COVID‐19 outbreak: The mediation effect of hedonic motivation. Human Behavior and Emerging Technologies, 3(3), 366-378.

Susana, O., Juanjo, M., Eva, T., and Ana, I. (2015). Improving graduate students learning through the use of moodle. Educational Research and Reviews, 15(5), 604-614.

Tarhini, A., Masa’deh, R. E., and Al-Busaidi, K. A., M, K. A. (2017). Factors influencing students’ adoption of e-learning: a structural equation modeling approach. Journal of International Education in Business, 10(2), 164-182.

Venkatesh , V., Morris , M. G., and Davis , G. B. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.

Venkatesh, V., and Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273-315.

Venkatesh, V., Thong , J., and Xu , X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quaterly, 36(1), 157–178.

Wright, C. R. (2003). Criteria for evaluating the quality of online courses. Alberta Distance Education and Training Association, 16(2), 185-200.

Wu, L., and Wu, Y.-F. (2020). An application of the UTAUT2 model for understanding user intention adopting Google Chromecast in Taiwan. Asian Journal of Information and Communications, 12(1), 90-107.

Zacharis, G., and Nikolopoulou, K. (2022). Factors predicting University students’ behavioral intention to use eLearning platforms in the post-pandemic normal: an UTAUT2 approach with ‘Learning Value’. Education and Information Technologies, 27(9), 12065-12082.

Zilinskiene, I. (2022). Exploring learning analytics for the course design improvement: The results of a pilot experiment. Baltic Journal of Modern Computing, 10(1), 36-54.

Zwain, A. A. A., and Haboobi, M. N. H. (2019). Investigating determinants of faculty and students’ acceptance of e-learning management systems using UTAUT2. Technology, 7(8), 280-293.




DOI: https://doi.org/10.17509/ijert.v4i2.64955

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