Key factors affecting users' preferences in using a learning management system (LMS): The moderating role of social influence, institutional initiatives and individual motives

Document Type : Original Article


Assistant Professor of Management, Sister Nivedita University, Kolkata, India


The Learning Management System (LMS) has been established in a number of Universities worldwide to help connect students and lecturers without the confines of the traditional classroom. The recent advancements in information and communication technologies have altered instructional contexts and re-shaped them into smart learning environments. Due to increasing number of available smart learning features, it has become indispensable to manage these features for effective and organized instructional processes. Currently, it is commonly seen that educational institutes operate their own LMS and provide various online smart learning features for a diverse group of students. This study aims to analyze key factors that can influence users’ preferences on LMS use and gain a deeper understanding of how to maximize the learning outcomes through LMS by considering six constructs, namely, Performance Expectancy, Effort Expectancy, Social Influence, Institutional Initiatives, Individual Motives and Behavioural Intension. This study involved 120 of the undergraduate and postgraduate students of a Private University of West Bengal and utilized the validated Technology Acceptance Model (TAM) to predict learners’ perceptions towards LMS adoption. Four essential success factors for LMS implementations have been emerged from the perceived dataset of the students of the University who have implemented LMS in their system. The study explores the potentiality of the acceptance of the Learning Management Systems perceived by the end users in the higher education system of West Bengal.


Main Subjects

Alkiş, N., Findik-Coşkunçay, D., & Yildirim, S. (2018). A Structural Model for Students' Adoption of Learning Management Systems: An Empirical Investigation in the Higher Education Context. Journal of Educational Technology & Society, 21(2), 13-27.
Baran, E. (2014). A Review of Research on Mobile Learning in Teacher Education. Journal of Educational Technology& Society, 17(4), 17-32.
Barrio-García, S., José, L., &Romero-Frías, E. (2015). Personal Learning Environments Acceptance Model: The Role of Need for Cognition, eLearning Satisfaction and Students' Perceptions. Journal of Educational Technology & Society, 18(3), 129-141.
Bervell, B., &Umar, I.N. (2014). A Decade of LMS Acceptance and Adoption Research in Sub-Sahara African Higher Education: A Systematic Review of Models, Methodologies, Milestones and Main Challenges. EURASIA Journal of Mathematics. Science and Technology Education, 13(11).
Bousbahi, F., &Alrazgan, M.S. (2015). Investigating IT Faculty Resistance to Learning Management SystemAdoption Using Latent Variables in an Acceptance Technology Model. The Scientific World Journal.
Buzzetto-More, N. (2015). Student Attitudes Towards the Integration of YouTube in Online, Hybrid, And Web- Assisted Courses: An Examination of the Impact of the Course Modality On Perception. MERLOT Journal of Online Learning and Teaching, 11(1), 55.
Das, J., &Majid, I. (2020). Assessment of e-Learning Readiness of Academic Staff & Students of Higher Education Institutions in Gujarat, India. Indian Journal of Educational Technology, 2(1), 31-45.
Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319-340.
DePietro, P. (2013). Mobile Education. Counterpoints, Transforming Education with New Media: Participatory Pedagogy, Interactive Learning, and Web 2.0, 435, 115-126.
Dias, S.B., Leontios, J., HadjileontiadisJosé, J., & Diniz, A. (2015). Fuzzy cognitive mapping of LMS users’ Quality of Interaction within higher education blended-learning environment. Elsevier, Expert Systems with Applications, 42(21), 7399-7423.
Ellahi, A. (2018). Social Networking Sites as Formal Learning Environments in Business Education. Journal of Educational Technology &Society, 21(4), 64-75.
Emelyanova, N., &Voronina, E. (2014). Introducing a Learning Management system at a Russian University: Students' and Teachers' Perceptions. National Research University Higher School of Economics (HSE), Russian Federation, The International Review of Research in Open and Distance Learning (IRR ODL).
Ghalandari, K. (2012). The Effect of Performance Expectancy, Effort Expectancy, Social Influence and Facilitating Conditions on Acceptance of E-Banking Services in Iran: The Moderating Role of Age and Gender. Middle-East Journal of Scientific Research, 12(6), 801-807.
Goh, W.W., Hong, J. L., & Gunawan, W. (2014). Exploring Lecturers’ Perceptions of Learning Management System: An Empirical Study Based on TAM. iJEP, 4(3), 48-54.
Venkatesh, V., Morris, M.G., Davis, G.B., & Davis, F.D. (2003). User Acceptance of Information Technology: Toward a Unified View, 27(3), 425-478.
Wu, C., Burton, N., Jeremy, J., Andrew, M., Byron, Z., Steve, S., & Gennady, S. (2012). Metamaterial-based integrated plasmatic absorber/emitter for solar thermo-photovoltaic systems.