EXAMINING THE LEARNING MANAGEMENT SYSTEM ADOPTION IN A STATE UNIVERSITY USING THE EXTENDED TECHNOLOGY ACCEPTANCE MODEL
Denson N. Liday | Nobelyn V Agapito
Discipline: Higher Education Research
Abstract:
This paper investigated the factors that determine how higher education teachers adopt and utilize a learning management system (LMS). It also verified the expanded technology acceptance model (TAM) in the context of a public institution. The link between eight TAM components was investigated using a non-experimental quantitative research technique with partial least squares-structural equation modeling (PLS-SEM). All faculty active users with LMS experience and training were included in the study sample. Overall, the fit and quality indices of the structural model were within acceptable boundaries. Several relationships among the model's eight components were confirmed, while others were not validated by this investigation. System quality and perceived usefulness, perceived self-efficacy and perceived ease of use, facilitating conditions and perceived ease of use, perceived usefulness and attitude towards use, attitude towards use and behavioral intention to use, and behavioral intention to use and actual use all had a significant and positive effect. The LMS used by the university. To be valuable to teachers, the system employed at the university LMS should be very performant. Faculty with greater perceived self-efficacy have a stronger sense of the system's perceived ease of use, whereas those with lower perceived competence find the system less useful and more difficult to use. If appropriate enabling conditions exist, faculty engaged users will develop favorable attitudes of the ease of usage. Furthermore, professors with favorable views regarding technology use were more likely to have higher behavioral intentions, which might result in actual technology use.
References:
- Aldiab, A., Chowdhury, H., Kootsookos, A., Alam, F., & Allhibi, H. (2019). Utilization of Learning Management Systems (LMSs) in higher education system: A case review for Saudi Arabia. Energy Procedia, 160, 731-737. https://doi.org/10.1016/j.egypro.2019.02.186
- Alenezi, A. (2018). Barriers to participation in learning management systems in Saudi Arabian universities. Education Research International, 2018. https://doi.org/10.1155/2018/9085914.
- Ali, W. (2020). Online and Remote Learning in Higher Education Institutes: A Necessity in Light of COVID-19 Pandemic. Higher Education Studies ,10(3):16. DOI:10.5539/hes.v10n3p16
- Alias, N. A., & Zainuddin, A. M. (2005). Innovation for Better Teaching and Learning: Adopting the Learning Management System. Malaysian Online Journal of Instructional Technology, 2(2), 27-40.
- Amora, J. T., & Fearnley, M. R. (2020). Learning Management System Adoption in Higher Education Using the Extended Technology Acceptance Model. Journal of Education, 8(2), 89-106. https://doi.org/10.22492/ije.8.2.05
- Commission on Higher Education. (2020). CHED Memorandum Order No.4 s. 2020. -Guidelines-on-the-Implementation-of-Flexible-Learning.pdf. https://ched.gov.ph/wp-content/uploads/.
- Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003.
- Fathema, N., & Sutton, K. L. (2013). Factors influencing faculty members’ learning management systems adoption behavior: An analysis using the technology acceptance model. International Journal of Trends in Economics Management & Technology, 2(6), 20–28.
- 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. Journal of Online Learning and Teaching, 11(2).
- Ferran, F. M. (2021). Extended Technology Acceptance Model to Examine the Use of Google Forms – based Lesson Playlist in Online Distance Learning. Recoletos Multidisciplinary Research Journal, 9(1), 147-161. https://doi.org/10.32871/rmrj2109.01.13
- Fishbein M. & Ajzen I. (1975) Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research. Addison-Wesley, Reading, MA.
- Kennedy, D. J. (2009). Virtual learning environments (VLEs): Here to stay, or on the brink of demise?. Plymouth Student Educator, 1(1), 58–56.
- Kock, N. (2015). WarpPLS 5.0 user manual. Script Warp Systems.
- Kock, N. (2020). WarpPLS 7.0 user manual: Version 7.0. Script Warp Systems.
- Lim, C. P., & Khine, M. S. (2006). Managing teachers' barriers to ICT integration in Singapore schools. Journal of Technology and Teacher Education, 14(1), 97-125.
- Mailizar M., Burg, D., & Maulina S. (2021). Examining university students’ behavioural intention to use e‑learning during the COVID‑19 pandemic: An extended TAM model. Education and Information Technologies, 26, 7057–7077. https://doi.org/10.1007/s10639-021-10557-5
- Mailizar, M., Almanthari, A., & Maulina, S. (2021). Examining Teachers’ Behavioral Intention to Use E-learning in Teaching of Mathematics: An Extended TAM Model. Contemporary Educational Technology, 13(2). https://doi.org/10.30935/cedtech/9709.
- Nikou, S. A., & Economides, A. A. (2019). Factors that influence behavioral intention to use mobile-based assessment: A STEM teachers’ perspective. British Journal of Educational Technology. 50(2), 587–600. https://doi.org/10.1111/bjet.12609
- Parkman, S., Litz, D., & Gromik, N. (2018). Examining pre-service teachers’ acceptance of technology-rich learning environments: A case study. Education and Information Technologies, 23, 1253–1275. https://doi.org/10.1007/s10639-017-9665-3
- Scherer, R., Siddiq, F., & Tondeur, J. (2019). The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers’ adoption of digital technology in education. Computers & Education, 128, 13–35. https://doi.org/10.1016/j.compedu.2018.09.009
- Schoonenboom, J. (2013). Using an adapted, task-level technology acceptance model to explain why instructors in higher education intend to use some learning management system tools more than others. Computers & Education, 71, 247–256. https://doi.org/10.1016/j.compedu.2013.09.016
- Shraim, K. & Khlaif, Z. (2010). An e-learning approach to secondary education in Palestine: Opportunities and challenges. Information Technology for Development, 16(3), 159-173. https://doi.org/10.1080/02681102.2010.501782
- Siyam, N. (2019). Factors impacting special education teachers’ acceptance and actual use of technology. Education and Information Technologies, 24(3), 2035–2057. https://doi.org/10.1007/s10639-018-09859-y
- Teo, T. (2010). Examining the influence of subjective norm and facilitating conditions on the intention to use technology among pre-service teachers: A structural equation modeling of an extended technology acceptance model. Asia Pacific Education Review, 11(2), 253–262. https://doi.org/10.1080/10494821003714632.
- Teo, T. (2012). Examining the intention to use technology among pre-service teachers: An integration of the Technology Acceptance Model and Theory of Planned Behavior. Interactive Learning Environments, 20(1), 3-18. DOI: 10.1080/10494821003714632.
- Torrisi-Steele, G., & Drew, S. (2013). The literature landscape of blended learning in higher education: the need for better understanding of academic blended practice. International Journal for Academic Development, 18(4), 371-383. http://dx.doi.org/10.1080/1360144X.2013.786720.
- Zanjani, N., Edwards, S., Nykvist, S., & Geva, S. (2016). LMS Acceptance: The Instructor Role. The Asia-Pacific Education Researcher, 25(4), 519-526. DOI: 10.1007/s40299-016-0277-2