Factors Influencing High School Teachers’ Adoption of Learning Management Systems: A Study in Northern Luzon, Philippines
DOI:
https://doi.org/10.69569/jip.2024.0595Keywords:
Educational technology, Extended TAM, Learning Management System, PhilippinesAbstract
The world faced different challenges brought about by the COVID–19 pandemic. The closure of schools brought significant disruptions to the learning process and, in effect, caused learning gaps among students. To adapt and facilitate continued learning, the Philippine Science High School (PSHS) System used the Knowledge Hub (kHub) as its central Learning Management System (LMS). This quantitative study examined factors influencing the acceptance of an extended Technology Acceptance Model (TAM) among teachers at PSHS-CVC. Data was gathered using an online survey and was analyzed using the WarpPLS software. Forty-one teachers, the majority female, with Master’s degrees, belong to higher teaching ranks, and with 6 – 10 years of teaching in the institution were the respondents of the study. Analysis of the data gathered indicated that system quality directly impacted both perceived usefulness and perceived ease of use. In turn, perceived ease of use influenced perceived usefulness, while perceived self-efficacy affected perceived ease of use. Perceived usefulness directly affected teachers' attitudes, intentions, and actual use of the LMS, which were also influenced by teaching experience. Of the 29 tested hypotheses, 10 were supported, confirming specific constructs of the extended TAM in this academic setting. Furthermore, the study showed that the adoption of LMS is greatly affected by the teaching experiences of the users, whether it is perceived to be beneficial to them, and if they intend to use the said LMS. This study provides insights into the acceptance factors for LMS adoption, contributing to strategies for effective technology use in education.
Downloads
References
Amora, J.T., & Fearnley, M.R. (2020). Learning management system adoption in higher education using the Extended Technology Acceptance Model. IAFOR Journal of Education, 8(2), 89-
106. https://doi.org/10.22492/ije.8.2.05
Belo, A. V. M., Prasetyo, Y.T., C. Roque, R. A., Benito, O. P., Cahigas, M. M., Gumasing, M. J., Borres, R. & Nadlifatin, R. (2024). Investigating students’ intention to utilize an e – learning platform in an aviation institution during the Covid – 19 pandemic. PLOS ONE, 19(12), e0308180. https://doi.org/10.1371/journal.pone.0308180
Chatterjee, P. (2023). Key factors affecting users’ preferences in using a learning management system (LMS): The moderating role of social influence, institutional initiatives and individual motives. New Applied Studies in Management, Economics & Accounting, 6(24), 71-82. e-ISSN:2783-3119
Cheng, Y. M. (2012). Effects of quality antecedents on e‐learning acceptance. Internet Research, 22(3), 361–390. https://doi.org/10.1108/10662241211235699
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. https://doi.org/10.1287/mnsc.35.8.982
DeLone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: A ten-year update. Journal of Management Information Systems, 19(4), 9-30. https://doi.org/10.1080/07421222.2003.11045748
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. https://tinyurl.com/356pbsvm
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. https://tinyurl.com/ha2usdr6
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
Garcia, M. (2017). E-learning technology adoption in the Philippines: An investigation of factors affecting Filipino college students’ acceptance of learning management systems. The International Journal of E – Learning and Educational Technologies in the Digital Media, 3(3), 118 – 130.
Ignacio, A. (2021). Online classes and learning in the Philippines during the Covid – 19 Pandemic. International Journal on Integrated Education, 4(3), 1 – 6. https://doi.org/10.31149/ijie.v4i3.1301
Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of e-Collaboration, 11(4), 1-10.
Kock, N. (2020). Using indicator correlation fit indices in PLS-SEM: Selecting the algorithm with the best fit. Data Analysis Perspectives Journal, 1(4), 1-4.
Kock, N., & Hadaya, P. (2018). Minimum sample size estimation in PLS‐SEM: The inverse square root and gamma‐exponential methods. Information Systems Journal, 28(1), 227–261.
Lavidas, K., Komis, V. & Achriani, A. (2022). Explaining faculty members’ behavioral intention to use learning management systems. J. Comput. Educ. 9(4), 707-725. https://doi.org/10.1007/s40692-021-00217-5
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. ISSN: 1309-517X (Online) 2021, 13(2), ep298. https://doi.org/10.30935/cedtech/9709
Mailizar M., Burg, D., & Maulina S. (2021). Examining university students’ behavioral intention to use e‐learning during the COVID‐19 pandemic: An extended TAM model. Education and Information Technologies. https://doi.org/10.1007/s10639-021-10557-5
McFarland, D.J., & Hamilton, D. (2006). Adding contextual specificity to the technology acceptance model. Computers in Human Behavior, 22(3), 427-447. https://doi.org/10.1016/j.chb.2004.09.009
Navarro, M. M., Prasetyo, Y. T., Young, M. N., Nadlifatin, R., Redi, A. A. N. P. (2021) The perceived satisfaction in utilizing learning management system among engineering students during the Covid – 19 pandemic: Integrating Task Technology Fit and extended technology acceptance model. Sustainability, 13, 10669. https://doi.org/10.3390/su131910669
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
Panergayo, A. A. & Aliazas, J. V. (2023). Google classroom adoption as learning management system in senior high school using technology acceptance model. 13(2), 871 – 883. doi: https://doi.org/10.23960/jpp.v13.i2.202355
Park, S. Y. (2009). An analysis of the technology acceptance model in understanding university students' behavioral intention to use e-learning. Educational Technology & Society, 12(3), 150-162. https://www.jstor.org/stable/jeductechsoci.12.3.150
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
Prasetyo, Y. T., Ong, A. K. S., Concepcion, G. K. F, Navalta, F. M. B., Robles, R. A. V., Tomagos, I. J. T., Young, M. N., Diaz, J. F. T., Nadlifatin, R., Redi, A. A. N. P. (2021). Determining factors affecting acceptance of e-learning platforms during the Covid – 19 pandemic: Integrating extended technology acceptance model and DeLone & McLean IS Success model. Sustainability, 13, 8365. https://doi.org/10.3390/su13158365
Pynoo, B., Devolder, P., Tondeur, J., Van Braak, J., Duyck, W., & Duyck, P. (2011). Predicting secondary school teachers’ acceptance and use of a digital learning environment: A cross- sectional study. Computers in Human Behavior, 27(1), 568–575. https://doi.org/10.1016/j.chb.2010.10.005
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
Sibuea, T. (2018). Applying the technology acceptance model in understanding academics’ behavior to adopt learning management system in their profession. Proceedings of LIA International Conference on Language and Culture 2018, Jakarta, Indonesia. 19-35
Tarhini, A., Hone, K., & Liu, X. (2014). Measuring the moderating effect of gender and age on e-learning acceptance in England: A structural equation modeling approach for an extended technology acceptance model. Journal of Educational Computing Research, 51(2), 163-184. https://doi.org/10.2190/EC.51.2.b
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. (2010). Development and validation of the E-learning Acceptance Measure (ElAM). Internet and Higher Education, 13(3), 148–152. https://doi.org/10.1016/j.iheduc.2010.02.001 Teo, T. (2011). Modeling the determinants of pre-service teachers’ perceived usefulness of e-learning. Computers & Education, 57(4), 2432-2440.
https://doi.org/10.1016/j.compedu.2011.06.011
Thong, J. Y., Hong, W., & Tam, K. Y. (2006). The effects of post-adoption beliefs on the expectation-confirmation model for information technology continuance. International Journal of Human-Computer Studies, 64(9), 799-810. https://doi.org/10.1016/j.ijhcs.2006.05.001
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the Technology Acceptance Model: Four longitudinal field studies. Management Science, 46(2), 186-204. https://doi.org/10.1287/mnsc.46.2.186.11926
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478. https://doi.org/10.2307/30036540
Zanjani, N., Edwards, S., Nykvist, S., & Geva, S. (2016). LMS Acceptance: The Instructor role. The Asia-Pacific Education Researcher, 25(4), 519-526. https://doi.org/10.1007/s40299-016- 0277-2
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Journal of Interdisciplinary Perspectives
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
