Determinants of Teachers’ Behavioral Intention to Use a Learning Management System (LMS) in a Public University in Sichuan

Authors

  • Shanshan Zhang

Keywords:

Perceived Usefulness, Performance Expectancy, Effort Expectancy, Social Influence, Behavioral Intention

Abstract

Purpose: The study investigates the influence of five independent variables (Perceived Usefulness, Attitude, Performance Expectancy, Effort Expectancy, and Social Influence) on dependent variables (Behavioral Intention). In addition, this study also aimed to identify significant differences between the variables. Research design, data, and methodology: The research employed the Index of Item-Objective Congruence (IOC) for validity and a Cronbach's Alpha in a pilot test (n=30) for reliability. The multiple linear regression analyzed 60 valid responses from teachers of Sichuan University to verify the significant relationship between variables. Following this, 30 teachers underwent a 14-week Strategic Plan (SP). Afterward, the quantitative results from post-SP and pre-SP were analyzed in the paired-sample t-test for comparison. Results: In multiple linear regression, the study revealed that Perceived Usefulness, Attitude, Performance Expectancy, Effort Expectancy, and Social Influence significantly impacted teacher's Behavioral Intention. Finally, the results from the paired-sample t-test for comparison demonstrated a significant difference in teachers' Behavioral Intention between the post-SP and pre-SP stages. Conclusions: This study aims to improve the behavior intention of teachers by using LMS to influence Perceived Usefulness, Attitude, Performance Expectancy, Effort Expectancy, and Social Influence in the background of the construction of Double First-class Universities in China.

Author Biography

Shanshan Zhang

West China Meadical Center, Sichuan University, China.

References

Abu-Al-Aish, A., & Love, S. (2013). Factors Influencing Students’ Acceptance of M-Learning: An Investigation in Higher Education. The International Review of Research in Open and Distance Learning, 14(5), 82-107. https://doi.org/10.19173/irrodl.v14i5.1631.

Agarwal, R., & Prasad, J. (1999). Are Individual Differences Germane to The Acceptance of New Information Technologies? Decision Sciences, 30(2), 361-91.

Ajzen, I. (1991). The Theory of Planned Behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211.

Alharbi, S., & Drew, S. (2014). Using the technology acceptance model in understanding academics' behavioural intention to use learning management systems. International Journal of Advanced Computer Science and Applications, 5(1), 143-155.

Bardakcı, S. (2019). Exploring high school students' educational use of YouTube. International Review of Research in Open and Distributed Learning, 20(2).

https://doi.org/10.19173/irrodl.v20i2.4074

Carter, C. B., & Williams, D. B. (1996). Transmission electron microscopy: A textbook for materials science. Springer.

Cheung, R., & Vogel, D. (2013). Predicting user acceptance of collaborative technologies: An extension of the technology acceptance model for e-learning. Computers & education, 63, 160-175.

Davis, F. D. (1986). A technology acceptance model for empirically testing new end-user information systems: Theory and results [Doctoral dissertation]. Massachusetts Institute of Technology.

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.

Dennis, A. R., Fuller, R. M., & Valacich, J. S. (2011). Media, tasks, and communication processes: A theory of media synchronicity. MIS Quarterly, 32(3), 575-600.

El Gayar, O. F., Saleh, M., Atiya, A. F., & El-Shishiny, H. (2011). An integrated framework for advanced hotel revenue management. International Journal of Contemporary Hospitality Management, 23(1), 84-98. https://doi.org/10.1108/09596111111101689

El-Masri, M., & Tarhini, A. (2017). Factors affecting the adoption of e-learning systems in Qatar and USA: Extending the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). Educational Technology Research and Development, 65(3), 743-763. https://doi.org/10.1007/s11423-016-9508-8

Gao, W., Emaminejad, S., Nyein, H. Y. Y., Challa, S., Chen, K., Peck, A., Fahad, H. M., Ota, H., Shiraki, H., Kiriya, D., Lien, D. H., Brooks, G. A., Davis, R. W., & Javey, A. (2016). Fully integrated wearable sensor arrays for multiplexed in situ perspiration analysis. Nature, 529(7587), 509-514. https://doi.org/10.1038/nature16521

Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2014). A primer on partial least squares structural equation modeling (PLS-SEM). SAGE Publications.

Hao, L. T., Duy, P. Q., An, M., Talbot, J., Iyer, C. C., Wolman, M., & Beattie, C. E. (2017). HuD and the survival motor neuron protein interact in motoneurons and are essential for motoneuron development, function, and mRNA regulation. The Journal of Neuroscience, 37(48), 11559-11571. https://doi.org/10.1523/JNEUROSCI.2017-17.2017

Iqbal, S., & Qureshi, I. A. (2012). M-learning adoption: A perspective from a developing country. International Review of Research in Open and Distance Learning, 13(3), 147-164. https://doi.org/10.19173/irrodl.v13i3.1152

Kaishawani, K., & Tripathi, N. (2012). Effect of art therapy and counseling on adolescents. Indian Journal of Health and Wellbeing, 3(3), 653-658.

Liu, L., Zhao, X., Liu, Y., Zhao, H., & Li, F. (2019). Dietary addition of garlic straw improved the intestinal barrier in rabbits. Journal of Animal Science, 97(10), 4248-4255.

https://doi.org/10.1093/jas/skz275

Miltgen, C. L., Popovič, A., & Oliveira, T. (2013). Determinants of end-user acceptance of biometrics: Integrating the "Big 3" of technology acceptance with privacy context. Decision Support Systems, 56, 103-114. https://doi.org/10.1016/j.dss.2013.05.010

Ngugi, W. T. (2016). Secure the base: Making Africa visible in the globe (1st ed.). Seagull Books.

Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill.

Oye, N. D., Iahad, N. A., & Ab. Rahim, N. (2011). The impact of UTAUT model and ICT theoretical framework on university academic staff: Focus on Adamawa State University, Nigeria. Australian Journal of Basic and Applied Sciences, 5(12), 1884-1890.

Raza, S. A., Qazi, W., Khan, K. A., & 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.

https://doi.org/10.1177/0735633120960421

Shi, J., & Huang, T. J. (2009). Continuous particle separation in a microfluidic channel via standing surface acoustic waves (SSAW). Lab on a Chip, 9(23), 3354-3359.

https://doi.org/10.1039/b915113c

Tan, M., & Teo, T. S. H. (2000). Factors influencing the adoption of Internet banking. Journal of the Association for Information Systems, 1(1), Article 5. https://doi.org/10.17705/1jais.00005

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

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. https://doi.org/10.2307/41410412

Wang, T., & Choi, W. (2017). Polydopamine-based concentric nanoshells with programmable architectures and plasmonic properties. Nanoscale, 9(44), 17405-17412. https://doi.org/10.1039/C7NR05451C

Ward, J. H. (1963). Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association, 58(301), 236-244. https://doi.org/10.1080/01621459.1963.10500845

Watjatrakul, B. (2013). Intention to use a free voluntary service: The effects of social influence, knowledge, and perceptions. Journal of Systems and Information Technology, 15(3), 202-220. https://doi.org/10.1108/13287261311328903

You, J. Q., Hu, X., Ashhab, S., & Nori, F. (2007). Low-decoherence flux qubit. Physical Review B, 75(14), 140515. https://doi.org/10.1103/PhysRevB.75.140515

Zhang, X., Zhao, Y., Zhang, M., Pang, X., Xu, J., Kang, C., & Zhao, L. (2012). Structural changes of gut microbiota during berberine-mediated prevention of obesity and insulin resistance in high-fat diet-fed rats. PLOS ONE, 7(8), e42529. https://doi.org/10.1371/journal.pone.0042529

Downloads

Published

2026-03-24

How to Cite

Zhang, S. (2026). Determinants of Teachers’ Behavioral Intention to Use a Learning Management System (LMS) in a Public University in Sichuan. Scholar: Human Sciences, 18(1), 192-201. Retrieved from https://assumptionjournal.au.edu/index.php/Scholar/article/view/8760