Factors Influencing Students’ Intention to Use E-Learning in Zhanjiang, China
Keywords:
E-learning, Behavioral intention to use, Higher education, ZhanjiangAbstract
Purpose: This research examined the factors influencing students’ behavioral intention to use e-learning in Zhanjiang, China. In the digital age, e-learning is increasingly favored for its convenience, flexibility, and sustainability. Given the widespread use of e-learning in education, it is necessary to conduct an in-depth study to examine the factors that influence students' use of e-learning. Research design, data, and methodology: The study started with a validity analysis using the Item-Objective Consistency Index (IOC) and a reliability analysis using Cronbach's Alpha (n=30). Multiple linear regression analyses were then conducted to test whether the variables had a significant relationship. Afterward, a 12-week Strategic plan (SP) was implemented for 30 students, and a paired-sample t-test was conducted on the quantitative results before and after the SP to determine the changes in students' behavioral intentions to use e-learning by implementing the Strategic plan. Results: This study verified that the six independent variables (Effort expectancy, Performance expectancy, Habit, facilitating conditions, Hedonic motivation, and social influence) significantly influence the dependent variable (Behavioral intention to use). Concurrently, the paired sample t-test comparing results demonstrated a statistically significant difference between the pre- and post-strategic plan phases regarding students' behavioral intention to use e-learning. Conclusions: This study identifies and validates several key factors significantly influencing students' behavioral intentions to use e-learning environments. In addition, this study provides a rich research opportunity and theoretical foundation for future in-depth exploration and analysis.
References
Alexander, B., Ashford-Rowe, K., Barajas-Murphy, N., Dobbin, G., Knott, J., McCormack, M., Pomerantz, J., Seilhamer, R., & Weber, N. (2019). EDUCAUSE horizon report:2019 higher education edition. Educause. https://library.educause.edu/-/media/files/library/2019/4/2019horizonreport.pdf
Aljawarneh, S. A. (2020). Reviewing and exploring innovative ubiquitous learning tools in higher Education. Journal of Computing in Higher Education, 32(1), 57-73. https://doi.org/10.1007/s12528-019-09207-0
Alkhwaldi, A. F., & Abdulmuhsin, A. A. (2022). Crisis-centric distance learning model in Jordanian higher education sector: factors influencing the continuous use of distance learning platforms during COVID-19 pandemic. Journal of International Education in Business, 15(2), 250-272. https://doi.org/10.1108/jieb-01-2021-0001
Alowayr, A. (2022). Determinants of mobile learning adoption: extending the unified theory of acceptance and use of technology (UTAUT). The International Journal of Information and Learning Technology, 39(1), 1-12.
https://doi.org/10.1108/ijilt-05-2021-0070
Buabeng-Andoh, C., & Baah, C. (2020). Pre-service teachers’ intention to use learning management system: an integration of UTAUT and TAM. Interactive Technology and Smart Education, 17(4), 455-474. https://doi.org/10.1108/itse-02-2020-0028
Cho, V., Cheng, T. C. E., & Lai, W. M. J. (2009). The role of perceived user-interface design in continued usage intention of self-paced e-learning tools. Computers & Education, 53(2), 216-227. https://doi.org/10.1016/j.compedu.2009.01.014
Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297-334. https://doi:10.1007/bf02310555
Darcin, A. E., Kose, S., Noyan, C. O., Nurmedov, S., Yılmaz, O., & Dilbaz, N. (2016). Smartphone addiction and its relationship with social anxiety and loneliness. Behavior and Information Technology, 35(7), 520-525. https://doi.org/10.1080/0144929x.2016.1158319
Duggal, S. (2022). Factors impacting acceptance of e-learning in India: learners’ perspective. Asian Association of Open Universities Journal, 17(2), 101-119.
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.
Fianu, E., Blewett, C., & Ampong, G. O. (2020). Toward the development of a model of student usage of MOOCs. Education+Training, 62(5), 521-541. https://doi.org/10.1108/et-11-2019-0262
Gunasinghe, A., Malaysia, S. A., Hamid, G. A., Khatibi, A., & Azam, S. M. F. (2020). The adequacy of UTAUT-3 in interpreting academician’s adoption to e-learning in higher education environments. Interactive Technology and Smart Education, 17(1), 86-106.
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: indeed, a silver bullet. Journal of Marketing Theory and Practice, 19(2),139-152.
Hair, J. F., Sarstedt, M., Hopkins, L., & Kuppelwieser, V. G. (2014). Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research. European Business Review, 26(2), 106–121.
Hu, H., & Lai, C. (2019). Comparing factors that influence learning management systems use on computers and on mobile. Information and Learning Sciences, 120(7),468-488.
Kurt, O. E. (2022). Learning with smartphones: the acceptance of m-learning in higher education. Online Information Review, 9(5), 215-221.
Liaw, S. S. (2008). Investigating students’ perceived satisfaction, behavioral intention, and effectiveness of E-learning: a case study of the blackboard system. Computers and Education, 51(2), 864-873.
https://doi.org/10.1016/j.compedu.2007.09.005
Masadeh, R. M. T., Tarhini, A., Bany, M. A., & Maqableh, M. (2016). Modeling factors affecting student’s usage behavior of E-learning systems in Lebanon. International Journal of Business and Management, 11(2), 299-312.
Mpungose, C. B. (2020). Is Moodle or WhatsApp the preferred e-learning platform at a South African university? First-year students’ experiences. Education and Information Technologies, 25(2), 927-941.
Okazaki, S., & Santos, L. M. R. (2012). Understanding e-learning adoption in Brazil: major determinants and gender effects. The International Review of Research in Open and Learning, 13(4), 91-106.
Perera, R. H. A. T., & Abeysekera, N. (2022). Factors affecting learners’ perception of e-learning during the COVID-19 pandemic. Asian Association of Open Universities Journal, 17(1), 84-100.
Raman, A., & Don, Y. (2013). Preservice teachers’ acceptance of learning management software: an application of the UTAUT2 model. International Education Studies, 6(7), 157-164.
Reyes-Mercado, P., Barajas-Portas, K., Kasuma, J., Almonacid-Duran, M., & Zamacona-Aboumrad, G. A. (2023). 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.
Riquelme, H. E., & Rios, R. E. (2010). The moderating effect of gender in the adoption of mobile banking. International Journal of Bank Marketing, 28(5),328-341.
Rudhumbu, N. (2022). Applying the UTAUT2 to predict the acceptance of blended learning by university students. Asian Association of Open Universities Journal, 17(1),15-36.
Salloum, S. A., & Shaalan, K. (2018). Factors affecting students’ acceptance of e-learning system in higher education using UTAUT and structural equation modeling approaches. In International Conference on Advanced Intelligent Systems and Informatics. Springer.
Samsudeen, S. N., & Mohamed, R. (2019). University students’ intention to use e-learning systems: A study of higher educational institutions in Sri Lanka. Interactive Technology and Smart Education, 16(3), 219-238.
Tamilmani, K., Rana, N. P., Prakasam, N., & Dwivedi, Y. K. (2019). The battle of brain vs Heart: a literature review and meta-analysis of ‘hedonic motivation’ use in UTAUT2.International Journal of Information Management, 46(1), 222-235.
Tandon, U., Mittal, A., Bhandari, H., & Bansal, K. (2022). Learning adoption by undergraduate architecture students: facilitators and inhibitors. Engineering, Construction and Architectural Management, 29(10), 4287-4312.
Tarhini, A., Hone, K., & Liu, X. (2014). The effects of individual differences on e-learning users’ behaviors in developing countries: a structural equation model. Computers in Human Behavior, 41,153-163.
Tarhini, A., Masadeh, R., Al-Busaidi, K. A., Mohammed, A. B., & Mahmoud, M. (2017). Factors influencing students’ adoption of e-learning: a structural equation modeling approach. Journal of International Education in Business, 10(2), 164-182.
Teo, T., & Noyes, J. (2014). Explaining the intention to use technology among pre-service teachers: a multi-group analysis of the unified theory of acceptance and use of technology. Interactive Learning Environments, 22(1), 51-66.
Thaker, H. M. T., Pitchay, A. A., & Hussain, H. I. (2022). Behavioral intention and adoption of internet banking among clients of Islamic banks in Malaysia: an analysis using UTAUT2. Journal of Islamic Marketing, 13(5), 1171-1197.
Twum, K. K., Ofori, D., Keney, G., & Korang-Yeboah, B. (2022). Using the UTAUT, personal innovativeness and perceived financial cost to examine student’s intention to use E-learning. Journal of Science and Technology Policy Management, 13(3), 713-737.
Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on Interventions. Decision Sciences, 39(2), 273-315.
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204.
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.
Venkatesh, V., Thong, J. Y., & Xu, X. (2016). Unified Theory of Acceptance and Use of Technology: A Synthesis and the Road Ahead. Journal of the Association for Information Systems, 17(5), 328-376.
Venkatesh, V., & Zhang, X. (2010). Unifified theory of acceptance and use of technology: US v-s China. Journal of Global Information Technology Management, 13(1), 527.
Yee, R. C. S. (2015). Perceptions of online learning in an Australian University: Malaysian students’ perspective–support for learning. International Journal of Information and Education Technology, 5(8), 587-592.
Zwain, A. A. A. (2019). Technological innovativeness and information quality as neoteric predictors of users’ acceptance of learning management system: An expansion of UTAUT2. Interactive Technology and Smart Education, 16(3), 239-54.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Zhengju Zhang

This work is licensed under a Creative Commons Attribution 4.0 International License.
The submitting author warrants that the submission is original and that she/he is the author of the submission together with the named co-authors; to the extend the submission incorporates text passages, figures, data, or other material from the work of others, the submitting author has obtained any necessary permission.
Articles in this journal are published under the Creative Commons Attribution License (CC-BY What does this mean?). This is to get more legal certainty about what readers can do with published articles, and thus a wider dissemination and archiving, which in turn makes publishing with this journal more valuable for you, the authors.

