Online learning intention among students from private universities in Malaysia: The role of past behavior and students’ planned behavior
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Abstract
This study examines the determinants that affect the intention of Malaysian private university students to continue using online learning. The study utilized structural equation modelling analysis to examine the connections between variables, employing the partial least squares method. A total of 564 data were collected from students enrolled in private higher education institutions. The suitability of the variable dimensions was established through reliability analysis. Key findings revealed that students’ past online learning behavior significantly impacts their attitude, subjective norms, and perceived behavioral control towards online learning. Specifically, past behavior was positively correlated with attitude, subjective norms, and perceived behavioral control. Additionally, significant positive relationships were observed between attitude and online learning intention, subjective norms and online learning intention, and perceived behavioral control and online learning intention. The results showed that students with positive past behaviors tend to hold favorable attitudes and social support and are capable of succeeding in online learning environments.
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References
Adams, D., Tan, M. H. J., Sumintono, B., & Oh, S. P. (2020). Blended learning engagement in higher education institutions: A differential item functioning analysis of students backgrounds. Malaysian Journal of Learning and Instruction, 17(1), 133–158. https://doi.org/10.32890/mjli2020.17.1.6
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179– 211. https://doi.org/10.4135/9781446249215.n22
Ajzen, I., & Schmidt, P. (2020). Changing behavior using the theory of planned behavior. In M. S. Hagger, L. D. Cameron, K. Hamilton, N. Hankonen, & T. Lintunen (Eds.), The handbook of behavior change (pp. 17–31). Cambridge: Cambridge University Press. https://doi.org/10.1017/9781108677318.002
Al-Adwan, A. S., Yaseen, H., Alsoud, A., Abousweilem, F., & Al-Rahmi, W. M. (2022). Novel extension of the UTAUT model to understand continued usage intention of learning management systems: the role of learning tradition. Education and Information Technologies, 27, 3567–3593. https://doi.org/10.1007/s10639-021-10758-y
Al-Kumaim, N. H., Alhazmi, A. K., Mohammed, F., Gazem, N. A., Shabbir, M. S., & Fazea, Y. (2021). Exploring the impact of the COVID-19 pandemic on university students learning life: An integrated conceptual motivational model for sustainable and healthy online learning. Sustainability, 13(5), 2546. https://doi.org/10.3390/su13052546
Allen, I. E., & Seaman, J. (2013). Changing course: Ten years of tracking online education in the United States. Institute of Education Sciences. https://eric.ed.gov/?id=eD541571
Altowairiki, N. (2021). Online collaborative learning: Analyzing the process through living the experience. International Journal of Technology in Education, 4(3), 413–427. https://doi.org/10.46328/ijte.95
Aroonsrimarakot, S., Laiphrakpam, M., Chathiphot, P., Saengsai, P., & Prasri, S. (2023). Online learning challenges in Thailand and strategies to overcome the challenges from the students perspectives. Education and Information Technologies, 28(7), 8153–8170. https://doi.org/10.1007/s10639-022-11530-6
Artino, A. R. (2010). Online or face-to-face learning? Exploring the personal factors that predict students choice of instructional format. Internet and Higher Education, 13(4), 272–276.
https://doi.org/10.1016/j.iheduc.2010.07.005
Becker, J.-M. (2020). Inner VIF vs. outer VIF. SmartPLS 3 – Small Talk Corner. https://forum.smartpls.com/viewtopic.php?t=26669
Bellingan, A. (2020). Mobile learning readiness: Psychological factors influencing students behavioral intention to adopt mobile learning in South Africa [Masters dissertation, University of South Africa]. Unisa Campus Repository. https://uir.unisa.ac.za/items/e11576c1-3bee-4e87-90c6-c11127cd8b23
Bender, T. (2023). Discussion-based online teaching to enhance student learning: Theory, practice and assessment. Taylor & Francis. https://doi.org/10.4324/9781003444282
Brown, M., Hoon, A. E., Edwards, M., Shabu, S., Okoronkwo, I., & Newton, P. M. (2023). A pragmatic evaluation of university student experience of remote digital learning during the COVID-19 pandemic, focusing on lessons learned for future practice. Plos One, 18(5), e0283742.
Buffel, V., Wouters, E., Cullati, S., Tancredi, S., Van Eeckert, N., & Van de Velde, S. (2024). The relation between economic stressors and higher education students mental health during the initial outbreak of the COVID-19 pandemic. Scandinavian Journal of Public Health. https://doi.org/10.1177/14034948231185938
Butt, S., Mahmood, A., Saleem, S., Rashid, T., & Ikram, A. (2021). Students performance in online learning environment: The role of task technology fit and actual usage of system during COVID-19. Frontiers in Psychology, 12, 759227. https://doi.org/10.3389/fpsyg.2021.759227
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.
https://doi.org/10.1016/j.compedu.2012.12.003
Chu, T. H., & Chen, Y. Y. (2016). With good we become good: Understanding e-learning adoption by theory of planned behavior and group influences. Computers and Education, 92–93, 37–52.
https://doi.org/10.1016/j.compedu.2015.09.013
Farley, I. A., & Burbules, N. C. (2022). Online education viewed through an equity lens: Promoting engagement and success for all learners. Review of Education, 10(3), e3367. https://doi.org/10.1002/rev3.3367
Gopal, R., Singh, V., & Aggarwal, A. (2021). Impact of online classes on the satisfaction and performance of students during the pandemic period of COVID 19. Education and Information Technologies, 26(6), 6923–6947.
Guo, H., Ye, Y., Lin, Y. C., Khan, A., Chen, S. C., & Liou, J. H. (2024). Evaluating the determinants on students switching intentions towards distance learning: an extension of the theory of planned behavior. Cogent Social Sciences, 10(1), 1-23. https://doi.org/10.1080/23311886.2024.2356721
Hair Jr, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Evaluation of reflective measurement models. In Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R: A Workbook (pp. 75–90). Springer International Publishing. https://link.springer.com/book/10.1007/978-3-030-80519-7
Han, H., Hsu, L.-T. J., & Sheu, C. (2010). Application of the theory of planned behavior to green hotel choice: Testing the effect of environmental friendly activities. Tourism Management, 31(3), 325–334.
https://doi.org/10.1016/j.tourman.2009.03.013
Harper, C. V, McCormick, L. M., & Marron, L. (2024). Face-to-face vs. blended learning in higher education: a quantitative analysis of biological science student outcomes. International Journal of Educational Technology in Higher Education, 21(1), 2.
Jafar, Adi, Ramli Dollah, Nordin Sakke, Mohammad Tahir Mapa, Ang Kean Hua, Oliver Valentine Eboy, Eko Prayitno Joko, Diana Hassan, and Chong Vun Hung. (2022). Assessing the challenges of e-learning in Malaysia during the pandemic of Covid-19 using the geo-spatial approach. Scientific Reports, 12(1), 1–10. https://doi.org/10.1038/s41598-022-22360-4
Jakobsdottir, G., Stefansdottir, R. S., Gestsdottir, S., Stefansson, V., Johannsson, E., Rognvaldsdottir, V., & Gisladottir, T. L. (2023). Changes in health-related lifestyle choices of university students before and during the COVID-19 pandemic: Associations between food choices, physical activity and health. Plos One, 18(6), e0286345. https://doi.org/10.1371/journal.pone.0286345
Kim, E.J., Kim, J. J., & Han, S.H. (2021). Understanding student acceptance of online learning systems in higher education: Application of social psychology theories with consideration of user innovativeness. Sustainability, 13(2), 896, 1-14. https://doi.org/10.3390/su13020896
Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of ECollaboration, 11(4), 1– 10. https://doi.org/10.4018/ijec.2015100101
Kovac, V. B., Cameron, D. L., & Høigaard, R. (2014). The extended theory of planned behavior and college grades: The role of cognition and past behavior in the prediction of students academic intentions and achievements. Educational Psychology, 36(4), 792–811. https://doi.org/10.1080/01443410.2014.923557
Kumari, S., Gautam, H., Nityadarshini, N., Das, B. K., & Chaudhry, R. (2021). Online classes versus traditional classes? Comparison during COVID-19. Journal of Education and Health Promotion, 10.
Larreamendy-Joerns, J., & Leinhardt, G. (2006). Going the distance with online education. Review of Educational Research, 76(4), 567–605. https://www.jstor.org/stable/4124415
Lau, L. S., Choong, Y. O., Wei, C. Y., Seow, A. N., Choong, C. K., Senadjki, A., & Ching, S. L. (2020). Investigating nonusers behavioral intention towards solar photovoltaic technology in Malaysia: The role of knowledge transmission and price value. Energy Policy, 144. https://doi.org/10.1016/j.enpol.2020.111651
Li, C., & Lalani, F. (2020). The COVID-19 pandemic has changed education forever. This is how. World Economic Forum. https://www.weforum.org/agenda/2020/04/coronavirus-education-global-covid19-online-digital-learning/
Liang, K., Zhang, Y., He, Y., Zhou, Y., Tan, W., & Li, X. (2017). Online behavior analysis-based student profile for intelligent e-Learning. Journal of Electrical and Computer Engineering, 1–7. https://doi.org/10.1155/2017/9720396
Lutfi, A., Saad, M., Almaiah, M. A., Alsaad, A., Al-Khasawneh, A., Alrawad, M., Alsyouf, A., & Al-Khasawneh, A. L. (2022).
Actual use of mobile learning technologies during social distancing circumstances: case study of King Faisal University students. Sustainability, 14(12), 7323. https://doi.org/10.3390/su14127323
Maheshwari, G. (2021). Factors affecting students intentions to undertake online learning: an empirical study in Vietnam. Education and Information Technologies, 26(6), 6629–6649. https://doi.org/10.1007/s10639-02110465-8
Maisha, K., & Shetu, S. N. (2023). Influencing factors of e-learning adoption amongst students in a developing country: the post-pandemic scenario in Bangladesh. Future Business Journal, 9(1), 37.
Mastour, H., Emadzadeh, A., Hamidi Haji Abadi, O., & Niroumand, S. (2023). Are students performing the same in Elearning and In-person education? An introspective look at learning environments from an Iranian medical school standpoint. BMC Medical Education, 23(1), 1–8. https://doi.org/10.1186/s12909-023-04159-7
McCool, L. B. (2023). Examining social presence, team cohesion, and collaborative writing in online teams. Business and Professional Communication Quarterly. https://doi.org/10.1177/23294906231156138
Mitchell, C., Cours Anderson, K., Laverie, D., & Hass, A. (2021). Distance be damned: The importance of social presence in a pandemic constrained environment. Marketing Education Review, 31(4), 294–310. https://doi.org/10.1080/10528008.2021.1936561
Mittelman, R., & Rojas-Méndez, J. (2018). Why Canadians give to charity: An extended theory of planned behavior model. International Review on Public and Nonprofit Marketing, 15(2), 189–204. https://doi.org/10.1007/s12208018-0197-3
Mohd, N. M., & Phuah, K. T. (2016). Understanding students behavioral intentions to use e-learning system in higher education institution in Klang valley, Malaysia. BERJAYA Journal of Services & Management, 6, 3–15.
https://journal.berjaya.edu.my/wp-content/uploads/2019/10/July-2016_3-15.pdf
MOHE. (2023). Statistik Pendidikan Tinggi 2022 - Bab 1: Makro Institusi Pendidikan Tinggi. https://www.mohe.gov.my/en/downloads/statistics
Nadeem, M., Oroszlanyova, M., & Farag, W. (2023). Effect of digital game-based learning on student engagement and motivation. Computers, 12(9), 177. https://doi.org/10.3390/computers12090177
Ndubisi, N. O. (2006). Factors of online learning adoption: A comparative juxtaposition of the theory of planned behavior and the technology acceptance model. International Journal on E- Learning, 5(4), 571–591.
Ouellette, J. A., & Wood, W. (1998). Habit and intention in everyday life: The multiple processes by which past behavior predicts future behavior. Psychological Bulletin, 124(1), 54–74. https://doi.org/10.1037/0033-2909.124.1.54
Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: a critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879
Prakaschandra, D. R., Meyer, R., & Bhagwan, R. (2023). Exploring the challenges and opportunities for learning during the COVID-19 pandemic: Academics and students experiences in the clinical technology undergraduate programme in South Africa. African Journal of Health Professions Education, 15(4), 2–7. https://doi.org/10.7196/AJHPE.2023.v15i4.830
Saleem, F., AlNasrallah, W., Malik, M. I., & Rehman, S. U. (2022). Factors affecting the quality of online learning during
COVID-19: Evidence from a developing economy. Frontiers in Education, 7, 1–13.
https://doi.org/10.3389/feduc.2022.847571
Sangeeta, & Tandon, U. (2020). Factors influencing adoption of online teaching by school teachers: A study during COVID-19 pandemic. Journal of Public Affairs, 2503, 1–11. https://doi.org/10.1002/pa.2503
Seow, A. N., Lam, S. Y., Choong, Y. O., & Choong, C. K. (2023). Online learning effectiveness in private higher education institutions: the mediating roles of emotions and students learning behavior. Quality Assurance in Education.
Sun, L., Zhou, X., & Sun, Z. (2019). Improving cycling behaviors of dockless bike-sharing users based on an extended theory of planned behavior and credit-based supervision policies in China. Frontiers in Psychology, 10(2189), 1–
https://doi.org/10.3389/fpsyg.2019.02189
Tang, L., & Zhu, X. (2024). Academic Self-Efficacy, Grit, and Teacher Support as Predictors of Psychological Well-being of Chinese EFL Students. Frontiers in Psychology, 14, 1332909. https://doi.org/10.3389/fpsyg.2023.1332909
Tang, M. (2019). Fostering creativity in intercultural and interdisciplinary teams: The VICTORY Model. Frontiers in Psychology, 10, 2020. https://doi.org/10.3389/fpsyg.2019.02020
Tang, T., Wang, H., Zhou, X., Gong, H., & Chen, F. (2020). Understanding electric bikers red-light running behavior:
Predictive utility of theory of planned behavior vs prototype willingness model. Journal of Advanced Transportation, 2020, 1–13. https://doi.org/10.1155/2020/7097302
Tannoubi, A., Quansah, F., Magouri, I., Chalghaf, N., Bonsaksen, T., Srem-Sai, M., Hagan, J. E., Handrianto, C., Azaiez, F., & Bragazzi, N. L. (2023). Modelling the associations between academic engagement, study process and grit on academic achievement of physical education and sport university students. BMC Psychology, 11(1), 418. https://doi.org/10.1186/s40359-023-01454-2
Wu, T.-T., Lee, H.-Y., Li, P.-H., Huang, C.-N., & Huang, Y.-M. (2024). Promoting self-regulation progress and knowledge construction in blended learning via ChatGPT-based learning aid. Journal of Educational Computing Research, 61(8), 3–31. https://doi.org/10.1177/07356331231191125
Yang, H. H., & Su, C. H. (2017). Learner behavior in a MOOC practice-oriented course: In empirical study integrating TAM and TPB. International Review of Research in Open and Distributed Learning, 18(5), 35–63.