Journal Screenshot

International Journal of Academic Research in Business and Social Sciences

Open Access Journal

ISSN: 2222-6990

The Relationship between Online Learning Acceptance and Emotional Well-Being among Undergraduates

Mohd Aidil Riduan Awang Kader, Nurul Nadia Abd Aziz, Suhanom Mohd Zaki, Musramaini Mustapha, Zaidatul Nadiah Abu Yazid

http://dx.doi.org/10.6007/IJARBSS/v12-i5/13206

Open access

Although online learning promotes many advantages, especially in adapting to the spread of the COVID-19 pandemic, minimal studies have examined the influence of online learning on undergraduates' emotional well-being. This study is crucial to prevent the adverse effects on emotional well-being such as depression, low self-esteem, and lack of self-efficacy, contributing to suicidal thoughts. In attempting to examine the factors that affect students' emotional well-being, this study utilized and extended Technology Acceptance Model (TAM) by adding emotional well-being as a new variable. This study aims to 1) investigate the predicting factors that contribute to online learning acceptance among undergraduates, and 2) examine the relationship between the actual use of online learning and emotional well-being among undergraduates. Online survey research and cross-sectional data were employed to test the conceptual framework and developed hypotheses. The respondents consisted of undergraduates from Universiti Teknologi MARA Pahang who underwent online learning. Structural equation modeling (SEM) was performed in this analysis to test the measurement model and to evaluate the hypotheses. The study found that all the predicting factors had a significant relationship with online learning acceptance except for perceived usefulness. Furthermore, the use of online learning has a significant relationship with emotional well-being whereby the students are able to adapt and feel convenient with the new learning method. The findings of this study are expected to assist educators or instructors in understanding the factors that contribute to online learning acceptance among undergraduate students and conducting their online learning in a way that may not be harmful to students’ emotional well-being.

Almaiah, M. A., & Alyoussef, I.Y. (2019). Analysis of the Effect of Course Design, Course Content Support, Course Assessment and Instructor Characteristics on the Actual Use of E-Learning System. IEE Access. 7. 171907-171922.
Alsabawy, A. Y., Cater-Steel, A., & Soar, J. (2016). Determinants of perceived usefulness of e-learning systems. Computers in Human Behavior, 64 (2016), 843-858. https://doi.org/10.1016/j.chb.2016.07.065
Awang, Z. (2015). SEM made simple: A gentle approach to learning Structural Equation Modeling. MPWS Rich Publication.
Castellacci, F., & Tveito, V. (2018). Internet use and well-being: A survey and a theoretical framework. Research Policy, 47, 308-325.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 319-340.
Diop, E. B., Zhao, S., & Duy, T. V. (2019). An extension of the technology acceptance model for understanding travelers’ adoption of variable message signs. PLoS ONE, 14(4), e0216007. https://doi.org/10.1371/journal.pone.0216007
Estacio, R. R., & Raga, R. C. (2017). Analyzing students online learning behavior in blended courses using Moodle. Asian Association of Open Universities Journal, 12(1), 52-68.
Farhan, W., Razmak, J., Demers, S., & Laflamme, S. (2019). E-learning systems versus instructional communication tools: Developing and testing a new e-learning user interface from the perspectives of teachers and students. Technology in Society, 59 (2019), 101192. https://doi.org/10.1016/j.techsoc.2019.101192
Hoang, S. (2015). Stress Among Undergraduate Distance Learners: A Cross-Sectional Study. Walden Dissertations and Doctoral Studies. 1196.
https://scholarworks.waldenu.edu/dissertations/1196
Holzer, J., Lüftenegger, M., Korlat, S., Pelikan, E., Salmela-Aro, K., Spiel, C., & Schober B. (2021). Higher Education in Times of COVID-19: University Students’ Basic Need Satisfaction, Self-Regulated Learning, and Well-Being. AERA Open, 7(1), 1–13. https://doi.org/10.1177/23328584211003164
Hussein, Z. (2017). Leading to Intention: The Role of Attitude in Relation to Technology Acceptance Model in E-Learning. Procedia Computer Science, 105 (2017), 159 – 164.
Ibrahim, R., Leng, N. S., Yusoff, R. C. M., Samy, G. N., Masrom, S., & Rizman, Z. I. (2017). E-Learning Acceptance Based on Technology Acceptance Model (TAM). Journal of Fundamental and Applied Sciences, 9(4S), 871-889.
Kerzic, D., Tomazevic, N., Aristovnik, A., & Umek, L. (2019). Exploring critical factors of the perceived usefulness of blended learning for higher education students. PLoS ONE, 14(11), e0223767. https://doi.org/10.1371/journal.pone.0223767
Leo, I. D., & Muis, K. R. (2020). Confused, now what? A Cognitive-Emotional Strategy Training (CEST) intervention for elementary students during mathematics problem solving. Contemporary Educational Psychology, 62(2020), 101879.
https://doi.org/10.1016/j.cedpsych.2020.101879
Li, L. Y., & Tsai, C. C. (2017). Accessing online learning material: Quantitative behavior patterns and their effects on motivation and learning performance. Computers & Education, 114, 286-297.
Ningsih, S., Yandri, H., Sasferi, N., & Juliawati, D. (2020). An Analysis of Junior High School Students’ Learning Stress Levels during the COVID-119 Outbreak: Review of Gender Differences. Psychocentrum Review, 2(2), 69-96.
Nurtjahjanti, H., Prasetyo, A. R., & Ardhiani, N. L. (2021). The role of resilience and readiness for change on students’ interest in learning: e-learning implementation during COVID-19. Cakrawala Pendidikan, 40(3), 750-761.
Peytcheva-Forsyth, R., Yovkova, B., & Aleksieva, L. (2018). Factors affecting students’ attitudes towards online learning - The case of Sofia University. AIP Conference Proceedings 2048, 020025 (2018). https://doi.org/10.1063/1.5082043
Porter, C. E., & Donthu, N. (2006). Using the technology acceptance model to explain how attitudes determine Internet usage: The role of perceived access barriers and demographics. Journal of Business Research, 59 (2006), 999–1007. https://doi:10.1016/j.jbusres.2006.06.003
Saade, R. G., Kira, D., Mak, T., & Nebebe, F. (2017). Anxiety and Performance in Online Learning. Proceedings of the Informing Science and Information Technology Education Conference, Vietnam, pp. 147-157. Santa Rosa, CA: Informing Science Institute. http://www.informingscience.org/Publications/3736
Sim, S. P., Sim, H. P., & Quah, C. (2020). Online Learning: A Post Covid-19 Alternative Pedagogy for University Students. Asian Journal of University Education, 16(4), 137-151.
Solangi, Z. A., Al Shahrani, F., & Pandhiani, S. M. (2018). Factors affecting successful implementation of elearning: Study of colleges and institutes sector RCJ Saudi Arabia. International Journal of Emerging Technologies in Learning, 13(6), 223–230. https://doi.org/10.3991/ijet.v13i06.8537
Sutarni, N., Ramdhany, A. M., Hufad, A., & Kurniawan, E. (2021). Self-regulated learning and digital learning environment: its’ effect on academic achievement during the pandemic. Cakrawala Pendidikan, 40(2), 374-388.
Tarhini, A., Hone, K., Liu, X., & Tarhini, T. (2017). Examining the moderating effect of individual-level cultural values on users’ acceptance of E-learning in developing countries: a structural equation modeling of an extended technology acceptance model. Interactive Learning Environments, 25(3), 306–328. https://doi.org/10.1080/10494820.2015.1122635
Tsaroucha, A., Kingston, P., Corp, N., Stewart, T., & Walton, I. (2012). The emotional needs audit (ENA): a report on its reliability and validity. Mental Health Review Journal, 17(2), 81-89.
Twenge, J. M., & Campbell, W .K. (2018). Associations between screen time and lower psychological well-being among children and adolescents: Evidence from a population-based study. Preventive Medicine Reports, 12, 271-283. https://doi.org/10.1016/j.pmedr.2018.10.003
Wang, C. X., Fang, T., & Gu, Y. X. (2020). Learning performance and behavioral patterns of online collaborative learning: Impact of cognitive load and affordances of different multimedia. Computers & Education, 143, 103683. https://doi.org/10.1016/j.compedu.2019.103683
Wang, L. Y. K., Lew, S. L., Lau, S. H., & Leow, M. C. (2019). Usability factors predicting continuance of intention to use cloud e-learning application. Heliyon, 5, e01788. https://doi.org/10.1016/j.heliyon.2019.e01788
Weng, F., Yang, R., Ho, H., & Su, H. (2018). A TAM-Based Study of the Attitude towards Use Intention of Multimedia among School Teachers. Appl. Syst. Innov. 2018, 1, 36; https://doi.org/10.3390/asi1030036.
Zhu, Y., Au, W., & Yates, G. C. R. (2013). University Students’ Attitudes Toward Online Learning in A Blended Course. Paper Presented at the AARE Annual Conference, Adelaide 2013. https://doi.org/10.2316/P.2011.750-054
Zheng, J., Jiang, N., & Dou, J. (2020). Autonomy Support and Academic Stress: A Relationship Mediated by Self-regulated Learning and Mastery Goal Orientation. New Waves Educational Research & Development, 23(2020), 43-63.
Zimmerman, B. J. (1990). Self-regulated learning and academic achievement: An overview. Educational Psychologist, 25(1), 3–17.

In-Text Citation: (Kader et al., 2022)
To Cite this Article: Kader, M. A. R. A., Abd Aziz, N. N., Zaki, S. M., Mustapha, M., & Abu Yazid, Z. N. (2022). The Relationship between Online Learning Acceptance and Emotional Well-Being among Undergraduates. International Journal of Academic Research in Business and Social Sciences. 12(5), 1791 – 1808.