Factors Affecting the Acceptance of E-learning by Learners in the Context of the Covid-19 Pandemic: A Hybrid Artificial Neural Network - SEM Method


  • Thi Quynh Ngoc Do Thu Dau Mot University, Vietnam



How to Cite

Do, T. Q. N. (2023). Factors Affecting the Acceptance of E-learning by Learners in the Context of the Covid-19 Pandemic: A Hybrid Artificial Neural Network - SEM Method. Vietnam Journal of Education, 7(1), 48–62. https://doi.org/10.52296/vje.2023.255


This research aims to determine the factors affecting the student’s intention of E-learning adoption in the context of the Covid-19 pandemic in Vietnam. An online survey was used to assess the proposed psychological determinants of E-learning adoption. Confirmatory factor analysis and structural equation modeling were conducted on the collected data (n=310) using the SPSS 20 and the AMOS 24 statistical software. The reliability and validity of the measurement were examined via Cronbach’s alpha, EFA, CFA while the strength and direction of the hypothesized causal paths among the constructs were analyzed via SEM. Finally, the results from SEM were used as the inputs for an Artificial Neural Network (ANN) model to predict acceptance factors. The results of the study indicated that there was a significant positive relationship between the attitude toward risk (Covid-19), PU, PE and the intention of E-learning adoption. Furthermore, combining SEM and neural networks enabled the capture of linear and complex nonlinear relationships between predictors and the dependent variables.


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Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211. https://doi.org/10.1016/0749-5978(91)90020-T

Ajzen, I. (2002). Perceived Behavioral Control, Self-Efficacy, Locus of Control and the Theory of Planned Behavior. Journal of Applied Social Psychology, 32(4), 665-683. https://doi.org/10.1111/j.1559-1816.2002.tb00236.x

Ali, G. E., & Magalhaes, R. (2008). Barriers to implementing e-learning: A Kuwaiti case study. International Journal of Training and Development, 12(1), 36-53. https://doi.org/10.1111/j.1468-2419.2007.00294.x

Bandura, A. (1986). The explanatory and predictive scope of self-efficacy theory. Journal of Social and Clinical Psychology, 4(3), 359-373. https://doi.org/10.1521/jscp.1986.4.3.359

Blum, A. (1992). Neural Networks in C++. Wiley.

Chan, F. T. S., Chong, A. Y. L. (2012). A SEM-neural network approach for understanding determinants of interorganizational system standard adoption and performances. Decision Support Systems, 54(1), 621-630. https://doi.org/10.1016/j.dss.2012.08.009

Chong, A. Y. L. (2013a). Predicting M-commerce adoption determinants: A neural network approach. Expert Systems with Applications, 40(2), 523-530. https://doi.org/10.1016/j.eswa.2012.07.068

Chong, A. Y. L. (2013b). A two-staged SEM-neural network approach for understanding and predicting the determinants of m-commerce adoption. Expert Systems with Applications, 40(4), 1240-1247. https://doi.org/10.1016/j.eswa.2012.08.067

Chong, A. Y. L., Liu, M. J., Luo, J., & Keng-Boon, O. (2015). Predicting RFID adoption in healthcare supply chain from the perspectives of users. International Journal of Production Economics, 159, 66-75. https://doi.org/10.1016/j.ijpe.2014.09.034

Compeau, D. R., & Higgins, C. A. (1995a). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2), 189-211. https://doi.org/10.2307/249688

Compeau, D. R., & Higgins, C. A. (1995b). Application of social cognitive theory to training for computer skills. Information Systems Research, 6(2), 118-143. http://www.jstor.org/stable/23011006

Concannon, F., Flynn, A., & Campbell, M. (2005). What campus-based students think about the quality and benefits of E-learning. British Journal of Educational Technology, 36(3), 501-512. https://doi.org/10.1111/j.1467-8535.2005.00482.x

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340. https://doi.org/10.2307/249008

Davis, F. D. (1993). User acceptance of information technology: system characteristics, user perceptions and behavioral impacts. International Journal of Man-Machine Studies, 38(3), 475-487. https://doi.org/10.1006/imms.1993.1022

Elbeck, M., & Tirtiroglu, E. (2008). Qualifying purchase intentions using queueing theory. Journal of Applied Quantitative Methods, 3(2), 167-178.

Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley.

Fornell, C., & Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18(1), 39-50. https://doi.org/10.2307/3151312

Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43, 115-135. https://doi.org/10.1007/s11747-014-0403-8

Howard, J. A., & Sheth, J. N. (1969). Theory of Buyer Behavior. NY: Wiley & Sons.

Ji, Z., Yang, Z., Liu, Z., & Yu, C. (2019). Investigating Users’ Continued Usage Intentions of Online Learning Applications. Information, 10(6). https://doi.org/10.3390/info10060198

Kanwal, F., & Rehman, M. (2017). Factors affecting e-learning adoption in developing countries–empirical evidence from Pakistan’s higher education sector. IEEE Access, 5, 10968-10978. https://doi.org/10.1109/ACCESS.2017.2714379

Kaplan-Leiserson, E. (2018). E-Learning glossary. Retrieved April 12, 2018 from https://www.puw.pl/sites/default/files/content_files/zasob_do_pobrania/355/elearn-gloss-learncircuits.pdf

Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). Guilford Press.

Leong, L. Y., Hew, T. S., Lee, V. H., & Ooi, K. B. (2015). An SEM-artificial-neural-network analysis of the relationships between SERVPERF, customer satisfaction and loyalty among low-cost and full-service airline. Expert Systems with Applications, 42(19), 6620-6634. https://doi.org/10.1016/j.eswa.2015.04.043

Leong, L. Y., Hew, T. S., Tan, G. W. H., & Ooi, K. B. (2013). Predicting the determinants of the NFC-enabled mobile credit card acceptance: A neural networks approach. Expert Systems with Applications, 40(14), 5604-5620. https://doi.org/10.1016/j.eswa.2013.04.018

Muthén, B., & Kaplan, D. (1985). A comparison of some methodologies for the factor analysis of nonnormal Likert variables. British Journal of Mathematical and Statistical Psychology, 38(2), 171-189. https://doi.org/10.1111/j.2044-8317.1985.tb00832.x

Negnevitsky, M. (2011). Artificial Intelligence: A Guide to Intelligent Systems, 3rd ed. Pearson Education, Essex, England.

Ooi, K. B., & Tan, G. W. H. (2016). Mobile technology acceptance model: An investigation using mobile users to explore smartphone credit card. Expert Systems with Applications, 59, 33-46. https://doi.org/10.1016/j.eswa.2016.04.015

Park, S. Y. (2009). An Analysis of the Technology Acceptance Model in Understanding University Students’ Behavioral Intention to Use e-Learning. Journal of Educational Technology & Society, 12(3), 150-162.

Pham, M., & Bui, N. T. A. (2020). Analysis of factors affecting the intention to participate in the E-Learning from the lecturer’s viewpoints: A case study of Vietnam. Ho Chi Minh City Open University Journal of Science, 15(1), 60-71.

Phan, T. N. T., Nguyen, N. T., & Nguyen, T. P. T. (2020). Feelings of regular students when experiencing online learning completely during the time of Covid-19 epidemic prevention. Ho Chi Minh City Open University Journal of Science, 15(4), 18-28.

Pituch, K. A. & Lee, Y. K. (2006). The influence of system characteristics on e-learning use. Computers & Education, 47(2), 222-244.

Roberts, T. S. (2004). Online Collaborative Learning: Theory and Practice. Hershey, PA: Information Science Publishing.

Sheela, K. G., & Deepa, S. N. (2013). Review on methods to fix number of hidden neurons in neural networks. Mathematical Problems in Engineering.

Sim, J. J., Tan, G. W. H., Wong, J. C. J., Ooi, K. B. & Hew, T. S. (2014). Understanding and predicting the motivators of mobile music acceptance - A multi stage MRA- artificial neural network approach. Telematics and Informatics, 31(4), 569-584. https://doi.org/10.1016/j.tele.2013.11.005

Tan, G. W. H., Ooi, K. B., Leong, L. Y., & Lin, B. (2014). Predicting the drivers of behavioral intention to use mobile learning: A hybrid SEM-Neural Networks approach. Computers in Human Behavior, 36, 198-213. https://doi.org/10.1016/j.chb.2014.03.052

Tan, M., & Teo, T. S. (2000). Factors influencing the adoption of Internet banking. Journal of the Association for information Systems, 1(1), 1-44. https://doi.org/10.17705/1jais.00005

Taylor, J. W. (1974). The Role of Risk in Consumer Behavior. Journal of Marketing, 38(2), 54-60. https://doi.org/10.2307/1250198

Venkatesh, V., & Bala, H. (2008). Technology Acceptance Model 3 and a Research Agenda on Interventions. Decision Sciences, 39(2), 273-315. https://doi.org/10.1111/j.1540-5915.2008.00192.x

Venkatesh, V. (1999). Creation of favorable user perceptions: Exploring the role of intrinsic motivation. MIS Quarterly, 23(2), 239-260. https://doi.org/10.2307/249753

Venkatesh, V., Morris, M., Davis, G., & Davis, F. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27(3), 425-478. https://doi.org/10.2307/30036540




How to Cite

Do, T. Q. N. (2023). Factors Affecting the Acceptance of E-learning by Learners in the Context of the Covid-19 Pandemic: A Hybrid Artificial Neural Network - SEM Method. Vietnam Journal of Education, 7(1), 48–62. https://doi.org/10.52296/vje.2023.255



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