The COVID-19 pandemic has challenged higher education worldwide. From face-to-face sessions between teachers and students, teaching and learning have to be done online to allow students to continue learning while preventing the spread of this infectious disease. Faced with a wide range of electronic learning systems (ELS) to choose from, this study examined the factors that could affect the students’ intent to re-use the ELS. There were 135 college students in a private university in Manila, Philippines, and 177 college students from a public university in Macau, China, who were surveyed to find out the factors that would influence their intent to re-use ELS. Using structural equation modeling (SMART-PLS), this study showed that there was a significant relationship between perceived ease of use (PE) and perceived usefulness (PU), which were the constructs of the technology acceptance model (TAM). This implies that those who find the ELS easy to use will also find it useful. This study also extended TAM to include intrinsic motivation (IM) and extrinsic motivation (EM) as moderating variables between satisfaction (ST) and intent to re-use the ELS (INT). The result showed that IM and EM did not moderate the relationship between ST and INT. This suggests that Chinese and Filipino students are not motivated to use the ELS. This may be because there is a need to make the ELS enjoyable and engaging. Perceived convenience (PC) was significant to PE and PU. This shows that those who find the ELS convenient find it easy to use and useful. The overall results showed that user training (UT) was significant to PE and PU. This implies that training can stress the usefulness and ease of adopting the ELS. Satisfaction (ST) was significant to INT. The results validate existing literature that those who find the technology easy to use and those who are satisfied with the learning systems are likely to re-use it. This study is one of the few studies in the Philippines that examined the factors that affected students’ re-use of the ELS. It has significant implications for educational institutions in terms of designing an ELS that would encourage students to re-use it, especially during this time when educational institutions have developed courses to be taught in an online environment even if there are vaccines for COVID-19.
Keywords: electronic learning system, behavioral intention, motivation, TAMAcharya, B., & Lee, J. (2018). Users’ perspective on the adoption of e-learning in developing countries: The case of Nepal with a conjoint-based discrete choice approach. Telematics and Informatics, 35 (6), 1733-–1743. https://doi.org/10.1016/j.tele.2018.05.002
Armitage, C., & Conner, M. (2001). Efficacy of the theory of planned behavior: a A meta-analytic review. British Journal of Social Psychology, 40 (4), 471-–499. https://doi.org/10.1348/014466601164939
Andersson, A., & Gronlund, A. (2017). A conceptual framework for e-learning in developing countries: a A critical review of research challenges. Journal of Information Systems in Developing Countries, 38(8), 1-–16. https://doi.org/10.1002/j.1681-4835.2009.tb00271.x
Azainil, R., Haryaka, U., Agus, F., & Kridalaksana, A. (2017). User satisfaction model for e-learning using smartphones. Procedia Computer Science, 116, 373-–380. https://doi.org/10.1016/j.procs.2017.10.070
Bautista, R., Osaki, T., & Suplico-Jeong, L. (2020). Japanese and Filipino college students as consumers: does country of origin affect their purchase intent? DLSU Business & Economics Review, 29(2), 1–18.
Bhattacherjee, A. (2001). Understanding information system continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351–370. http://doi.org/10.2307/3250921
Bissessar, C. (2018). An application of Hofstede’s cultural dimension among female educational leaders. Education Sciences, 8(77),. 1-–15. http://doi.org/10.3390/educsci8020077
Boateng, R., Mbrokoh, A.S., Boateng, L., Senyo, P. K., & Ansong, E. (2016). Determinants of e-learning adoption among students of developing countries. International Journal of Information and Learning Technology 33(4), 248-–262.https://doi.org/10.1108/IJILT-02-2016-0008
Carvajal, Adrian A. (2014). Social transformations facilitated by Facebook on the Filipino generation Y. International Journal of Education and Research, 2 (10), 77-–90.
Chang, C., Yan, C., & Tseng, J. (2012). Perceived convenience in an extended technology acceptance model: mobile Mobile technology and English learning for college students. Australasian Journal of Educational Technology, 28 (5), 809-–826. https://doi.org/10.14742/ajet.818
Chang, C.,; Hajiyev, J., & Su, C. (2017). Examining the students’ behavioral intention to use e-learning in Azerbaijan? The general extended technology acceptance model for the E-learning approach. Computers & Education, 111, 128-–143. https://doi.org/10.1016/j.compedu.2017.04.010
Churchill, G., & Iacobucci, D. (2002). Marketing research: Methodological foundations (8th editioned.). . Harcourt College Publishers.
Crawford, J.,; Butler-Henderson, K.,; Rudolp, J.,; Malkawi, B.,; Glowatz, M.,; Burton, R.,; Magni, P., & Lam, S. (2020). COVID-19: 20 countries’ higher education intra-period digital pedagogy responses. Journal of Applied Learning & Teaching, 3 (1). 9-–28. https://doi.org/10.37074/jalt.2020.3.1.7
Cronbach, L. (1951). Coefficient and the internal structure of tests. Psychometrika, 16, 297-–355.
Davis, F., & Venkatesh, V. (2000). A theoretical extension of the technology acceptance model: four Four longitudinal field studies. Management Science, 46 (2), 186–-204. https://doi.org/10.1287/mnsc.46.2.186.11926
Deci, E., Koestner, R., & Ryan, R. (1999). A meta-analytic review of experiments examining the effects of extrinsic rewards on intrinsic motivation. Psychological Bulletin, 125(6), 627–-668. https://psycnet.apa.org/doi/10.1037/0033-2909.125.6.627
Deci, E., & Ryan, R. (2008). Facilitating optimal motivation and psychological well-
being across life’s domains. Canadian Psychology/Psychologie Canadienne, 49(1), 14–-23. https://psycnet.apa.org/doi/10.1037/0708-5591.49.1.14
Deci, E., & Ryan, R. (1985). The general causality orientation scale: selfSelf-determination in personality. Journal of Research in Personality, 19(2), 109-–134. https://doi.org/10.1016/0092-6566(85)90023-6
Del Barrio-García, S., Arquero, J., & Romero-Frías, E. (2015). Personal learning environments acceptance model: the role of need for cognition, e-Learning satisfaction and students’ perceptions. Educational Technology & Society, 18 (3), 129–141.
Dela Pena-Bandalaria, M. (2007). Impact of ICTs on open and distance learning in a developing country setting: the The Philippine experience. International Review of Research in Open and Distance Learning, 8 (1), 1-–15.
Dholakia, U. (2006). How customer self-determination influences relational marketing outcomes: Evidence from longitudinal field studies. Journal of Marketing Research, 43(1), 109-–120. https://doi.org/10.1509%2Fjmkr.43.1.109
Eastin, M. (2002). Diffusion of e-commerce: analysis of the adoption of four e-commerce activities. Telematics and Informatics, Vol. 19 (3), 251-–267. http://dx.doi.org/10.1016/S0736-5853(01)00005-3
Garcia, K. (2016, March 21). Education and the internet for a sustainable PH. Rappler. Retrieved from https://www.rappler.com/brandrap/tech-and-innovation/126587-education-internet-sustainable-ph.
Hayashi, F. (2012). Mobile payments: What’s in it for consumers?. Economic Review, Federal Reserve Bank of Kansas City, 97(QI), 35-–66.
Hofstede Insights. (n.d.)., Country comparison. Retrieved July 5, 2019 from https://www.hofstede-insights.com/country-comparison/china,the-philippines/
Hu, L., & Bentler, P. (1999). Cut-off criteria for fit indexes in covariance structure analysis: conventional Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6 (1), 1-–55. https://doi.org/10.1080/10705519909540118
Igbaria, M., & Iivari, J. (1995). The effects of self-efficacy on computer usage. Omega, 23(6), 587–605. https://doi.org/10.1016/0305-0483(95)00035-6
Igbaria, M., Zinatelli, N., Cragg, P., & Cavaye, A. L. M. (1997). Personal computing acceptance factors in small firms: a structural equation model. MIS Quarterly, 21(3), 279–305. http://doi.org/10.2307/249498
Joo, Y.,; Kim, N., & Kim, N. (2016). Factors predicting online university students’ use of a mobile learning management system (m-LMS). Educational Technology Research and Development,. (64(#), 611–630. https://doi.org/10.1007/s11423-016-9436-7
Ketchen, D. (2013). A primer on partial least squares structural equation modeling. Long Range Planning, 46 (1-2), 184-–185.
Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of e-Collaboration, 11(4), 1-–10. http://doi.org/10.4018/ijec.2015100101
Lee, Y.,; Hsieh, Y., & Chen, Y. (2013). An investigation of employees’ use of e-learning systems: Applying the technology acceptance model. Behaviour & Information, 32 (2), 173-–189. https://doi.org/10.1080/0144929X.2011.577190
Liao, Z., & Cheung, M. (2002). Internet-based e-banking and consumer attitudes: An empirical study.”, Information & Management, 39 (4), 283-–295. https://doi.org/10.1016/S0378-7206(01)00097-0
Liaw, S. (2008). Investigating students’ perceived satisfaction, behavioral intention, and effectiveness of e-learning: A case study of the Blackboard system. Computers & Education, 51(2), 864-–873. https://doi.org/10.1016/j.compedu.2007.09.005
Marcial, D., Caballero, D., Rendal, J., & Patrimonio, G. (2015). I am offline: Measuring barriers to open online learning in the Philippines. Information Technologies and Learning Tools, 45(1), 28-–40. https://doi.org/10.33407/itlt.v45i1.1170
Marshall, B., Mills, R., & Olsen, D. (2008). The role of end-user training in technology acceptance. Review of Business Information System, 12(2), 151-–162. https://doi.org/10.33407/itlt.v45i1.1170
Mohammadi, H. (2015). Investigating user’s perspectives on e-learning: an integration of TAM and IS success model. Computers in Human Behavior, 45, 359-–374. https://doi.org/10.1016/j.chb.2014.07.044
Pandey, S., Chawla, D., Suplico-Jeong, L., Bautista, R., & Santos, J. (2020). An experimental approach to examine the antecedents of attitude, intention, and loyalty towards cause-related marketing: the The case of India and the Philippines. Global Business Review, 21(1), 1-–20. https://doi.org/10.1177/0972150919901186
Park, S. (2009). An analysis of the technology acceptance model in understanding university students` behavioral intention to use e-learning. Educational Technology & Society, 12 (3), 150–162.
Peterson, R. (1994). A meta-analysis of Cronbach’s coefficient alpha. Journal of Consumer Research,. (21), 381-–391. https://doi.org/10.2307/2489828
Prensky, M. (2001). Digital natives, digital immigrants Part 1. On the Horizon, 9 (5) 1-–6. https://doi.org/10.1108/10748120110424816
Ringle, C., Wende, S., & Becker, J. (2015). SMARTPLS3. Bonningstedt: SmartPLS. Retrieved from http://www.smartpls.com.
Robinson, J. P.,; Shaver, P. R., & Wrightsman, L. S. (1991). Criteria for scale selection and evaluation. In J. P. Robinson, P. R. Shaver, & L. S. Wrightsman (Eds). .), Measures of personality and social psychological attitudes (pp. 1-–15). Academic Press. https://doi.org/10.1016/B978-0-12-590241-0.50005-8
Roth, G., Assor, A., Niemiec, C., Ryan, R., & Deci, E. (2009). The emotional and academic consequences of parental conditional regard: Comparing conditional positive regard, conditional negative regard, and autonomy support as parenting practices. Developmental Psychology, 45 (4), 1119-–1142. https://psycnet.apa.org/doi/10.1037/a0015272
Ryan, R., Rigby, S., & King, K. (1993). Two types of religious internalization and their relations to religious orientations and mental health. Journal of Personality and Social Psychology, 65 (3), 586-–596. https://psycnet.apa.org/doi/10.1037/0022-3514.65.3.586
Sandel, T., Ou, C., Wangchuk, D., Ju, B., & Duque, M. (2019). Unpacking and describing interaction on Chinese WeChat: A methodological approach. Journal of Pragmatics, 143, 228-–241. http://dx.doi.org/10.1016/j.pragma.2018.08.011
Toquero, C. M. (2020). Challenges and opportunities for higher education amid the COVID-19 pandemic: The Philippine context. Pedagogical Research, 5(4), em0063. https://doi.org/10.29333/pr/7947
Williams, G., & Deci, E. (1996). Internalization of biopsychosocial values by medical students: a A test of self-determination theory. Journal of Personality and Social Psychology, 70 (4), 767-–779. https://doi.org/10.1037//0022-3514.70.4.767
Yoon, C., & Kim, S. (2007). Convenience and TAM in a ubiquitous computing environment: The case of wireless LAN. Electronic Commerce Research and Applications, 6(1), 102-–112. https://doi.org/10.1016/j.elerap.2006.06.009
Zheng, Q., Chen, L., & Burgos, D. (2018). The development of MOOCs in China. Springer r Singapore. https://doi.org/10.1007/978-981-10-6586-6