A study on the influencing factors of university students’ online persistent learning supported by intelligent technology in the post-pandemic era: an empirical study with PLS-SEM

References

• Abdullah, F., Ward, R., & Ahmed, E. (2016). Investigating the influence of the most commonly used external variables of TAM on students' perceived ease of use (PEOU) and perceived usefulness (PU) of e-portfolios. Computers in Human Behavior, 63(10), 75–90. https://doi.org/10.1016/j.chb.2016.05.014
   Google Scholar
• Adeshola, I., & Agoyi, M. (2022). Examining factors influencing e-learning engagement among university students during covid-19 pandemic: A mediating role of “learning persistence”. Interactive Learning Environments, 29(1), 1–28. https://doi.org/10.1080/10494820.2022.2029493
   Google Scholar
• Aibinu, A. A., & Al-Lawati, A. M. (2010). Using PLS-SEM technique to model construction organizations’ willingness to participate in e-bidding. Automation in Construction, 19(6), 714–724. https://doi.org/10.1016/j.autcon.2010.02.016
   Web of Science ®Google Scholar
• Akter, S., Ray, P., & D’Ambra, J. (2013). Continuance of mHealth services at the bottom of the pyramid: The roles of service quality and trust. Electronic Markets, 23(1), 29–47. https://doi.org/10.1007/s12525-012-0091-5
   Web of Science ®Google Scholar
• Alalwan, A. A., Dwivedi, Y., Rana, N. P., & Williams, M. D. (2016). Consumer adoption of mobile banking in Jordan: Examining the role of usefulness, ease of use, perceived risk and self-efficacy. Journal of Enterprise Information Management, 29(1), 118–139. https://doi.org/10.1108/JEIM-04-2015-0035
   Web of Science ®Google Scholar
• Alam, M., & Singh, P. (2021). Performance feedback interviews as affective events: An exploration of the impact of emotion regulation of negative performance feedback on supervisor–employee dyads. Human Resource Management Review, 31(2), 100740. https://doi.org/10.1016/j.hrmr.2019.100740
   Web of Science ®Google Scholar
• Alghamdi, A., Karpinski, A. C., Lepp, A., & Barkley, J. (2020). Online and face-to-face classroom multitasking and academic performance: Moderated mediation with self-efficacy for self-regulated learning and gender. Computers in Human Behavior, 102(1), 214–222. https://doi.org/10.1016/j.chb.2019.08.018
   Google Scholar
• Andel, S. A., de Vreede, T., Spector, P. E., Padmanabhan, B., Singh, V. K., & De Vreede, G. J. (2020). Do social features help in video-centric online learning platforms? A social presence perspective. Computers in Human Behavior, 113(12), Article 106505. https://doi.org/10.1016/j.chb.2020.106505
   Google Scholar
• Ayu, H. D., Saputro, S., Sarwanto, S., & Mulyani, S. (2023). Reshaping technology-based projects and their exploration of creativity. Eurasia Journal of Mathematics, Science and Technology Education, 19(1), em2217. https://doi.org/10.29333/ejmste/12814
   Google Scholar
• Benson, A. (2002). Using online learning to meet workforce demand: A case study of stakeholder influence. Quarterly Review of Distance Education, 3(4), 443–452.
   Google Scholar
• Caskurlu, S., Maeda, Y., Richardson, J. C., & Lv, J. (2020). A meta-analysis addressing the relationship between teaching presence and students' satisfaction and learning. Computers & Education, 157(11), Article 103966. https://doi.org/10.1016/j.compedu.2020.103966
   Google Scholar
• Chen, C., Jones, K. T., & Xu, S. (2018). The association between students’ style of learning preferences, social presence, collaborative learning and learning outcomes. Journal of Educators Online, 15(1), n1. https://doi.org/10.9743/JEO2018.15.1.3
   Google Scholar
• Chen, C., Sonnert, G., Sadler, P. M., Sasselov, D., & Fredericks, C. (2020). The impact of student misconceptions on student persistence in a MOOC. Journal of Research in Science Teaching, 57(6), 879–910. https://doi.org/10.1002/tea.21616
   Web of Science ®Google Scholar
• Chen, C. C., & Huang, P. H. (2023). The effects of STEAM-based mobile learning on learning achievement and cognitive load. Interactive Learning Environments, 31(1), 100–116. https://doi.org/10.1080/10494820.2020.1761838
   Google Scholar
• Chen, P. Y., & Hwang, G. J. (2019). An empirical examination of the effect of self-regulation and the unified theory of acceptance and use of technology (UTAUT) factors on the online learning behavioural intention of college students. Asia Pacific Journal of Education, 39(1), 79–95. https://doi.org/10.1080/02188791.2019.1575184
   Web of Science ®Google Scholar
• Cheung, D. H. C., Ng, A. K. L., Kiang, K. M., & Chan, H. H. Y. (2020). Creating a community of inquiry in the science classroom: An effective pedagogy for teaching diverse students? Journal of Further and Higher Education, 44(1), 1–13. https://doi.org/10.1080/0309877X.2018.1491959
   Web of Science ®Google Scholar
• Chiang, H. S., & Hsiao, K. L. (2015). YouTube stickiness: The needs, personal, and environmental perspective. Internet Research, 25(1), 85–106. https://doi.org/10.1108/IntR-11-2013-0236
   Web of Science ®Google Scholar
• Chiu, T. K. (2022). School learning support for teacher technology integration from a self-determination theory perspective. Educational Technology Research and Development, 70(3), 931–949. https://doi.org/10.1007/s11423-022-10096-x
   PubMed Web of Science ®Google Scholar
• Chiu, T. K. F., & Churchill, D. (2016). Adoption of mobile devices in teaching: Changes in teacher beliefs, attitudes, and anxiety. Interactive Learning Environments, 24(2), 317–327. https://doi.org/10.1080/10494820.2015.1113709
   Web of Science ®Google Scholar
• Chung, E., Subramaniam, G., & Dass, L. C. (2020). Online learning readiness among university students in Malaysia amidst COVID-19. Asian Journal of University Education, 16(2), 45–58. https://doi.org/10.24191/ajue.v16i2.10294
   Google Scholar
• Cicha, K., Rizun, M., Rutecka, P., & Strzelecki, A. (2021). COVID-19 and higher education: First-year students’ expectations toward distance learning. Sustainability, 13(4), 1889. https://doi.org/10.3390/su13041889
   Web of Science ®Google Scholar
• Cleveland-Innes, M., & Campbell, P. (2012). Emotional presence, learning, and the online learning environment. The International Review of Research in Open and Distributed Learning, 13(4), 269–292. https://doi.org/10.19173/irrodl.v13i4.1234
   Web of Science ®Google Scholar
• Crues, R. W., Henricks, G. M., Perry, M., Bhat, S., Anderson, C. J., Shaik, N., & Angrave, L. (2018). How do gender, learning goals, and forum participation predict persistence in a computer science MOOC? ACM Transactions on Computing Education (TOCE), 18(4), 1–14. https://doi.org/10.1145/3152892
   Web of Science ®Google Scholar
• Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003. https://doi.org/10.1287/mnsc.35.8.982
   Web of Science ®Google Scholar
• Deng, W., Lei, W., Guo, X., Li, X., Ge, W., & Hu, W. (2022). Effects of regulatory focus on online learning engagement of high school students: The mediating role of self-efficacy and academic emotions. Journal of Computer Assisted Learning, 38(3), 707–718. https://doi.org/10.1111/jcal.12642
   Web of Science ®Google Scholar
• Edmunds, J. A., Gicheva, D., Thrift, B., & Hull, M. (2021). High tech, high touch: The impact of an online course intervention on academic performance and persistence in higher education. The Internet and Higher Education, 49(14), 1–10. https://doi.org/10.1016/j.iheduc.2020.100790
   Google Scholar
• Fuller, R. M., Vician, C., & Brown, S. A. (2006). E-learning and individual characteristics: The role of computer anxiety and communication apprehension. Journal of Computer Information Systems, 46(4), 103–115. https://doi.org/10.1080/08874417.2006.11645917
   Web of Science ®Google Scholar
• Garrison, D. R. (2011). E-learning in the 21st century: A framework for research and practice (2nd ed.). Routedge.
   Google Scholar
• Garrison, D. R., Anderson, T., & Archer, W. (2001). Critical thinking, cognitive presence, and computer conferencing in distance education. American Journal of Distance Education, 15(1), 7–23. https://doi.org/10.1080/08923640109527071
   Google Scholar
• Gelbrich, K., Hagel, J., & Orsingher, C. (2021). Emotional support from a digital assistant in technology-mediated services: Effects on customer satisfaction and behavioral persistence. International Journal of Research in Marketing, 38(1), 176–193. https://doi.org/10.1016/j.ijresmar.2020.06.004
   Web of Science ®Google Scholar
• Goel, P., Parayitam, S., Sharma, A., Rana, N. P., & Dwivedi, Y. K. (2022). A moderated mediation model for e-impulse buying tendency, customer satisfaction and intention to continue e-shopping. Journal of Business Research, 142(3), 1–16. https://doi.org/10.1016/j.jbusres.2021.12.041
   Google Scholar
• Götz, O., Liehr-Gobbers, K., & Krafft, M. (2010). Evaluation of structural equation models using the partial least squares (PLS) approach. In V. E. Vinzi, W. W. Chin, J. Henseler, & H. Wang (Eds.), Handbook of partial least squares (pp. 691–711). Springer.
   Google Scholar
• Grabner-Kräuter, S., & Kaluscha, E. A. (2003). Empirical research in on-line trust: A review and critical assessment. International Journal of Human-Computer Studies, 58(6), 783–812. https://doi.org/10.1016/S1071-5819(03)00043-0
   Web of Science ®Google Scholar
• Gu, D., Guo, J., Liang, C., Lu, W., Zhao, S., Liu, B., & Long, T. (2019). Social media-based health management systems and sustained health engagement: TPB perspective. International Journal of Environmental Research and Public Health, 16(9), 1495. https://doi.org/10.3390/ijerph16091495
   PubMed Web of Science ®Google Scholar
• Hair, J., Hollingsworth, C. L., Randolph, A. B., & Chong, A. Y. L. (2017). An updated and expanded assessment of PLS-SEM in information systems research. Industrial Management & Data Systems.
   Google Scholar
• Hair J. F. Jr, Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2021). A primer on partial least squares structural equation enrolment (PLS-SEM). Sage Publications.
   Google Scholar
• Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(41), 2–4. https://doi.org/10.1108/EBR-11-2018-0203.
   Google Scholar
• Hajiyev, J. (2018). Assessing students™ attitude and intention to use M-learning in higher education. Journal of Contemporary Educational Research, 2(2). https://doi.org/10.26689/jcer.v2i2.317
   Google Scholar
• Han, J., & Geng, X. (2023). University students' approaches to online learning technologies: The roles of perceived support, affect/emotion and self-efficacy in technology-enhanced learning. Computers & Education, 194(3), 104695. https://doi.org/10.1016/j.compedu.2022.104695
   Google Scholar
• Henderson, J., & Corry, M. (2021). Teacher anxiety and technology change: A review of the literature. Technology, Pedagogy & Education, 30(4), 573–587. https://doi.org/10.1080/147593.2021.1931426
   Web of Science ®Google Scholar
• Hew, J. J., Lee, V. H., Ooi, K. B., & Lin, B. (2016). Mobile social commerce: The booster for brand loyalty? Computers in Human Behavior, 59(6), 142–154. https://doi.org/10.1016/j.chb.2016.01.027
   Web of Science ®Google Scholar
• Hew, K. F., & Cheung, W. S. (2014). Students' and instructors' use of massive open online courses (MOOCs): Motivations and challenges. Educational Research Review, 12(6), 45–58. https://doi.org/10.1016/j.edurev.2014.05.001
   Google Scholar
• Hew, K. F., Huang, W., Du, J., & Jia, C. (2023). Using chatbots to support student goal setting and social presence in fully online activities: Learner engagement and perceptions. Journal of Computing in Higher Education, 35(1), 40–68. https://doi.org/10.1007/s12528-022-09338-x
   PubMed Web of Science ®Google Scholar
• Hiltz, S. R., & Turoff, M. (2005). Education goes digital: The evolution of online learning and the revolution in higher education. Communications of the ACM, 48(10), 59–64. https://doi.org/10.1145/1089107.1089139
   Web of Science ®Google Scholar
• Hong, I. B. (2015). Understanding the consumer’s online merchant selection process: The roles of product involvement, perceived risk, and trust expectation. International Journal of Information Management, 35(3), 322–336. https://doi.org/10.1016/j.ijinfomgt.2015.01.003
   Web of Science ®Google Scholar
• Hsiao, C. H., Chang, J. J., & Tang, K. Y. (2016). Exploring the influential factors in continuance usage of mobile social Apps: Satisfaction, habit, and customer value perspectives. Telematics and Informatics, 33(2), 342–355. https://doi.org/10.1016/j.tele.2015.08.014
   Web of Science ®Google Scholar
• Hsu, C. L., & Lin, C. C. (2016). Effect of perceived value and social influences on mobile app stickiness and in-app purchase intention. Technological Forecasting and Social Change, 108(7), 42–53.
   Google Scholar
• Huang, B., & Hew, K. F. (2017, April). Factors influencing learning and factors influencing persistence: A mixed-method study of MOOC learners’ motivation. In Proceedings of the 2017 International Conference on Information System and data mining (pp. 103–110).
   Google Scholar
• Hwang, G. J., Hsu, T. C., Lai, C. L., & Hsueh, C. J. (2017). Interaction of problem-based gaming and learning anxiety in language students' English listening performance and progressive behavioral patterns. Computers & Education, 106(3), 26–42. https://doi.org/10.1016/j.compedu.2016.11.010
   Google Scholar
• Ibili, E., Resnyansky, D., & Billinghurst, M. (2019). Applying the technology acceptance model to understand maths teachers’ perceptions towards an augmented reality tutoring system. Education and Information Technologies, 24(5), 2653–2675. https://doi.org/10.1007/s10639-019-09925-z
   Web of Science ®Google Scholar
• Islam, M. S., Karia, N., Khaleel, M., Fauzi, F. B. A., Soliman, M., Khalid, J., Bhuiyan, M. Y. A., Mamun, M. A. A. (2019). Intention to adopt mobile banking in Bangladesh: An empirical study of emerging economy. International Journal of Business Information Systems, 31(1), 136–151. https://doi.org/10.1504/IJBIS.2019.099530
   Google Scholar
• Jiang, M., & Koo, K. (2020). Emotional presence in building an online learning community among non-traditional graduate students. Online Learning, 24(4), 93–111. https://doi.org/10.24059/olj.v24i4.2307
   Web of Science ®Google Scholar
• Jung, Y., & Lee, J. (2018). Learning engagement and persistence in massive open online courses (MOOCS). Computers & Education, 122(7), 9–22. https://doi.org/10.1016/j.compedu.2018.02.013
   Google Scholar
• Kaczkó, É, & Ostendorf, A. (2023). Critical thinking in the community of inquiry framework: An analysis of the theoretical model and cognitive presence coding schemes. Computers & Education, 193, Article 104662. https://doi.org/10.1016/j.compedu.2022.104662
   Web of Science ®Google Scholar
• Khalid, N. M. (2014). Factors affecting course satisfaction of online Malaysian university students [Doctoral dissertation]. Colorado State University.
   Google Scholar
• Khammat Al-iessa, A. H., Amiryousefi, M., & Afrouz, M. (2023). Relationship between Iraqi EFL students’ perceptions of community of inquiry and their attention and engagement in online classes. Teaching English Language, 14(1), 1–28. https://doi.org/10.22132/TEL.2023.164844
   Google Scholar
• Kim, N. H., So, H. J., & Joo, Y. J. (2021). Flipped learning design fidelity, self-regulated learning, satisfaction, and continuance intention in a university flipped learning course. Australasian Journal of Educational Technology, 37(4), 1–19. https://doi.org/10.14742/ajet.6046
   Web of Science ®Google Scholar
• Kim, S. H., Bae, J. H., & Jeon, H. M. (2019). Continuous intention on accommodation apps: Integrated value-based adoption and expectation–confirmation model analysis. Sustainability, 11(6), 1578. https://doi.org/10.3390/su11061578
   Web of Science ®Google Scholar
• Kwateng, K., Osei Atiemo, K. A., & Appiah, C. (2019). Acceptance and use of mobile banking: An application of UTAUT2. Journal of Enterprise Information Management, 32(1), 118–151. https://doi.org/10.1108/JEIM-03-2018-0055
   Web of Science ®Google Scholar
• Lakhal, S., Khechine, H., & Mukamurera, J. (2021). Explaining persistence in online courses in higher education: A difference-in-differences analysis. International Journal of Educational Technology in Higher Education, 18(1), 1–32. https://doi.org/10.1186/s41239-021-00251-4
   Web of Science ®Google Scholar
• Lakhal, S., Khechine, H., & Pascot, D. (2013). Student behavioural intentions to use desktop video conferencing in a distance course: Integration of autonomy to the UTAUT model. Journal of Computing in Higher Education, 25(2), 93–121. https://doi.org/10.1007/s12528-013-9069-3
   Web of Science ®Google Scholar
• Law, K. M., Geng, S., & Li, T. (2019). Student enrolment, motivation and learning performance in a blended learning environment: The mediating effects of social, teaching, and cognitive presence. Computers & Education, 136(7), 1–12. https://doi.org/10.1016/j.compedu.2019.02.021
   Google Scholar
• Lee, M. C. (2010). Explaining and predicting users’ continuance intention toward e-learning: An extension of the expectation–confirmation model. Computers & Education, 54(2), 506–516. https://doi.org/10.1016/j.compedu.2009.09.002
   Web of Science ®Google Scholar
• Lee, S. W., Sung, H. J., & Jeon, H. M. (2019). Determinants of continuous intention on food delivery apps: Extending UTAUT2 with information quality. Sustainability, 11(11), 3141. https://doi.org/10.3390/su11113141
   Web of Science ®Google Scholar
• Li, D., Browne, G. J., & Wetherbe, J. C. (2006). Why do internet users stick with a specific web site? A relationship perspective. International Journal of Electronic Commerce, 10(4), 105–141. https://doi.org/10.2753/JEC1086-4415100404
   Web of Science ®Google Scholar
• Li, K., Johnsen, J., & Canelas, D. A. (2021). Persistence, performance, and goal setting in massive open online courses. British Journal of Educational Technology, 52(3), 1215–1229. https://doi.org/10.1111/bjet.13068
   Web of Science ®Google Scholar
• Li, W., Chen, D., Li, Q., & Liu, L. (2021b). Internal characteristics and relationship model of online learning emotional presence. Modern Distance Education Research, 33(4), 82–91.
   Google Scholar
• Li, W., Mao, Y., & Zhou, L. (2021c). The impact of interactivity on user satisfaction in digital social Reading: Social presence as a mediator. International Journal of Human–Computer Interaction, 37(17), 1636–1647. https://doi.org/10.1080/10447318.2021.1898850
   Web of Science ®Google Scholar
• Liang, L. J., Choi, H. C., & Joppe, M. (2018). Exploring the relationship between satisfaction, trust and switching intention, repurchase intention in the context of Airbnb. International Journal of Hospitality Management, 69(1), 41–48. https://doi.org/10.1016/j.ijhm.2017.10.015
   Web of Science ®Google Scholar
• Lien, C. H., Cao, Y., & Zhou, X. (2017). Service quality, satisfaction, stickiness, and usage intentions: An exploratory evaluation in the context of WeChat services. Computers in Human Behavior, 68(3), 403–410. https://doi.org/10.1016/j.chb.2016.11.061
   Google Scholar
• Lim, J. (2018). Disciplinary differences in a community of inquiry [Doctoral dissertation]. Purdue University.
   Google Scholar
• Lim, J., & Richardson, J. C. (2021). Predictive effects of undergraduate students' perceptions of social, cognitive, and teaching presence on affective learning outcomes according to disciplines. Computers & Education, 161(2), 104063. https://doi.org/10.1016/j.compedu.2020.104063
   Google Scholar
• Lin, J. C. C. (2007). Online stickiness: Its antecedents and effect on purchasing intention. Behaviour & Information Technology, 26(6), 507–516. https://doi.org/10.1080/01449290600740843
   Web of Science ®Google Scholar
• Liu, Y., Zhang, M., Qi, D., & Zhang, Y. (2022). Understanding the role of learner engagement in determining MOOCs satisfaction: A self-determination theory perspective. Interactive Learning Environments, 29(1), 1–15.
   Google Scholar
• Liu, Z., Kong, X., Liu, S., Yang, Z., & Zhang, C. (2022). Looking at MOOC discussion data to uncover the relationship between discussion pacings, learners' cognitive presence and learning achievements. Education and Information Technologies, 27(6), 8265–8288.
   PubMed Web of Science ®Google Scholar
• Majeski, R. A., Stover, M., & Valais, T. (2018). The community of inquiry and emotional presence. Adult Learning, 29(2), 53–61. https://doi.org/10.1177/1045159518758696
   Web of Science ®Google Scholar
• Mamolo, L. A. (2022). Online learning and students’ mathematics motivation, self-efficacy, and anxiety in the “New Normal”. Education Research International, 2022(1), 1–10. https://doi.org/10.1155/2022/9439634
   Google Scholar
• Mendoza, N. B., Yan, Z., & King, R. B. (2023). Supporting students' intrinsic motivation for online learning tasks: The effect of need-supportive task instructions on motivation, self-assessment, and task performance. Computers & Education, 193(2), 104663. https://doi.org/10.1016/j.compedu.2022.104663
   Google Scholar
• Miah, M. S., Singh, J. S. K., & Rahman, M. A. (2023). Factors influencing technology adoption in online learning among private university students in Bangladesh post COVID-19 pandemic. Sustainability, 15(4), 3543. https://doi.org/10.3390/su15043543
   Web of Science ®Google Scholar
• Nikolopoulou, K., Gialamas, V., & Lavidas, K. (2021). Habit, hedonic motivation, performance expectancy and technological pedagogical knowledge affect teachers’ intention to use mobile internet. Computers and Education Open, 2(12), 100041. https://doi.org/10.1016/j.caeo.2021.100041
   Google Scholar
• Norz, L. M., Dornauer, V., Hackl, W. O., & Ammenwerth, E. (2023). Measuring social presence in online-based learning: An exploratory path analysis using log data and social network analysis. The Internet and Higher Education, 56, Article 100894. https://doi.org/10.1016/j.iheduc.2022.100894
   Web of Science ®Google Scholar
• Oh, Y., & Lee, S. (2016). The effects of online interactions on the relationship between learning-related anxiety and intention to persist among e-learning students with visual impairment. International Review of Research in Open and Distributed Learning: IRRODL, 17(6), 89–107. https://doi.org/10.19173/irrodl.v17i6.2581
   Web of Science ®Google Scholar
• Ooi, K. B., Hew, J. J., & Lee, V. H. (2018). Could the mobile and social perspectives of mobile social learning platforms motivate learners to learn continuously? Computers & Education, 120(3), 127–145. https://doi.org/10.1016/j.compedu.2018.01.017
   Google Scholar
• 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(10), 33–46. https://doi.org/10.1016/j.eswa.2016.04.015
   Google Scholar
• Oyarzun, B., Stefaniak, J., Bol, L., & Morrison, G. R. (2018). Effects of learner-to-learner interactions on social presence, achievement and satisfaction. Journal of Computing in Higher Education, 30(1), 154–175. https://doi.org/10.1007/s12528-017-9157-x
   Web of Science ®Google Scholar
• Padesky, L. B., Robertson, D. A., Barney, A., & Clark, N. (2023). Using digital tools to promote teacher agency and enhance teacher professional learning. Innovations in Literacy Professional Learning: Strengthening Equity, Access, and Sustainability, 27(2), 191–205.
   Google Scholar
• Pangarso, A., & Setyorini, R. (2023). The drivers of e-learning satisfaction during the early COVID-19 pandemic: Empirical evidence from an Indonesian private university. Cogent Education, 10(1), Article 2149226. https://doi.org/10.1080/2331186X.2022.2149226
   Web of Science ®Google Scholar
• Park, J. H., & Lee, H. (2021). The influence of self-directed learning and learning commitment on learning persistence intention in online learning: Mediating effect of learning motivation. International Journal of Advanced Culture Technology, 9(4), 9–17. https://doi.org/10.17703/IJACT.2021.9.4.9
   Google Scholar
• Parker, N. (2021). Best pedagogical practices for online instructor emotional presence. Journal of Practical Nurse Education and Practice, 1(1), 14–30. https://doi.org/10.29173/jpnep3
   Google Scholar
• Pina, J. M., & Bordonaba-Juste, V. (2018). Students’ experience with online simulation games: From computer anxiety to satisfaction. Interacting with Computers, 30(2), 162–171. https://doi.org/10.1093/iwc/iwy003
   Web of Science ®Google Scholar
• Putwain, D. W., Becker, S., Symes, W., & Pekrun, R. (2018). Reciprocal relations between students' academic enjoyment, boredom, and achievement over time. Learning and Instruction, 54(4), 73–81. https://doi.org/10.1016/j.learninstruc.2017.08.004
   Google Scholar
• Qayyum, A., Zipf, S., Gungor, R., & Dillon, J. M. (2019). Financial aid and student persistence in online education in the United States. Distance Education, 40(1), 20–31. https://doi.org/10.1080/01587919.2018.1553561
   Web of Science ®Google Scholar
• Rabin, E., Henderikx, M., Yoram, M. K., & Kalz, M. (2020). What are the barriers to learners’ satisfaction in MOOCs and what predicts them? The role of age, intention, self-regulation, self-efficacy and motivation. Australasian Journal of Educational Technology, 36(3), 119–131. https://doi.org/10.14742/ajet.5919
   Web of Science ®Google Scholar
• Rachmawati, I. K., Bukhori, M., Majidah, Y., Hidayatullah, S., & Waris, A. (2020). Analysis of use of mobile banking with acceptance and use of technology (UTAUT). International Journal of Scientific and Technology Research, 9(8), 534–540.
   Google Scholar
• Rahi, S., Mansour, M. M. O., Alghizzawi, M., & Alnaser, F. M. (2019). Integration of UTAUT model in internet banking adoption context: The mediating role of performance expectancy and effort expectancy. Journal of Research in Interactive Marketing, 13(3), 411–435. https://doi.org/10.1108/JRIM-02-2018-0032
   Web of Science ®Google Scholar
• Rajeh, M. T., Abduljabbar, F. H., Alqahtani, S. M., Waly, F. J., Alnaami, I., Aljurayyan, A., & Alzaman, N. (2021). Students’ satisfaction and continued intention toward e-learning: A theory-based study. Medical Education Online, 26(1), 1961348. https://doi.org/10.1080/10872981.2021.1961348
   PubMed Web of Science ®Google Scholar
• Räsänen, K., Pietarinen, J., Pyhältö, K., Soini, T., & Väisänen, P. (2020). Why leave the teaching profession? A longitudinal approach to the prevalence and persistence of teacher turnover intentions. Social Psychology of Education, 23(4), 837–859. https://doi.org/10.1007/s11218-020-09567-x
   Web of Science ®Google Scholar
• Rashid, T., & Asghar, H. M. (2016). Technology use, self-directed learning, student engagement and academic performance: Examining the interrelations. Computers in Human Behavior, 63(10), 604–612. https://doi.org/10.1016/j.chb.2016.05.084
   Google Scholar
• Richardson, J. C., Maeda, Y., Lv, J., & Caskurlu, S. (2017). Social presence in relation to students’ satisfaction and learning in the online environment: A meta-analysis. Computers in Human Behavior, 71(7), 402–417. https://doi.org/10.1016/j.chb.2017.02.001
   Google Scholar
• Rios, T., Elliott, M., & Mandernach, B. J. (2018). Efficient instructional strategies for maximizing online student satisfaction. Journal of Educators Online, 15(3), n3. https://doi.org/10.9743/jeo.2018.15.3.7
   Google Scholar
• Rizvi, S., Rienties, B., Rogaten, J., & Kizilcec, R. F. (2022). Beyond one-size-fits-all in MOOCs: Variation in learning design and persistence of learners in different cultural and socioeconomic contexts. Computers in Human Behavior, 126(1), 106973. https://doi.org/10.1016/j.chb.2021.106973
   Google Scholar
• Ross, J., Sinclair, C., Knox, J., & Macleod, H. (2014). Teacher experiences and academic identity: The missing components of MOOC pedagogy. Journal of Online Learning and Teaching, 10(1), 57–69.
   Google Scholar
• Rovai, A. P. (2003). In search of higher persistence rates in distance education online programs. The Internet and Higher Education, 6(1), 1–16. https://doi.org/10.1016/S1096-7516(02)00158-6
   Google Scholar
• Saito, K., Dewaele, J. M., Abe, M., & In’nami, Y. (2018). Motivation, emotion, learning experience, and second language comprehensibility development in classroom settings: A cross-sectional and longitudinal study. Language Learning, 68(3), 709–743. https://doi.org/10.1111/lang.12297
   Web of Science ®Google Scholar
• Shea, P., Hayes, S., Uzuner-Smith, S., Gozza-Cohen, M., Vickers, J., & Bidjerano, T. (2014). Reconceptualizing the community of inquiry framework: An exploratory analysis. The Internet and Higher Education, 23(10), 9–17. https://doi.org/10.1016/j.iheduc.2014.05.002
   Google Scholar
• Shea, P., Pickett, A. M., & Pelz, W. E. (2003). A follow-up investigation of teaching presence in the SUNY networks. Journal of Asychronous Learning Networks, 7(2), 61–80.
   Google Scholar
• Shen, B., Wang, Y., Yang, Y., & Yu, X. (2023). Relationships between Chinese university EFL learners' academic emotions and self-regulated learning strategies: A structural equation model. Language Teaching Research, 2023(1), 1–26. https://doi.org/10.1177/13621688221144832
   Google Scholar
• Shiau, W. L., & Luo, M. M. (2012). Factors affecting online group buying intention and satisfaction: A social exchange theory perspective. Computers in Human Behavior, 28(6), 2431–2444. https://doi.org/10.1016/j.chb.2012.07.030
   Web of Science ®Google Scholar
• Shiau, W. L., & Luo, M. M. (2013). Continuance intention of blog users: The impact of perceived enjoyment, habit, user involvement and blogging time. Behaviour & Information Technology, 32(6), 570–583. https://doi.org/10.1080/0144929X.2012.671851
   Web of Science ®Google Scholar
• Szymanski, D. M., & Henard, D. H. (2001). Customer satisfaction: A meta-analysis of the empirical evidence. Journal of the Academy of Marketing Science, 29(1), 16–35. https://doi.org/10.1177/0092070301291002
   Web of Science ®Google Scholar
• Taghizadeh, S. K., Rahman, S. A., Nikbin, D., Alam, M. M. D., Alexa, L., Ling Suan, C., & Taghizadeh, S. (2021). Factors influencing students’ continuance usage intention with online learning during the pandemic: A cross-country analysis. Behaviour & Information Technology, 41(9), 1–21. https://doi.org/10.1080/0144929X.2021.1912181
   Web of Science ®Google Scholar
• Tai, K. H., Hong, J. C., Tsai, C. R., Lin, C. Z., & Hung, Y. H. (2022). Virtual reality for car-detailing skill development: Learning outcomes of procedural accuracy and performance quality predicted by VR self-efficacy, VR using anxiety, VR learning interest and flow experience. Computers & Education, 182(6), 1–14. https://doi.org/10.1016/j.compedu.2022.104458
   Google Scholar
• Tang, D., Fan, W., Zou, Y., George, R. A., Arbona, C., & Olvera, N. E. (2021). Self-efficacy and achievement emotions as mediators between learning climate and learning persistence in college calculus: A sequential mediation analysis. Learning & Individual Differences, 92(12), 1–11. https://doi.org/10.1016/j.lindif.2021.102094
   Google Scholar
• Teilegen, A. (1985). Structures of mood and personality and their relevance to assessing anxiety, with an emphasis on self-report. In A. H. Tuma & J. D. Maser (Eds.), Anxiety and the anxiety disorders (pp. 681–706). Routledge.
   Google Scholar
• Thatcher, J. B., & Perrewe, P. L. (2002). An empirical examination of individual traits as antecedents to computer anxiety and computer self-efficacy. MIS Quarterly, 26, 381–396. https://doi.org/10.2307/4132314
   Web of Science ®Google Scholar
• Thibaut, L., Knipprath, H., Dehaene, W., & Depaepe, F. (2018). The influence of teachers' attitudes and school context on instructional practices in integrated STEM education. Teaching and Teacher Education, 71(7), 190–205. https://doi.org/10.1016/j.tate.2017.12.014
   Google Scholar
• Thongmak, M. (2021). A model for enhancing employees' lifelong learning intention online. Learning & Motivation, 75(8), 1–14. https://doi.org/10.1016/j.lmot.2021.101733
   Google Scholar
• Turk, M., Heddy, B. C., & Danielson, R. W. (2022). Teaching and social presences supporting basic needs satisfaction in online learning environments: How can presences and basic needs happily meet online? Computers & Education, 180(4), 1–15. https://doi.org/10.1016/j.compedu.2022.104432
   Google Scholar
• Verhagen, T., Van Nes, J., Feldberg, F., & Van Dolen, W. (2014). Virtual customer service agents: Using social presence and personalization to shape online service encounters. Journal of Computer-Mediated Communication, 19(3), 529–545. https://doi.org/10.1111/jcc4.12066
   Web of Science ®Google Scholar
• Wang, G., & Qing, X. (2023). Analyzing online and offline mixed teaching model for university students during and after COVID-19. Interactive Learning Environments, 14(1), 1–16. https://doi.org/10.1080/10494820.2022.2127781
   Google Scholar
• Wang, S., Tlili, A., Zhu, L., & Yang, J. (2021). Do playfulness and university support facilitate the adoption of online education in a crisis? COVID-19 as a case study based on the technology acceptance model. Sustainability, 13(16), 9104. https://doi.org/10.3390/su13169104
   Web of Science ®Google Scholar
• Wang, Y., & Baker, R. (2018). Grit and intention: Why do learners complete MOOCs? The International Review of Research in Open and Distributed Learning, 19(3). https://doi.org/10.19173/irrodl.v19i3.3393
   Web of Science ®Google Scholar
• Wang, Y., Cao, Y., Gong, S., Wang, Z., Li, N., & Ai, L. (2022). Interaction and learning engagement in online learning: The mediating roles of online learning self-efficacy and academic emotions. Learning and Individual Differences, 94(2), 1–10. https://doi.org/10.1016/j.lindif.2022.102128
   Google Scholar
• Wei, H. C., & Chou, C. (2020). Online learning performance and satisfaction: Do perceptions and readiness matter? Distance Education, 41(1), 48–69. https://doi.org/10.1080/01587919.2020.1724768
   Web of Science ®Google Scholar
• Wertz, R. E. (2022). Learning presence within the community of inquiry framework: An alternative measurement survey for a four-factor model. The Internet and Higher Education, 52(1), 1–15. https://doi.org/10.1016/j.iheduc.2021.100832
   Google Scholar
• Wong, C. H., Tan, G. W. H., Loke, S. P., & Ooi, K. B. (2015). Adoption of mobile social networking sites for learning? Online Information Review, 39(6), 762–778. https://doi.org/10.1108/OIR-05-2015-0152
   Web of Science ®Google Scholar
• Wong, R. (2023). When no one can go to school: Does online learning meet students’ basic learning needs? Interactive Learning Environments, 31(1), 434–450. https://doi.org/10.1080/10494820.2020.1789672
   Google Scholar
• Xavier, M., & Meneses, J. (2022). Persistence and time challenges in an open online university: A case study of the experiences of first-year learners. International Journal of Educational Technology in Higher Education, 19(1), 1–17. https://doi.org/10.1186/s41239-022-00338-6
   PubMed Web of Science ®Google Scholar
• Xiong, Y., Li, H., Kornhaber, M. L., Suen, H. K., Pursel, B., & Goins, D. D. (2015). Examining the relations among student motivation, engagement, and retention in a MOOC: A structural equation modeling approach. Global Education Review, 2(3), 23–33.
   Google Scholar
• Yan, Y., Cheng, X., Zhou, C., Yang, X., & Li, Y. Q. (2021). The perceptions of anatomy teachers for different majors during the COVID-19 pandemic: A national Chinese survey. Medical Education Online, 26(1), Article 1897267. https://doi.org/10.1080/10872981.2021.1897267
   PubMed Web of Science ®Google Scholar
• Yang, Q., Pang, C., Liu, L., Yen, D. C., & Tarn, J. M. (2015). Exploring consumer perceived risk and trust for online payments: An empirical study in China’s younger generation. Computers in Human Behavior, 50(9), 9–24. https://doi.org/10.1016/j.chb.2015.03.058
   Google Scholar
• Yang, Y., Wen, Y., & Song, Y. (2023). A systematic review of technology-enhanced self-regulated language learning. Educational Technology & Society, 26(1), 31–44. https://doi.org/10.30191/ETS.202301_26(1).0003
   Web of Science ®Google Scholar
• Yu, J., Huang, C., Han, Z., He, T., & Li, M. (2020). Investigating the influence of interaction on learning persistence in online settings: Moderation or mediation of academic emotions? International Journal of Environmental Research and Public Health, 17(7), 2320. https://doi.org/10.3390/ijerph17072320
   PubMed Web of Science ®Google Scholar
• Zhang, L., Carter Jr, R. A., Qian, X., Yang, S., Rujimora, J., & Wen, S. (2022). Academia’s responses to crisis: A bibliometric analysis of literature on online learning in higher education during COVID-19. British Journal of Educational Technology, 53(3), 620–646. https://doi.org/10.1111/bjet.13191
   PubMed Web of Science ®Google Scholar
• Zhu, M., Herring, S. C., & Bonk, C. J. (2019). Exploring presence in online learning through three forms of computer-mediated discourse analysis. Distance Education, 40(2), 205–225. https://doi.org/10.1080/01587919.2019.1600365
   Web of Science ®Google Scholar
• Zou, W., Hu, X., Pan, Z., Li, C., Cai, Y., & Liu, M. (2021). Exploring the relationship between social presence and learners’ prestige in MOOC discussion forums using automated content analysis and social network analysis. Computers in Human Behavior, 115(2), Article 106582. https://doi.org/10.1016/j.chb.2020.106582
   Google Scholar