• Ajzen, I. (1991). The theory of planned behavior.Organizational Behavior and Human Decision Processes, 50(2), 179–211.

• Al-Shehri, M. (2017). The effectiveness of D2L system: An evaluation of teaching-learning process in the Kingdom of Saudi Arabia.International Journal of Advanced Computer Science and Applications, 8(1).https://doi.org/10.14569/IJACSA.2017.080156

• Althunibat, A. (2015). Determining the factors influencing students’ intention to use m-learning in Jordan higher education.Computers in Human Behavior, 52, 65–71.https://doi.org/10.1016/j.chb.2015.05.046.

  Article  Google Scholar 

• Botero, G. G., Questier, F., Cincinnato, S., He, T., & Zhu, C. (2018). Acceptance and usage of mobile assisted language learning by higher education students.Journal of Computing in Higher Education, 30(3), 426–451.https://doi.org/10.1007/s12528-018-9177-1.

  Article  Google Scholar 

• Brislin, R. W. (1970). Back-translation for cross-cultural research.Journal of Cross-Cultural Psychology, 1(3), 185–216.

  Article  Google Scholar 

• Chang, C. T., Hajiyev, J., & Su, C. R. (2017). Examining the students’ behavioral intention to use e-learning in Azerbaijan: The general extended technology acceptance model for e-learning approach.Computers & Education, 111, 128–143.https://doi.org/10.1016/j.compedu.2017.04.010.

  Article  Google Scholar 

• Chatterjee, S., & Bhattacharjee, K. K. (2020). Adoption of artificial intelligence in higher education: A quantitative analysis using structural equation modelling.Education Information Technology.https://doi.org/10.1007/s10639-020-10159-7.

• Cohen, J. (1988).Statistical power analysis for the behavioral sciences (2nd ed.). New York: Academic Press.

  MATH  Google Scholar 

• Conole, G. (2016). MOOCs as disruptive technologies: Strategies for enhancing the learner experience and quality of MOOCs.RED: Revista de Educacion a Distancia, 50, 1–18.https://doi.org/10.6018/red/50/2.

  Article  Google Scholar 

• Curley, N. (2013). Saudi Arabia’s Rwaq builds an online courseware platform for the Middle East. Wamda Blog. Retrieved fromhttp://www.wamda.com/2013/12/saudi-arabia-rwaq-online-courseware-mooc-middle-east.

• Davis, F. D. (1986).A technology acceptance model for empirically testing new end-user information systems: Theory and results. Massachusetts: Sloan School of Management, Massachusetts Institute of Technology.

  Google Scholar 

• 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.

  Article  Google Scholar 

• Davis, F. D., Bogozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models.Management Science, 35, 982–1003.https://doi.org/10.1287/mnsc.35.8.982.

  Article  Google Scholar 

• Dwivedi, Y. K., Rana, N. P., Janssen, M., Lal, B., Williams, M. D., & Clemente, M. (2017). An empirical validation of a unified model of electronic government adoption (UMEGA).Government Information Quarterly, 34(2), 211–230.https://doi.org/10.1016/j.giq.2017.03.001.

  Article  Google Scholar 

• Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M., & Williams, M. D. (2019). Re-examining the unified theory of acceptance and use of technology (UTAUT): Towards a revised theoretical model.Information Systems Frontiers, 21(3), 719–734.https://doi.org/10.1007/s10796-017-9774-y.

  Article  Google Scholar 

• Fianu, E., Blewett, C., Ampong, G., & Ofori, K. (2018). Factors affecting MOOC usage by students in selected Ghanaian universities.Education Sciences, 8(2), 70.https://doi.org/10.3390/educsci8020070.

  Article  Google Scholar 

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

  Google Scholar 

• Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error.Journal of Marketing Research, 13, 39–50.https://doi.org/10.1177/002224378101800104.

  Article  Google Scholar 

• Gao, S., & Yang, Y. (2015). Exploring users’ adoption of MOOCs from the perspective of the institutional theory.WHICEB 2015 Proceedings, 282–290.https://doi.org/10.1504/ijnvo.2016.10002515.

• Gunasinghe, A., Abd Hamid, J., Khatibi, A., & Azam, S. F. (2019). Academicians' acceptance of online learning environments: A review of information system theories and models.Global Journal of Computer Science and Technology, 39, 31–39.https://doi.org/10.34257/GJCSTHVOL19IS1PG31.

  Article  Google Scholar 

• Hair Jr., J., Sarstedt, M., Hopkins, L., & Kuppelwieser, V. (2014). Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research.European Business Review, 26(2), 106–121.https://doi.org/10.1108/EBR-10-2013-0128.

  Article  Google Scholar 

• Hair, J. F., Black, W. C., Babin, B., Anderson, R., & Tatham, R. L. (2006).Multivariate data analysis (6th ed.). New Jersey: Prentice Hall.

  Google Scholar 

• Hamdan, F., Nordin, N., Khalid, F., Muslimin, M. S., & Norman, H. (2018). Technology acceptance of online managerial finance training via massive open online courses for life long learning.Advanced Science Letters, 24(4), 2365–2369.https://doi.org/10.1166/asl.2018.10955.

  Article  Google Scholar 

• Hoi, V. N. (2020). Understanding higher education learners' acceptance and use of mobile devices for language learning: A Rasch-based path modeling approach.Computers & Education, 146, 103761.https://doi.org/10.1016/j.compedu.2019.103761.

  Article  Google Scholar 

• Hsu, C.-L., & Lu, H.-P. (2004). Why do people play online games? An extended TAM with social influences and flow experience.Information and Management, 41, 853–868.https://doi.org/10.1016/j.im.2003.08.014.

  Article  Google Scholar 

• Kim, T. G., Lee, J. H., & Law, R. (2008). An empirical examination of the acceptance behavior of hotel front office systems: An extended technology acceptance model.Tourism Management, 29(3), 500–513.https://doi.org/10.1016/j.tourman.2007.05.016.

  Article  Google Scholar 

• Kim, T., Suh, Y. K., Lee, G., & Choi, B. G. (2010). Modelling roles of task-technology fit and self-efficacy in hotel employees’ usage behaviors of hotel information systems.International Journal of Tourism Research, 12(6), 709e725–709e725.https://doi.org/10.1002/jtr.787.

  Article  Google Scholar 

• King Khalid University. (2012).KKU MOOC. eLD news. Abha: KSA Retrieved fromhttp://oldelc.kku.edu.sa/en/node/3/2060.

  Google Scholar 

• Lwoga, E. T., & Komba, M. (2015). Antecedents of continued usage intentions of web-based learning management system in Tanzania.Education+Training, 57(7), 738–756.https://doi.org/10.1108/et-02-2014-0014.

  Article  Google Scholar 

• Ma, L., & Lee, C. S. (2020). Drivers and barriers to MOOC adoption: Perspectives from adopters and non-adopters.Online Information Review, 44(3), 671–684.https://doi.org/10.1108/OIR-06-2019-0203.

  Article  Google Scholar 

• Malek, A., & Karim, A. (2010). An empirical investigation into the role of enjoyment, computer anxiety, computer self-efficacy and internet experience in influencing the students’ intention to use e-learning: A case study.The Turkish Online Journal of Educational Technology, 9, 22–34.

  Google Scholar 

• Mikalef, P., Pappas, I. O., & Giannakos, M. (2016). An integrative adoption model of video-based learning.The International Journal of Information and Learning Technology, 33(4), 219–235.https://doi.org/10.1108/ijilt-01-2016-0007.

  Article  Google Scholar 

• Mulik, S., Srivastava, M., & Yajnik, N. (2018). Extending the UTAUT model to examine MOOC adoption.NMIMS Management Review, 36(2), 26–44.

  Google Scholar 

• Nunnally, J. C., Bernstein, I. H., & Berge, J. M. T. (1967).Psychometric theory (Vol. 226). New York: McGraw-Hill.

  Google Scholar 

• Pellas, N. (2014). The influence of computer self-efficacy, meta cognitive self-regulation and self-esteem on student engagement in online learning programs: Evidence from the virtual world of second life.Computers in Human Behavior, 35, 157–170.https://doi.org/10.1016/j.chb.2014.02.048.

  Article  Google Scholar 

• Pituch, K. A., & Lee, Y. K. (2006). The influence of system characteristics on e-learning use.Computers & Education, 47(2), 222–244.https://doi.org/10.1016/j.compedu.2004.10.007.

  Article  Google Scholar 

• Ruipérez-Valiente, J. A., Halawa, S., Slama, R., & Reich, J. (2019). Using multi-platform learning analytics to compare regional and global MOOC learning in the Arab world.Computers & Education, 146, 103776.https://doi.org/10.1016/j.compedu.2019.103776.

  Article  Google Scholar 

• Sarstedt, M., Hair, J. F., Ringle, C. M., Thiele, K. O., & Gudergan, S. P. (2016). Estimation issues with PLS and CBSEM: Where the bias lies!.Journal of Business Research, 69(10), 4010–3998.

• Sattari, A., Abdekhoda, M., & Gavgani, V. Z. (2017). Determinant factors affecting the web based training acceptance by health students, applying the UTAUT model.International Journal of Emerging Technologies in Learning (iJET), 12(10), 112–126.https://doi.org/10.3991/ijet.v12i10.7258.

  Article  Google Scholar 

• Straub, D. W. (1989). Validating instruments in MIS research.MIS Quarterly, 13(2), 146–169.https://doi.org/10.2307/248922.

  Article  Google Scholar 

• Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models.Information Systems Research, 6(2), 144–176.https://doi.org/10.1287/isre.6.2.144.

  Article  Google Scholar 

• Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions.Decision Sciences, 39, 273–315.https://doi.org/10.1111/j.1540-5915.2008.00192.x.

  Article  Google Scholar 

• Venkatesh, V., Morris, M. G., Davis, F. D., & Davis, G. B. (2003). User acceptance of information technology: Toward a unified view.MIS Quarterly, 27, 425–478.https://doi.org/10.2307/30036540.

  Article  Google Scholar 

• Wu, B., & Chen, X. (2017). Continuance intention to use MOOCs: Integrating the technology acceptance model (TAM) and task technology fit (TTF) model.Computers in Human Behavior, 67, 221–232.https://doi.org/10.1016/j.chb.2016.10.028.

  Article  Google Scholar 

• Zhao, Y., Wang, A., & Sun, Y. (2020). Technological environment, virtual experience, and MOOC continuance: A stimulus–organism–response perspective.Computers & Education, 144, 103721.https://doi.org/10.1016/j.compedu.2019.103721.

  Article  Google Scholar 

Download references