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How to cite this paper
APA: Majali, T., Al-kyid, K., Alhassan, I., Barkat, S & Almajali, R. (2022). COVID-19 fears and e-learning platforms acceptance among Jordanian university students. International Journal of Data and Network Science, 6(3), 905-914.
Chicago/Turabian: Majali, T., Al-kyid, K., Alhassan, I., Barkat, S & Almajali, R. 2022. "COVID-19 fears and e-learning platforms acceptance among Jordanian university students." International Journal of Data and Network Science 6, no. 3 (2022): 905-914.
AMA: Majali, T., Al-kyid, K., Alhassan, I., Barkat, S & Almajali, R. COVID-19 fears and e-learning platforms acceptance among Jordanian university students. International Journal of Data and Network Science. 2022;6(3):905-914.
References
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Al-Adwan, A., Al-Adwan, A., & Smedley, J. (2013). Exploring students acceptance of e-learning using Technology Ac-ceptance Model in Jordanian universities. International Journal of Education and Development using ICT, 9(2).
Alkandari, B. (2015). An investigation of the factors affecting students' acceptance and intention to use e-learning systems at Kuwait University: Developing a technology acceptance model in e-learning environments (Doctoral dissertation, Cardiff Metropolitan University).
Binyamin, S. S., Rutter, M. J., & Smith, S. (2020). The moderating effect of gender and age on the students' acceptance of learning management systems in Saudi higher education. Knowledge Management & E-Learning: An International Journal, 12(1), 30-62.
Davis, F.D., Bagozzi, RP, & Warshaw, P.R. (1989). User acceptance of computer technology: a comparison of two theo-retical models. Management Science, 35(8), 982-1003
Hayashi, A., Chen, C., Ryan, T., & Wu, J. (2020). The role of social presence and moderating role of computer self-efficacy in predicting the continuance usage of e-learning systems. Journal of Information Systems Education, 15(2), 5.
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.
Pituch, K. A., & Lee, Y. K. (2006). The influence of system characteristics on e-learning use. Computers & Education, 47(2), 222-244.
Salloum, S. A., Al-Emran, M., Monem, A. A., & Shaalan, K. (2017). A survey of text mining in social media: facebook and twitter perspectives. Advanced Science Technological Engineering System Journal, 2(1), 127-133.
Salloum, S. A., Al-Emran, M., Shaalan, K., & Tarhini, A. (2019). Factors affecting the E-learning acceptance: A case study from UAE. Education and Information Technologies, 24(1), 509-530.
Selim, H. M. (2007). Critical success factors for e-learning acceptance: Confirmatory factor models. Computers & Educa-tion, 49(2), 396-413.
Tarhini, A., Hone, K. S., & Liu, X. (2013). Factors affecting students' acceptance of e-learning environments in develop-ing countries: a structural equation modeling approach.
Tran, K. N. N. (2016). The Adoption of Blended E-learning Technology in Vietnam using a Revision of the Technology Acceptance Model. Journal of Information Technology Education, 15.
Al-Aulamie, A. (2013). Enhanced technology acceptance model to explain and predict learners’ behavioural intentions in learning management systems.
Al-Busaidi, K. A. (2013). An empirical investigation linking learners’ adoption of blended learning to their intention of full e-learning. Behaviour & Information Technology, 32(11), 1168–1176.
Al-Maroof, R. S., Salloum, S. A., Hassanien, A. E., & Shaalan, K. (2020). Fear from COVID-19 and technology adoption: the impact of Google Meet during Coronavirus pandemic. Interactive Learning Environments, 1–16.
Alharbi, S., & Drew, S. (2014). Using the technology acceptance model in understanding academics’ behavioural intention to use learning management systems. International Journal of Advanced Computer Science and Applications, 5(1), 143–155.
Almaiah, M. A., Jalil, M. A., & Man, M. (2016). Extending the TAM to examine the effects of quality features on mobile learning acceptance. Journal of Computers in Education, 3(4), 453–485.
Alshurideh, M., Salloum, S. A., Al Kurdi, B., & Al-Emran, M. (2019). Factors affecting the social networks acceptance: an empirical study using PLS-SEM approach. Proceedings of the 2019 8th International Conference on Software and Computer Applications, 414–418.
Arbaugh, J. B. (2000). Virtual classroom versus physical classroom: An exploratory study of class discussion patterns and student learning in an asynchronous Internet-based MBA course. Journal of Management Education, 24(2), 213–233.
Atkinson, S. E., & Wilson, P. W. (1995). Comparing mean efficiency and productivity scores from small samples: a boot-strap methodology. Journal of Productivity Analysis, 6(2), 137–152.
Ayodele, S. O., Oga, O. E., Bundot, Y. G., & Ogbari, M. E. (2016). Role of power supply towards e-learning acceptance: VBSEM-AMOS. 2016 6th International Conference on Information Communication and Management (ICICM), 151–155.
Bataineh, K. B., Atoum, M. S., Alsmadi, L. A., & Shikhali, M. (2021). A silver lining of coronavirus: Jordanian Universi-ties turn to distance education. International Journal of Information and Communication Technology Education (IJICTE), 17(2), 1–11.
Chen, C. W. (2010). Brief introduction of new instruction–network learning. Living Technology Education Journal, 34(4), 10–16.
Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern Methods for Business Re-search, 295(2), 295–336.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.
Freeman, M. A., & Capper, J. M. (1999). Exploiting the web for education: An anonymous asynchronous role simulation. Australasian Journal of Educational Technology, 15(1).
Hair Jr, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2016). A primer on partial least squares structural equation mod-eling (PLS-SEM). Sage publications.
Haryanto, H., & Kultsum, H. U. (2016). E-learning program adoption: Technology acceptance model approach. Proceed-ing of the International Conference on Teacher Training and Education, 2(1), 616–622.
Hsia, J.-W., Chang, C.-C., & Tseng, A.-H. (2014). Effects of individuals’ locus of control and computer self-efficacy on their e-learning acceptance in high-tech companies. Behaviour & Information Technology, 33(1), 51–64.
Jaber, O. A. (2016). An examination of variables influencing the acceptance and usage of E-learning systems in Jordanian higher education institutions. Cardiff Metropolitan University.
Khor, E. T. (2014). An analysis of ODL student perception and adoption behavior using the technology acceptance model. International Review of Research in Open and Distributed Learning, 15(6), 275–288.
Krejcie, R. V, & Morgan, D. W. (1970). Determining sample size for research activities. Educational and Psychological Measurement, 30(3), 607–610.
Liao, H.-L., & Lu, H.-P. (2008). The role of experience and innovation characteristics in the adoption and continued use of e-learning websites. Computers & Education, 51(4), 1405–1416.
Mac Callum, K., & Jeffrey, L. (2014). Factors impacting teachers’ adoption of mobile learning. Journal of Information Technology Education, 13, 141.
Mahmodi, M. (2017). The analysis of the factors affecting the acceptance of E-learning in higher education. Interdiscipli-nary Journal of Virtual Learning in Medical Sciences, 8(1).
Nchunge, D. M., Sakwa, M., & Mwangi, W. (2012). User’s perception on ICT adoption for education support in schools: a survey of secondary school teacher’s in Thika district Kenya. International Journal of Humanities and Social Science, 2(10), 17–29.
Nicomedes, C. J., & Avila, R. M. (2020). An analysis on the panic of Filipinos during COVID-19 pandemic in the Philip-pines. Unpublished Manuscript. Https://Doi. Org/10.13140/RG, 2(17355.54565).
Revythi, A., & Tselios, N. (2019). Extension of technology acceptance model by using system usability scale to assess be-havioral intention to use e-learning. Education and Information Technologies, 24(4), 2341–2355.
Salari, M., Yaghmayee, F., Mehdizade, S., Vafadar, Z., & Afzali, M. (2009). Factors related to accept of" e-learning" in nursing students. Education Strategies in Medical Sciences, 2(3), 103–108.
Salloum, S. A., & Al-Emran, M. (2018). Factors affecting the adoption of E-payment systems by university students: Ex-tending the TAM with trust. International Journal of Electronic Business, 14(4), 371–390.
Sanayei, A., & Salimian, H. (2013). The analysis of effecting factors on virtual education acceptance with emphasis on in-ternal factors. Technology of Education Journal (TEJ), 7(3), 149-158.
Sandjojo, N., & Wahyuningrum, T. (2015). Measuring e-learning systems success: Implementing D & M is success mod-el. 2015 4th International Conference on Interactive Digital Media (ICIDM), 1–6.
Smart, K. L., & Cappel, J. J. (2006). Students’ perceptions of online learning: A comparative study. Journal of Infor-mation Technology Education: Research, 5(1), 201–219.
Tan, G. W.-H., Ooi, K.-B., Sim, J.-J., & Phusavat, K. (2012). Determinants of mobile learning adoption: An empirical analysis. Journal of Computer Information Systems, 52(3), 82–91.
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.
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(4), 381–396.
Traxler, J. (2018). Distance learning—Predictions and possibilities. Education Sciences, 8(1), 35.
Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11(4), 342–365.
Wongvilaisakul, W., & Lekcharoen, S. (2015). The acceptance of e-Learning using SEM approach: A case of IT Literacy development for PIM students. 2015 12th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), 1–6.
Woodrow, L. (2011). College English writing affect: Self-efficacy and anxiety. System, 39(4), 510–522.
Wu, W. V., Yen, L. L., & Marek, M. (2011). Using online EFL interaction to increase confidence, motivation, and ability. Journal of Educational Technology & Society, 14(3), 118–129.
Al-Adwan, A., Al-Adwan, A., & Smedley, J. (2013). Exploring students acceptance of e-learning using Technology Ac-ceptance Model in Jordanian universities. International Journal of Education and Development using ICT, 9(2).
Alkandari, B. (2015). An investigation of the factors affecting students' acceptance and intention to use e-learning systems at Kuwait University: Developing a technology acceptance model in e-learning environments (Doctoral dissertation, Cardiff Metropolitan University).
Binyamin, S. S., Rutter, M. J., & Smith, S. (2020). The moderating effect of gender and age on the students' acceptance of learning management systems in Saudi higher education. Knowledge Management & E-Learning: An International Journal, 12(1), 30-62.
Davis, F.D., Bagozzi, RP, & Warshaw, P.R. (1989). User acceptance of computer technology: a comparison of two theo-retical models. Management Science, 35(8), 982-1003
Hayashi, A., Chen, C., Ryan, T., & Wu, J. (2020). The role of social presence and moderating role of computer self-efficacy in predicting the continuance usage of e-learning systems. Journal of Information Systems Education, 15(2), 5.
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.
Pituch, K. A., & Lee, Y. K. (2006). The influence of system characteristics on e-learning use. Computers & Education, 47(2), 222-244.
Salloum, S. A., Al-Emran, M., Monem, A. A., & Shaalan, K. (2017). A survey of text mining in social media: facebook and twitter perspectives. Advanced Science Technological Engineering System Journal, 2(1), 127-133.
Salloum, S. A., Al-Emran, M., Shaalan, K., & Tarhini, A. (2019). Factors affecting the E-learning acceptance: A case study from UAE. Education and Information Technologies, 24(1), 509-530.
Selim, H. M. (2007). Critical success factors for e-learning acceptance: Confirmatory factor models. Computers & Educa-tion, 49(2), 396-413.
Tarhini, A., Hone, K. S., & Liu, X. (2013). Factors affecting students' acceptance of e-learning environments in develop-ing countries: a structural equation modeling approach.
Tran, K. N. N. (2016). The Adoption of Blended E-learning Technology in Vietnam using a Revision of the Technology Acceptance Model. Journal of Information Technology Education, 15.
Al-Aulamie, A. (2013). Enhanced technology acceptance model to explain and predict learners’ behavioural intentions in learning management systems.
Al-Busaidi, K. A. (2013). An empirical investigation linking learners’ adoption of blended learning to their intention of full e-learning. Behaviour & Information Technology, 32(11), 1168–1176.
Al-Maroof, R. S., Salloum, S. A., Hassanien, A. E., & Shaalan, K. (2020). Fear from COVID-19 and technology adoption: the impact of Google Meet during Coronavirus pandemic. Interactive Learning Environments, 1–16.
Alharbi, S., & Drew, S. (2014). Using the technology acceptance model in understanding academics’ behavioural intention to use learning management systems. International Journal of Advanced Computer Science and Applications, 5(1), 143–155.
Almaiah, M. A., Jalil, M. A., & Man, M. (2016). Extending the TAM to examine the effects of quality features on mobile learning acceptance. Journal of Computers in Education, 3(4), 453–485.
Alshurideh, M., Salloum, S. A., Al Kurdi, B., & Al-Emran, M. (2019). Factors affecting the social networks acceptance: an empirical study using PLS-SEM approach. Proceedings of the 2019 8th International Conference on Software and Computer Applications, 414–418.
Arbaugh, J. B. (2000). Virtual classroom versus physical classroom: An exploratory study of class discussion patterns and student learning in an asynchronous Internet-based MBA course. Journal of Management Education, 24(2), 213–233.
Atkinson, S. E., & Wilson, P. W. (1995). Comparing mean efficiency and productivity scores from small samples: a boot-strap methodology. Journal of Productivity Analysis, 6(2), 137–152.
Ayodele, S. O., Oga, O. E., Bundot, Y. G., & Ogbari, M. E. (2016). Role of power supply towards e-learning acceptance: VBSEM-AMOS. 2016 6th International Conference on Information Communication and Management (ICICM), 151–155.
Bataineh, K. B., Atoum, M. S., Alsmadi, L. A., & Shikhali, M. (2021). A silver lining of coronavirus: Jordanian Universi-ties turn to distance education. International Journal of Information and Communication Technology Education (IJICTE), 17(2), 1–11.
Chen, C. W. (2010). Brief introduction of new instruction–network learning. Living Technology Education Journal, 34(4), 10–16.
Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern Methods for Business Re-search, 295(2), 295–336.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.
Freeman, M. A., & Capper, J. M. (1999). Exploiting the web for education: An anonymous asynchronous role simulation. Australasian Journal of Educational Technology, 15(1).
Hair Jr, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2016). A primer on partial least squares structural equation mod-eling (PLS-SEM). Sage publications.
Haryanto, H., & Kultsum, H. U. (2016). E-learning program adoption: Technology acceptance model approach. Proceed-ing of the International Conference on Teacher Training and Education, 2(1), 616–622.
Hsia, J.-W., Chang, C.-C., & Tseng, A.-H. (2014). Effects of individuals’ locus of control and computer self-efficacy on their e-learning acceptance in high-tech companies. Behaviour & Information Technology, 33(1), 51–64.
Jaber, O. A. (2016). An examination of variables influencing the acceptance and usage of E-learning systems in Jordanian higher education institutions. Cardiff Metropolitan University.
Khor, E. T. (2014). An analysis of ODL student perception and adoption behavior using the technology acceptance model. International Review of Research in Open and Distributed Learning, 15(6), 275–288.
Krejcie, R. V, & Morgan, D. W. (1970). Determining sample size for research activities. Educational and Psychological Measurement, 30(3), 607–610.
Liao, H.-L., & Lu, H.-P. (2008). The role of experience and innovation characteristics in the adoption and continued use of e-learning websites. Computers & Education, 51(4), 1405–1416.
Mac Callum, K., & Jeffrey, L. (2014). Factors impacting teachers’ adoption of mobile learning. Journal of Information Technology Education, 13, 141.
Mahmodi, M. (2017). The analysis of the factors affecting the acceptance of E-learning in higher education. Interdiscipli-nary Journal of Virtual Learning in Medical Sciences, 8(1).
Nchunge, D. M., Sakwa, M., & Mwangi, W. (2012). User’s perception on ICT adoption for education support in schools: a survey of secondary school teacher’s in Thika district Kenya. International Journal of Humanities and Social Science, 2(10), 17–29.
Nicomedes, C. J., & Avila, R. M. (2020). An analysis on the panic of Filipinos during COVID-19 pandemic in the Philip-pines. Unpublished Manuscript. Https://Doi. Org/10.13140/RG, 2(17355.54565).
Revythi, A., & Tselios, N. (2019). Extension of technology acceptance model by using system usability scale to assess be-havioral intention to use e-learning. Education and Information Technologies, 24(4), 2341–2355.
Salari, M., Yaghmayee, F., Mehdizade, S., Vafadar, Z., & Afzali, M. (2009). Factors related to accept of" e-learning" in nursing students. Education Strategies in Medical Sciences, 2(3), 103–108.
Salloum, S. A., & Al-Emran, M. (2018). Factors affecting the adoption of E-payment systems by university students: Ex-tending the TAM with trust. International Journal of Electronic Business, 14(4), 371–390.
Sanayei, A., & Salimian, H. (2013). The analysis of effecting factors on virtual education acceptance with emphasis on in-ternal factors. Technology of Education Journal (TEJ), 7(3), 149-158.
Sandjojo, N., & Wahyuningrum, T. (2015). Measuring e-learning systems success: Implementing D & M is success mod-el. 2015 4th International Conference on Interactive Digital Media (ICIDM), 1–6.
Smart, K. L., & Cappel, J. J. (2006). Students’ perceptions of online learning: A comparative study. Journal of Infor-mation Technology Education: Research, 5(1), 201–219.
Tan, G. W.-H., Ooi, K.-B., Sim, J.-J., & Phusavat, K. (2012). Determinants of mobile learning adoption: An empirical analysis. Journal of Computer Information Systems, 52(3), 82–91.
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.
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(4), 381–396.
Traxler, J. (2018). Distance learning—Predictions and possibilities. Education Sciences, 8(1), 35.
Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11(4), 342–365.
Wongvilaisakul, W., & Lekcharoen, S. (2015). The acceptance of e-Learning using SEM approach: A case of IT Literacy development for PIM students. 2015 12th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), 1–6.
Woodrow, L. (2011). College English writing affect: Self-efficacy and anxiety. System, 39(4), 510–522.
Wu, W. V., Yen, L. L., & Marek, M. (2011). Using online EFL interaction to increase confidence, motivation, and ability. Journal of Educational Technology & Society, 14(3), 118–129.