The study’s goal was to investigate the impact of e-learning determinants on student satisfaction and intention to use e-learning tools. The dependent and independent variables in this study were based on the technological acceptance model. The study examines three determinants, including usefulness, ease of use, and facilitating conditions, as independent variables, while student satisfaction and intention to use were used as dependent variables. Additionally, this study is unique by adding student satisfaction as a dependent variable and a mediator to examine the relationship between e-learning determinants and intention to use. A questionnaire was prepared and distributed to 324 undergraduate students from Jordan’s private universities on the basis of a convenience sample. The proposed hypotheses were investigated using the quantitative techniques of regression in SPSS and SEM in AMOS. The findings of this study revealed that student satisfaction and intention to use e-learning were positively impacted by e-learning determinants. It found that intention to use was positively impacted by student satisfaction. Furthermore, e-learning intention to use was found to be positively impacted by e-learning determinants via student satisfaction. Universities and other educational institutions are advised to identify the appropriate e-learning determinants that satisfy students’ demands and motivate them to use e-learning tools in light of the study’s findings. Private universities can accomplish their goals, stay ahead of the competition, and obtain a competitive advantage by properly understanding e-learning determinants, student satisfaction, and the application of successful e-learning solutions.

1. Abbad, M. M. M. (2021). Using the UTAUT model to understand students’ usage of e-learning systems in developing countries. Education and Information Technologies, 26(6), 7205–7224. https://doi.org/10.1007/s10639-021-10573-5
2. Abbasi, S., Ayoob, T., Malik, A., & Memon, S. I. (2020). Perceptions of students regarding e-learning during Covid-19 at a private medical college. Pak J Med Sci., 3, S57–S61. https://doi.org/10.12669/pjms.36.COVID 19-S4.2766
3. Abdullah, S. I. N.W., Arokiyasamy, K., Goh, S.L., et al. (2022). University students’ satisfaction and future outlook towards forced remote learning during a global pandemic. Smart Learn Environ, 9(15). https://doi.org/10.1186/s40561-022-00197-8
4. Ajina, A. S., Joudeh, J. M. M., Ali, N. N., Zamil, A. M., et al. (2023). The effect of mobile-wallet service dimensions on customer satisfaction and loyalty: an empirical study. Cogent Business & Management, 10(2). https://doi.org/10.1080/23311975.2023.2229544
5. 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
6. Ajzen, I. (2020). The theory of planned behavior: frequently asked questions. Human Behavior and Emerging Technologies, 2(4), 314–324. https://doi.org/10.1002/hbe2.195
7. Al-Ali, A., Alsmairat, M. A., Qawasmeh, R., et al. (2024). Exploring the role of digital citizenship and digital empowerment to enhance academic performance of business students. International Journal of Data and Network Science, 8(2), 1275–1284. https://doi.org/10.5267/j.ijdns.2023.11.007
8. Al-Emran, M., Arpaci, I., & Salloum, S. A. (2020). An empirical examination of continuous intention to use m-learning: an integrated model. Educ Inf Technol, 25, 2899–2918. https://doi.org/10.1007/s10639-019-10094-2
9. Al-Fraihat, D., Joy, M., Masa’deh, R., & Sinclair, J. (2020). Evaluating e-learning systems success: an empirical study. Computers in Human Behavior, 102, 67–86. https://doi.org/10.1016/j.chb.2019.08.004
10. Al-Gasawneh, J. A, Hasan, M., Joudeh, J. M. M., et al. (2023). Mediating role of e-word of mouth on the relationship between visual social media marketing and customer purchase intention in Jordanian real estate companies. Quality-Access to Success, 24(193). https://doi.org/10.47750/qas/24.193.21
11. Alharbi, H. M., Ab Jalil, H., Muhd Khaizer Omar, M. K., & Puad, M. H. M. (2022). Demographic factors of teachers’ behavioral intention on Madrasati utilization in Riyadh public schools. Educational Administration: Theory and Practice, 28(4), 13–33.
12. Alkailani, M., & Nusairat, N. (2022). What motivates Jordanians to adopt mobile commerce? An empirical study of the most relevant factors. International Journal of Data and Network Science, 6(2), 487–496. https://doi.org/10.5267/j.ijdns.2021.12.005
13. Al-Mekhlafi, A. B. A., Othman, I., Kineber, A. F., et al. (2022). Modeling the impact of massive open online courses (MOOC) implementation factors on continuance intention of students: PLS-SEM approach. Sustainability, 14(9), 5342. https://doi.org/10.3390/su14095342
14. Ashrafi, A., Zareravasan, A., Rabiee Savoji, S., & Amani, M. (2022). Exploring factors influencing students’ continuance intention to use the learning management system (LMS): A multi-perspective framework. Interactive Learning Environments, 30(8), 1475–1497. https://doi.org/10.1080/10494820.2020.1734028
15. Baber, H. (2020). Determinants of students’ perceived learning outcome and satisfaction in online learning during the pandemic of COVID19. Journal of Education and e-Learning Research, 7(3), 285–292. https://doi.org/10.20448/journal.509.2020.73.285.292
16. Bahati, B., Fors, U., Hansen, P., et al. (2019). Measuring learner satisfaction with formative e-assessment strategies. International Journal of Emerging Technologies in Learning (iJET), 14(07), 61–79. https://doi.org/10.3991/ijet.v14i07.9120
17. Banihashem, S. K., Noroozi, O., Brok, P. D., et al. (2023). Modeling teachers’ and students’ attitudes, emotions, and perceptions in blended education: towards post-pandemic education. The International Journal of Management Education, 21(2), 100803. https://doi.org/10.1016/j.ijme.2023.100803
18. Binyamin, S. S., Rutter, M., & Smith, S. (2019). Extending the technology acceptance model to understand students’ use of learning management systems in Saudi higher education. International Journal of Emerging Technologies in Learning, 14(03), 4–21. https://doi.org/10.3991/ijet.v14i03.9732
19. Caffaro, F., Bisaglia, C., Cutini, M., et al. (2018). A method to evaluate the perceived ease of use of human-machine interface in agricultural tractors equipped with continuously variable transmission (CVT). Spanish Journal of Agricultural Research, 15(4), e0210. https://doi.org/10.5424/sjar/2017154-10726
20. Chahal, J., & Rani, N. (2022). Exploring the acceptance for e-learning among higher education students in India: combining technology acceptance model with external variables. Journal of Computing in Higher Education, 34(3), 844–867. https://doi.org/10.1007/s12528-022-09327-0
21. Chung, E., Subramaniam, G., & Christ Dass, L. (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
22. Daneji, A. A., Ayub, A. F. M., & Khambari, M. N. M. (2019). The effects of perceived usefulness, confirmation and satisfaction on continuance intention in using massive open online course (MOOC). Knowledge Management & E-Learning, 11(2), 201–214. https://doi.org/10.34105/j.kmel.2019.11.010
23. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319. https://doi.org/10.2307/249008
24. 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
25. El-Aasar, S. A., & Farghali, G. (2022). Predictive study of the factors and challenges affecting the usability of e-learning platforms in the light of COVID-19. International Journal of Education in Mathematics, Science and Technology, 10(3), 568–589. https://doi.org/10.46328/ijemst.2428
26. Fauzi, A., Wandira, R., Sepri, D., & Hafid, A. (2021). Exploring students’ acceptance of google classroom during the Covid-19 pandemic by using the technology acceptance model in West Sumatera universities. Electronic Journal of E-Learning, 19(4), 233–240. https://doi.org/10.34190/ejel.19.4.2348
27. Feng, D., Xiang, C., Vongurai, R., & Pibulcharoensit, S. (2022). Investigation on satisfaction and performance of online education among fine arts major undergraduates in Chengdu public universities. AU-GSB E-Journal, 15(2), 169–177. https://doi.org/10.14456/augsbejr.2022.82
28. Gautam, K. P., Sharma, D., Wangpo, K., & Dema, S. (2021). the acceptance and use of the virtual learning environment (VLE) in higher education: a contextual study of Royal University of Bhutan. Asian Research Journal of Arts & Social Sciences, 20–36. https://doi.org/10.9734/arjass/2021/v13i330215
29. Gefen, D., & Straub, D. (2005). A Practical guide to factorial validity using PLS-Graph: Tutorial and annotated example. Communications of the Association for Information Systems, 16. https://doi.org/10.17705/1cais.01605
30. Getuli, V., Capone, P., Bruttini, A., & Isaac, S. (2020). BIM-based immersive Virtual Reality for construction workspace planning: A safety-oriented approach. Automation in Construction, 114, 103160. https://doi.org/10.1016/j.autcon.2020.103160
31. Giannakos, M. N., Mikalef, P., & Pappas, I. O. (2021). systematic literature review of e-learning capabilities to enhance organizational learning. Information Systems Frontiers, 24(2), 619–635. https://doi.org/10.1007/s10796-020-10097-2
32. Giray, G. (2021). An assessment of student satisfaction with e-learning: An empirical study with computer and software engineering undergraduate students in Turkey under pandemic conditions. Education and Information Technologies, 26(6), 6651–6673. https://doi.org/10.1007/s10639-021-10454-x
33. Hair, J. F., Black, W. C., Babin, B. J., et al. (2019). Multivariate data analysis. UK, Cengage Learning, EMEA.
34. Hammouri, Q. M., Abu-Shanab, E. A., & Nusairat, N. M. (2021). Attitudes toward implementing e-government in health insurance administration. International Journal of Electronic Government Research, 17(2), 1–18. https://doi.org/10.4018/ijegr.2021040101
35. He, W., Zhao, L., & Su, Y. S. (2022). Effects of online self-regulated learning on learning ineffectiveness in the context of COVID-19. The International Review of Research in Open and Distributed Learning, 23(2), 25–43. https://doi.org/10.19173/irrodl.v23i2.5775
36. Ho, N. T. T., Sivapalan, S., Pham, H. H., et al. (2020). Students’ adoption of e-learning in emergency situation: The case of a Vietnamese university during COVID-19. Interactive Technology and Smart Education, 18(2), 246–269. https://doi.org/10.1108/itse-08-2020-0164
37. Hoq, M. Z. (2020). E-learning during the period of pandemic (COVID-19) in the Kingdom of Saudi Arabia: An empirical study. American Journal of Educational Research, 8(7), 457–464. https://doi.org/10.55057/ijares.2021.3.4.17
38. Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118
39. Huet, J. M., & Tcheng, H. (2010). What if telecoms - were the key to the third industrial revolution? Pearson Education, France.
40. Jatmikowati, T. E., Rachman, A. U., & Adwitiya, A. B. (2020). Technology acceptance model in using e-learning on early childhood teacher education program’s student during pandemic. Jurnal Obsesi: Jurnal Pendidikan Anak Usia Dini, 5(2), 1501–1511. https://doi.org/10.31004/obsesi.v5i2.801
41. Joudeh, J. M. M., & Dandis, A. O. (2018). Service quality, customer satisfaction and loyalty in an internet service provider. International Journal of Business and Management, 13(8), 108. https://doi.org/10.5539/ijbm.v13n8p108
42. Joudeh, J. M. M., Khraiwish, A., Ali, N. N., et al. (2021). Evaluating attitudes and intention to use of personal protective equipment (PPE) during the COVID-19 pandemic. Academy of Strategic Management Journal, 20(6), 1–15.
43. Kalsoom, B. S., Mumtaz, M., Dahir Mohamed, N., et al. (2022). Online education amid COVID-19 crisis. IJERI: International Journal of Educational Research and Innovation, 18, 240–259. https://doi.org/10.46661/ijeri.6429
44. Kaur, B., Kaur, J., Pandey, S. K., & Joshi, S. (2023). E-service quality: development and validation of the scale. Global Business Review, 24(5), 953–971. https://doi.org/10.1177/0972150920920452
45. Kee, D. M. H., Liew, J. H. E., Heng, J., et al. (2023). Post pandemic: university students’ satisfaction on the use of e-learning. Asian Pacific Journal of Management and Education, 6(3), 1–15. https://doi.org/10.32535/apjme.v6i3.2634
46. Khan, M. A., Kamal, T., Illiyan, A., & Asif, M. (2021). School students’ perception and challenges towards online classes during COVID-19 pandemic in India: An econometric analysis. Sustainability, 13, 1–15. https://doi.org/10.3390/su13094786
47. Kim, B., & Park, M. J. (2017). Effect of personal factors to use ICTs on e-learning adoption: comparison between learner and instructor in developing countries. Information Technology for Development, 24(4), 706–732. https://doi.org/10.1080/02681102.2017.1312244
48. Li, N., Zhang, X., & Limniou, M. (2021). A country’s national culture affects virtual learning environment adoption in higher education: a systematic review (2001–2020). Interactive Learning Environments, 31(7), 4407–4425. https://doi.org/10.1080/10494820.2021.1967408
49. Li, Y., Duan, Y., Fu, Z., & Alford, P. (2011). An empirical study on behavioural intention to reuse e‐learning systems in rural China. British Journal of Educational Technology, 43(6), 933–948. https://doi.org/10.1111/j.1467-8535.2011.01261.x
50. Liao, Y. K., Wu, W. Y., Le, T. Q., & Phung, T. T. (2022). The integration of the technology acceptance model and value-based adoption model to study the adoption of e-learning: The moderating role of e-WoM. Sustainability, 14(2), 815. https://doi.org/10.3390/su14020815
51. Lin, K. M., Chen, N. S., & Fang, K. (2011). Understanding e-learning continuance intention: a negative critical incidents perspective. Behaviour & Information Technology, 30(1), 77–89. https://doi.org/10.1080/01449291003752948
52. MacCallum, R. C., Browne, M. W., & Sugawara, H. M. (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological Methods, 1(2), 130–149. https://doi.org/10.1037/1082-989x.1.2.130
53. Marani, I. N., Subarkah, A., & Wijayanto, A. (2020). The use of computer-mediated communication (CMC) in distance learning during COVID-19 pandemic: pros and cons. In: Proceedings of the 6th International Conference on Social and Political Sciences. pp. 95–102. https://doi.org/10.2991/assehr.k.201219.015
54. Mariia, R., & Strzelecki, A. (2020). Students’ acceptance of the COVID-19 impact on shifting higher education to distance learning in Poland. International Journal of Environmental Research and Public Health, 17(18), 6468. https://doi.org/10.3390/ijerph17186468
55. Muthuprasad, T., Aiswarya, S., Aditya, K. S., & Jha, G. K. (2020). Students’ perception and preference for online education in India during COVID-19 pandemic. Social Sciences and Humanities Open, 3, 1–11. https://doi.org/10.1016/j.ssaho.2020.100101
56. Nambiar, D. (2020). The impact of online learning during COVID-19: students’ and teachers’ perspective. International Journal of Indian Psychology, 8(2), 783–793.
57. Noroozi, O., Alqassab, M., Taghizadeh Kerman, N., et al. (2024). Does perception mean learning? Insights from an online peer feedback setting. Assessment & Evaluation in Higher Education, 1–15. https://doi.org/10.1080/02602938.2024.2345669
58. Nusairat, N. M., Al-Gasawneh, J. A., Aloqool, A., et al. (2021). The relationship between internet of things and search engine optimization in Jordanian tele-communication companies: The mediating role of user behavior. International Journal of Data and Network Science, 163–172. https://doi.org/10.5267/j.ijdns.2021.6.016
59. Ogunode, N. J. (2020). Nigerian universities and their sustainability: Challenges and way forward. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3695789
60. Pelling, E. L., & White, K. M. (2009). The theory of planned behavior applied to young people’s use of social networking web sites. CyberPsychology & Behavior, 12(6), 755–759. https://doi.org/10.1089/cpb.2009.0109
61. Poong, Y. S., Yamaguchi, S., & Takada, J. I. (2016). Investigating the drivers of mobile learning acceptance among young adults in the World Heritage town of Luang Prabang, Laos. Information Development, 33(1), 57–71. https://doi.org/10.1177/0266666916638136
62. Punnoose, A. C. (2012). Determinants of intention to use eLearning based on the technology acceptance model. Journal of Information Technology Education: Research, 11, 301–337. https://doi.org/10.28945/1744
63. Robin, R., & Dandis, A. O. (2021). Business as usual through contact tracing app: what influences intention to download? Journal of Marketing Management, 37(17–18), 1903–1932. https://doi.org/10.1080/0267257x.2021.2017323
64. Sahin, I., & Shelley, M. (2008). Considering students’ perceptions: the distance education student satisfaction model. Educational Technology & Society, 11(3), 216–223.
65. Sakarji, S. R., Mohd Nor, K., Mohd Razali, M., et al. (2019). Investigating students acceptance of e-learning using technology acceptance model among diploma in office management and technology students at Uitm Melaka. Journal of Information System and Technology Management, 13–26. https://doi.org/10.35631/jistm.413002
66. Sarstedt, M., Ringle, C. M., Hair, J. F. (2021). Partial Least Squares Structural Equation Modeling. In: Homburg, C., Klarmann, M., Vomberg, A. E. (editors). Handbook of Market Research. Springer, Cham. https://doi.org/10.1007/978-3-319-05542-8_15-2
67. Seddon, P. B. (1997). A re-specification and extension of the DeLone and McLean model of IS success. Information Systems Research, 8(3), 240–253. https://doi.org/10.1287/isre.8.3.240
68. Sekaran, U., & Bougie, R. (2016). Research methods for business. New York. John Wiley & Sons.
69. Selamat, Z., & Jaffar, N. (2010). Information technology acceptance: from perspective of Malaysian bankers. International Journal of Business and Management, 6(1). https://doi.org/10.5539/ijbm.v6n1p207
70. Shaiba, H., John, M., & Meshoul, S. (2023). Female Saudi college students’ e-learning experience amidst COVID-19 pandemic: an investigation and analysis. Heliyon, 9(1), e12768. https://doi.org/10.1016/j.heliyon.2022.e12768
71. Shevlin, M., & Miles, J. (1998). Effects of sample size, model specification and factor loadings on the GFI in confirmatory factor analysis. Personality and Individual Differences, 25(1), 85–90. https://doi.org/10.1016/s0191-8869(98)00055-5
72. Small, G. W., Lee, J., Kaufman, A., et al. (2022). Brain health consequences of digital technology use. Dialogues in clinical neuroscience, 22(2), 179–187. https://doi.org/10.31887/DCNS.2020.22.2/gsmall
73. Sukendro, S., Habibi, A., Khaeruddin, K., et al. (2020). Using an extended technology acceptance model to understand students’ use of e-learning during Covid-19: Indonesian sport science education context. Heliyon, 6(11), e05410. https://doi.org/10.1016/j.heliyon.2020.e05410
74. Taat, M. S., & Francis, A. (2019). Factors Influencing the Students’ Acceptance of E-Learning at Teacher Education Institute: an exploratory study in Malaysia. International Journal of Higher Education, 9(1), 133. https://doi.org/10.5430/ijhe.v9n1p133
75. Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics. New York, Allyn & Bacon.
76. Teo, T. (2011). Factors influencing teachers’ intention to use technology: model development and test. Computers & Education, 57(4), 2432–2440. https://doi.org/10.1016/j.compedu.2011.06.008
77. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926
78. Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157. https://doi.org/10.2307/41410412
79. Wang, B., & Ha-Brookshire, J. (2018). Perceived usefulness and perceived ease of use of new technologies described by Chinese textile and apparel firm owners and managers. Available online: https://lib.dr.iastate.edu/itaa_proceedings (accessed on 11 January 2024).
80. Wongwatkit, C., Panjaburee, P., Srisawasdi, N., & Seprum, P. (2020). Moderating effects of gender differences on the relationships between perceived learning support, intention to use, and learning performance in a personalized e-learning. Journal of Computers in Education, 7(2), 229–255. https://doi.org/10.1007/s40692-020-00154-9
81. Wu, J. H., Tennyson, R. D., & Hsia, T. L. (2010). A study of student satisfaction in a blended e-learning system environment. Computers & Education, 55(1), 155–164. https://doi.org/10.1016/j.compedu.2009.12.012
82. Young, A., & Norgard, C. (2006). Assessing the quality of online courses from the students’ perspective. The Internet and Higher Education, 9(2), 107–115. https://doi.org/10.1016/j.iheduc.2006.03.001
83. Yukselturk, E., & Yildirim, Z. (2008). Investigation of interaction, online support, course structure, and flexibility as the contributing factors to students’ satisfaction in an online certificate program. Educational Technology and Society, 11(4), 51–6.
84. Zamil A. M., & Areiqat, A. Y. (2020). The impact of accreditation of higher education institutions in enhancing the quality of the teaching process. Talent Development & Excellence, 12, 1912–1921.