Abstract
As a result of the pandemic, but also of the rapid advancement of technology in general, e-learning has emerged as a popular method of education, providing students with flexibility and accessibility. Understanding the factors that influence students’ levels of learning and accomplishment in this digital learning environment is therefore critical for teachers and institutions seeking to increase the effectiveness of teaching and knowledge transfer via e-learning platforms. A number of variables that might improve or impair student use, learning, and performance affect how successful e-learning actually is. In order to maximize the benefits of e-learning and guarantee successful student results, educators and policymakers must have a thorough understanding of these elements. The purpose of this study is to investigate the impact of extrinsic and intrinsic factors on students’ use, learning level, and performance in the setting of e-learning in higher education in two countries. This study evaluates the impact of extrinsic elements such as course content, e-learning system quality, institutional and teacher support, as well as intrinsic aspects such as personal innovativeness, self-efficacy, and information sharing in two countries. The study takes a quantitative approach, and the analysis was carried out using the structural equations method to examine the combined influence of numerous extrinsic and intrinsic elements on the use of e-learning, as well as learning level and performance.The research results show that the course content and e-learning system, personal innovativeness, self-efficacy, and knowledge sharing have a positive influence on the intention to use e-learning. Also, the intention of using an e-learning system will increase the actual use of e-learning technologies, which will ultimately result in better learning performance. The findings of this study will help educators, policymakers, and e-learning platform developers create effective ways for optimizing student experiences and promoting good learning outcomes in higher education settings.
Similar content being viewed by others
Data Availability
The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
References
Afzal, H., Ali, I., Aslam Khan, M., & Hamid, K. (2010). A study of University Students’ motivation and its relationship with their academic performance. International Journal of Business and Management, 5(4), p80. https://doi.org/10.5539/ijbm.v5n4p80.
Agarwal, R., & Prasad, J. (1998). A conceptual and operational definition of personal innovativeness in the domain of Information Technology. Information Systems Research, 9(2), 204–215. https://doi.org/10.1287/isre.9.2.204.
Ain, N., Kaur, K., & Waheed, M. (2016). The influence of learning value on learning management system use: An extension of UTAUT2. Information Development, 32(5), 1306–1321. https://doi.org/10.1177/0266666915597546.
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.
Ajzen, I., & Fishbein, M. (1980). Understanding Attitudes and Predicting Social Behavior. Prentice Hal.
Al-Adwan, A., & Smedley, J. K. (2012). Implementing e-learning in the jordanian higher Education Systems: Factors affecting impact. International Journal of Education and Development Using Information and Communication Technology, 8(1), 121–135.
Al-Adwan, A., Al-Adwan, A., & Smedley, J. (2013). Exploring students’ acceptance of e-learning using Technology Acceptance Model in jordanian universities. International Journal of Education and Development Using ICT, 8(1), 4–18.
Al-Adwan, A. S., Nofal, M., Akram, H., Albelbisi, N. A., & Al-Okaily, M. (2022). Towards a sustainable adoption of E-Learning Systems: The role of Self-Directed Learning. Journal of Information Technology Education: Research, 21, 245–267. https://doi.org/10.28945/4980.
Al-Emran, M., & Mezhuyev, V. (2020). Examining the Effect of Knowledge Management Factors on Mobile Learning Adoption Through the Use of Importance-Performance Map Analysis (IPMA). In A. E. Hassanien, K. Shaalan, & M. F. Tolba (Eds.), Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2019 (Vol. 1058, pp. 449–458). Springer International Publishing. https://doi.org/10.1007/978-3-030-31129-2_41.
Al-Emran, M., & Teo, T. (2020). Do knowledge acquisition and knowledge sharing really affect e-learning adoption? An empirical study. Education and Information Technologies, 25(3), 1983–1998. https://doi.org/10.1007/s10639-019-10062-w.
Al-Emran, M., Abbasi, G. A., & Mezhuyev, V. (2021). Evaluating the Impact of Knowledge Management Factors on M-Learning Adoption: A Deep Learning-Based Hybrid SEM-ANN Approach. In M. Al-Emran & K. Shaalan (Eds.), Recent Advances in Technology Acceptance Models and Theories (Vol. 335, pp. 159–172). Springer International Publishing. https://doi.org/10.1007/978-3-030-64987-6_10.
Al-Maroof, R. S., Alhumaid, K., & Salloum, S. (2020). The continuous intention to use E-Learning, from two different perspectives. Education Sciences, 11(1), 6. https://doi.org/10.3390/educsci11010006.
AL-Nuaimi, M. N., Sawafi, O. S. A., Malik, S. I., Al-Emran, M., & Selim, Y. F. (2022). Evaluating the actual use of learning management systems during the covid-19 pandemic: An integrated theoretical model. Interactive Learning Environments, 1–26. https://doi.org/10.1080/10494820.2022.2055577.
Al-Rahmi, W. M., Alias, N., Othman, M. S., Alzahrani, A. I., Alfarraj, O., Saged, A. A., & Rahman, N. S. A. (2018). Use of E-Learning by University students in malaysian higher Educational Institutions: A case in Universiti Teknologi Malaysia. Ieee Access : Practical Innovations, Open Solutions, 6, 14268–14276. https://doi.org/10.1109/ACCESS.2018.2802325.
Aldholay, A. H., Abdullah, Z., Ramayah, T., Isaac, O., & Mutahar, A. M. (2018). Online learning usage and performance among students within public universities in Yemen. International Journal of Services and Standards, 12(2), 163. https://doi.org/10.1504/IJSS.2018.091842.
Almaiah, M. A., Al-Khasawneh, A., & Althunibat, A. (2020). Exploring the critical challenges and factors influencing the E-learning system usage during COVID-19 pandemic. Education and Information Technologies, 25(6), 5261–5280. https://doi.org/10.1007/s10639-020-10219-y.
Alqahtani, M. A., Alamri, M. M., Sayaf, A. M., & Al-Rahmi, W. M. (2022). Investigating students’ perceptions of Online Learning Use as a Digital Tool for Educational Sustainability during the COVID-19 pandemic. Frontiers in Psychology, 13. https://doi.org/10.3389/fpsyg.2022.886272.
Alqurashi, E. (2016). Self-Efficacy in Online Learning environments: A Literature Review. Contemporary Issues in Education Research (CIER), 9(1), 45–52. https://doi.org/10.19030/cier.v9i1.9549.
Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411–423. https://doi.org/10.1037//0033-2909.103.3.411.
Ansong-Gyimah, K. (2020). Students’ perceptions and continuous intention to Use E-Learning Systems: The case of Google Classroom. International Journal of Emerging Technologies in Learning (IJET), 15(11), 236. https://doi.org/10.3991/ijet.v15i11.12683.
Aparicio, M., Bacao, F., & Oliveira, T. (2016). Cultural impacts on e-learning systems’ success. The Internet and Higher Education, 31, 58–70. https://doi.org/10.1016/j.iheduc.2016.06.003.
Artino, A. R. (2007). Motivational beliefs and perceptions of instructional quality: Predicting satisfaction with online training*: Predicting satisfaction with online training. Journal of Computer Assisted Learning, 24(3), 260–270. https://doi.org/10.1111/j.1365-2729.2007.00258.x.
Babie, S., Cicin-Sain, M., & Bubas, G. (2016). A study of factors influencing higher education teachers’ intention to use E-learning in hybrid environments. 2016 39th International Convention on Information and Communication Technology Electronics and Microelectronics (MIPRO), 998–1003. https://doi.org/10.1109/MIPRO.2016.7522285.
Bandura, A. (1997). Self-efficacy: The exercise of control. W.H. Freeman.
Basri, W. S., Alandejani, J. A., & Almadani, F. M. (2018). ICT Adoption Impact on Students’ Academic Performance: Evidence from Saudi Universities. Education Research International, 2018, 1–9. https://doi.org/10.1155/2018/1240197.
Bento, F., Costa, C. J., & Aparicio, M. (2017). S.I. success models, 25 years of evolution. 2017 12th Iberian Conference on Information Systems and Technologies (CISTI), 1–6. https://doi.org/10.23919/CISTI.2017.7975884.
Brown, L. V. (2007). Psychology of motivation. Nova Science Publishers.
Čevra, B., Kapo, A., Zaimović, T., & Turulja, L. (2022). E-learning in Organizations: Factors affecting individual Job Performances. International Journal of Emerging Technologies in Learning (IJET), 17(02), 189–208. https://doi.org/10.3991/ijet.v17i02.26967.
Chen, M., Wang, X., Wang, J., Zuo, C., Tian, J., & Cui, Y. (2021). Factors affecting College Students’ continuous intention to Use Online Course platform. SN Computer Science, 2(2), 114. https://doi.org/10.1007/s42979-021-00498-8.
Cheng, Y. Y., Tung, W. F., Yang, M. H., & Chiang, C. T. (2019). Linking relationship equity to brand resonance in a social networking brand community. Electronic Commerce Research and Applications, 35(July 2018), 100849. https://doi.org/10.1016/j.elerap.2019.100849.
Cheung, R., & Vogel, D. (2013). Predicting user acceptance of collaborative technologies: An extension of the technology acceptance model for e-learning. Computers & Education, 63, 160–175. https://doi.org/10.1016/j.compedu.2012.12.003.
Chiu, C. M., Chiu, C. S., & Chang, H. C. (2007). Examining the integrated influence of fairness and quality on learners’ satisfaction and web-based learning continuance intention. Information Systems Journal, 17(3), 271–287. https://doi.org/10.1111/j.1365-2575.2007.00238.x.
Cidral, W. A., Oliveira, T., Felice, M. D., & Aparicio, M. (2018). E-learning success determinants: Brazilian empirical study. Computers & Education, 122, 273–290. https://doi.org/10.1016/j.compedu.2017.12.001.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–339.
DeLone, W. H., & McLean, E. R. (1992). Information Systems Success: The Quest for the Dependent Variable. Information Systems Research, 3(1), 60–95. https://doi.org/10.1287/isre.3.1.60.
DeLone, W. H., & McLean, E. R. (2003). The DeLone and McLean Model of Information Systems Success: A ten-year update. Journal of Management Information Systems, 19(4), 9–30. https://doi.org/10.1080/07421222.2003.11045748.
Elfaki, N., Abdulraheem, I., & Abdulrahim, R. (2019). Impact of E-learning VS traditional learning on students’ performance and attitude. International Journal of Medical Research & Health Sciences, 8(10), 76–82.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate Data Analysis (7th Edition). Prentice Hall; 7 edition.
Harandi, S. R. (2015). Effects of e-learning on students’ motivation. Procedia - Social and Behavioral Sciences, 181, 423–430. https://doi.org/10.1016/j.sbspro.2015.04.905.
Hassanzadeh, A., Kanaani, F., & Elahi, S. (2012). A model for measuring e-learning systems success in universities. Expert Systems with Applications, 39(12), 10959–10966. https://doi.org/10.1016/j.eswa.2012.03.028.
Hew, T. S., & Kadir, S. L. S. A. (2016). Predicting the acceptance of cloud-based virtual learning environment: The roles of self determination and Channel Expansion Theory. Telematics and Informatics, 33(4), 990–1013. https://doi.org/10.1016/j.tele.2016.01.004.
Ho, N. T. T., Sivapalan, S., Pham, H. H., Nguyen, T. M., Van Pham, A. T., & Dinh, H. V. (2020). Students’ adoption of e-learning in emergency situation: The case of a vietnamese university during COVID-19. Interactive Technology and Smart Education, 17(4), 1–24.
Huang, C. H. (2021). Exploring the continuous usage intention of Online Learning Platforms from the perspective of Social Capital. Information, 12(4), 141. https://doi.org/10.3390/info12040141.
Hurt, H. T., Joseph, K., & Cook, C. D. (2013). Individual Innovativeness (II) from Measurement Instrument Database for the Social Scienc. www.midss.ie.
Im, T., & Kang, M. (2019). Structural Relationships of factors which Impact on Learner Achievement in Online Learning Environment. The International Review of Research in Open and Distributed Learning, 20(1), 112–124. https://doi.org/10.19173/irrodl.v20i1.4012.
Islam, A. K. M. N. (2013). Investigating e-learning system usage outcomes in the university context. Computers & Education, 69, 387–399. https://doi.org/10.1016/j.compedu.2013.07.037.
Islam, A. K. M. N. (2016). E-learning system use and its outcomes: Moderating role of perceived compatibility. Telematics and Informatics, 33(1), 48–55. https://doi.org/10.1016/j.tele.2015.06.010.
Jameel, A., Hamzah, A. K., Shaikhli, T. A., & Alanssari, A. I. (2021). System characteristics and behavioural intention to use E-Learning. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(10), 7724–7733.
Jameel, A. S., Karem, M. A., & Ahmad, A. R. (2022). Behavioral Intention to Use E-Learning Among Academic Staff During COVID-19 Pandemic Based on UTAUT Model. In M. Al-Emran, M. A. Al-Sharafi, M. N. Al-Kabi, & K. Shaalan (Eds.), Proceedings of International Conference on Emerging Technologies and Intelligent Systems (Vol. 299, pp. 187–196). Springer International Publishing. https://doi.org/10.1007/978-3-030-82616-1_17.
Jawad, Y. A. L. A., & Shalash, B. (2020). The impact of E-Learning strategy on students’ academic achievement. Case Study: Al- Quds Open University. International Journal of Higher Education, 9(6), 44. https://doi.org/10.5430/ijhe.v9n6p44.
Kew, S. N., Petsangsri, S., Ratanaolarn, T., & Tasir, Z. (2018). Examining the motivation level of students in e-learning in higher education institution in Thailand: A case study. Education and Information Technologies, 23(6), 2947–2967. https://doi.org/10.1007/s10639-018-9753-z.
Khechine, H., Lakhal, S., & Ndjambou, P. (2016). A meta-analysis of the UTAUT model: Eleven years later: A meta-analysis of the UTAUT model: Eleven years later. Canadian Journal of Administrative Sciences / Revue Canadienne Des Sciences de l’Administration, 33(2), 138–152. https://doi.org/10.1002/cjas.1381.
Kim, J., & Lee, K. H. (2017). Influence of integration on interactivity in social media luxury brand communities. Journal of Business Research October, 0–1. https://doi.org/10.1016/j.jbusres.2017.10.001.
Kim, B., & Park, M. J. (2018). 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.
Koufaris, M., & Hampton-Sosa, W. (2004). The development of initial trust in an online company by new customers. Information and Management, 41(3), 377–397. https://doi.org/10.1016/j.im.2003.08.004.
Kurt, Ö. E. (2019). Examining an e-learning system through the lens of the information systems success model: Empirical evidence from Italy. Education and Information Technologies, 24(2), 1173–1184. https://doi.org/10.1007/s10639-018-9821-4.
Kurtlu, A., & Uçar, M. (2022). A scale development study on the expectations of university students from the accounting course in the digitalization process. Anali Ekonomskog Fakulteta u Subotici, 48, 155–173. https://doi.org/10.5937/AnEkSub2248155K.
Lawson-Body, A., Willoughby, L., Lawson-Body, L., & Tamandja, E. M. (2020). Students’ acceptance of E-books: An application of UTAUT. Journal of Computer Information Systems, 60(3), 256–267. https://doi.org/10.1080/08874417.2018.1463577.
Lee, J. K., & Lee, W. K. (2008). The relationship of e-Learner’s self-regulatory efficacy and perception of e-Learning environmental quality. Computers in Human Behavior, 24(1), 32–47. https://doi.org/10.1016/j.chb.2006.12.001.
Lehlohonolo, S. (2019). Exploring the Impact of Institutional Support on Students’ E-Learning Intentions: Moderating Effect of Age, Gender and Internet Access. ADVED 2019- 5th International Conference on Advances in Education and Social Sciences, 221–230. https://www.ocerints.org/adved19_e-publication/papers/256.pdf.
Lin, H. C., & Chang, C. M. (2018). What motivates health information exchange in social media? The roles of the social cognitive theory and perceived interactivity. Information and Management, 55(6), 771–780. https://doi.org/10.1016/j.im.2018.03.006.
Liu, Y., Li, H., & Carlsson, C. (2010). Factors driving the adoption of m-learning: An empirical study. Computers & Education, 55(3), 1211–1219. https://doi.org/10.1016/j.compedu.2010.05.018.
Lu, J., Yao, J. E., & Yu, C. S. (2005). Personal innovativeness, social influences and adoption of wireless internet services via mobile technology. The Journal of Strategic Information Systems, 14(3), 245–268. https://doi.org/10.1016/j.jsis.2005.07.003.
Masrom, M. (2007). Technology Acceptance Model and E-learning. 12th International Conference on Education, Sultan Hassanal Bolkiah Institute of Ed.
Mathieson, K. (1991). Predicting user intentions: Comparing the Technology Acceptance Model with the theory of Planned Behavior. Information Systems Research, 2(3), 173–191. https://doi.org/10.1287/isre.2.3.173.
Mohammadi, H. (2015). Investigating users’ 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.
Moon, J. W., & Kim, Y. G. (2001). Extending the TAM for a World-Wide-Web context. Information & Management, 38(4), 217–230. https://doi.org/10.1016/S0378-7206(00)00061-6.
Nguyen, H. T. H., Pham, H. V., Vu, N. H., & Hoang, H. T. (2020). Factors influencing students’ intention to use E-learning system: A case study conducted in Vietnam. International Journal of Emerging Technologies in Learning (IJET), 15(18), 165. https://doi.org/10.3991/ijet.v15i18.15441.
Osei, H. V., Kwateng, K. O., & Boateng, K. A. (2022). Integration of personality trait, motivation and UTAUT 2 to understand e-learning adoption in the era of COVID-19 pandemic. Education and Information Technologies, 27(8), 10705–10730. https://doi.org/10.1007/s10639-022-11047-y.
Paola Torres Maldonado, U., Feroz Khan, G., Moon, J., & Rho, J., J (2011). E-learning motivation and educational portal acceptance in developing countries. Online Information Review, 35(1), 66–85. https://doi.org/10.1108/14684521111113597.
Petrov, V., Drašković, Z., Ćelić, Đ., & Rus, M. (2023). Determinants of learning outcomes with online teaching based on students’ perception. Strategic Management, online-first, https://doi.org/10.5937/StraMan2300047P.
Pham, Q. T., & Huynh, M. C. (2018). Learning achievement and knowledge transfer: The impact factor of e-learning system at Bach Khoa University, Vietnam. International Journal of Innovation, 6(3), 194–206. https://doi.org/10.5585/iji.v6i2.235.
Ratna, P. A., & Mehra, S. (2015). Exploring the acceptance for e-learning using technology acceptance model among university students in India. International Journal of Process Management and Benchmarking, 5(2), 194. https://doi.org/10.1504/IJPMB.2015.068667.
Rovai, A. P., Wighting, M. J., Baker, J. D., & Grooms, L. D. (2009). Development of an instrument to measure perceived cognitive, affective, and psychomotor learning in traditional and virtual classroom higher education settings. The Internet and Higher Education, 12(1), 7–13. https://doi.org/10.1016/j.iheduc.2008.10.002.
Saadé, R. G., Nebebe, F., & Tan, W. (2007). Viability of the Technology Acceptance Model in Multimedia Learning environments: A comparative study. Interdisciplinary Journal of E-Skills and Lifelong Learning, 3(1), 175–184. https://doi.org/10.28945/392.
Salamat, L., Ahmad, G., Bakht, M., & Saifi, I. (2018). Effects of e-learning on students’ academic learning at university level. Asian Innovative Journal of Social Science & Humanities, 2(2), 1–12.
Salleh, S. M., Yusof, H. S. M., Mohammed, N. H., Zahari, A. S. M., & Hamzah, S. F. M. (2020). Knowledge Sharing in Online Community: A Review. Journal of Physics: Conference Series, 1529(2), 022052. https://doi.org/10.1088/1742-6596/1529/2/022052.
Salloum, S. A., Alhamad, A. Q. M., Al-Emran, M., Monem, A. A., & Shaalan, K. (2019). Exploring students’ Acceptance of E-Learning through the development of a Comprehensive Technology Acceptance Model. Ieee Access : Practical Innovations, Open Solutions, 7, 128445–128462. https://doi.org/10.1109/ACCESS.2019.2939467.
Sekerdej, M., & Szwed, P. (2021). Perceived self-efficacy facilitates critical reflection on one’s own group. Personality and Individual Differences, 168, 110302. https://doi.org/10.1016/j.paid.2020.110302.
Selim, H. M. (2007). Critical success factors for e-learning acceptance: Confirmatory factor models. Computers & Education, 49(2), 396–413. https://doi.org/10.1016/j.compedu.2005.09.004.
Seta, H. B., Wati, T., Muliawati, A., & Hidayanto, A. N. (2018). E-Learning Success Model: An Extention of DeLone & McLean IS’ Success Model. Indonesian Journal of Electrical Engineering and Informatics (IJEEI), 6(3). https://doi.org/10.52549/ijeei.v6i3.505.
Shadiev, R., Yu, J., & Sintawati, W. (2021). Exploring the impact of learning activities supported by 360-Degree Video Technology on Language Learning, intercultural communicative competence development, and knowledge sharing. Frontiers in Psychology, 12, https://doi.org/10.3389/fpsyg.2021.766924.
Shih, M., Feng, J., & Tsai, C. C. (2008). Research and trends in the field of e-learning from 2001 to 2005: A content analysis of cognitive studies in selected journals. Computers & Education, 51(2), 955–967. https://doi.org/10.1016/j.compedu.2007.10.004.
Shroff, R. H., Deneen, C. C., & Ng, E. M. W. (2011). Analysis of the technology acceptance model in examining students’ behavioural intention to use an e-portfolio system. Australasian Journal of Educational Technology, 27(4), https://doi.org/10.14742/ajet.940.
Siron, Y., Wibowo, A., & Narmaditya, B. S. (2020). Factors affecting the adoption of e-learning in Indonesia: Lesson from Covid-19. Journal of Technology and Science Education, 10(2), 282. https://doi.org/10.3926/jotse.1025.
Sukendro, S., Habibi, A., Khaeruddin, K., Indrayana, B., Syahruddin, S., Makadada, F. A., & Hakim, H. (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.
Sun, P. C., Tsai, R. J., Finger, G., Chen, Y. Y., & Yeh, D. (2008). What drives a successful e-Learning? An empirical investigation of the critical factors influencing learner satisfaction. Computers & Education, 50(4), 1183–1202. https://doi.org/10.1016/j.compedu.2006.11.007.
Tam, C., & Oliveira, T. (2016). Understanding the impact of m-banking on individual performance: DeLone & McLean and TTF perspective. Computers in Human Behavior, 61, 233–244. https://doi.org/10.1016/j.chb.2016.03.016.
Tarhini, A., Hone, K., & Liu, X. (2014). Measuring the moderating effect of gender and age on E-Learning Acceptance in England: A structural equation modeling Approach for an Extended Technology Acceptance Model. Journal of Educational Computing Research, 51(2), 163–184. https://doi.org/10.2190/EC.51.2.b.
Tawafak, R. M., Malik, S. I., Mathew, R., Ashfaque, M. W., Jabbar, J., AlNuaimi, M. N., ElDow, A., & Alfarsi, G. (2021). A combined model for continuous intention to Use E-Learning System. International Journal of Interactive Mobile Technologies (IJIM), 15(03), 113. https://doi.org/10.3991/ijim.v15i03.18953.
Taylor, N. J. (2007). Public grid computing participation: An exploratory study of determinants. Information & Management, 44(1), 12–21. https://doi.org/10.1016/j.im.2006.05.004.
Tsai, C. L., Cho, M. H., Marra, R., & Shen, D. (2020). The Self-Efficacy Questionnaire for Online Learning (SeQoL). Distance Education, 41(4), 472–489. https://doi.org/10.1080/01587919.2020.1821604.
Turner, M., Kitchenham, B., Brereton, P., Charters, S., & Budgen, D. (2010). Does the technology acceptance model predict actual use? A systematic literature review. Information and Software Technology, 52(5), 463–479. https://doi.org/10.1016/j.infsof.2009.11.005.
Twum, K. K., Ofori, D., Keney, G., & Korang-Yeboah, B. (2022). Using the UTAUT, personal innovativeness and perceived financial cost to examine student’s intention to use E-learning. Journal of Science and Technology Policy Management, 13(3), 713–737. https://doi.org/10.1108/JSTPM-12-2020-0168.
Urbach, N., Smolnik, S., & Riempp, G. (2010). An empirical investigation of employee portal success. The Journal of Strategic Information Systems, 19(3), 184–206. https://doi.org/10.1016/j.jsis.2010.06.002.
Vassilikopoulou, A., Lepetsos, A., & Siomkos, G. (2018). Crises through the consumer lens: The role of trust, blame and risk. Journal of Consumer Marketing, 35(5), 502–511. https://doi.org/10.1108/JCM-02-2016-1721.
Venkatesh, M., Davis, & Davis (2003). User Acceptance of Information Technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540.
Venkatesh, T., & Xu (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.
Vladova, G., Ullrich, A., Bender, B., & Gronau, N. (2021). Students’ Acceptance of Technology-Mediated teaching – how it was Influenced during the COVID-19 pandemic in 2020: A study from Germany. Frontiers in Psychology, 12, 636086. https://doi.org/10.3389/fpsyg.2021.636086.
Wang, Y. S., Wang, H. Y., & Shee, D. Y. (2007). Measuring e-learning systems success in an organizational context: Scale development and validation. Computers in Human Behavior, 23(4), 1792–1808. https://doi.org/10.1016/j.chb.2005.10.006.
Wang, Y. S., Wu, M. C., & Wang, H. Y. (2009). Investigating the determinants and age and gender differences in the acceptance of mobile learning. British Journal of Educational Technology, 40(1), 92–118. https://doi.org/10.1111/j.1467-8535.2007.00809.x.
Wang, C. H., Shannon, D. M., & Ross, M. E. (2013). Students’ characteristics, self-regulated learning, technology self-efficacy, and course outcomes in online learning. Distance Education, 34(3), 302–323. https://doi.org/10.1080/01587919.2013.835779.
Wang, H., Tlili, A., Lehman, J. D., Lu, H., & Huang, R. (2021). Investigating feedback implemented by instructors to support online competency-based learning (CBL): A multiple case study. International Journal of Educational Technology in Higher Education, 18(1), 5. https://doi.org/10.1186/s41239-021-00241-6.
Wen, G. K. Y., Ern, E. C. J., Khoo, X. Q., Sim, C., Yap, J. J., Teh, S. Y., & A STUDY OF BEHAVIORAL INTENTION OF UNDERGRADUATES TOWARDS THE USAGE OF E- LEARNING SYSTEMS. (2022). International Journal of Modern Education, 4(14), 10–20. https://doi.org/10.35631/IJMOE.414002.
Yi, M. Y., & Hwang, Y. (2003). Predicting the use of web-based information systems: Self-efficacy, enjoyment, learning goal orientation, and the technology acceptance model. International Journal of Human-Computer Studies, 59(4), 431–449. https://doi.org/10.1016/S1071-5819(03)00114-9.
Yuen, A. J. K., & Ma, W. W. K. (2004). Knowledge sharing and teacher acceptance of Web based learning system. In R. Atkinson, C. McBeath, D. Jonas-Dwyer, & R. Phillips, Beyond the Comfort Zone: Proceedings of the 21st ASCILITE Conference (pp. 975–983).
Zapata, L., De La Fuente, J., Martínez Vicente, J. M., González Torres, M. C., & Artuch, R. (2016). Relations between the personal self-regulation and learning approach, coping strategies, and self-regulation learning, in university students (PROCESS). International Journal of Developmental and Educational Psychology. Revista INFAD de Psicología, 4(1), 175. https://doi.org/10.17060/ijodaep.2014.n1.v4.601.
Zhang, Z., Cao, T., Shu, J., & Liu, H. (2022). Identifying key factors affecting college students’ adoption of the e-learning system in mandatory blended learning environments. Interactive Learning Environments, 30(8), 1388–1401. https://doi.org/10.1080/10494820.2020.1723113.
Zimmerman, B. J. (2000). Self-Efficacy: An essential motive to learn. Contemporary Educational Psychology, 25(1), 82–91. https://doi.org/10.1006/ceps.1999.1016.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
There were no conflicts of interest in this research.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendix
Appendix
Survey questionnaire.
Dimension | Item |
---|---|
Course content | Students receive information about the work through the e-learning system where the goals, concepts, and main messages of the course are defined. |
Desired learning outcomes that are defined and marketed through the e-learning system are summarized in clearly written statements. | |
E-learning is designed to encourage us to work together using problem-solving to better understand topics. | |
The content of the course is well communicated on the e-learning platform. | |
The content of the course is constantly updated on the e-learning platform. | |
E-learning system | The e-learning system offers flexibility in terms of time and place of use. |
The e-learning system has a well-designed user interface. | |
The e-learning system enables quick access to information. | |
The e-learning system is reliable. | |
The steps for completing tasks in the e-learning system have a logical sequence. | |
Collaboration tools such as forums, built into the e-learning system, are effective. | |
Institutional support (Selim, 2007) | Students are provided with detailed information about the e-learning program. |
Students have information that can help them access materials in digital form. | |
Easily accessible technical assistance is provided to all students throughout the course/program. | |
Teacher support | Professors/assistants clearly explain how communication channels should be used during course attendance. |
Professors/assistants manage student expectations regarding the type and timeliness of responses to student communications. | |
Professors/assistants can solve student problems related to the use of e-learning in the course. | |
Personal innovativeness (Hurt et al., 2013) | My peers often ask me for advice or information. |
I enjoy trying new ideas. | |
I’m looking for new ways to do a certain job | |
Self-efficacy (Artino, 2007) | I can complete my learning activities using an e-learning system even though I have never used such a system. |
I could have completed my learning activities using the e-learning system if I had seen someone else using it before I tried to use it. | |
I could complete my learning activities using an e-learning system if I had built-in help. | |
Knowledge sharing | The e-learning system facilitates the process of knowledge exchange anytime and anywhere. |
The e-learning system supports conversations with my teacher and fellow students. | |
Sharing my knowledge through the e-learning system strengthens the relationship with my teacher and fellow students. | |
The e-learning system allows me to share different types of resources with my teacher and fellow students. | |
The e-learning system facilitates collaboration among students. | |
Intention to use (Chiu et al., 2007) | I want to use e-learning in my learning activities in the future. |
In the future, I will continue to use e-learning as much as possible in my learning activities. | |
I intend to increase the use of e-learning in my learning activities in the future. | |
Use (Y.-S. Wang et al., 2007) | I often use the e-learning system during my studies. |
In most cases, I use the e-learning system because I want to, not because I have to. | |
I use the e-learning system a lot. | |
Performance (Zapata et al., 2016) | I am satisfied with the way I learned. |
I have achieved the proposed learning objectives. | |
I learned adequately from the suggested materials. | |
I am interested in further specialization in this field. | |
I am motivated to learn lessons. | |
I understand the teaching material well. | |
I learned “how to learn better” on this topic. | |
I planned my study and carried it out well. | |
Learning level (Rovai et al., 2009) | I can organize teaching material into a logical structure. |
As a result of the course, I changed my views on the subject matter. | |
I feel more confident as a result of what I learned in the course. |
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Kapo, A., Milutinovic, L.D., Rakovic, L. et al. Enhancing e-learning effectiveness: analyzing extrinsic and intrinsic factors influencing students’ use, learning, and performance in higher education. Educ Inf Technol (2023). https://doi.org/10.1007/s10639-023-12224-3
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10639-023-12224-3