The Factors That Affect Electronic Learning Students' Behavioural Intentions In The Higher Education Tourism And Hospitality Disciplines
DOI:
https://doi.org/10.56294/dm2025691Keywords:
TAM, E-L, Learners’ intention, UAEAbstract
Introduction: This study aims to explore the factors influencing the intention of hospitality and tourism students in the UAE to adopt e-learning using the Technology Acceptance Model (TAM). E-learning has become an essential tool in higher education, particularly in response to the COVID-19 pandemic. The research seeks to identify the key determinants that affect students' willingness to engage with e-learning platforms.
Methods: A cross-sectional survey was conducted in two phases, involving 278 undergraduate students from a UAE university. The survey assessed various TAM constructs such as perceived usefulness, ease of use, system characteristics, and hedonic motivation. Data were analyzed using SmartPLS software and Structural Equation Modeling (SEM) to test the relationships between the variables.
Results: The study found that perceived usefulness and ease of use were the most significant factors influencing students' intention to adopt e-learning. Other influential factors included e-learning resources, platform functionality, subjective norms, and e-learning support. Additionally, hedonic motivation played an important role in enhancing students' engagement with e-learning.
Conclusions: The findings suggest that higher education institutions should focus on improving the perceived usefulness and ease of use of e-learning platforms while ensuring robust system functionality and support. The study contributes to the understanding of technology adoption in non-technical fields, offering insights that can inform e-learning strategies, especially in the context of future pandemics or disruptions.
References
Alkhazali, Z., Aldabbagh, I., & Abu-Rumman, A. (2019). TQM potential moderating role to the relationship between HRM practices, KM strategies and organizational performance: the case of Jordanian banks. Academy of Strategic Management Journal, 18(3), 1-16.
Al Zoubi, M and Alzoubi S (2023). Exploring The Relationship Between Robot Employees' Perceptions and Robot-Induced Unemployment Under COVID-19 In The Jordanian Hospitality Sector. International Journal of Data Science 11 4. DOI: https://doi.org/10.5267/j.ijdns.2023.8.007
Al Zoubi, M (2023). An extension of the diffusion of innovation theory for business intelligence adoption: A maturity perspective on project management. Uncertain Supply Chain Management 11 2 465–472 DOI: https://doi.org/10.5267/j.uscm.2023.3.003
Al Zoubi, M et al (2023). The moderating role of internal control system on the relationship between service quality of accounting information system and customer satisfaction: a study of some selected customers from commercial banks in Jordan. Uncertain Supply Chain Management 12 DOI: https://doi.org/10.5267/j.uscm.2023.8.015
Al Zoubi, et al. (2023). The influence of soft and hard quality management practises on quality improvement and performance in UAE higher education. International Journal of Data Science 11 3.
Abbad, M. (2021). Using the UTAUT model to understand students’ usage of e-learning systems in developing countries, Education and Information Technologies, Vol. 26 No. 6, pp. 7205-7224. DOI: https://doi.org/10.1007/s10639-021-10573-5
Abuhammad, D. (2020). Barriers to distance learning during the COVID-19 outbreak: a qualitative review from parents’ perspective. Heliyon ;6(11):e05482. https://doi.org/10.1016/J.HELIYON.2020.E05482. DOI: https://doi.org/10.1016/j.heliyon.2020.e05482
Ain, N., Kaur, K., & Waheed, M. (2016). The influence of learning value on learning management system use: an extension of UTAUT2, Information Development, Vol. 32 No. 5, pp. 1306-1321, doi: 10.1177/0266666915597546. DOI: https://doi.org/10.1177/0266666915597546
Ajzen, I. (1991). The theory of planned behavior. Organ Behav Hum Decis Process; 50 (2):179–211. DOI: https://doi.org/10.1016/0749-5978(91)90020-T
Al-Adwan , A., Albelbisi, N., Hujran, O., Al-Rahmi, W., & Alkhalifah, A. (2021). Developing a holistic success model for sustainable e-learning: a structural equation modeling approach. Sustainability ;13(16):9453. https://doi.org/10.3390/ su13169453. DOI: https://doi.org/10.3390/su13169453
Alalwan , A., Dwivedi, Y., Rana , Q., & Williams, M. (2015). Consumer adoption of Internet banking in Jordan: examining the role of hedonic motivation, habit, self-efficacy and trust. J Financ Serv Mark ;20(2):145–57. https://doi.org/ 10.1057/FSM.2015.5/FIGUR. DOI: https://doi.org/10.1057/fsm.2015.5
Almaiah , M., Al-Khasawneh , A., & Althunibat , A. (2020). Exploring the critical challenges and factors influencing the e-learning system usage during COVID-19 pandemic. Educ Inf Technol ;25(6):5261–80. https://doi.org/10.1007/S10639-020- 10219-Y/FIGURES/3. DOI: https://doi.org/10.1007/s10639-020-10219-y
Al-Okaily, m., Alqudah, h., Matar, A., Lutfi, A., & Taamneh, E. (2020). Dataset on the acceptance of e-learning system among universities students’ under the COVID-19 pandemic conditions. Data Brief;32(5):106176. https://doi.org/10.1016/J. DIB.2020.106176. DOI: https://doi.org/10.1016/j.dib.2020.106176
Al-Qirim, N., Rouibah, K., Tarhini, A., Serhani, M., Yammahi, A., & Yammahi, M. (2018). Towards a personality understanding of information technology students and their IT learning in UAE university, Education and Information Technologies, Vol. 23 No. 1, pp. 29-40. DOI: https://doi.org/10.1007/s10639-017-9578-1
Alzoubi, A., & Alzoubi, M. (2020). Determinants of E-Learning Based on Cloud Computing adoption: Evidence from a Students' Perspective in Jordan; Vol. 29, No. 4, (2020), pp. 1361-1370,.
Broadbent, J. (2017). Comparing online and blended learner’s self-regulated learning strategies and academic performance. Internet High Educ ;33(2):24–32. https://doi.org/10.1016/j.iheduc.2017.01.004. DOI: https://doi.org/10.1016/j.iheduc.2017.01.004
Bruso , J., Stefaniak , J., & Bol, L. (2020). An examination of personality traits as a predictor of the use of self-regulated learning strategies and considerations for online instruction. Educ Technol Res Dev;68(5):2659–83. https://doi.org/ 10.1007/s11423-020-09797-y. DOI: https://doi.org/10.1007/s11423-020-09797-y
Cheng, Y. (2011). Antecedents and consequences of e‐learning acceptance. Information Systems Journal, 21(3), 269-299. https://doi.org/10.1111/j.1365-2575.2010.00356.x. DOI: https://doi.org/10.1111/j.1365-2575.2010.00356.x
Conrad, K., Upadhyaya, S., & Joa, C. (2015). Bridging the divide: using UTAUT to predict multigenerational, Computers in Human Behaviour, Vol. 01 No. 50, pp. 186-196, doi: 10.1016/j.chb.2015.03.032. DOI: https://doi.org/10.1016/j.chb.2015.03.032
Davis , D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q;13(3):319–40. https://doi.org/10.2307/ 249008. DOI: https://doi.org/10.2307/249008
DeLone, W., & McLean, E. (2004). Measuring E-commerce success: applying the DeLone & McLean Information Systems Success Model. International Journal of Electronic Commerce, 9(1), 31-47. https://doi.org/10.1080/10864415.2004.11044317. DOI: https://doi.org/10.1080/10864415.2004.11044317
Gillis , A., & Krull, M. (2020). COVID-19 remote learning transition in spring 2020: class structures, student perceptions, and inequality in college courses. Teach Sociol ;48(4):283–99. ttps://doi.org/10.1177/0092055X20954263. DOI: https://doi.org/10.1177/0092055X20954263
Hair. (2017). A primer on partial least squares structural equation modeling (PLS-SEM).
Hanh , T., Hau , V., & Pham, N. (2023). Factors influencing students' intention to use e-learning system a case study conducted in Vietnam Hue Thi Hoang. Emerging Technologies in Learning, 9(4), 165-181. doi:https://doi.org/10.3991/ijet.v15i18.15441 DOI: https://doi.org/10.3991/ijet.v15i18.15441
Hanham, J., Lee , B., & Teo, T. (2021). The influence of technology acceptance, academic selfefficacy, and gender on academic achievement through online tutoring. Comput Educ ;172(13):104252. https://doi.org/10.1016/J. COMPEDU.2021.104252. DOI: https://doi.org/10.1016/j.compedu.2021.104252
Jameel, A., Karem, M., & Ahmad, A. (2021). Behavioral intention to use E-learning among academic staff during COVID-19 pandemic based on UTAUT model, International Conference on Emerging Technologies and Intelligent Systems, pp. 187-196. DOI: https://doi.org/10.1007/978-3-030-82616-1_17
Kamalasena, B., & irisena, A. (2021). Factors influencing the adoption of E-learning by university students in Sri Lanka: application of UTAUT-3 model during covid-19 pandemic, Wayamba Journal of Management, Vol. 12 No. 2, pp. 99-124. DOI: https://doi.org/10.4038/wjm.v12i2.7533
King, C., & So, K. (2014). Creating a virtual learning community to engage international students. Journal of Hospitality & Tourism Education, 26(3), 136–146. doi:10.1080/10963758.2014.936255. DOI: https://doi.org/10.1080/10963758.2014.936255
Lee , J., Song, H., & Hong, A. (2019). Exploring factors, and indicators for measuring students’ sustainable engagement in e-learning. Sustainability ;11(4). https://doi.org/10.3390/su11040985. Article 985. DOI: https://doi.org/10.3390/su11040985
Lee, C., & Hsieh, M. (2009). The influence of mobile self-efficacy on attitude towards mobile advertising, International Conference on New Trends in Information and Service Science (NISS’09), Beijing, China, pp. 1231-1236. DOI: https://doi.org/10.1109/NISS.2009.91
Lee, M. (2010). Explaining and predicting users’ continuance intention toward Elearning: An extension of the expectation–confirmation model. Computers & Education, 54(2), 506-516. https://doi.org/10.1016/j.compedu.2009.09.002. DOI: https://doi.org/10.1016/j.compedu.2009.09.002
Mafuna, W., & Wadesango, N. (2016). Exploring lecturers’ acceptance level of learning management system (LMS) at applying the extended technology acceptance model (TAM), Journal of Social Sciences, Vol. 48 Nos 1/2, pp. 63-70, doi: 10.1080/09718923.2016.1. DOI: https://doi.org/10.1080/09718923.2016.11893571
Martin , F., Sun , T., & Westine, C. (2020). A systematic review of research on online teaching and learning from 2009 to 2018. Comput Educ ;159:104009. https://doi. org/10.1016/J.COMPEDU.2020.104009. DOI: https://doi.org/10.1016/j.compedu.2020.104009
Muhmmad , A., Rakibul , B., Muhammad, Z., & Muhaiminul , I. (2023). Analyzing students’ e-learning usage and post-usage outcomes in higher education. Computers and Education Open, 5(17), 1-11. doi:https://doi.org/10.1016/j.caeo.2023.100146 DOI: https://doi.org/10.1016/j.caeo.2023.100146
Murphy, J., Kalbaska, N., Williams, A., Ryan, P., & Cantoni, L. (2014). Massive open online courses: Strategies and research areas. Journal of Hospitality & Tourism Education, 26, 39–43. doi:10.1080/10963758.2014.880618. DOI: https://doi.org/10.1080/10963758.2014.880618
Nawaz, S., & Mohamed, R. (2020). Acceptance of mobile learning by higher educational institutions in Sri Lanka: an UTAUT2 approach, Journal of Critical Reviews, Vol. 7 No. 12, pp. 1036-1049, doi: 10.31838/jcr.07.12.183. DOI: https://doi.org/10.31838/jcr.07.12.183
Pang, S., Penfold, P., & Wong, S. (2010). Chinese learners’ perceptions of blended learning in a hospitality and tourism management program. Journal of Hospitality & Tourism Education, 22(1), 15–22. doi:10.1080/10963758.2010.10696965. DOI: https://doi.org/10.1080/10963758.2010.10696965
Park, S. (2009). An analysis of the technology acceptance model in understanding university students’ behavioral intention to use e-learning. J Educ Technol Soc;12(3):150–62.
Perera, R., & Nalin, A. (2022). Factors affecting learners’perception of e-learning during the COVID-19 pandemic. Asian Association of Open Universities Journal, 17(5), 84-100. doi:https://www.emerald.com/insight/2414-6994.htm DOI: https://doi.org/10.1108/AAOUJ-10-2021-0124
Persico, D., Manca, S., & Pozzi, F. (2014). Adapting the technology acceptance model to evaluate the innovative potential of e-learning systems. Computers in Human Behavior, 30, 614–622. doi:10.1016/j.chb.2013.07.045. DOI: https://doi.org/10.1016/j.chb.2013.07.045
Podsakoff et al. (2003). Common method biases in behavioral research: a critical review of the literature and recommended remedies, Journal of Applied Psychology, Vol. 88 No. 5, pp. 879-903. DOI: https://doi.org/10.1037/0021-9010.88.5.879
Raman , A., Thannimalai, R., Rathakrishnan , R., & Ismail, S. (2022). Investigating the influence of intrinsic motivation on behavioral intention and actual use of technology in moodle platforms. Int J Instr;15(1):1003–24. https://doi.org/10.29333/ iji.2022.15157a. DOI: https://doi.org/10.29333/iji.2022.15157a
Robbins, S., & Stylianou, A. (2003). Global corporate web sites: an empirical investigation of content and design. Information & Management, 40(3), 205-212. https://doi.org/10.10 16/s0378-7206(02)00002-2. DOI: https://doi.org/10.1016/S0378-7206(02)00002-2
Samsudeen, S., & Mohamed, R. (2019). University students’ intention to use e-learning systems: a study of higher educational institutions in Sri Lanka”, Interactive Technology and Smart Education, Vol. 16 No. 3, pp. 219-238, doi: 10.1108/ITSE-11-2018-0092. DOI: https://doi.org/10.1108/ITSE-11-2018-0092
Sangra, A., Vlachopoulos, D., & Cabrera, N. (2012). Building an inclusive definition of e-learning: An approach to the conceptual framework. International Review of Research in Open and Distance Learning, 13(2), 145–159. DOI: https://doi.org/10.19173/irrodl.v13i2.1161
Schepers, J., & Wetzels, J. (2007). A meta-analysis of the technology acceptance model: investigating subjective norm and moderation effects. Inf Manag ;44(1): 90–103. https://doi.org/10.1016/J.IM.2006.10.007. DOI: https://doi.org/10.1016/j.im.2006.10.007
Sitar-Taut , D. (2021). Mobile learning acceptance in social distancing during the COVID- 19 outbreak: the mediation effect of hedonic motivation. Hum Behav Emerg Technol ;3(3):366–78. https://doi.org/10.1002/HBE2.261. DOI: https://doi.org/10.1002/hbe2.261
Smith, B., & Merchant, E. (2001). Designing an attractive web site: variables of importance. Paper presented at the Proceedings of the 32nd Annual Conference of the Decision Sciences Institute, San Francisco, CA.
Tamer, M., Eleri , J., & Faten, M. (2016). Technological Factors Influencing University Tourism and Hospitality Students’ Intention to Use E-Learning: A Comparative Analysis of Egypt and the United Kingdom, Journal of Hospitality & Tourism Education, 28:4, 189-201. 10.1080/10963758.2016.1226845. DOI: https://doi.org/10.1080/10963758.2016.1226845
Teo , T., & Noyes, J. (2011). An assessment of the influence of perceived enjoyment and attitude on the intention to use technology among pre-service teachers: a structural equation modeling approach. Comput Educ ;57(2):1645–53. https://doi.org/10.1016/J.COMPEDU.20. DOI: https://doi.org/10.1016/j.compedu.2011.03.002
To , P., & Lin , T. (2007). Shopping motivations on Internet: a study based on utilitarian and hedonic value. Technovation ;27(12):774–87. https://doi. org/10.1016/J.TECHNOVATION.2007.01.001. DOI: https://doi.org/10.1016/j.technovation.2007.01.001
Venkatesh, V., & Davis, D. (2000). A theoretical extension of the technology acceptance model: four longitudinal field studies. Manage Sci ;46(2):186–204. https:// doi.org/10.1287/mnsc.46.2.186.11926. DOI: https://doi.org/10.1287/mnsc.46.2.186.11926
Wei , H., & Chou, C. (2020). Online learning performance and satisfaction: do perceptions and readiness matter? Distance Educ ;41(1):48–69. https://doi.org/ 10.1080/01587919.2020.1724768. DOI: https://doi.org/10.1080/01587919.2020.1724768
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Copyright (c) 2025 Mohammed Mhmood Al Matalka, Hazim Ryad Momani, Mohammad Khasawneh, Salim khanfar , Zaid Akram AL-Malahmeh, Amer Hani Al-Qassem, Ammar Mohammad Al-Ramadan, Mohammad Alzoubi , Ashraf Alfandi (Author)

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