Factors Influencing the Intention to Use Computer Technology for E-Learning Among University Students

Authors

  • Juliana Rosmidah Jaafar Centre for Foundation and General Studies, Infrastructure University Kuala Lumpur (IUKL), Malaysia
  • Nalinah Poongavanam Centre for Foundation and General Studies, Infrastructure University Kuala Lumpur (IUKL), Malaysia
  • Lee Su Yee Centre for Foundation and General Studies, Infrastructure University Kuala Lumpur (IUKL), Malaysia
  • Jaya Chitra Ramalu Centre for Foundation and General Studies, Infrastructure University Kuala Lumpur (IUKL), Malaysia

DOI:

https://doi.org/10.32890/jps2023.26.1

Keywords:

Intention student, computer, technology, e-Learning

Abstract

The utilisation of gadgets and computer technology has become a significant aspect of e-learning. This condition requires students to use various online platforms for learning purposes. The current study aims to explore the potential factor in explaining the intention to use computer technology during e-learning among private university students. Online questionnaires were distributed to 174 university students from various programmes in a university in Selangor. The items in the questionnaire covered three independent variables: perceived ease of use of technology, perceived usefulness of technology, and computer self-efficacy. The dependent variable is the intention to use computer technology in e-learning. Pearson Correlation was used to assess the relationship between the independent and dependent variables. Furthermore, Hierarchal Multiple Regression analysis was used to determine the predictor of the intention to use computer technology. The result showed that the intention to use computer technology for e-learning was significantly related to technology’s perceived ease of use, usefulness, and computer self-efficacy. However, when computer self-efficacy is controlled, students’ intention to use computer technology is driven by their perceived notion that technology is easy to use and usefulness of the technology. The study concludes that when students perceive technology as easy to use and sense its practicality, they will have a stronger intention of using technology for learning. This research implies that a systematic strategy should be practised by integrating computer technology into teaching and learning in the current situation. Future studies could further explore the potential of differences across students’ backgrounds towards e-learning. 

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References

Ab Jalil, H., Ma’rof, A., & Omar, R. (2019). Attitude and behavioral intention to develop and use MOOCs among academics. International Journal of Emerging Technologies in Learn-ing (iJET), 14(24), 31-41.

Abdullah, Z. D., & Mustafa, K. I. (2019). The underlying factors of computer self-efficacy and the relationship with students’ academic achievement. International Journal of Research in Education and Science, 5(1), 346-354.

Agarwal, R., & Karahanna, E. (2000). Time flies when you’re having fun: Cognitive absorption and beliefs about information technology usage. MIS Quarterly, 24(4), 665-694.

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

Al Kurdi, B., Alshurideh, M. T., & Salloum, S. A. (2020). Investigating a theoretical framework for e-learning technology acceptance. International Journal of Electrical and Computer Engineering (IJECE), 10(6), 6484-6496.

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, 6, 14268-14276.

Bao, Y., Xiong, T., Hu, Z., & Kibelloh, M. (2013). Exploring gender differences on general and specific computer self-efficacy in mobile learning adoption. Journal of Educational Computing Research, 49(1), 111-132.

Buabeng-Andoh, C. (2020). Exploring university students’ intention to use mobile learning: A research model approach. Education and Information Technologies, 26(4).

Carini, R. M., Kuh, G. D., & Klein, S. P. (2006) Student engagement and student learning: Testing the linkages*. Research in Higher Education, 47(1), 1-32.

Chen, Y. C., Lin, Y. C., Yeh, R. C., & Lou, S. J. (2013). Examining factors affecting college students’ intention to use web-based instruction systems: Towards an integrated model. Turkish Online Journal of Educational Technology, 12(2), 111-121.

Fishbein, M., & Cappella, J. N. (2006). The role of theory in developing effective health communications. Journal of Communication, 56(1), 1-17.

Gu, X., Zhu, Y., & Guo, X. (2013). Meeting the ‘‘digital natives’’: Understanding the acceptance of technology in classrooms. Educational Technology and Society, 16(1), 392-402.

Hassanein, K., Head, M., & Wang, F. (2010). Understanding student satisfaction in a mobile learning environment: The role of internal and external facilitators. Paper presented at the 2010 Ninth International Conference on Mobile Business and 2010 Ninth Global Mobility Roundtable (ICMB-GMR). https://doi.org/10.1109/ICMB-GMR.2010.38

Hayashi, A., Chen, C., Ryan, T., & Wu, J. (2004). The role of social presence and moderating role of computer self efficacy in predicting the continuance usage of e-learning sys-tems. Journal of Information Systems Education, 15(2), 139-154.

Hill, T., Smith, N. D., & Mann, M. F. (1978). Role of efficacy expectation in predicting the decision to use advanced technologies: The case of computers. Journal of Applied Psychol-ogy, 72(2), 307-313.

Hopson, M. H., Simms, R. L., & Knezek, G. A. (2002). Using a technology-enriched environment to improve higher-order thinking skills. Journal of Research on Technology in Education, 34(2), 109–119.

Hsin, H. C., Shu, C. C., & Min, H. K. (2015). A study of EFL college students’ acceptance of mobile learning. Procedia - Social and Behavioral Sciences, 176, 333-339.

Huang, F., Teo, T., & Scherer, R. (2020). Investigating the antecedents of university students’perceived ease of using the Internet for learning. Interactive Learning Environments, 7, 1-17.

Kim, E. J., Kim, J. J., & Han, S. H. (2021). Understanding student acceptance of online learning systems in higher education: Application of social psychology theories with consideration of user innovativeness. Sustainability, 13(2), 896.

Kim, H. W., & Kankanhalli, A. (2009). Investigating user resistance to information systems implementation: A status quo bias perspective. MIS Quarterly, 33, 567–582.

Krause, M., Pietzner, V., Dori, Y. J., & Eilks, I. (2017). Differences and developments in attitudes and self-efficacy of prospective chemistry teachers concerning the use of ICT in education. Eurasia Journal of Mathematics, Science and Technology Education, 13(8), 4405-4417.

Macharia, J. K. N., & Pelser, T. G. (2012). Key factors that influence the diffusion and infusion of information and communication technologies in Kenyan higher education. Studies in Higher Education, 39(4).

Maheshwari, G. (2021). Factors affecting students’ intentions to undertake online learning: An empirical study in Vietnam. Education and Information Technologies, 1-21.

Marakas, G., Yi, M., & Johnson, R. D. (1998). The multilevel and multifaceted character of computer self-efficacy: Toward clarification of the construct and an integrative framework for research. Information Systems Research, 9, 126-163.

Margaryan, A., Littlejohn, A., & Vojt, G. (2011). Are digital natives a myth or reality? University students’ use of digital technologies. Computers and Education, 56(2), 429-440.

Martinez, J. (2020). Take this pandemic moment to improve education. Retrieved from EdSource: https://edsource. org/2020/take-this-pandemic-moment-to-improve-educa-tion/633500.

Park, S. Y., Nam, M. W., & Cha, S. B. (2012). University students’ behavioral intention to use mobile learning: Evaluating the technology acceptance model. British Journal of Educa-tional Technology, 43(4), 592-605.

Rizun, M., & Strzelecki, A. (2020). Students’ acceptance of the COVID-19 impact on shifting higher education to distance learning in Poland. International Journal of Environmental Re-search and Public Health 2020, 17(18), 6468.

Saleh, S. M. (2013). The self-efficacy in acceptance of information technology in public sector. Asian Journal of Business and Management Sciences, 2(9), 44-55. http://repo.uum.edu. my/9460/

Seliaman, M. E., & Al-Turki, M. S. (2012). Mobile learning adoption in Saudi Arabia. World Academy of Science, Engineering and Technology, 6(9), 356-358.

Seppala, P., & Alamaki, H. (2003). Mobile learning in teacher training. Journal of Computer Assisted Learning, 19(3), 330-335.

Su, Y., Zhu, Z., Chen, J., Jin, Y., Wang, T., Lin, C. L., & Xu, D. (2021). Factors influencing entrepreneurial intention of university students in China: Integrating the perceived university support and theory of planned behavior. Sustainability, 13(8), 4519.

Sun, Y., & Gao, F. (2020). An investigation of the influence of intrinsic motivation on students’ intention to use mobile devices in language learning. Educational Technology Research and Development, 68, 1181-1198.

Tahar, A., Riyadh, H. A., Sofyani, H., & Purnomo, W. E. (2020). Perceived ease of use, perceived usefulness, perceived security and intention to use e-filing: The role of technolo-gy readiness. The Journal of Asian Finance, Economics and Business (JAFEB), 7(9), 537-547.

Tartavulea, C. V., Albu, C. N., Albu, N., Dieaconescu, R. I., & Petre, S. (2020). Online teaching practices and the effectiveness of the educational process in the wake of the COVID-19 pandemic. Amfiteatru Economic, 22(55), 920-936.

Teo, T., & Zhou, M. (2014). Explaining the intention to use technology among university students: A structural equation modeling approach. Journal of Computing in Higher Education, 26(2), 124-142.

Terrion, J. L., & Aceti, V. (2012). Perceptions of the effects of clicker technology on student learning and engagement: A study of freshmen chemistry students. Research in Learning Technology, 20, 1-11.

Thongsri, N., Shen, L., & Bao, Y. (2020). Investigating academic major differences in perception of computer self-efficacy and intention toward e-learning adoption in Chi-na. Innovations in Education and Teaching International, 57(5), 577-589.

Tung, F. C., & Chang, S. C. (2008). Nursing students’ behavioral intention to use online courses: A questionnaire survey. International Journal of Nursing Studies, 45, 1299-1309.

Venkatesh, V., Thong, J. Y., Chan, F. K., Hu, P. J. H., & Brown, S. A. (2011). Extending the two-stage information systems continuance model: Incorporating UTAUT predictors and the role of context. Information Systems Journal, 21(6), 527–555.

Wong, K. T., Osman, R., Goh, Swee, C. P., & Rahmat, M. K. (2013a). Understanding student teachers’ behavioural intention to use technology: Technology Acceptance Model (TAM) validation and testing. International Journal of Instruction, 6(1),89-104.

Wong, K. T., Teo, T., & Russo, S. (2013b). Interactive whiteboard acceptance: Applicability of the UTAUT model among student teachers. The Asia Pacific Education Researcher, 22(1), 1-10.

Yang, S. (2012). Exploring college students’ attitudes and self-efficacy of mobile learning. The Turkish Online Journal of Educational Technology, 11(4), 148-154.

Zainab, B., Awais, B. M., & Alshagawi, M. (2017). Factors affecting e-training adoption: An examination of perceived cost, computer self-efficacy and the technology acceptance model. Behaviour & Information Technology, 36(12), 1261-1273.

Zhai, X., & Shi, L. (2020). Understanding how the perceived

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Published

31-07-2023

How to Cite

Jaafar, J. R., Poongavanam, N., Lee, S. Y., & Ramalu, J. C. (2023). Factors Influencing the Intention to Use Computer Technology for E-Learning Among University Students. Jurnal Pembangunan Sosial, 26, 1-23. https://doi.org/10.32890/jps2023.26.1

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