The Application of UTAUT on elearning Usage Among Physics Students of International Schools in Bangkok, Thailand
DOI:
https://doi.org/10.14456/shserj.2023.3Keywords:
eLearning, Technology Adoption, Behavioral Intention, Use Behavior, StudentsAbstract
Purpose: Students have been introduced to eLearning during COVID-19, and it has been continued to have a strong impact on the future use. Therefore, this research aims to identify factors impacting the behavioral intention and use behavior of eLearning among the high school students who have been studying physics in the final two years (Grade 11 and 12) of international schools in Bangkok, Thailand, ascertained by performance expectancy, effort expectancy, social influence, facilitating conditions and habit. Research design, data, and methods: Researchers applied quantitative methods of questionnaire distribution to 500 participants, underlying the sampling techniques of judgmental, stratified random and convenience samplings. Constructs were prior approved by Item Objective Congruence (IOC) Index. Pilot testing of 30 participants with Cronbach’s Alpha reliability test was satisfied. The data were analyzed with descriptive analysis, Confirmatory Factor Analysis (CFA), and Structural Equation Model (SEM). Results: Results indicate the strongest relationship between the behavioral intention and use behavior of eLearning. Furthermore, performance expectancy, efforts expectancy, facilitating conditions, and habit significantly affect behavioral intention. Facilitating conditions and habit have a significant impact on use behavior. Conclusion: This study recommends that schools should improve e-learning system in order to enhance student behavioral intention and use behavior for their future education and career.
References
Alalwan, A. A., Dwivedi, Y. K., & Rana, N. P. (2017). Factors influencing adoption of mobile banking by Jordanian bank customers: extending UTAUT2 with trust. International Journal of Information Management, 37(3), 99-110. http://doi.org/10.1016/j.ijinfomgt.2017.01.002.
Ali, F., Nair, P., & Hussain, K. (2016). An Assessment of Students’ Acceptance & Usage of Computer Supported Collaborative Classrooms in Hospitality and Tourism Schools. The Journal of Hospitality Leisure Sport and Tourism, 18, 51-60. http://doi.org/10.1016/j.jhlste.2016.03.002
Alraja, M. (2015). User acceptance of information technology: A field study of an e-mail system adoption from the individual students’ perspective. Mediterranean Journal of Social Sciences, 6(6), 19-25.
Ambarwati, R. (2020). The Role of Facilitating Conditions and User Habits: A Case of Indonesian Online Learning Platform. Journal of Asian Finance Economics and Business, 7(10), 481-489.
Global Market Insights. (2022, April 1). E-Learning Market. https://www.gminsights.com/industry-analysis/elearning-market-size
Baabdullah, A. M. (2018). Consumer adoption of mobile social network games (M-SNGs) in Saudi Arabia: The role of social influence, hedonic motivation and trust. Technology in Society, 53, 91-102.
Baki, R., Birgoren, B., & Aktepe, A. (2018). A Meta Analysis of Factors Affecting Perceived Usefulness and Perceived Ease of Use in The Adoption of eLearning Systems. Turkish Online Journal of Distance Education, 19(4), 4-42.
https://doi.org/10.17718/tojde.471649
Baptista, G., & Oliveira, T. (2015). Understanding mobile banking: the unified theory of acceptance and use of technology combined with cultural moderators. Computers in Human Behavior, 50, 418-430.
Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238-246.
https://doi.org/10.1037/0033-2909.107.2.238
Berry, A. M. (2017). Behavioral Intention and Use Behavior of Social Networking Websites among Senior Adults [Doctoral dissertation]. Nova Southeastern University.
https://nsuworks.nova.edu/gscis_etd/1025
Carter, L., & Salyers, V. (2015). A Model for Meaningful ELearning at Canadian Universities. In J. Keengwe (Ed.), Handbook of Research on Educational Technology Integration and Active Learning (pp. 78-113). IGI Global. https://doi.org/10.4018/978-1-4666-8363-1.ch005
Chang, A. (2012). UTAUT and UTAUT 2: A review and agenda for future research. The Winners, 13(2), 106 - 114.
Chao, C. M. (2019). Factors Determining the Behavioral Intention to Use Mobile Learning: An Application and Extension of the UTAUT Model. Frontiers in Psychology, 10, 1-14.
https://doi.org/10.3389/fpsyg.2019.01652
Cook, R. G., & Sutton, R. (2014). Administrators’ Assessments of Online Courses and Student Retention in Higher Education: Lessons Learned. In S. Mukerji, & P. Tripathi (Eds.), Handbook of Research on Transnational Higher Education (pp. 138-150). IGI Global.
https://doi.org/10.4018/978-1-4666-4458-8.ch008
Davis, F. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 13(3), 319-339.
Duangekanong, S. (2022). Applications of Artificial Intelligence for Strategic Management of Organization. ABAC ODI JOURNAL Vision. Action. Outcome, 9(2), 202-217.
https://doi.org/10.14456/abacodijournal.2022.13
Evoh, C. J. (2011). ICT in Education Development in Africa: Policy and Institutional Frameworks. In E. Adomi (Eds.), Handbook of Research on Information Communication Technology Policy: Trends, Issues and Advancements (pp. 283-305). IGI Global. https://doi.org/10.4018/978-1-61520-847-0.ch017
Fornell, C. G., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50.
Gao, L., Vongurai, R., Phothikitti, K., & Kitcharoen, S. (2022). Factors Influencing University Students’ Attitude and Behavioral Intention Towards Online Learning Platform in Chengdu, China. ABAC ODI JOURNAL Vision. Action. Outcome, 9(2), 21-37.
https://doi.org/10.14456/abacodijournal.2022.2
Hair, J. F., Black, W. C., Babin., B. J., Anderson., R. E., & L.Tatham., R. (2006). Multivariate Data Analysis (6th ed.). Pearson Prentice Hall.
Herrero, Á., & San Martín, H. (2017). Explaining the adoption of social networks sites for sharing user-generated content: a revision of the UTAUT2. Computers in Human Behavior, 71, 209-217.
http://dx.doi.org/10.1016/j.ijpe.2016.08.035.
https://doi.org/10.1109/HICSS.2014.409
Huang, K. Y., & Chuang, Y. R. (2017). Aggregated model of TTF with UTAUT2 in an employment website context. Journal of Data Science, 15(2), 187-204.
Lallmahomed, M. Z., Lallmahomed, N., & Lallmahomed, G. M. (2017). Factors influencing the adoption of e-Government services in Mauritius. Telematics and Informatics, 34(4), 57-72.
Latip, M. S. A., Tamrin, M., Noh, I., Rahim, F. A., & Latip, S. N. N. A. (2022). Factors Affecting e-Learning Acceptance among Students: The Moderating Effect of Self-efficacy. International Journal of Information and Education Technology, 12(2), 116-122. https://doi.org/10.18178/ijiet.2022.12.2.1594
Limayem, M., Hirt, S. G., & Cheung, C. M. (2007). How habit limits the predictive power of intention: the case of information systems continuance. MIS quarterly, 31(4), 705-737.
Macedo, I. M. (2017). Predicting the acceptance and use of information and communication technology by older adults: an empirical examination of the revised UTAUT2. Computers in Human Behavior, 75, 935-948.
Mahande, R., & Malago, J. (2019). An eLearning acceptance evaluation through utaut model in a postgraduate program, ‖. Journal of Education Online, 16(2), 1-14.
Marlina, E., Tjahjadi, B., & Ningsih, S. (2021). Factors affecting student performance in eLearning: a case study of higher educational institutions in Indonesia. Journal of Asian Finance, Economics and Business, 8(4), 993-1001.
Ngampornchai, A., & Adams, J. (2016). Students’ acceptance and readiness for E-learning in Northeastern Thailand. International Journal of Educational Technology in Higher Education, 13(34), 1-13. https://doi.org/10.1186/s41239-016-0034-x
Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill.
Oechslein, O., Fleischmann, M., & Hess, T. (2014). An Application of UTAUT2 on Social Recommender Systems: Incorporating Social Information for Performance Expectancy. Proceedings of the Annual Hawaii International Conference on System Sciences (pp. 3297-3306). IEEE.
OPEC. (2021). Office of the Private Education Commission. https://opec.go.th/
Pedroso, C. B., Silva, A. L., & Tate, W. L. (2016). Sales and Operations Planning (S&OP): insights from a multi-case study of Brazilian organizations. International Journal of Production Economics, 182, 213-229.
Raith, C. P. (2019). Students' Formal and Informal Information Sources: From Course Materials to User-Generated Content. In P. Ordóñez de Pablos, M. Lytras, X. Zhang, & K. Chui (Eds.), Opening Up Education for Inclusivity Across Digital Economies and Societies (pp. 209-232). IGI Global. https://doi.org/10.4018/978-1-5225-7473-6.ch011
Salloum, S., & Shaalan, K. (2018). Investigating students' acceptance of ELearning system in Higher Educational Environments in the UAE: Applying the Extended Technology Acceptance Model (TAM) [Unpublished master dissertation]. The British University.
Sharma, S., Pradhan, K., Satya, S., & Vasudevan, P. (2005). Potentiality of earthworms for waste management and in other uses-a review. The Journal of American Science, 1(1), 4-16.
Sica, C., & Ghisi, M. (2007). The Italian versions of the Beck Anxiety Inventory and the Beck Depression Inventory-II: Psychometric properties and discriminant power. In M. A. Lange (Ed.), Leading-edge psychological tests and testing research (pp. 27-50). Nova Science Publishers.
Soper, D. S. (2022, May 24). A-priori Sample Size Calculator for Structural Equation Models. Danielsoper.
www.danielsoper.com/statcalc/default.aspx
Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics (5th ed.). Allyn & Bacon/Pearson Education.
Tadesse, M., Lin, H., Xu, B., & Yang, L. (2018). Personality Predictions Based on User Behavior on the Facebook Social Media Platform. IEEE Access, 6, 61959-61969. https://doi.org/10.1109/ACCESS.2018.2876502
Taiwo, A. A., & Downe, A. G. (2013). The theory of user acceptance and use of technology (UTAUT): A meta-analytic review of empirical findings. Journal of Theoretical & Applied Information Technology, 49(1), 48-58.
Tan, P. (2013). Applying the UTAUT to understand factors affecting the use of English eLearning websites in Taiwan. Sage Open, 3(4), 1-12.
Tayebinik, M., & Puteh, M. (2012). Blended learning or eLearning? International Management Advances in Computer Science and Telecommunications, 3(1), 103-110.
Venkatesh, V., Morris, M., Davis, G., & Davis, F. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478.
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, 12(1), 157-178.
Wu, J. H., & Wang, Y. M. (2006). Measuring KMS Success: A Respecification of the DeLone and McLean’s Model. Journal of Information & Management, 43, 728-739.