Influential Factors on English E-Learning Behavioral Intention and Usage Among Undergraduates at Chengdu University, Sichuan, China
Keywords:
Hedonic Motivation, Self- efficacy, Facilitating Conditions, Behavioral Intention, Use BehaviorAbstract
Purpose: The research investigates factors impacting the E-learning Behavioral Intention and Use behavior of undergraduates in non-English majors of Chengdu University who represent the undergraduates of Sichuan Province in China. Research design, data, and methodology: 496 sample data from the target group was gathered using a questionnaire and the quantitative approach. After the index of item-objective congruence (IOC), and Cronbach's Alpha, the Confirmatory factor analysis (CFA) was applied to test the data to verify the causal link between the variables and the model's goodness of fit. Finally, the Structural Equation Model (SEM) was again applied to conclude the impact strength of each variable. Results: All six hypotheses are supported at the p-value ranging from, showing a significant impact. The impact strengths of the factors are in the order of behavioral intention to use behavior, hedonic motivation to behavioral intention, self- efficacy to behavioral intention, effort expectancy to behavioral intention, facilitating conditions to use behavior, and performance expectancy to behavioral intention. Conclusions: In order to spread English E-learning among undergraduates in China, governments, university administrators, and English E-learning cooperating companies should pay full attention to the impacting factors investigated in this research and follow up policies and measurements to create a comfortable English E-learning setting.
References
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, 7205-7224. https://doi.org/10.1007/s10639-021-10573-5
Abdou, D., & Jasimuddin, S. M. (2020). The use of the UTAUT model in the adoption of E-learning technologies: an empirical study in France based banks. Journal of Global Information Management. 4(28), 38-51.
https://doi.org/10.4018/jgim.2020100103
Ali, M., Raza, S. A., Qazi, W., & Puah, C.-H. (2018). Assessing e-learning system in higher education institutes: Evidence from structural equation modelling. Interactive Technology and Smart Education, 15(1), 59-78. https://doi.org/10.1108/itse-02-2017-0012
Allen, M., Titsworth, S., & Hunt, S. K. (2009). Quantitative Research Communication (1st ed.). Sage Publications Inc.
Awwad, M. S., & Al-Majali, S. M. (2015). Electronic library services acceptance and use: An empirical validation of unified theory of acceptance and use of technology. The Electronic Library, 33(6), 1100-1120. https://doi.org/10.1108/el-03-2014-0057
Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74-94. https://doi.org/10.1007/bf02723327
Barrett, P. (2007). Structural equation modelling: Adjudging model fit. Personality and Individual Differences, 42(5), 815-824. https://doi.org/10.1016/j.paid.2006.09.018
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
Blunch, N. (2013). Introduction to Structural Equation Modeling using IBM SPSS Statistics and AMOS (2nd ed.). 55 City.
Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen and J. S. Long (Eds.), Testing structural equation models (pp. 136-162). Sage.
Chandio, F. H., Abbasi, M. S., Nizamani, H. A., & Nizamani, Q. U. A. (2013). Online banking information systems acceptance: a structural equation modelling analysis. International Journal of Business Information Systems, 12(2), 177.
https://doi.org/10.1504/ijbis.2013.052050
Chen, W., & Hirschheim, R. (2004). A paradigmatic and methodological examination of information systems research from 1991 to 2001. Information Systems Journal, 14(3), 197-235. https://doi.org/10.1111/j.1365-2575.2004.00173.x
Chua, P. Y., Rezaei, S., Gu, M.-L., Oh, Y., & Jambulingam, M. (2018). Elucidating social networking apps decisions: Performance expectancy, effort expectancy and social influence. Nankai Business Review International, 9(2), 118-142. https://doi.org/10.1108/nbri-01-2017-0003
Compeau, D. R., & Higgins, C. A. (1995). Computer Self-Efficacy: Development of a Measure and Initial Test. MIS Quarterly, 19(2), 189. https://doi.org/10.2307/249688
Gunasinghe, A., Hamid, A. J., Khatibi, A., & Azam, F. S. M. (2020). The adequacy of UTAUT-3 in interpreting academician's adoption to e-Learning in higher education environments. Interactive Technology and Smart Education, 17(1), 86-106. https://doi.org/10.1108/itse-05-2019-0020
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). Prentice-Hall.
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariant Data Analysis (6th ed.). Pearson International Edition.
Halabí, V., & More-Espuivel, R. (2017). Sample Sizes Using Structural Equation Modeling with Latent Variables: A Practical Method. Act alidades Investigative en Educación, 17(1), 1-34.
Hennink, M., Hutter, H., & Bailey, A. (2020). Qualitative research methods (2nd ed.). Sage Publications Ltd.
Hulland, J. (1999). Use Of Partial Least Squares (PLS) In Strategic Management Research: A Review of Four Recent Studies. Strategic Management Journal, 20(2), 195-204. https://doi.org/10.1002/(sici)1097-0266(199902)20:2<195::aid-smj13>3.3.co;2-z
Johnson, R. B., & Christensen, L. (2020). Educational Research: Quantitative, Qualitative, and Mixed Approaches (7th ed.). Sage.
Kafetsios, K., Nezlek, J. B., & Vassiou, A. (2011). A multilevel analysis of relationships between leaders’ and subordinates’ emotional intelligence and emotional outcomes. Journal of Applied Social Psychology, 41(5), 1121-1144.
Kankanhalli, A., Tan, B. C. Y., & Wei, K.-K. (2005). Contributing Knowledge to Electronic Knowledge Repositories: An Empirical Investigation. MIS Quarterly, 29(1), 113. https://doi.org/10.2307/25148670
Kline, R. B. (2016). Principles and Practice of Structural Equation Modeling (4th ed.). The Guilford Press.
Lam, T., Cho, V., & Qu, H. (2007). A study of hotel employee behavioral intentions towards adoption of information technology. International Journal of Hospitality Management, 26(1), 49-65. https://doi.org/10.1016/j.ijhm.2005.09.002
Lewis, B. R., Snyder, C. A., & Rainer, K. R. (1995). An empirical assessment of the Information Resources Management construct. Journal of Management Information Systems, 12(1), 199-223. https://doi.org/10.1080/07421222.1995.11518075
Lowry, P. B., & Gaskin, J. (2014). Partial Least Squares (PLS) Structural Equation Modeling (SEM) for Building and Testing Behavioral Causal Theory: When to Choose It and How to Use It. IEEE Transactions on Professional Communication, 57(2), 123-146. https://doi.org/10.1109/tpc.2014.2312452 https://doi.org/10.1109/TPC.2014.2312452
Maldonado, T. U. P., Khan, F. 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
McFarland, D. J. (2001). The role of age and efficacy on technology acceptance: implications for e-learning. Proceedings of the World Conference on the WWW and Internet, 2-7.
Mikalef, P., Pappas, I. O., & Giannakos, M. (2016). An integrative adoption model of video-based learning. The International Journal of Information and Learning Technology, 33(4), 219-235. https://doi.org/10.1108/ijilt-01-2016-0007
Mittal, A., Mantri, A., Tandon, U., & Dwivedi, Y. K. (2022). A unified perspective on the adoption of online teaching in higher education during the COVID-19 pandemic. Information Discovery and Delivery, 50(2), 117-132. https://doi.org/10.1108/idd-09-2020-0114
Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill.
Ogula, P. A. (2005). Research methods (1st ed.). Cuea Publications.
Pedroso, R., Zanetello, L., Guimarães, L., Pettenon, M., Gonçalves, V., Scherer, J., Kessler, F., & Pechansky, F. (2016). Confirmatory factor analysis (CFA) of the Crack Use Relapse Scale (CURS). Archives of Clinical Psychiatry (São Paulo), 43(3), 37-40. https://doi.org/10.1590/0101-60830000000081
Samsudeen, N. 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, 3(6), 219-238.
Saunders, M., Lewis, P., & Thornhill, A. (2016). Research methods for business students (7th ed.). Pearson
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
Sullivan, T. J., Priestley, M. J. N., & Calvi, G. M. (2012). A Model Code for the Displacement-Based Seismic Design of Structures (1st ed.). IUSS Press
Tandon, U., Mittal, A., Bhandari, H., & Bansa, K. (2021). E E-learning adoption by undergraduate architecture students: facilitators and inhibitors. Engineering, Construction and Architectural Management, 29(10), 4287-4312. https://doi.org/10.1108/ecam-05-2021-0376
Tarhini, A., El-Masri, M., Ali, M., & Serrano, A. (2016). Extending the UTAUT model to understand the customers' acceptance and use of internet banking in Lebanon: A structural equation modeling approach. Information Technology & People, 29(4), 830-849. https://doi.org/10.1108/itp-02-2014-0034
Tarhini, A., Masa'deh, R. E., Al-Busaidi, K. A., Mohammed, A. B., & Maqableh, M. (2017). Factors influencing students' adoption of e-learning: a structural equation modeling approach. Journal of International Education in Business, 10(2), 164-182. https://doi.org/10.1108/jieb-09-2016-0032
Taylor, S., & Todd, P. (1995). Decomposition and Crossover Effects in the Theory of Planned Behavior: A Study of Consumer Adoption Intentions. International Journal of Research in Marketing, 12(2), 137-155. https://doi.org/10.1016/0167-8116(94)00019-k
Thongsri, N., Shen, L., Bao, Y., & Alharbi, I. M. (2018). Integrating UTAUT and UGT to explain behavioral intention to use M-learning: A developing country's perspective. Journal of Systems and Information Technology, 20(3), 278-297.
https://doi.org/10.1108/jsit-11-2017-0107
Twum, K. K., Ofori, D., Keney, G., & Korang-Yeboah, B. (2021). 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
Williams, A. P., Seager, R., Abatzoglou, J. T., Cook, B. I., Smerdon, J. E., & Cook, E. R. (2015). Contribution of anthropogenic warming to California drought during 2012-2014. Geophysical Research Letters, 42(16), 6819-6828.
https://doi.org/10.1002/2015gl064924
Zhang, Y., Fang, Y., Wei, K.-K., & Wang, Z. (2012). Promoting the intention of students to continue their participation in e-learning systems: The role of the communication environment. Information Technology & People, 25(4), 356-375. https://doi.org/10.1108/09593841211278776
Zimmerman, B. J., & Kitsantas, A. (1999). Acquiring writing revision skill: Shifting from process to outcome self-regulatory goals. Journal of Educational Psychology, 91(2), 241-250. https://doi.org/10.1037/0022-0663.91.2.241
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Shuhui Wang
This work is licensed under a Creative Commons Attribution 4.0 International License.
The submitting author warrants that the submission is original and that she/he is the author of the submission together with the named co-authors; to the extend the submission incorporates text passages, figures, data, or other material from the work of others, the submitting author has obtained any necessary permission.
Articles in this journal are published under the Creative Commons Attribution License (CC-BY What does this mean?). This is to get more legal certainty about what readers can do with published articles, and thus a wider dissemination and archiving, which in turn makes publishing with this journal more valuable for you, the authors.
