Factors Influencing Online Learning System Usage Among Fourth-Year Students in Higher Education in Sichuan, China
DOI:
https://doi.org/10.14456/abacodijournal.2022.9Keywords:
perceived ease of use, perceived usefulness, information quality, system quality, service qualityAbstract
This research aims to determine factors influencing online learning usage of students in higher education in Sichuan, China. The conceptual framework presents key constructs, including perceived ease of use, perceived usefulness, information quality, system quality, service quality, attitude toward use, satisfaction, behavioral intention, and actual use. The researcher applied a quantitative approach to collect data by distributing online questionnaires to 500 fourth-year students in three private universities. The sampling method used was nonprobability sampling, including judgmental sampling, quota sampling, and convenience sampling. Before the data collection, an index of item objective congruence (IOC) was used to validate items in the questionnaire, and Cronbach's Alpha Coefficient reliability test was used to measure the reliability of the questionnaire by conducting a pilot test with 40 participants. Afterward, data analysis was carried out employing descriptive analysis, confirmation factor analysis (CFA), and structural equation modeling (SEM). The findings revealed that the strongest significant relationship was the attitude toward use and behavioral intention, followed by behavioral intention and actual use, system quality and behavioral intention, and perceived ease of use and attitude toward use. Conversely, there were no supported relationships of service quality and behavioral intention, followed by information quality and behavioral intention, perceived usefulness and attitude toward use, and satisfaction and behavioral intention. Henceforth, academic practitioners and universities are recommended to provide an effective system, high service support standard, and promote the benefits of the online learning system to students.
References
Adeyinka, T., & Mutula, S. (2010). A proposed model for evaluating the success of WebCT course content management system. Computers in Human Behavior, 26(6), 1795–1805.
Al-Adwan, A., Al-Alwan, A., & Smedley, J. (2013). Exploring students acceptance of e-learning using Technology Acceptance Model in Jordanian universities. International Journal of Education and Development Using Information and Communication Technology, 9(2), 4–18.
Ajzen, I. (2005). Attitudes, Personality And Behaviour (2nd ed.). Open University Press.
Ajzen, I., & Fishbein, M. (1980). Understanding Attitudes and Predicting Social Behavior. Prentice-Hall.
Aristovnik, A., Keržič, D., Tomaževič, N., & Umek, L. (2016). Demographic determinants of usefulness of e-learning tools among students of public administration. Interactive Technology and Smart Education, 13(4), 289 - 304. https://doi.org/10.1108/ITSE-09-2016-0033
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
Bouchrika, I. (2020, June 30). 50 Online Education Statistics: 2020/2021 Data on Higher Learning & Corporate Training. Research.com. https://research.com/education/online-education-statistics#TOC2
Calli, L., Balcikanli, C., Calli, F., Cebeci, H. I., & Seymen, O. F. (2013). Identifying factors that contribute to the Satisfaction of students in e-learning. Turkish Online Journal of Distance Education, 14(1), 85–101.
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(7), 1–14. https://doi.org/10.3389/fpsyg.2019.01652
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
Chuttur, M. (2009). Overview of the technology acceptance model: Origins, development and future directions. Sprouts: Working Papers on Information Systems, 9.
Daghan, G., & Akkoyunlu, B. (2016). Modeling the continuance usage intention of online learning environments. Computers in Human Behavior, 60, 198–211. https://doi.org/10.1016/j.chb.2016.02.066
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly: Management Information Systems, 13(3), 319–339. https://doi.org/10.2307/249008
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User Acceptance of Computer Technology: A Comparison of Two Theoretical Models. Management Science, 35(8), 982–1003. https://doi.org/10.1287/mnsc.35.8.982
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/10.1016/0012-1606(78)90250-6
Dhull, I., & Arora, S. (2019). Online Learning. International Education & Research Journal, 3(8), 32-34.
Efiloğlu Kurt, Ö. (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
Fagan, M. H., Neill, S., & Wooldridge, B. R. (2008). Exploring the intention to use computers: An empirical investigation of the role of intrinsic motivation, extrinsic motivation, and perceived ease of use. Journal of Computer Information Systems, 48(3), 31–37. https://doi.org/10.1080/08874417.2008.11646019
Farahat, T. (2012). Applying the Technology Acceptance Model to Online Learning in the Egyptian Universities. Procedia - Social and Behavioral Sciences, 64, 95–104. https://doi.org/10.1016/j.sbspro.2012.11.012
Ferdiansyah, S., Supiastutik, & Angin, R. (2020). Thai students' experiences of online learning at indonesian universities in the time of the covid-19 pandemic. Journal of International Students, 10(3), 58–74. https://doi.org/10.32674/jis.v10iS3.3199
Fornell, C., & Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18(1), 39–50.
Freeze, R. D., Alshare, K. a, Lane, P. L., & Joseph Wen, H. (2010). IS success model in e-learning context based on students' perceptions. Journal of Information Systems Education, 21(2), 173.
Granić, A., & Marangunić, N. (2019). Technology acceptance model in educational context: A systematic literature review. British Journal of Educational Technology, 50(5), 2572–2593. https://doi.org/10.1111/bjet.12864
Hair, J., Black, W., Babin, B., Anderson, R., & Tatham, R. (2006). Multivariate Data Analysis (6th ed.). Pearson Education
Hanif, A., Jamal, F. Q., & Imran, M. (2018). Extending the technology acceptance model for use of e-learning systems by digital learners. IEEE Access, 6, 73395–73404. https://doi.org/10.1109/ACCESS.2018.2881384
Hsu, L. (2021). What makes good LMOOCs for EFL learners? Learners' personal characteristics and Information System Success Model. Computer Assisted Language Learning, (In press). https://doi.org/https://doi.org/10.1080/09588221.2021.1899243
Imsa-ard, P. (2020). Thai University Students' Perceptions Towards the Abrupt Transition to "Forced" Online Learning in the COVID-19 Situation. Journal of Education Khon Kaen University, 3(3), 30–44. https://doi.org/10.14456/edkkuj.2020.16
Jiang, G., Liu, F., Liu, W., Liu, S., Chen, Y., & Xu, D. (2021). Effects of information quality on information adoption on social media review platforms: moderating role of perceived risk, Data Science and Management, 1(1),13-22.
King, W. R., & He, J. (2006). A meta-analysis of the technology acceptance model. Information and Management, 43(6), 740–755. https://doi.org/10.1016/j.im.2006.05.003
Lin, H. F. (2007). Measuring online learning systems success: Applying the updated DeLone and McLean model. Cyberpsychology and Behavior, 10(6), 817–820. https://doi.org/10.1089/cpb.2007.9948
Marangunić, N., & Granić, A. (2015). Technology acceptance model: a literature review from 1986 to 2013. Universal Access in the Information Society, 14(1), 81–95. https://doi.org/10.1007/s10209-014-0348-1
Masrom, M. (2007, May). Technology acceptance model and E-learning [Paper presentation]. 12th International Conference on Education, Bandar Seri Begawan, Brunei.
Nunnally, J. C., & Bernstein, I. R. (1994). Psychometric theory (3rd ed.). McGraw-Hill.
Park, D. H., & Lee. J. (2008). eWOM overload and its effect on consumer behavioral intention depending on consumer involvement. Electronic Commerce Research Applicarion, 7(4), 386-398.
Pedroso, R., Zanetello, L., Guimaraes, L., Pettenon, M., Goncalves, V., Scherer, J., Kessler, F., & Pechansky, F. (2016). Confirmatory factor analysis (CFA) of the crack use relapse scale (CURS). Archives of Clinical Psychiatry, 43(3), 37-40.
Petter, S., & McLean, E. R. (2009). A meta-analytic assessment of the DeLone and McLean IS success model: An examination of IS success at the individual level. Information and Management, 46(3), 159–166. https://doi.org/10.1016/j.im.2008.12.006
Petter, S., DeLone, W., & McLean, E. (2008). Measuring information systems success: Models, dimensions, measures, and interrelationships. European Journal of Information Systems, 17(3), 236–263. https://doi.org/10.1057/ejis.2008.15
Ramayah, T., Ahmad, N. H., & Lo, M. C. (2010). The role of quality factors in intention to continue using an e-learning system in Malaysia. Procedia - Social and Behavioral Sciences, 2(2), 5422–5426. https://doi.org/10.1016/j.sbspro.2010.03.885
Sharma, G. P., Verma, R. C., & Pathare, P. (2005). Mathematical modeling of infrared radiation thin layer drying of onion slices. Journal of Food Engineering, 71(3), 282–286.
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
Siti, C., Md, L., Nur, L., Binti, D., Mohd, I., Afiq, D., & Tazilah, K. (2021). Application of Technology Acceptance Model (Tam) Towards Online Learning During Covid-19 Pandemic: Accounting Students Perspective. International Journal of Business, Economics and Law, 24(1), 13–20.
Sivo, S. A., Ku, C. H., & Acharya, P. (2018). Understanding how university student perceptions of resources affect technology acceptance in online learning courses. Australasian Journal of Educational Technology, 34(4), 72–91. https://doi.org/10.14742/ajet.2806
Soper, D. (2022). A priori sample size calculator for structural equation models. Free Statistics Calculators. https://www.danielsoper.com/statcalc/calculator.aspx?id=89
Šumak, B., Heričko, M., & Pušnik, M. (2011). A meta-analysis of e-learning technology acceptance: The role of user types and e-learning technology types. Computers in Human Behavior, 27(6), 2067–2077. https://doi.org/10.1016/j.chb.2011.08.005
Thongsri, N., Shen, L., & Bao, Y. (2019). Investigating factors affecting learner's perception toward online learning: evidence from ClassStart application in Thailand. Behaviour and Information Technology, 38(12), 1243–1258. https://doi.org/10.1080/0144929X.2019.1581259
Urbach, N., & Müller, B. (2012). The updated DeLone and McLean model of information systems success. In Y. K. Dwivedi, M. R. Wade, & S. L. Schneberger (Eds.), Information Systems Theory: Explaining and Predicting Our Digital Society (Vol 1) (pp. 1–18). Springer. https://doi.org/10.1007/978-1-4419-6108-2
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/http://dx.doi.org/10.1016/j.proci.2004.08.141
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 27(3), 425-478.
Wu, J. H., & Wang, Y. M. (2006). Measuring KMS success: A respecification of the DeLone and McLean's model. Information and Management, 43(6), 728–739. https://doi.org/10.1016/j.im.2006.05.002
Xue, E., Li, J., & Xu, L. (2020). Online education action for defeating COVID-19 in China: An analysis of the system, mechanism and mode. Educational Philosophy and Theory, 0(0), 1–13. https://doi.org/10.1080/00131857.2020.1821188
Yousafzai, S. Y., Foxall, G. R., & Pallister, J. G. (2007). Technology acceptance: a meta-analysis of the TAM: Part 1. Journal of Modelling in Management, 2(3), 281–304. https://doi.org/10.1108/17465660710834453
Zhu, X., & Liu, J. (2020). Education in and After Covid-19: Immediate Responses and Long-Term Visions. Postdigital Science and Education, 2(3), 695–699. https://doi.org/10.1007/s42438-020-00126-3
