Influential Factors of Satisfaction and Continuance Intention on E-Learning Among Students Majoring in Radio and Television Directing in Eastern China
Article Sidebar
Main Article Content
Abstract
Purpose: This research investigates the factors that impact the satisfaction and continuance intention of students majoring in Radio and Television Directing at private art schools in Eastern China. The key variables are perceived ease of use, perceived usefulness, informative quality, service quality, system quality, satisfaction and continuance intention. Research design, data, and methodology: This study adopts a quantitative research approach, and the researcher collected data from students majoring in Radio and Television Directing at three private art schools in Eastern China. Confirmatory factor analysis was employed to assess the reliability and discriminant validity of the framework model, and Structural Equation Modeling was used to examine the relationships and influences among the variables. Results: The research results demonstrate that information quality is the most significant factor influencing students' satisfaction with online learning, followed by perceived usefulness, ease of use, and system quality. Additionally, the relationship between satisfaction and continuance intention to use online learning is supported. Nevertheless, service quality has no impact on satisfaction. Conclusions: The conceptual framework proposed in this study exhibits high reliability and validity. Educational institutions should allocate resources more effectively, and online learning platforms should provide a better online learning experience to help students enhance their motivation and engagement, ultimately leading to better learning outcomes.
Downloads
Article Details
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.
References
Aharony, N., & Bar-Ilan, J. (2016). Students' Perceptions on MOOCs: An Exploratory Study. Interdisciplinary Journal of e-Skills and Lifelong Learning, 12, 145-162. http://doi.org/10.28945/3540.
Ainur, A. K., Sayang, M. D., Jannoo, Z., & Yap, B. W. (2017). Sample Size and NonNormality Effects on Goodness of Fit Measures in Structural Equation Models. Pertanika Journal of Science and Technology, 25(2), 575-586.
Al-Busaidi, K. A., & Al-Shihi, H. (2012). Key factors to instructors' satisfaction of learning management systems in blended learning. J Comput High Educ, 24(1), 18-39. http://doi.org/ 10.1007/s12528-011-9051-x
Al-Gahtani, S. S. (2016). Empirical investigation of e-learning acceptance and assimilation: a structural equation model. Applied Computing & Informatics, 1(2), 1-24. http://doi.org/10.1016/j.aci.2014.09.001
Allen, M., Titsworth, S., & Hunt, S. K. (2009). Quantitative Research Communication (1st ed.). SAGE Publications.
Almarashdeh, I. (2016). Sharing instructors experience of learning management system: A technology perspective of user satisfaction in distance learning course. Computers in Human Behavior, 63, 249-255. http://doi.org/10.1016/j.chb.2016.05.013
Al Natour, S., & Woo, C. (2020). The determinants of learner satisfaction with the online video presentation method. Internet Research, 31(1), 234-261. http://doi.org/10.1108/intr-04-2019-0155
Alsabawy, A. Y., Cater-Steel, A., & Soar, J. (2016). Determinants of perceived usefulness of e-learning systems. Computers in Human Behavior, 64, 843-858. http://doi.org/10.1016/j.chb.2016.07.065
Al-Samarraie, H., Teng, B. K., Alzahrani, A. I., & Alalwan, N. (2017). E-learning continuance satisfaction in higher education: a unified perspective from instructors and students. Studies in Higher Education, 1(2), 1-17. http://doi.org/10.1080/03075079.2017.1298088
Aparicio, M., Fernando, B., & Oliveira, T. (2017). Grit in the path to e-learning success. Computers in Human Behavior, 66, 388-399. http://doi.org/10.1016/j.chb.2016.10.009
Arbaugh, J. B. (2000a). How classroom environment and student engagement affect learning in Internet-based MBA courses. Business Communication Quarterly, 63(4), 9-26. http://doi.org/ 10.1177/108056990006300402
Arbaugh, J. B. (2000b). Virtual Classroom Characteristics and Student Satisfaction with Internet-Based MBA Courses. Journal of Management Education, 24(1), 32-54. http://doi.org/10.1177/105256290002400104
Arbaugh, J. B., & Duray, R. (2002). Technological and Structural Characteristics, Student Learning and Satisfaction with Web-Based Courses: An Exploratory Study of Two On-Line MBA Programs. Management Learning, 33(3), 331-347. http://doi.org/10.1177/1350507602333003
Arif, M., Ameen, K., & Rafiq, M. (2017). Assessing distance education students’ satisfaction with web-based services a pakistani's perspective. Online Information Review, 41(2), 202-218. http://doi.org/10.1108/OIR-07-2016-0172
Arteaga Sánchez, R., Duarte Hueros, A., & García Ordaz, M. (2013). E-learning and the University of Huelva: a study of WebCT and the technological acceptance model. Campus-Wide Information Systems, 30(2), 135-160. http://doi.org/10.1108/10650741311306318
Baturay, M. H. (2011). Relationships among sense of classroom community, perceived cognitive learning and satisfaction of students at an e-learning course. Interactive Learning Environments, 19(5), 563-575. http://doi.org/10.1080/10494821003644029
Bhattacherjee, A. (2001). Understanding information systems continuance: an expectation-confirmation model. MIS Quarterly, 25(3), 351-370. http://doi.org/10.2307/3250921
Campbell, D. T., & Fiske, D. W. (1959). Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin, 56(2), 81-105. https://doi.org/10.1037/h0046016
Chang, C. C. (2013). Exploring the determinants of e-learning systems continuance intention in academic libraries. Library Management, 34(1/2), 40-55. http://doi.org/10.1108/01435121311298261
Chen, C.-C., Lee, C.-H., & Hsiao, K.-L. (2018). Comparing the determinants of non-MOOC and MOOC continuance intention in Taiwan: Effects of interactivity and openness. Library Hi Tech, 36(4), 705-719. http://doi.org/10.1108/LHT-11-2016-0129
Cheng, D., Liu, G., Qian, C., & Song, Y. F. (2008). Customer acceptance of internet banking: Integrating trust and quality with UTAUT model. IEEE International Conference on Service Operations & Logistics. http://doi.org/10.1109/SOLI.2008.4686425
Cheng, J. T., Tracy, J. L., Foulsham, T., Kingstone, A., & Henrich, J. (2013). Two ways to the top: Evidence that dominance and prestige are distinct yet viable avenues to social rank and influence. Journal of Personality and Social Psychology, 104(1), 103–125. https://doi.org/10.1037/a0030398
Cheng, Y.-M. (2012). The Effects of Information Systems Quality on Nurses' Acceptance of the Electronic Learning System. Journal of Nursing Research, 20(1), 19-31.
Cheng, Y.-M. (2019). How does task-technology fit influence cloud-based e-learning continuance and impact?. Education + Training, 61(4), 480-499. http://doi.org/10.1108/ET-09-2018-0203
Cheng, Y. M. (2020). Students' satisfaction and continuance intention of the cloud-based e-learning system: roles of interactivity and course quality factors. Education and Training, 62(9), 1037-1059. https://doi.org/10.1108/ET-10-2019-0245
Chiu, C.-M., Cheng, H.-L., Huang, H.-Y., & Chen, C.-F. (2013). Exploring individuals' subjective wellbeing and loyalty towards social network sites from the perspective of network externalities: the Facebook case. International Journal of Information Management, 33(3), 539-552. http://doi.org/10.1016/j.ijinfomgt.2013.01.007
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. http://doi.org/10.1111/j.1365-2575.2007.00238.x
Chiu, C. M., Hsu, M., & Sun, S. (2005). Usability, Quality, Value, and e-Learning Continuance Decisions. Computers & Education, 45, 399-416. http://doi.org/10.1016/j.compedu.2004.06.001
Chopra, G., Madan, P., Jaisingh, P., & Bhaskar, P. (2019). Effectiveness of e-learning portal from students' perspective. Interactive Technology and Smart Education, 16(2), 94-116. http://doi.org/10.1108/ITSE-05-2018-0027
Davis, F. D. (1986). A Technology Acceptance Model for Empirically Testing New End-User Information Systems: Theory and Results (1st ed.). Sloan School of Management, Massachusetts Institute of Technology.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. Mis Quarterly, 13(3), 319-340. 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
DeCuir-Gunby, J. T., & Schutz, P. A. (2017). Developing a mixed methods proposal: A practical guide for beginning researchers (1st ed.) SAGE Publications.
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. http://doi.org/10.1080/07421222.2003.11045748
EU Digital Education Action Plan. (2021). Digital Education Action Plan (2021-2027). https://education.ec.europa.eu/focus-topics/digital-education/action-plan
Farid, S., Ahmad, R., Alam, M., Akbar, A., & Chang, V. (2018). A sustainable quality assessment model for the information delivery in E-learning systems. Information Discovery and Delivery, 46(1), 1-25. https://doi.org/10.1108/IDD-11-2016-0047
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 18(1), 39-50. https://doi.org/10.2307/3151312
Hair, J., Black, W., Babin, B., Anderson, R., & Tatham, R. (2006). Multivariate Data Analysis (6th ed.). Pearson Prentice Hall.
Hair, J., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate Data Analysis (7th ed.). Pearson Education International
Hair, J., Celsi, M., Money, A., Samouel, P., & Page, M. (2015). The essentials of business research methods (3rd ed.). Routledge
Hair, J. F., Arthur, H. M., Samouel, P., & Mike, P. (2007). Research Methods for Business (6th ed.). John Wiley and Sons.
Hossain, M. A. (2016). Assessing m-Health success in Bangladesh. Journal of Enterprise Information Management, 29(5), 774-796. http://doi.org/10.1108/JEIM-02-2014-0013
Hsiao, C. H., Chang, J. J., & Tang, K. Y. (2016). Exploring the influential factors in continuance usage of mobile social apps: satisfaction, habit, and customer value perspectives. Telematics and Informatics, 33(2), 342-355. http://doi.org/10.1016/j.tele.2015.08.014
Hu, L. T., & Bentler, P. M. (1999). Cutoff Criteria for Fit Indexes in Covariance Structure Analysis: Conventional Criteria versus New Alternatives. Structural Equation Modeling, 6, 1-55. http://dx.doi.org/10.1080/10705519909540118
Jamal, A., & Naser, K. (2003). Factors influencing customer satisfaction in the retail banking sector in Pakistan. International Journal of Commerce & Management, 13(2), 29-53. https://doi.org/10.1108/eb047465
Jung, T., Chung, N., & Leue, M. C. (2015). The determinants of recommendations to use augmented reality technologies: The case of a Korean theme park. Tourism Management, 49, 75-86. http://doi.org/10.1016/j.tourman.2015.02.013
Kim, S. S., & Malhotra, N. K. (2005). A longitudinal model of continued IS use: an integrative view of four mechanisms underlying postadoption phenomena. Management Science, 51(5), 741-755. https://doi.org/10.1287/mnsc.1040.0326
Larsen, T. J., Sørebø, A. M., & Sørebø, Ø. (2009). The role of task-technology fit as users' motivation to continue information system use. Computers in Human Behavior, 25(3), 778-784. http://doi.org/10.1016/j.chb.2009.02.006
Lee, D. Y., & Lehto, M. R. (2013). User acceptance of YouTube for procedural learning: An extension of the Technology Acceptance Model. Computers & Education, 61, 193-208. http://doi.org/10.1016/j.compedu.2012.10.001
Lee, J. K., & Lee, W. K. (2008). The relationship of e-learner's self-regulatory efficacy and perception of e-learning environmental quality. Computers in Human Behavior, 24(1), 32-47. http://doi.org/10.1016/j.chb.2006.12.001
Lee, Y. H., Hsieh, Y. C., & Chen, Y. H. (2013). An investigation of employees' use of e-learning systems: applying the technology acceptance model. Behavior & Information Technology, 32(2), 173-189. http://doi.org/10.1080/0144929x.2011.577190
Lewis, B. R. (1993). Service quality: recent developments in financial services. International Journal of Bank Marketing, 11(6), 19-25. http://doi.org/10.1108/02652329310045729
Liao, C., Chen, J. L., & Yen, D. C. (2007). Theory of planning behavior and customer satisfaction in the continued use of e-service: an integrated model. Computers in Human Behavior, 23, 2804-2822. http://doi.org/10.1016/j.chb.2006.05.006
Liaw, S.-S. (2008). Investigating students' perceived satisfaction, behavioral intention, and effectiveness of e-learning: A case study of the Blackboard system. Computers & Education, 51(2), 864-873. http://doi.org/10.1016/j.compedu.2007.09.005
Likert, R. (1932). A technique for the measurement of attitudes. Archives of psychology, 22, 140-155.
Lim, H., Lee, S. G., & Nam, K. (2007). Validating e-learning factors affecting training effectiveness. International Journal of Information Management, 27(1), 22-35. http://doi.org/10.1016/j.ijinfomgt.2006.08.002
Lin, H. F. (2010). An application of fuzzy and for evaluating course website quality. Computers & Education, 54(4), 877-888. http://doi.org/10.1016/j.compedu.2009.09.017
Lin, H.-F., & Lee, G.-G. (2006). Determinants of success for online communities: an empirical study. Behavior & Information Technology, 25(6), 479-488. http://doi.org/10.1080/01449290500330422
Lin, K. M., Chen, N. S., & Fang, K. T. (2010). Understanding e-learning continuance intention: A negative critical incidents perspective. Behavior & Information Technology, 30(1), 77-89. http://doi.org/10.1080/01449291003752948
Lin, W. S., & Wang, C. H. (2012). Antecedents to continued intentions of adopting e-learning system in blended learning instruction: a contingency framework-based models of information system success and technology fit. Computers & Education, 58(1), 88-99. http://doi.org/10.1016/j.compedu.2011.07.008
Malhotra, N., & Birks, D. (2007). Marketing Research: an applied approach (3rd ed.). Pearson Education
Ministry of Education of the People's Republic of China. (2019, September 23). The Central Committee of the Communist Party of China and the State Council issued the "2035 Modernization of Education in China", Official website of the Ministry of Education of China. http://www.moe.gov.cn/jyb_xwfb/s6052/moe_838/201902/t20190223_370857.html
Mirabolghasemi, M., Shasti, R., & Choshaly, S. H. (2021). An investigation into the determinants of blended leaning satisfaction from efl learners' perspective. Interactive Technology and Smart Education, 18(1), 69-84. http://doi.org/10.1108/ITSE-07-2020-0117
Mohammadi, H. (2015). Investigating users' perspectives on e-learning: An integration of TAM and IS success model. Computers in Human Behavior, 45, 359-374. http://doi.org/10.1016/j.chb.2014.07.044
Najmul Islam, A. K. M. (2015). The moderation effect of user-type (educators vs. students) in learning management system continuance. Behavior & Information Technology, 34(12), 1160-1170. http://doi.org/10.1080/0144929X.2015.1004651
Ozkan, S., & Koseler, R. (2009). Multi-dimensional students' evaluation of e-learning systems in the higher education context: An empirical investigation. Computers & Education, 53(4), 1285-1296. http://doi.org/10.1016/j.compedu.2009.06.011
Petrick, J. F., & Backman, S. J. (2002). An examination of the construct of perceived value for the prediction of golf travelers' intentions to revisit. Journal of Travel Research, 41(1), 38-45. https://doi.org/10.1177/004728750204100106
Piattoeva, N., & Gurova, G. (2020). Digitalization of Russian Education: Changing Actors and Spaces of Governance (1st ed.). The Palgrave Handbook of Digital Russia Studies
Rakoczy, K., Pinger, P., Hochweber, J., Klieme, E., Scheutze, B., & Besser, M. (2019). Formative assessment in mathematics: mediated by feedback's perceived usefulness and students' self-efficacy. Learning and Instruction, 60, 154-165. https://doi.org/10.1016/j.learninstruc.2018.01.004
Ramirez-correa, P. E., Rondan-Cataluña, J. F., Arenas-Gaitan, J., & Alfaro-Perez, J. L. (2016). Moderating effect of learning styles on a learning management system's success. Telematics and Informatics, 7(2) 139-150. http://dx.doi.org/10.1016/j.tele.2016.04.006
Ranaweera, C., & Prabhu, J. (2003). The influence of satisfaction, trust and switching barriers on customer retention in a continuous purchasing setting. International Journal of Service Industry Management, 14(4), 374-395. https://doi.org/10.1108/09564230310489231
Ranganathan, C., & Ganapathy, S. (2002). Key dimensions of business-to-consumer web sites. Information & Management, 39(6), 457-465. https://doi.org/10.1016/S0378-7206(01)00112-4
Robey, D., & Farrow, D. (1982). User Involvement in Information System Development: A Conflict Model and Empirical Test. Management Science, 28(1), 73-85. http://doi.org/10.1287/mnsc.28.1.73
Saadé, R. G., & Kira, D. (2009). Computer anxiety in e-learning: the effect of computer self-efficacy. Journal of Information Technology Education, 8, 177-91. http://doi.org/10.28945/166
Schermelleh-engel, K., Moosbrugger, H., & Müller, H. (2003). Evaluating the Fit of Structural Equation Models: Tests of Significance and Descriptive Goodness-of-Fit Measures. Methods of Psychological Research Online, 8(8), 23–74.
Schreiber, J. B., Stage, F. K., King, J., Nora, A., & Barlow, E. A. (2006). Reporting structural equation modeling and confirmatory factor analysis results: A review. The Journal of Educational Research, 99(6), 323-337. https://doi.org/10.3200/JOER.99.6.323-338
Seddon, P., & Kiew, M. Y. (1996). A Partial Test and Development of DeLone and McLean's Model of IS Success. Australasian Journal of Information Systems, 4(1), 90-109. http://doi.org/10.3127/ajis.v4i1.379
Sela, E., & Sivan, Y. Y. (2009). Enterprise e-learning success Factors: An analysis of practitioners' perspective. Interdisciplinary Journal of E-learning and Learning Objects, 5, 335-343. https://www.learntechlib.org/p/44839/.
Sharma, S. K., Gaur, A., Saddikuti, V., & Rastogi, A. (2017). Structural equation model (SEM)-neural network (NN) model for predicting quality determinants of e-learning management systems. Behavior and Information Technology, 36(10-12), 1-14. http://doi.org/10.1080/0144929x.2017.1340973
Shih, H. P. (2004). An empirical study on predicting user acceptance of e-shopping on the web. Information & Management, 41, 351-368. http://doi.org/10.1016/S0378-7206(03)00079-X
Singh, A., & Sharma, A. (2021). Acceptance of moocs as an alternative for internship for management students during covid-19 pandemic: an Indian perspective. International Journal of Educational Management, 35(6), 1231-1244. https://doi.org/10.1108/IJEM-03-2021-0085
Soper, D. (2015). Calculator: A-priori sample size for structural equation models. https://www.danielsoper.com/statcalc/calculator.aspx?id=89
Stone, R. W., & Baker-Eveleth, L. (2013). Students' expectation, confirmation, and continuance intention to use electronic textbooks. Computers in Human Behavior, 29(3), 984-990. http://doi.org/10.1016/j.chb.2012.12.007
Swaid, S. I., & Wigand, R. T. (2009). The customer perspective of e-service quality: an empirical study. In C. Standing (Eds.), Electronic Markets (pp. 36-61). Palgrave Macmillan, London. http://doi.org/10.1057/9780230274235_3
Szymanski, D. M., & Hise, R. T. (2000). E-satisfaction: an initial examination. Journal of Retailing, 76(3), 309-322. https://doi.org/10.1016/S0022-4359(00)00035-X
Tan, X., & Kim, Y. (2015). User acceptance of SaaS-based collaboration tools: a case of Google Docs. Journal of Enterprise Information Management, 28(3), 423-442. http://doi.org/10.1108/JEIM-04-2014-0039
Urbach, N., & Müller, B. (2012). The updated DeLone and McLean model of information systems success. In Y. Dwivedi, M. Wade & S. Schneberger (Eds.), Information Systems Theory: Explaining and Predicting Our Digital Society (Vol. 1, pp.1-18). Springer. http://doi.org/10.1007/978-1-4419-6108-2_1
Wu, Y. (2021, November 27). Grasp the new infrastructure and move towards high quality -- vigorously promote the innovative development of MOOC and online teaching, from The Chinese Ministry of Education. http://www.moe.gov.cn/jyb_xwfb/s5147/202111/t20211130_583368.html
Wu, Y. (2022, May 17). Ten years of Education, from The Chinese Ministry of Education. http://www.moe.gov.cn/fbh/live/2022/54453/
Xinli, H. (2015). Effectiveness of information technology in reducing corruption in China. The Electronic Library, 33(1), 52-64. http://doi.org/10.1108/EL-11-2012-0148
Xu, F., Tian, M., Xu, G., Reyes, A. B., & Shen, W. (2017). Understanding Chinese users' switching behavior of cloud storage services. The Electronic Library, 35(2), 214-232. http://doi.org/10.1108/EL-04-2016-0080
Zheng, Y., Zhao, K., & Stylianou, A. (2013). The impacts of information quality and system quality on users' continuance intention in information-exchange virtual communities: An empirical investigation. Decision Support Systems, 56, 513-524. http://doi.org/10.1016/j.dss.2012.11.008