Critical Factors Shaping University Students’ Satisfaction and Continued Engagement with E-Learning in Sichuan, China
Keywords:
Perceived Usefulness, Confirmation, Perceived Ease of Use, Satisfaction, Continuance IntentionAbstract
Purpose: This study explored the key factors impacting university students' satisfaction and intention to use e-learning at Xihua University in Sichuan, China. The conceptual framework proposed relationships among Interactivity, Course Content Quality, Perceived Usefulness, Confirmation, Perceived Ease of Use, Satisfaction, and Continuance Intention. Research design, data, and methodology: A quantitative approach was employed, with a sample size of 500 students from Xihua University. The sampling strategy included judgmental sampling for school selection, quota sampling to determine the sample size, and convenience sampling for data collection and questionnaire distribution online. Data analysis was conducted using structural equation modeling (SEM) and confirmatory factor analysis (CFA) to assess model fit, reliability, and construct validity. Results: The study revealed that interactivity, course content quality, perceived usefulness, confirmation, and ease of use significantly influenced students' satisfaction. Satisfaction was found to mediate the effect of these factors on students' continuance intention to use e-learning. Conclusions: The statistical analysis confirmed all six research hypotheses, indicating that the study successfully met its objectives. To enhance the effectiveness of e-learning, it is recommended that policymakers and program developers at Xihua University increase their investment in factors that impact student satisfaction and continuance intention and optimize the allocation of these resources.
References
Alami, Y., & El Idrissi, I. (2022). Students' adoption of e-learning: Evidence from a Moroccan business school in the COVID-19 era. Arab Gulf Journal of Scientific Research, 40(1), 54-78.
https://doi.org/10.1108/AGJSR-05-2022-0052
Alraimi, K. M., Orcher, J., & Alzahrani, A. I. (2015). Examining the influence of social influence, facilitating conditions, and hedonic motivation on students' adoption of e-learning. Computers & Education, 82, 99-112. https://doi.org/10.1016/j.compedu.2014.11.015
Anderson, T. (2003). Getting the mix right again: An updated and theoretical rationale for interaction. The International Review of Research in Open and Distributed Learning, 4(2). https://doi.org/10.19173/irrodl.v4i2.149
Anderson, T. (2008). The theory and practice of online learning (1st ed.). Athabasca University Press.
Arbaugh, J. B. (2000). Virtual classroom characteristics and student satisfaction with internet-based MBA courses. Journal of Management Education, 24(1), 32-54.
Arbaugh, J. B., Desai, S., & Rau, B. (2008). The influence of course delivery method on student performance: An analysis of online and face-to-face courses. Journal of Asynchronous Learning Networks, 12(3), 1-20.
Awang, H. Z. (2012). Structural Equation Modelling Using AMOS Graphic (1st ed.). Kuala Lumpur: UiTM Press.
Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238-246.
Bernard, R. M., Abrami, P. C., Borokhovski, E., Wade, C. A., Tamim, R., Surkes, M. A., & Bethel, E. C. (2004). How does distance education compare with classroom instruction? A meta-analysis of the empirical literature. Review of Educational Research, 74(3), 379-439.
https://doi.org/10.3102/00346543074003379
Bhattacharjee, S. (2001). E-commerce: A comprehensive analysis. Information Technology and Management, 2(1), 1-15. https://doi.org/10.1023/A:1013303717691
Chang, C.-C. (2013). The effects of service quality, customer satisfaction, and switching barriers on customer loyalty in the mobile telecommunication industry. Journal of Business Research, 66(8), 1164-1170. https://doi.org/10.1016/j.jbusres.2012.03.008
Cheng, Y. (2012). Effects of quality antecedents on e-learning acceptance. Internet Research, 22(3), 361-390. https://doi.org/10.1108/10662241211235699
Cheng, Y. M. (2020). How does task-technology fit influence cloud-based e-learning continuance and impact on performance?. Computers & Education, 150, 103850.
https://doi.org/10.1016/j.compedu.2020.103850
Clark, R. C., & Mayer, R. E. (2011). E-learning and the science of instruction: Proven guidelines for consumers and designers of multimedia learning (3rd ed.). Pfeiffer.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.
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/10.1080/07421222.2003.11045748
Dixson, M. D. (2015). Measuring student engagement in the online course: The Online Student Engagement scale (OSE). Online Learning, 19(4), 143-157.
Eze, S. C., Chinedu-Eze, V. C., & Bello, A. O. (2018). The utilization of e-learning facilities in the educational delivery system of Nigeria: A study of M-University. International Journal of Educational Technology in Higher Education, 15(1), 34. https://doi.org/10.1186/s41239-018-0113-4
Ferrer, J., Ringer, A., Saville, K., Parris, M., & Kashi, K. (2022). Students’ motivation and engagement in higher education: The importance of attitude to online learning. Higher Education, 83(2), 317-338. https://doi.org/10.1007/s10734-021-00702-7
Garrison, D. R., & Anderson, T. (2003). E-learning in the 21st century: A framework for research and practice (1st ed.). Routledge.
Hartog, D. N., & Verburg, R. M. (2004). High-performance work systems, organizational culture, and firm effectiveness. Human Resource Management Journal, 14(1), 55-78.
https://doi.org/10.1111/j.1748-8583.2004.tb00085.x
Hong, S.-J., & Tam, K. Y. (2014). Understanding the acceptance of digital libraries: An empirical study extending the Technology Acceptance Model. Information & Management, 44(4), 340-351. https://doi.org/10.1016/j.im.2006.03.002
Kember, D., McKay, J., Sinclair, K., & Wong, M. (2010). A four-dimensional framework for describing the nature of academic staff's teaching in higher education. Higher Education, 60(4), 347-362. https://doi.org/10.1007/s10734-009-9255-4
Kuo, Y.-C., Walker, A. E., Belland, B. R., & Schroder, K. E. (2014). Interaction, internet self-efficacy, and self-regulated learning as predictors of student satisfaction in online education courses. Internet and Higher Education, 20, 35-50. https://doi.org/10.1016/j.iheduc.2013.10.001
Lee, M.-C., & Choi, H. (2013). A study on the effects of confirmation on satisfaction, trust, and switching intention in electronic commerce. Information Systems Research, 24(3), 681-697. https://doi.org/10.1287/isre.1120.0458
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. https://doi.org/10.1016/j.compedu.2007.09.005
Lin, C., Wu, H., & Zhang, S. (2005). Confirmation of expectations and satisfaction with internet shopping: The role of internet self-efficacy. International Journal of Electronic Commerce, 9(3), 107-126. https://doi.org/10.1080/10864415.2005.11043950
Marsh, H. W., Hau, K. T., & Wen, Z. (2004). In search of golden rules: Comment on hypothesis-testing approaches to setting cutoff values for fit indexes and dangers in overgeneralizing Hu and Bentler's (1999) findings. Structural Equation Modeling: A Multidisciplinary Journal, 11(3), 320-341. https://doi.org/10.1207/s15328007sem1103_2
Moore, M. G., & Kearsley, G. (2005). Distance education: A systems view of online learning (2nd ed.). Wadsworth.
Nikou, S. A., & Economides, A. A. (2017). Mobile-based assessment: Investigating the factors that influence behavioral intention to use. Computers & Education, 109, 56-73. https://doi.org/10.1016/j.compedu.2017.02.010
Oliver, R. L. (1980). A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of Marketing Research, 17(4), 460-469. https://doi.org/10.1177/002224378001700405
Park, S. Y. (2009). An analysis of the technology acceptance model in understanding university students' behavioral intention to use e-learning. Educational Technology & Society, 12(3), 150-162.
Pedroso, R. S., Oliveira, M. S., & Galduróz, J. C. F. (2016). Confirmatory factor analysis (CFA) of the Crack Use Relapse Scale (CURS). Archives of Clinical Psychiatry (São Paulo), 43(3), 43-47. https://doi.org/10.1590/0101-60830000000007
Robinson, C. C., & Hullinger, H. (2010). New benchmarks in higher education: Student engagement in online learning. Journal of Education for Business, 84(2), 101-109.
https://doi.org/10.1080/08832320903449807
Roca, J. C., Chiu, C. M., & Martínez, F. J. (2006). Understanding e-learning continuance intention: An extension of the technology acceptance model. International Journal of Human-Computer Studies, 64(8), 683-696. https://doi.org/10.1016/j.ijhcs.2006.01.003
Sharma, S., Mukherjee, S., Kumar, A., & Dillon, W. R. (2005). A simulation study to investigate the use of cutoff values for assessing model fit in covariance structure models. Journal of Business Research, 58(7), 935-943. https://doi.org/10.1016/j.jbusres.2003.07.002
Sica, C., & Ghisi, M. (2007). The Italian versions of the Beck Anxiety Inventory and the Beck Depression Inventory-II: Psychometric properties and discriminant power. Journal of Psychopathology and Behavioral Assessment, 29(2), 114-122. https://doi.org/10.1007/s10862-007-9048-0
Sun, P.-C., Tsai, R. J., Finger, G., Chen, Y.-Y., & Yeh, D. (2008). What drives a successful e-learning? An empirical investigation of the critical factors influencing learner satisfaction. Computers & Education, 50(4), 1183-1202. https://doi.org/10.1016/j.compedu.2006.11.007
UNESCO. (2020, June 20). Global education monitoring report 2020: Inclusion and education: All means all. United Nations Educational, Scientific and Cultural Organization. https://reliefweb.int/report/world/global-education-monitoring-report-2020
West, M. A. (2002). Sparkling fountains or stagnant ponds: An integrative model of creativity and innovation implementation in work groups. Applied Psychology, 51(3), 355-387.
https://doi.org/10.1111/1464-0597.00951
Wu, J.-H., & Wang, S.-C. (2005). What drives mobile commerce? An empirical evaluation of the revised technology acceptance model. Information & Management, 42(5), 719-729.
https://doi.org/10.1016/j.im.2004.07.001
Wu, J. H., & Wang, Y. M. (2006). Measuring KMS success: A respecification of the DeLone and McLean's model. Information & Management, 43(6), 728-739. https://doi.org/10.1016/j.im.2006.05.002
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