Measuring Satisfaction and Continuance Intention of Undergraduates towards E-Learning in Chengdu, China
DOI:
https://doi.org/10.14456/shserj.2024.54Keywords:
E-Learning, Perceived Usefulness, Satisfaction, Information Quality, Continuance IntentionAbstract
Purpose: This study evaluates the factors significantly influencing e-learning satisfaction and continuance intention among undergraduate dance choreography students at three private universities in Chengdu, China. The present study investigates the impact of confirmation, system quality, service quality, perceived usefulness, satisfaction, and information quality on students' satisfaction and continuance intention towards e-learning. Research design, data, and methodology: The researcher used a quantitative approach to collect the data by distribuing an online questionnaire to 492 undergraduates majoring in dance choreography from three target universities. The sampling techniques involve purposive, quota and conveneince sampling. The relationships between the study variables were determined through factor analysis (CFA) and structural equation modeling (SEM). Results: The results of data analysis showed that confirmation, system quality, service quality, perceived usefulness, and information quality significantly impact satisfaction. It also indicated that perceived usefulness exerts the greatest impact on satisfaction. Futhermore, perceived usefulness and satisfaction significantly impact continuance intention. Conclusions: To ensure students’ continuance intention and sustained use of e-learning, university administrators, teaching staff, and e-learning developers should focus on the development on the quality and ease of use of e-learning system to enhance the learning efficiency and performance of students.
References
Aboelmaged, M. G. (2018). Predicting the success of Twitter in healthcare. Online Information Review, 42(6), 898-922. https://doi.org/10.1108/oir-01-2017-0018
Abu Seman, S. A., Hashim, M. J., Mohd Roslin, R., & Mohd Ishar, N. I. (2019). Millennial Learners’ Acceptance and Satisfaction of Blended Learning Environment. Asian Journal of University Education, 15(3), 129.
https://doi.org/10.24191/ajue.v15i3.7845
Albelbisi, N. A., Al-Adwan, A. S., & Habibi, A. (2021). Impact of Quality Antecedents on Satisfaction Toward Mooc. Turkish Online Journal of Distance Education, 22(2), 164-175. https://doi.org/10.17718/tojde.906843
Alraimi, K. M., Zo, H., & Ciganek, A. P. (2015). Understanding the MOOCs continuance: The role of openness and reputation. Computers & Education, 80, 28-38. https://doi.org/10.1016/j.compedu.2014.08.006
Amalia, E. (2019). Good governance for zakat institutions in Indonesia: A Confirmatory factor analysis. Pertanika Journal Social Sciences & Humanities, 27(3), 1815-1827.
Bagozzi, R., & 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.
Bhattacherjee, A. (2001). Understanding Information Systems Continuance: An Expectation-Confirmation Model. MIS Quarterly, 25(3), 351-370. https://doi.org/10.2307/3250921
Box, G. E. P. (1979). Some problems of statistics and everyday life. Journal of the American Statistical Association, 74(365), 1-4. https://doi.org/10.1080/01621459.1979.10481600
Chang, C. (2013). Exploring the determinants of e‐learning systems continuance intention in academic libraries. Library Management, 34(1/2), 40-55. https://doi.org/10.1108/01435121311298261
Chen, Y., Chen, C., Lin, Y., & Yeh, R. (2007). Predicting College Student’ Use of E-Learning Systems: An attempt to extend technology acceptance model. Pacific Asia Conference on Information Systems, 4(6), 1-10.
Cheng, Y. M. (2014). What drives nurses’ blended e-learning continuance intention? Educational Technology and Society, 17(4), 203-215.
Cheng, Y. M. (2019). How does task-technology fit influence cloud-based e-learning continuance and impact?. Education + Training, 61(4), 480-499. https://doi.org/10.1108/et-09-2018-0203
Cheng, Y. M. (2020). Will robo-advisors continue? Roles of task-technology fit, network externalities, gratifications, and flow experience in facilitating continuance intention. Kybernetes, 50(6), 1751-1783. https://doi.org/10.1108/k-03-2020-0185
Cheng, Y. M. (2021). Can tasks and learning be balanced? A dual-pathway model of cloud-based e-learning continuance intention and performance outcomes. Kybernetes, 51(1), 210-240. https://doi.org/10.1108/k-07-2020-0440
Ching, C. W., Maarof, N., & Lumpu, K. (2021). Effect of Student Satisfaction on e-learning Quality and Learning Outcome Among Malaysian Undergraduate Nursing Students. The Journal of Educators Online, 18(3), 1-10.
https://doi.org/10.9743/jeo.2021.18.3.2
Chiu, C. M., Hsu, M. H., Sun, S. Y., Lin, T. C., & Sun, P. C. (2005). Usability, quality, value, and e-learning continuance decisions. Computers & Education, 45(4), 399-416. https://doi.org/10.1016/j.compedu.2004.06.001
Chow, W. S., & Shi, S. (2014). Investigating Students’ Satisfaction and Continuance Intention toward E-learning: An Extension of the Expectation – Confirmation Model. Procedia - Social and Behavioral Sciences, 141, 1145-1149.
https://doi.org/10.1016/j.sbspro.2014.05.193
Dang, N. (2022). Research on high-quality construction of online education in the new era. Achievement, 1(19), 7-8.
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
Dehghan, A., Dugger, J., Dobrzykowski, D., & Balazs, A. (2014). The antecedents of student loyalty in online programs. International Journal of Educational Management, 28(1), 15-35. https://doi.org/10.1108/ijem-01-2013-0007
DeLone, H., & McLean, E. R. (2004). Measuring e-Commerce Success: Applying the DeLone & McLean Information Systems Success Model. International Journal of Electronic Commerce, 9(1), 31-47. https://doi.org/10.1080/10864415.2004.11044317
Dillon, A., & Morris, M. G. (1996). User acceptance of information technology: Theories and models. Annual Review of Information Science and Technology, 31, 3-32. https://doi.org/10.2307/30036540
Elifoğlu-Kurt, Ö. (2016). Bilgi Sistemleri Başarı Modeli İle Bir e-öğrenme sistemi değerlendirmesi. Yönetim Bilişim Sistemleri Dergisi, 1(3), 140-149.
Fearnley, M. R., & Amora, J. T. (2020). Learning Management System Adoption in Higher Education Using the Extended Technology Acceptance Model. IAFOR Journal of Education, 8(2), 89-106. https://doi.org/10.22492/ije.8.2.05
Fianu, E., Blewett, C., Ampong, G., & Ofori, K. (2018). Factors Affecting MOOC Usage by Students in Selected Ghanaian Universities. Education Sciences, 8(2), 1-15. https://doi.org/10.3390/educsci8020070
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate Data Analysis (7th ed.). Pearson.
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2013). Partial least squares structural equation modeling: rigorous applications, better results, and higher acceptance. Long Range Planning, 46(1/2), 1-12. https://doi.org/10.1016/j.lrp.2013.01.001
Halilovic, S., & Cicic, M. (2013). Antecedents of information systems user behaviour – extended expectation-confirmation model. Behaviour & Information Technology, 32(4), 359-370. https://doi.org/10.1080/0144929x.2011.554575
Havik, T., & Westergård, E. (2020). Do teachers matter? Students’ perceptions of classroom interactions and student engagement. Scandinavian Journal of Educational Research, 64(4), 488-507. https://doi.org/10.1080/00313831.2019.1577754
Hennig-Thurau, T., Langer, M. F., & Hansen, U. (2001). Modeling and Managing Student Loyalty. Journal of Service Research, 3(4), 331-344. https://doi.org/10.1177/109467050134006
Hsu, M. H., & Chiu, C. M. (2004). Predicting electronic service continuance with a decomposed theory of planned behaviour. Behaviour & Information Technology, 23(5), 359-373. https://doi.org/10.1080/01449290410001669969
Hussein, L. A., & Hilmi, M. F. (2021). The Influence of Convenience on the Usage of Learning Management System. Electronic Journal of E-Learning, 19(6), 504-515. https://doi.org/10.34190/ejel.19.6.2493
Jiang, D. C., & Wang, M. Y. (2015). Research on the current situation and development countermeasures of online education in China. E-Business Journal, 1(9), 68-69.
Kennett-Hensel, P. A., Lacey, R., & Sneath, J. Z. (2009). Coping with a natural disaster: Losses, emotions, and impulsive and compulsive buying. Marketing Letters, 20(1), 45-60
Kim, S., Lee, J., Yoon, S. H., & Kim, H. W. (2022). How can we achieve better e-Learning success in the new normal?. Internet Research, 33(1), 410-441. https://doi.org/10.1108/intr-05-2021-0310
Kulikowski, K., Przytula, S., & Sulkowski, L. (2021). Emergency forced pandemic e-learning – feedback from students for HEI management. Open Learning: The Journal of Open, Distance and e-Learning, 36(3), 245-262.
https://doi.org/10.1080/02680513.2021.1942810
Landrum, B. (2020). Examining Students’ Confidence to Learn Online, Self-Regulation Skills and Perceptions of Satisfaction and Usefulness of Online Classes. Online Learning, 24(3), 128-146. https://doi.org/10.24059/olj.v24i3.2066
Lee, M. C. (2010). Explaining and predicting users’ continuance intention toward e-learning: An extension of the expectation–confirmation model. Computers & Education, 54(2), 506-516. https://doi.org/10.1016/j.compedu.2009.09.002
Li, Y., & Kitcharoen, S. (2022). Determinants of Undergraduates’ Continuance Intention and Actual Behavior to Play Mobile Games in Chongqing, China. AU-GSB E-JOURNAL, 15(2), 206-214. https://doi.org/10.14456/augsbejr.2022.86
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, X., & Yong, L. L. (2022). Research on online teaching quality management in universities in the era of artificial intelligence. Chinese Journal of Multimedia and Internet Teaching, 1(9), 67-70.
Lodico, G., Spaulding, T., & Voegtle, H. (2006). Method in Educational Research: From Theory to Practice (1st ed.). Jossey-Bass.
MacCallum, R. C. (2003). Working with imperfect models. Multivariate Behavioral Research, 38, 113-139.
https://doi.org/10.1207/s15327906mbr3801_5
Mailizar, M., Burg, D., & Maulina, S. (2021). Examining university students’ behavioural intention to use e-learning during the COVID-19 pandemic: An extended TAM model. Education and Information Technologies, 26(6), 7057-7077.
https://doi.org/10.1007/s10639-021-10557-5
Masrom, M. (2007, May 21-24). Technology acceptance model and e-learning [Paper presented]. The 12th International Conference on Education, Sultan Hassanal Bolkiah Institute of Education, Universiti Brunei Darussalam.
Md Noh, N. H., & Amron, M. T. (2021). Exploring Cloud Computing Readiness and Acceptance in Higher Education Institution: A PLS-SEM Approach. Asian Journal of University Education, 17(4), 367.
https://doi.org/10.24191/ajue.v17i4.16193
Muhamad Sufi, A. S. (2019). A study on the acceptance of Augmented Reality in classroom based on TAM. [Unpublished Master’s Thesis]. Universiti Teknologi MARA (UiTM) Shah Alam.
Ngai, E. W. T., Poon, J. K. L., & Chan, Y. H. C. (2005). Empirical examination of the adoption of WebCT using TAM. In Computers and Education, 48, 250-267. https://doi.org/10.1016/j.compedu.2004.11.007
Nunnally, J. C. (1978). Psychometric Theory (2nd ed.). McGraw-Hill.
Oghuma, A. P., Libaque-Saenz, C. F., Wong, S. F., & Chang, Y. (2016). An expectation-confirmation model of continuance intention to use mobile instant messaging. Telematics and Informatics, 33(1), 34-47. https://doi.org/10.1016/j.tele.2015.05.006
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
Ouyang, Y. X., Tang, C., Rong, W. G., Zhang, L., Yin, C. T., & Xiong, Z. (2017). Task-technology fit aware expectation-confirmation model towards understanding of MOOCs continued usage. Proceedings of the 50th Hawaii International Conference on System Sciences, 174-183. https://doi.org/10.24251/hicss.2017.020
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, 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.
http://dx.doi.org/10.1016/j.ijpe.2016.08.035.
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
Petter, S., DeLone, W., & McLean, E. R. (2013). Information Systems Success: The Quest for the Independent Variables. Journal of Management Information Systems, 29(4), 7-62. https://doi.org/10.2753/mis0742-1222290401
Pituch, K. A., & Lee, Y. K. (2006). The influence of system characteristics on e-learning use. Computers &Amp; Education, 47(2), 222-244. https://doi.org/10.1016/j.compedu.2004.10.007
Rabaai, A. A. (2009). Assessing information systems success vmodels: empirical comparison (Research in Progress). 20th Australian Conference on Information Systems, 1(2), 2-4.
Rahim, R. B. A., & Razak, F. Z. A. (2021). The impact of system quality on satisfaction to use learning management system: E-campus perspective. Journal of Physics: Conference Series, 1793(1), 1-4.
https://doi.org/10.1088/1742-6596/1793/1/012021
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
Rosenberg, M. J. (2000). E-Learning: Strategies for Delivering Knowledge in the Digital Age (1st ed.). McGraw Hill.
Salkind, J. (2017). Exploring Research (9th ed.). Pearson Press.
Sanchez-Franco, M. J. (2009). The moderating effects of involvement on the relationships between satisfaction, trust, and commitment in e-banking. Journal of Interactive Marketing, 23(3), 247-258. https://doi.org/10.1016/j.intmar.2009.04.007
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, 8(2), 23-74.
Schmitt, N., & Stults, D. M. (1986). Methodology review: Analysis of Multitrait-Multimethod Matrices. Applied Psychological Measurement, 10(1), 1-22. https://doi.org/10.1177/014662168601000101
Seddon, P. B. (1997). A specification and extension of the DeLone and McLean model of IS success. Information Systems Research, 8(3), 240-253. https://doi.org/10.1287/isre.8.3.240
Sørebø, Y., Halvari, H., Gulli, V. F., & Kristiansen, R. (2009). The role of self-determination theory in explaining teachers’ motivation to continue to use e-learning technology. Computers & Education, 53(4), 1177-1187.
https://doi.org/10.1016/j.compedu.2009.06.001
Taherdoost, H. (2017). Determining Sample Size; How to Calculate Survey Sample Size. International Journal of Economics and Management Systems, 2, 237-239.
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. https://doi.org/10.1108/jeim-04-2014-0039
Thong, J. Y., Hong, S. J., & Tam, K. Y. (2006). The effects of post-adoption beliefs on the expectation-confirmation model for information technology continuance. International Journal of Human-Computer Studies, 64(9), 799-810.
https://doi.org/10.1016/j.ijhcs.2006.05.001
Wang, Y.-S., Wang, H.-Y., & Shee, D. Y. (2007). Measuring e-learning systems success in an organizational context: Scale development and validation. Computers in Human Behavior, 23(4), 1792-1808. https://doi.org/10.1016/j.chb.2005.10.006
Weng, F., Yang, R.-J., Ho, H.-J., & Su, H.-M. (2018). A TAM-based study of attitude towards use intention of multimedia among school teachers. In Applied System Innovation 1(36), 1-10. https://doi.org/10.3390/asi1030036
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
Xu, F. (2015). Research of the MOOC study behavior influencing factors. In Proceedings of the International Conference on Advanced Information and Communication Technology for Education. 5(1), 1-10.
https://doi.org/10.2991/icaicte-15.2015.5
Xu, F., Tian, M., Xu, G., Reyes Ayala, B., & Shen, W. (2017). Understanding Chinese users’ switching behaviour of cloud storage services. The Electronic Library, 35(2), 214-232. https://doi.org/10.1108/el-04-2016-0080
Yang, F., & Zheng, K. M. (2022). A study on emotional education strategies for college students in a live teaching environment. Science and Technology Information, 1(20), 161-164.
Zeng, Y., & Liu, M. (2022). The path of digital educational publishing in the context of online education. All Media Exploration, 3(9), 109-111.
Zhang, C. G. (2022). The practice and development prospect of webcasting teaching in the perspective of education modernization. Theory and Modernization, 1(1), 74-82.
Zhang, Z., Shi, J. J., & Sun, H. J. (2022). Meaning, Main Problems and Countermeasures of Building Online Education Platforms in the Perspective of Lifelong. Education Science and Technology Wind, 2(12), 37-39.
Zhen, L. H. (2022). Exploring the hot spots and frontiers of online education research in China in the past 5 years. Software Guide, 2(9), 230-235.
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