What Drives Satisfaction and Continuance Intention to Use E-Learning? : A Case of Dance Academy Students in Chengdu, China


  • Mengke Li Mr.


DOI: 10.14456/abacodijournal.2024.19
Published: 2024-04-24


e-learning, service quality, information quality, satisfaction, continuance intention


This study aims to explore the factors that significantly impact the e-learning satisfaction and continuance intention of dance academy students in Chengdu, China. The Technology Acceptance Model (TAM), Expectation Confirmation Model (ECM), and Information Systems Success Model (ISSM) serve as the foundation for the conceptual framework in this study. The study explores the key constructs from previous studies to propose a conceptual framework, including service quality, perceived ease of use, perceived usefulness, confirmation, information quality, satisfaction, and continuance intention. The quantitative questionnaire was distributed to 476 undergraduate students in Dance Academy at Sichuan University. The sampling methods include judgmental, quota and convenience sampling. Additionally, this study used confirmatory factor analysis and structural equation modeling as statistical analysis methods. The analysis showed that all six hypotheses were supported.  Students will be more likely to use e-learning in the future if they are very satisfied with their online learning experience. The usefulness of e-learning is also significantly impacted by perceived ease of use, information quality and service quality. 



Ahn, T., Ryu, S., & Han, I. (2004). The impact of the online and offline features on the user acceptance of internet shopping malls. Electronic Commerce Research and Applications, 3(4), 405-420. https://doi.org/10.1016/j.elerap.2004.05.001

Alharbi, S., & Drew, S. (2014). Using the technology acceptance model in understanding academics’ behavioural intention to use learning management systems. International Journal of Advanced Computer Science and Applications, 5(1), 143-155.


Armstrong, C. S., Banerjee, S., & Corona, C. (2013). Factor-loading uncertainty and expected returns. The Review of Financial Studies, 26(1), 158-207.


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

Baki, R., Birgoren, B., & Aktepe, A. (2018). A Meta Analysis of Factors Affecting Perceived Usefulness and Perceived Ease of Use in the Adoption of E-Learning Systems. Turkish Online Journal of Distance Education, 19(4), 4-42.


Baturay, M. H. (2010). 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. https://doi.org/10.1080/10494821003644029

Baylari, A., & Montazer, G. A. (2009). Design a personalized e-learning system based on item response theory and artificial neural network approach. Expert Systems with Applications, 36(4), 8013-8021. https://doi.org/10.1016/j.eswa.2008.10.080

Bhattacherjee, A. (2001). An empirical analysis of the antecedents of electronic commerce service continuance. Decision Support Systems, 32(2), 201-214.


Bhattacherjee, A. (2001a). Understanding information systems continuance: an expectation-Confirmation Model. MIS Quarterly, 25(3), 351. https://doi.org/10.2307/3250921

Chang, L. (1994). A psychometric evaluation of 4-point and 6-point Likert-type scale in relation to reliability and validity. Applied Psychological Measurement, 18, 205-215.

Chen, Z. S. C., Yang, S. J. H., & Huang, J. J. S. (2015). Constructing an e-portfolio-based integrated learning environment supported by library resource. The Electronic Library, 33(2), 273-291. https://doi.org/10.1108/el-07-2013-0118

Cheng, Y.-M. (2014a). Extending the expectation-confirmation model with quality and flow to explore nurses continued blended e-learning intention. Information Technology & People, 27(3), 230-258. https://doi.org/10.1108/itp-01-2013-0024

Cheng, Y.-M. (2014b). What drives nurses’ blended e-learning continuance intention?. Educational Technology and Society, 17(4), 203-215.

Cheng, Y.-M. (2014c). Why do users intend to continue using the digital library? An integrated perspective. Aslib Journal of Information Management, 66(6), 640-662. https://doi.org/10.1108/ajim-05-2013-0042

Cheng, Y.-M. (2018). What drives cloud ERP continuance? An integrated view. Journal of Enterprise Information Management, 31(5), 724-750.


Cheng, Y.-M. (2019). How does task-technology fit influence cloud-based e-learning continuance and impact? Education + Training, 61(4), 480-499.


Cheng, Y.-M. (2020a). Quality antecedents and performance outcome of cloud-based hospital information system continuance intention. Journal of Enterprise Information Management, 33(3), 654-683. https://doi.org/10.1108/jeim-04-2019-0107

Cheng, Y.-M. (2022). What roles do quality and cognitive absorption play in evaluating cloud-based e-learning system success? Evidence from medical professionals. Interactive Technology and Smart Education, 1(2), 1741-5659.


Chuo, Y., Liu, C., & Tsai, C. (2015). Effectiveness of e-learning in hospitals. Technology and Health Care, 23(1), 157-160. https://doi.org/10.3233/thc-15094

Crawford, N., & McKenzie, L. (2011). E-learning in context: An assessment of student inequalities in a university outreach program. Australasian Journal of Educational Technology, 27(3), 531-545. https://doi.org/10.14742/ajet.959

Daneji, A. A., Ayub, A. F. M. B., & Khambari, M. N. M. (2019). The effects of perceived usefulness, confirmation and satisfaction on continuance intention in using Massive Open Online Course (MOOC). Knowledge Management & E-Learning, 11(2), 201-214.

Daud, Y. R., Amin, M. R. M. B., & Karim, J. A. (2020). Antecedents of student loyalty in open and distance learning institutions: An empirical analysis. International Review of Research in Open and Distributed Learning, 21(3), 18-42.


Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.


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. (1992). Information systems success: the quest for the dependent variable. Information Systems Research, 3(1), 60-95.


Eom, S. B., & Ashill, N. J. (2018). A system’s view of e-learning success model. Decision Sciences Journal of Innovative Education, 16(1), 42-76.


Fan, X., Duangekanong, S., & Xu, M. (2021). Factors affecting college students’ intention to use English U-learning in Sichuan, China. AU-GSB E-JOURNAL, 14(2), 118-129. https://doi.org/10.14456/augsbejr.2021.20

Filippini, R., Forza, C., & Vinelli, A. (1998). Trade-off and compatibility between performance: definitions and empirical evidence. International Journal of Production Research, 36(12), 3379-3406.

Freeze, R. D., Alshare, K. A., Lane, P. L., & Wen, H. J. (2010). IS success model in e-learning context based on students’ perceptions?. Journal of Information Systems Education, 21(2), 173-184.

Gao, L., Waechter, K. A., & Bai, X. (2015). Understanding consumers’ continuance intention towards mobile purchase: a theoretical framework and empirical study: a case of China. Computers in Human Behavior, 53, 249-262.


Granic, A., & Marangunic, N. (2019). Technology acceptance model in educational context: a systematic literature review. British Journal of Educational Technology, 50(5), 2572-2593.

Gupta, P., & Kaushik, N. (2018). Dimensions of service quality in higher education–critical review (students’ perspective). International Journal of Educational Management, 32(4), 580-605. https://doi.org/10.1108/ijem-03-2017-0056

Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (2010). Multivariate data analysis (7th ed.). Prentice Hall.

Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. D., & Tatham, R. L. (2006). Multivariate data analysis (6th ed.). Upper Saddle River.

Hang, C. H. (2021). Using PLS-SEM model to explore the influencing factors of learning satisfaction in blended learning. Education Sciences, 11(5), 249.

Hong, S. J., Thong, J. Y. L., & Tam, K. Y. (2006). Understanding continued information technology usage behavior: a comparison of three models in the context of mobile internet. Decision Support Systems, 42(3), 1819-1834.


Hsiao, K. L., Lin, K. Y., Wang, Y. T., & Zhang, Z. M. (2019). Continued use intention of lifestyle mobile applications: the Starbucks app in Taiwan. The Electronic Library, 37(5), 893-913. https://doi.org/10.1108/el-03-2019-0085

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), 1-55. https://doi.org/10.1080/10705519909540118

Ibrahim, R., Leng, N. S., Yusoff, R. C. M., Samy, G. N., Masrom, S., & Rizman, Z. I. (2017). E-learning acceptance based on technology acceptance model (TAM). Journal of Fundamental and Applied Sciences, 9(4), 871-889.


Ifenxi. (2020, August). China Online Education Industry Trends report.


iResearch. (2020). 2020 China Online Education Industry Research Report.


Jeong, H. (2011). An investigation of user perceptions and behavioral intentions towards the e-library. Library Collections, Acquisitions, and Technical Services, 35(2), 45-60. https://doi.org/10.1080/14649055.2011.10766298

Joo, B. K. B. (2010). Organizational commitment for knowledge workers: The roles of perceived organizational learning culture, leader-member exchange quality, and turnover intention. Human Resource Development Quarterly, 21, 69-85. http://dx.doi.org/10.1002/hrdq.20031

Karkar, A. J. M., Fatlawi, H. K., & A l-Jobouri, A. A. (2020). Highlighting e‑learning adoption challenges using data analysis techniques: University of Kufa as a case study. Electronic Journal of e-Learning, 18(2), 136-149.


Lee, B.-C., Yoon, J.-O., & Lee, I. (2009). Learners' acceptance of e-learning in South Korea: Theories and results. Computers & Education, 53(4), 1320-1329.


Legris, P., Ingham, J., & Collerette, P. (2003). Why do people use information technology? A critical review of the Technology Acceptance Model. Information & Management, 40(3), 191-204. https://doi.org/10.1016/s0378-7206(01)00143-4

Liaw, S. S., Huang, H. M., & Chen, G. D. (2007). Surveying instructor and learner attitudes toward e-learning. Computers & Education, 49(4), 1066-1080.


Lin, C. S., Wu, S., & Tsai, R. J. (2005). Integrating perceived playfulness into expectation-confirmation model for web portal context. Information and Management, 42(5), 683-693. https://doi.org/10.1016/j.im.2004.04.003

Lin, H. F. (2007). The role of online and offline features in sustaining virtual communities: an empirical study. Internet Research, 17(2), 119-138.


Lin, W.-S., & Wang, C.-H. (2012). Antecedences to continued intentions of adopting e-learning system in blended learning instruction: A contingency framework based on models of information system success and task-technology fit. Computers & Education, 58(1), 88-99. https://doi.org/10.1016/j.compedu.2011.07.008

Long, J. S. (1983). Confirmatory factor analysis (1st ed.). Sage Publications Press.

Mahmoodi, Z., Esmaelzadeh-Saeieh, S., Lotfi, R., Baradaran-Eftekhari, M., Akbari Kamrani, M., Mehdizadeh-Tourzani, Z., & Salehi, K. (2017). The evaluation of a virtual education system based on the DeLone and McLean model: A path analysis. F1000 Research, 6, 1631. https://doi.org/10.12688/f1000research.12278.2

Mailizar, M., Almanthair, A., & Maulina, S. (2021). Examining teachers' behavioral intention to use e-learning in teaching of mathematics: An extended TAM model. Contemporary Educational Technology, 13(2), 298.

Marjanovic, U., Delic, M., & Lalic, B. (2016). Developing a model to assess the success of e-learning systems: evidence from a manufacturing company in transitional economy. Information Systems and e-Business Management, 14(2), 253-272.


McDowell, I. (1987). A guide to rating scales and questionnaires (3rd ed.). Oxford University Press.

Ngai, E. W. T., Poon, J. K. L., & Chan, Y. H. C. (2007). Empirical examination of the adoption of WebCT using TAM. Computers & Education, 48(20), 250-267.


Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill.

Ojo, A. I. (2017). Validation of the DeLone and McLean information systems success model. Healthcare Informatics Research, 23(1), 60-66.


Park, N., Roman, R., Lee, S., & Chung, J. E. (2009). User acceptance of a digital library system in developing countries: an application of the technology acceptance model. International Journal of Information Management, 29(3), 196-209.


Paudel, P. (2021). Online education during and after covid-19 in higher education. International Journal on Studies in Education, 3(2), 70-85.


Pirolli, P., & Card, S. (1999). Information Foraging. Psychological Review, 106(4), 643-675. https://doi.org/10.1037/0033-295x.106.4.643

Prodanova, J., San-Martin, S., & Sanchez-Beato, E. J. (2021). Quality requirements for continuous use of e-learning systems at public vs. private universities in Spain. Digital Education Review, 1(40), 33-50. https://doi.org/10.1344/der.2021.40.33-50

Rahardja, U., Hariguna, T., & Aini, Q. (2019). Understanding the impact of determinants in game learning acceptance: An empirical study. International Journal of Education and Practice, 7(3), 136-145. https://doi.org/10.18488/journal.61.2019.73.136.145

Ramayah, T., & Lee, J. W. C. (2012). System characteristics, satisfaction, and e-learning usage: a structural equation model (SEM). TOJET: The Turkish Online Journal of Educational Technology, 11(2), 196-206.

Ranganathan, C., & Ganapathy, S. (2002). Key dimensions of business-to-consumer web sites. Information & Management, 39(6), 457-465.


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

Rogers, E. M. (1983). Diffusion of Innovations (4th ed.). Macmillan Publishing Co.

Rughoobur-Seetah, S., & Hosanoo, Z. A. (2021). An evaluation of the impact of confinement on the quality of e-learning in higher education institutions. Quality Assurance in Education, 29(4), 422-444. https://doi.org/10.1108/qae-03-2021-0043

Russell, D. W. (2002). In search of underlying dimensions: The use (and abuse) of factor analysis in Personality and Social Psychology Bulletin. Personality and Social Psychology Bulletin, 28(12), 1629-1646. https://doi.org/10.1177/014616702237645

Shipley, B. (2002). Cause and Correlation in Biology: A User’s Guide to Path Analysis (2nd ed.). Structural Equations and Causal Inference.

Silva, P. (2015). Davis’s technology acceptance model information seeking behavior and technology adoption: Theories and trends. IGI Global, 1(2), 205-219.

Singh, A. S., & Masuku, M. B. (2012). Fundamentals of applied research and sampling techniques, International Jr. of Medical and Applied Sciences, 2(4), 124-132.

Tam, C., Loureiro, A., & Oliveira, T. (2020). The individual performance outcome behind e-commerce: Integrating information systems success and overall trust. Internet Research, 30(2), 439-462. https://doi.org/10.1108/intr-06-2018-0262

Thong, J. Y. L., 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.


Thong, J. Y. L., Hong, W., & Tam, K.-Y. (2002). Understanding user acceptance of digital libraries: what are the roles of interface characteristics, organizational context, and individual differences? International Journal of Human-Computer Studies, 57(3), 215-242. https://doi.org/10.1016/s1071-5819(02)91024-4

Venkatesh, V., & Bala, H. (2008). Technology Acceptance Model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273-315.





How to Cite

Li, M. (2024). What Drives Satisfaction and Continuance Intention to Use E-Learning? : A Case of Dance Academy Students in Chengdu, China. ABAC ODI JOURNAL Vision. Action. Outcome, 11(2), 337-356. https://doi.org/10.14456/abacodijournal.2024.19