Factors Impacting Art Major Undergraduates’ Continuance Intention to Use E-Leaning: A Case in a Public University of Chongqing


  • Ke Jin


DOI: 10.14456/shserj.2024.8
Published: 2024-03-01


E-Learning, Perceived Usefulness, Perceived Ease of Use, Satisfaction, Continuance Intention


Purpose: This article explores the significant factors impacting undergraduate art majors’ continuance intention toward e-learning at Southwest University in Chongqing, China. The major variables for the development of conceptual framework are information quality, system quality, service quality, perceived usefulness, perceived ease of use, satisfaction, and continuance intention. Research design, data, and methodology: The investigator conducted a quantitative evaluation approach with 493 samples and administered a statistical questionnaire to undergraduate students at Southwest University in Chongqing, China. Non-probability sampling processes were employed in this research to acquire data from the research. Item-objective congruence (IOC) index for content validity Cronbach's Alpha for pilot test (n=40) were assessed before the data collection. The Confirmatory Factor Analysis (CFA) and Structural Equation Model (SEM) were used for statistical assessment, which included goodness of model fits, validity, and reliability test. Results: Satisfaction has the strongest effect on continuance intention. Information quality, system quality, service quality, perceived usefulness and perceived usefulness significantly affect satisfaction. Conclusions:  To meet the research objectives, all hypotheses have been supported. As a response, education department administrators at public universities are recommended to evaluate the primary contributors for the contemporary online learning deployment methodology to enhance art major undergraduates’ learning satisfaction and continuance intention.

Author Biography

Ke Jin

School of Fine Arts and Design, Guangzhou University, China.


Al-Ammari, J., & Hamad, S. (2008). Factors Influencing the Adoption of e-learning at UOB. Conference: International Arab Conference on Information Technology, 22(2), 1-10.

Aldholay, A., Isaac, O., Abdullah, Z., Abdulsalam, R., & Al-Shibami, A. H. (2018). An extension of Delone and McLean IS success model with self-efficacy: Online learning usage in Yemen. The International Journal of Information and Learning Technology, 35(4), 285-304. https://doi.org/10.1108/ijilt-11-2017-0116

Allen, M., Titsworth, S., & Hunt, S. K. (2009). Quantitative Research Communication (1st ed.). Sage Publications. https://doi.org/10.4135/9781452274881

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. https://doi.org/10.1016/j.chb.2016.05.013

Annamdevula, S., & Bellamkonda, R. S. (2016). The Effects of Service Quality on Student Loyalty: The Mediating Role of Student Satisfaction. Journal of Modelling in Management, 11(2), 446-462. https://doi.org/10.1108/jm2-04-2014-0031

Astin, A. W. (1993). The Jossey-Bass Higher and Adult Education Series What Matters in College? Four Critical Years Revisited. Liberal Education, 79(4), 4-12.

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

Bentler, P. M., & Bonett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 88(3), 588-606. https://doi.org/10.1037/0033-2909.88.3.588

Bismala, L., Manurung, Y. H., Siregar, G., & Andriany, D. (2022). The Impact of E-Learning Quality and Students’ Self-Efficacy Toward the Satisfaction in the Using of E-Learning. Malaysian Online Journal of Educational Technology, 10(2), 141-150. https://doi.org/10.52380/mojet.2022.10.2.362

Chang, C. (2012). 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

Cheng, Y.-M. (2012). Effects of quality antecedents on e-learning acceptance. Internet Research, 22(3), 361-390. https://doi.org/10.1108/10662241211235699

Cheng, Y.-M. (2014). 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. (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. (2021). Can gamification and interface design aesthetics lead to MOOCs' success? Education + Training, 63(9), 1346-1375. https://doi.org/10.1108/et-09-2020-0278

Cheung, M. W. L. (2015). Meta‐Analysis: A Structural Equation Modeling Approach (1st ed.). John Wiley & Sons, Ltd.

Chopra, G., Madan, P., Jaisingh, P., & Bhaskar, P. (2019). Effectiveness of e-learning portal from students’ perspective: A structural equation model (SEM) approach. Interactive Technology and Smart Education, 16(4), 1-24.

Davis, F. D. (1993). User Acceptance of Information Technology: System Characteristics, User Perceptions and Behavioral Impacts. International Journal of Man-Machine Studies, 38(3), 475-487. https://doi.org/10.1006/imms.1993.1022

DeLone, W. H., & McLean, E. R. (2016). Information systems success measurement. Found. Trends Inf. Syst, 2(1), 1-116.


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. https://doi.org/10.1080/002075498192111

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/10.1177/002224378101800104

Greenemeier, L. (2009, March 18). Remembering the day, the World Wide Web was born. Scientific American. Scientific American. http://www.sciam.com/ article.cfm?id=daythe-web-was-born

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

Hu, L., & Bentler, P. M. (1999). Cutoff Criteria for Fit Indexes in Covariance Structure Analysis: Conventional Criteria Versus New Alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55. https://doi.org/10.1080/10705519909540118

Hussein, M. H., OW, S. H., Ibrahim, I., & Mahmoud, M. A. (2021). Measuring instructors continued intention to reuse Google Classroom in Iraq: a mixed method study during COVID-19. Interactive Technology and Smart Education, 18(3), 380-402. https://doi.org/10.1108/itse-06-2020-0095

Israel, D. (1992). Determining Sample Size. University of Florida Cooperative Extension Service (1st ed.). Institute of Food and Agriculture Sciences.

Kumar, R., Ravindran, G., & Sudharani, D. (2012). An Empirical Study on Service Quality Perceptions and Continuance Intention in Mobile Banking Context in India. Journal of Internet Banking & Commerce, 17(1), 1-22.

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

Marsh, H. W., & Hocevar, D. (1985). Application Of Confirmatory Factor Analysis to The Study of Self-Concept: First- And Higher-Order Factor Models and Their Invariance Across Groups. Psychological Bulletin, 97(3), 562-582.


Masrek, M. N., & Gaskin, J. E. (2016). Assessing users’ satisfaction with web digital library: the case of University Technology MARA. The International Journal of Information and Learning Technology, 33(1), 36-56.


Mirabolghasemi, M., Shasti, R., & Hosseinikhah Choshaly, S. (2021). An investigation into the determinants of blended leaning satisfaction from EFL learners' perspective. Interactive Technology and Smart Education, 18(1), 69-84.


Mouakket, S., & Bettayeb, A. M. (2015). Investigating the Factors Influencing Continuance Usage Intention of Learning Management Systems by University Instructors the Blackboard System Case. International Journal of Web Information Systems, 11(4), 491-509. https://doi.org/10.1108/ijwis-03-2015-0008

Nagy, J. T. (2018). Evaluation of Online Video Usage and Learning Satisfaction: An Extension of the Technology Acceptance Model. The International Review of Research in Open and Distributed Learning, 19(1). https://doi.org/10.19173/irrodl.v19i1.2886

Oliver, R. L. (1993). Cognitive, Affective, And Attribute Bases of The Satisfaction Response. Journal of Consumer Behavior, 20(3), 418-430. https://doi.org/10.1086/209358

Özüdogru, G. (2022). Pre-Service Teachers’ E-learning Styles and Attitudes towards E-learning. Inquiry in Education, 14(1), 1-15.

Panigyrakis, G. G., & Chatzipanagiotou, K. C. (2006). The Impact of Design Characteristics and Support Services on the Effectiveness of Marketing Information Systems: An Empirical Investigation. Review of Business Information Systems, 10(2), 91-104. https://doi.org/10.19030/rbis.v10i2.5328

Petter, S., & Mclean, E. (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.

Rughoobur-Seetah, S., & Zuberia, 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

Seddon, P. B. (1997). A Re-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

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

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

Wang, Y., & Wang, T. (2022). Transformation and Upgrading of Digital Education Industry in Digital Economy Perspective. Yuejiang Academic Journal, 14(6), 127-137.

Wu, J.-H., Tennyson, R. D., & Hsia, T.-L. (2010). A study of student satisfaction in a blended e-learning system environment. Computers & Education, 55(1), 155-164. https://doi.org/10.1016/j.compedu.2009.12.012

Yusoff, Y. M., Muhammad, Z., Zahari, M. S. M., Pasah, E. S., & Robert, E. (2009). Individual Differences, Perceived Ease of Use, and Perceived Usefulness in the E-Library Usage. Computer and Information Science, 2(1), 76-83. https://doi.org/10.5539/cis.v2n1p76




How to Cite

Jin, K. (2024). Factors Impacting Art Major Undergraduates’ Continuance Intention to Use E-Leaning: A Case in a Public University of Chongqing. Scholar: Human Sciences, 16(1), 68-76. https://doi.org/10.14456/shserj.2024.8