Educational Technology Research and Development

, Volume 65, Issue 5, pp 1195–1214 | Cite as

Understanding the quality factors that influence the continuance intention of students toward participation in MOOCs

  • Ming Yang
  • Zhen ShaoEmail author
  • Qian Liu
  • Chuiyi Liu
Research Article


The massive open online course (MOOC) is emerging as the new paradigm for modern education. The success of MOOCs depends on learners’ continued usage. Drawing upon the information systems success model (IS success model) and technology acceptance model, a theoretical model for studying learners’ continuance intentions toward participation in MOOCs was developed. Based on survey data from 294 respondents, structural equation modeling was employed to assess the model. The results of this analysis indicate that system quality, course quality, and service quality were significant antecedents of the continuance intention of individuals, and the effect of course quality and service quality were mediated by perceived usefulness. The results contribute to the extant literatures in the context of MOOCs learning by identifying the critical quality factors, and provide managerial guidelines for MOOCs utilization and generalization. The implications of the present findings for research and managerial practice are discussed.


MOOCs E-learning Continuance intention IS success model Quality factors 



This work was supported in part by grants from the National Natural Science Foundation of China (Nos. 71301172, 71301035) and the Beijing Higher Education Young Elite Teacher Project (No. YETP1001).


  1. Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice—a review and recommended 2-step approach. Psychological Bulletin, 103(3), 411–423.CrossRefGoogle Scholar
  2. Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173–1182.CrossRefGoogle Scholar
  3. Benbasat, I., & Barki, H. (2007). Quo vadis, TAM? Journal of the Association for Information Systems, 8(4), 211–218.Google Scholar
  4. Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351–370.CrossRefGoogle Scholar
  5. Bhattacherjee, A., & Sonford, C. (2006). Influence processes for information technology acceptance: An elaboration likelihood model. MIS Quarterly, 30(4), 805–825.Google Scholar
  6. Bhuasiri, W., Xaymoungkhoun, O., Zo, H., Rho, J. J., & Ciganek, A. P. (2012). Critical success factors for e-learning in developing countries: A comparative analysis between ICT experts and faculty. Computers & Education, 58(2), 843–855.CrossRefGoogle Scholar
  7. Calisir, F., Gumussoy, C. A., Bayraktaroglu, A. E., & Karaali, D. (2014). Predicting the intention to use a web-based learning system: perceived content quality, anxiety, perceived system quality, image, and the technology acceptance model. Human Factors and Ergonomics in Manufacturing and Service Industries, 24(5), 515–531.CrossRefGoogle Scholar
  8. Chang, S. C., & Tung, F. C. (2008). An empirical investigation of students’ behavioral intentions to use the online learning course websites. British Journal of Educational Technology, 39(1), 71–83.Google Scholar
  9. Chen, I. Y. L. (2007). The factors influencing members’ continuance intentions in professional virtual communities: A longitudinal study. Journal of Information Science, 33(4), 451–467.CrossRefGoogle Scholar
  10. Cheng, B., Wang, M. H., Yang, S. J. H., Kinshuk, & Peng, J. (2011). Acceptance of competency-based workplace e-learning systems: Effects of individual and peer learning support. Computers & Education, 57(1), 1317–1333.CrossRefGoogle Scholar
  11. Chin, W. W., Marcolin, B. L., & Newsted, P. R. (2003). A partial least squares latent variable modeling approach for measuring interaction effects: Results from a Monte Carlo simulation study and an electronic-mail emotion/adoption study. Information Systems Research, 14(2), 189–217.CrossRefGoogle Scholar
  12. 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.CrossRefGoogle Scholar
  13. 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.CrossRefGoogle Scholar
  14. Cusumano, M. A. (2013). Are the costs of ‘Free’ too high in online education? Communications of the ACM, 56(4), 26–29.CrossRefGoogle Scholar
  15. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer-technology—a comparison of 2 theoretical-models. Management Science, 35(8), 982–1003.CrossRefGoogle Scholar
  16. Davis, F. D., & Venkatesh, V. (1996). A critical assessment of potential measurement biases in the technology acceptance model: Three experiments. International Journal of Human-Computer Studies, 45(1), 19–45.CrossRefGoogle Scholar
  17. De Freitas, S. I., Morgan, J., & Gibson, D. (2015). Will MOOCs transform learning and teaching in higher education? Engagement and course retention in online learning provision. British Journal of Educational Technology, 46(3), 455–471.CrossRefGoogle Scholar
  18. DeLone, W. H., & McLean, E. R. (1992). Information systems success: The quest for the dependent variable. Information Systems Research, 3(1), 60–95.CrossRefGoogle Scholar
  19. 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.CrossRefGoogle Scholar
  20. Fini, A. (2009). The technological dimension of a massive open online course: The case of the CCK08 course tools. International Review of Research in Open and Distance Learning, 10(5), 26.Google Scholar
  21. Hew, K. F., & Cheung, W. S. (2014). Students’ and instructors’ use of massive open online courses (MOOCs): Motivations and challenges. Educational Research Review, 12, 45–58.CrossRefGoogle Scholar
  22. Hu, J. H. P., Brown, S. A., Thong, J. Y. L., Chan, F. K. Y., & Tam, K. Y. (2009). Determinants of service quality and continuance intention of online services: The case of e Tax. Journal of the American Society for Information Science and Technology, 60(2), 292–306.CrossRefGoogle Scholar
  23. 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.CrossRefGoogle Scholar
  24. Lewis, W., Agarwal, R., & Sambamurthy, V. (2003). Sources of influence on beliefs about information technology use: an empirical study of knowledge workers. MIS Quarterly, 27(4), 657–678.Google Scholar
  25. Li, Y., Duan, Y., Fu, Z., & Alford, P. (2012). An empirical study on behavioral intention to reuse e-learning systems in rural China. British Journal of Educational Technology, 43(6), 933–948.CrossRefGoogle Scholar
  26. Liaw, S. S., Huang, H. M., & Chen, G. D. (2007). An activity-theoretical approach to investigate learners’ factors toward e-learning systems. Computers in Human Behavior, 23(4), 1906–1920.CrossRefGoogle Scholar
  27. Lin, K. M. (2011). E-Learning continuance intention: Moderating effects of user e-learning experience. Computers & Education, 56(2), 515–526.CrossRefGoogle Scholar
  28. Lin, K.-M., Chen, N.-S., & Fang, K. (2011). Understanding e-learning continuance intention: a negative critical incidents perspective. Behavior and Information Technology, 30(1), 77–89.CrossRefGoogle Scholar
  29. Lin, J. C., & Lu, H. (2000). Towards an understanding of the behavioral intention to use a web site. International Journal of Information Management, 20(3), 197–208.CrossRefGoogle Scholar
  30. Molla, A., & Licker, P. (2001). E-commerce systems success: An attempt to extend and respecify the DeLone and McLean model of IS success. Journal of Electronic Commerce Success, 2(4), 131–141.Google Scholar
  31. Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1985). A conceptual-model of service quality and its implications for future-research. Journal of Marketing, 49(4), 41–50.CrossRefGoogle Scholar
  32. Pavlou, P. A., & Fygenson, M. (2006). Understanding and predicting electronic commerce adoption: An extension of the theory of planned behavior. MIS Quarterly, 30(1), 115–143.Google Scholar
  33. Rai, A., Lang, S. S., & Welker, R. B. (2002). Assessing the validity of IS success models: An empirical test and theoretical analysis. Information Systems Research, 13(1), 50–69.CrossRefGoogle Scholar
  34. Roca, J. C., Chiu, C. M., & Martinez, 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.CrossRefGoogle Scholar
  35. Saeed, K. A., Hwang, Y., & Yi, M. Y. (2003). Toward an integrative framework for online consumer behavior research: A meta-analysis approach. Journal of End User Computing, 15(4), 1–26.CrossRefGoogle Scholar
  36. Sanchez-Gordon, S., and Lujan-Mora, S. (2013). Accessibility considerations of massive online open courses as creditable courses in engineering programs. 6th International Conference of Education, Research and Innovation (Iceri 2013), 5853-5862.Google Scholar
  37. Seddon, P. B. (1997). A respecification and extension of the DeLone and McLean model of IS success. Information Systems Research, 8(3), 240–253.CrossRefGoogle Scholar
  38. Spector, J. M. (2014). Remarks on MOOCSMOOCS and Mini-MOOCSMOOCS. Educational Technology Research and Development, 62(3), 385–392.CrossRefGoogle Scholar
  39. 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.CrossRefGoogle Scholar
  40. Temme, D., Kreis, H., & Hildebrandt, L. (2006). PLS Path Modeling-A Software Review, SFB 694 discussion paper. Institute of Marketing: Humboldt-University Berlin, Berlin.Google Scholar
  41. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204.CrossRefGoogle Scholar
  42. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.Google Scholar
  43. Wang, H. C., & Chiu, Y. F. (2011). Assessing e-learning 2.0 system success. Computers & Education, 57(2), 1790–1800.CrossRefGoogle Scholar
  44. 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.CrossRefGoogle Scholar
  45. Wu, B., & Zhang, C. (2014). Empirical study on continuance intentions towards E-Learning 2.0 systems. Behavior & Information Technology, 33(10), 1027–1038.CrossRefGoogle Scholar
  46. Yousef, A. M. F., Chatti, M. A., Schroeder, U., and Wosnitza, M. (2014). What drives a successful MOOCS? An empirical examination of criteria to assure design quality of MOOCs. Paper presented at the 14th IEEE International Conference on Advanced Learning Technologies (ICALT)—Advanced Technologies for Supporting Open Access to Formal and Informal Learning, Athens, Greece.Google Scholar
  47. Zhang, Y., Fang, Y., Wei, K., & Wang, Z. (2012). Promoting the intention of students to continue their participation in e-learning systems: the role of the communication environment. Information Technology & People, 25(4), 356–375.CrossRefGoogle Scholar
  48. Zhou, T. (2013). An empirical examination of continuance intention of mobile payment services. Decision Support Systems, 54(2), 1085–1091.CrossRefGoogle Scholar

Copyright information

© Association for Educational Communications and Technology 2017

Authors and Affiliations

  1. 1.Department of Information ManagementCentral University of Finance and EconomicsBeijingChina
  2. 2.School of ManagementHarbin Institute of TechnologyHarbinChina
  3. 3.Academe of Internet EconomicsCentral University of Finance and EconomicsBeijingChina
  4. 4.Central University of Finance and EconomicsBeijingChina

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