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Influencing factors on students' continuance intention to use Learning Management System (LMS)

Published:23 August 2019Publication History

ABSTRACT

In the last decades, universities and higher education institutes have widely employed learning management system (LMS) to monitor and manage online learning and teaching. Contrary to the significant role of LMS in educational settings, most research has focused on initial acceptance, and few attempts have been made to investigate factors influencing students' continuance intention to use LMS. The present study is an effort towards this research direction by proposing an integrated model of Expectation-Conformation Theory (ECT), and Technology Acceptance Model (TAM). The proposed model was tested using statistical data from 153 students from an online university. To verify the proposed theoretical model, we ran partial least squares (PLS)/ structured equation modeling (SEM). The findings of this study revealed that the perceived usefulness is the strongest predictor of students' continuance intention. Surprisingly, our results also indicated that students' attitude toward LMS and their satisfaction level exert no significant influence on continuance intention.

References

  1. Cheng, M. and Yuen, A.H.K. 2018. Student continuance of learning management system use: A longitudinal exploration. Computers & Education. 120, (2018), 241--253.Google ScholarGoogle ScholarCross RefCross Ref
  2. McGill, T.J. and Klobas, J.E. 2009. A task--technology fit view of learning management system impact. Computers & Education. 52, 2 (2009), 496--508.Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Nurakun Kyzy, Z., Ismailova, R., and Dündar, H. 2018. Learning management system implementation: a case study in the Kyrgyz Republic. Interactive Learning Environments, (2018), 1--13.Google ScholarGoogle Scholar
  4. Lee, M.-C. 2010. Explaining and predicting users' continuance intention toward e-learning: An extension of the expectation--confirmation model. Computers & Education. 54, 2 (2010), 506--516.Google ScholarGoogle Scholar
  5. Bhattacherjee, A. 2001. Understanding information systems continuance: an expectation-confirmation model. MIS quarterly, (2001), 351--370.Google ScholarGoogle Scholar
  6. Bhattacherjee, A., Perols, J., and Sanford, C. 2008. Information technology continuance: A theoretic extension and empirical test. Journal of Computer Information Systems. 49, 1 (2008), 17--26.Google ScholarGoogle ScholarCross RefCross Ref
  7. Bøe, T., Gulbrandsen, B., and Sørebø, Ø. 2015. How to stimulate the continued use of ICT in higher education: Integrating information systems continuance theory and agency theory. Computers in Human Behavior. 50, (2015), 375--384.Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Tsai, Y.-h., et al. 2018. The effects of metacognition on online learning interest and continuance to learn with MOOCs. Computers & Education. 121, (2018), 18--29.Google ScholarGoogle ScholarCross RefCross Ref
  9. Chang, I.-C., Liu, C.-C., and Chen, K. 2014. The effects of hedonic/utilitarian expectations and social influence on continuance intention to play online games. Internet Research. 24, 1 (2014), 21--45.Google ScholarGoogle ScholarCross RefCross Ref
  10. Lin, C.S., Wu, S., and Tsai, R.J. 2005. Integrating perceived playfulness into expectation-confirmation model for web portal context. Information & management. 42, 5 (2005), 683--693.Google ScholarGoogle Scholar
  11. Davis, F.D. 1989. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, (1989), 319--340.Google ScholarGoogle Scholar
  12. Anderson, E.W. and Sullivan, M.W. 1993. The antecedents and consequences of customer satisfaction for firms. Marketing science. 12, 2 (1993), 125--143.Google ScholarGoogle Scholar
  13. Eveleth, D.M., Baker-Eveleth, L.J., and Stone, R.W. 2015. Potential applicants' expectation-confirmation and intentions. Computers in Human Behavior. 44, (2015), 183--190.Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Mouakket, S. 2015. Factors influencing continuance intention to use social network sites: The Facebook case. Computers in Human Behavior. 53, (2015), 102--110.Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Jahanmir, S.F. and Cavadas, J. 2018. Factors affecting late adoption of digital innovations. Journal of Business Research. 88, (2018), 337--343.Google ScholarGoogle ScholarCross RefCross Ref
  16. Liao, C., Chen, J.-L., and Yen, D.C. 2007. Theory of planning behavior (TPB) and customer satisfaction in the continued use of e-service: An integrated model. Computers in Human Behavior. 23, 6 (2007), 2804--2822.Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Wen, C., Prybutok, V.R., and Xu, C. 2011. An integrated model for customer online repurchase intention. Journal of Computer Information Systems. 52, 1 (2011), 14--23.Google ScholarGoogle Scholar
  18. Stone, R.W. and Baker-Eveleth, L. 2013. Students' expectation, confirmation, and continuance intention to use electronic textbooks. Computers in Human Behavior. 29, 3 (2013), 984--990.Google ScholarGoogle Scholar
  19. Bhattacherjee, A. and Lin, C.-P. 2015. A unified model of IT continuance: three complementary perspectives and crossover effects. European Journal of Information Systems. 24, 4 (2015), 364--373.Google ScholarGoogle ScholarCross RefCross Ref
  20. Oghuma, A.P., et al. 2016. An expectation-confirmation model of continuance intention to use mobile instant messaging. Telematics and Informatics. 33, 1 (2016), 34--47.Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Fishbein, M. and Ajzen, I., Belief, attitude, intention, and behavior: An introduction to theory and research. 1975: Addison-Wesley.Google ScholarGoogle Scholar
  22. Al-Debei, M.M., Al-Lozi, E., and Papazafeiropoulou, A. 2013. Why people keep coming back to Facebook: Explaining and predicting continuance participation from an extended theory of planned behaviour perspective. Decision support systems. 55, 1 (2013), 43--54.Google ScholarGoogle Scholar
  23. Hsu, M.-H., et al. 2007. Knowledge sharing behavior in virtual communities: The relationship between trust, self-efficacy, and outcome expectations. International journal of human-computer studies. 65, 2 (2007), 153--169.Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Oliver, R.L. 1980. A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of marketing research, (1980), 460--469.Google ScholarGoogle Scholar
  25. Thong, J.Y., Hong, S.-J., and 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 (2006), 799--810.Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Lin, T.-C., et al. 2012. The integration of value-based adoption and expectation--confirmation models: An example of IPTV continuance intention. Decision Support Systems. 54, 1 (2012), 63--75.Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Kim, B. 2010. An empirical investigation of mobile data service continuance: Incorporating the theory of planned behavior into the expectation--confirmation model. Expert Systems with Applications. 37, 10 (2010), 7033--7039.Google ScholarGoogle Scholar
  28. Mohammadi, H. 2015. Social and individual antecedents of m-learning adoption in Iran. Computers in Human Behavior. 49, (2015), 191--207.Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Venkatesh, V. and Morris, M.G. 2000. Why don't men ever stop to ask for directions? Gender, social influence, and their role in technology acceptance and usage behavior. MIS quarterly, (2000), 115--139.Google ScholarGoogle Scholar
  30. Siamagka, N.-T., et al. 2015. Determinants of social media adoption by B2B organizations. Industrial Marketing Management. 51, (2015), 89--99.Google ScholarGoogle ScholarCross RefCross Ref
  31. Van der Heijden, H. 2003. Factors influencing the usage of websites: the case of a generic portal in The Netherlands. Information & Management. 40, 6 (2003), 541--549.Google ScholarGoogle Scholar
  32. Ringle, C.M., Wende, S., and Becker, J.-M. 2015. SmartPLS 3. Boenningstedt: SmartPLS GmbH, http://www. smartpls. com, (2015).Google ScholarGoogle Scholar
  33. Henseler, J., Hubona, G., and Ray, P.A. 2016. Using PLS path modeling in new technology research: updated guidelines. Industrial management & data systems. 116, 1 (2016), 2--20.Google ScholarGoogle Scholar
  34. Cohen, J. 1992. Quantitative methods in psychology: A power primer. Psychol. Bull. 112, (1992), 1155--1159.Google ScholarGoogle Scholar

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    • Published in

      cover image ACM Other conferences
      ICICM '19: Proceedings of the 9th International Conference on Information Communication and Management
      August 2019
      210 pages
      ISBN:9781450371889
      DOI:10.1145/3357419

      Copyright © 2019 ACM

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      Publication History

      • Published: 23 August 2019

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