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.
- 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 ScholarCross Ref
- 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 ScholarDigital Library
- 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 Scholar
- 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 Scholar
- Bhattacherjee, A. 2001. Understanding information systems continuance: an expectation-confirmation model. MIS quarterly, (2001), 351--370.Google Scholar
- 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 ScholarCross Ref
- 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 ScholarDigital Library
- 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 ScholarCross Ref
- 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 ScholarCross Ref
- 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 Scholar
- Davis, F.D. 1989. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, (1989), 319--340.Google Scholar
- 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 Scholar
- 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 ScholarDigital Library
- Mouakket, S. 2015. Factors influencing continuance intention to use social network sites: The Facebook case. Computers in Human Behavior. 53, (2015), 102--110.Google ScholarDigital Library
- Jahanmir, S.F. and Cavadas, J. 2018. Factors affecting late adoption of digital innovations. Journal of Business Research. 88, (2018), 337--343.Google ScholarCross Ref
- 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 ScholarDigital Library
- 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 Scholar
- 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 Scholar
- 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 ScholarCross Ref
- 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 ScholarDigital Library
- Fishbein, M. and Ajzen, I., Belief, attitude, intention, and behavior: An introduction to theory and research. 1975: Addison-Wesley.Google Scholar
- 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 Scholar
- 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 ScholarDigital Library
- Oliver, R.L. 1980. A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of marketing research, (1980), 460--469.Google Scholar
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- 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 Scholar
- Mohammadi, H. 2015. Social and individual antecedents of m-learning adoption in Iran. Computers in Human Behavior. 49, (2015), 191--207.Google ScholarDigital Library
- 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 Scholar
- Siamagka, N.-T., et al. 2015. Determinants of social media adoption by B2B organizations. Industrial Marketing Management. 51, (2015), 89--99.Google ScholarCross Ref
- 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 Scholar
- Ringle, C.M., Wende, S., and Becker, J.-M. 2015. SmartPLS 3. Boenningstedt: SmartPLS GmbH, http://www. smartpls. com, (2015).Google Scholar
- 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 Scholar
- Cohen, J. 1992. Quantitative methods in psychology: A power primer. Psychol. Bull. 112, (1992), 1155--1159.Google Scholar
Index Terms
- Influencing factors on students' continuance intention to use Learning Management System (LMS)
Recommendations
Factors influencing continuance intention to use social network sites
Factors influencing continuance intention to use Facebook are examined.Satisfaction and perceived usefulness influence continuance intention.Enjoyment and subjective norms influence continuance intention.Habit mediates the relationship between ...
Predicting Users' Continuance Intention Toward E-payment System: An Extension of the Technology Acceptance Model
This paper synthesized the technology acceptance model (TAM), to explain and predict the users' intentions to continue using e-payment system. The hypothesized model was validated empirically using a sample data collected from a modified e-payment ...
Continuance intention to use MOOCs
The purpose of this study is to propose a unified model integrating the technology acceptance model (TAM), task fit technology (TTF) model, MOOCs features and social motivation to investigate continuance intention to use MOOCs. A sample of 252 ...
Comments