Exploring factors influencing students’ continuance intention to use the learning management system (LMS): a multi-perspective framework

References

• Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179–211.
   Web of Science ®Google Scholar
• Al-Debei, M. M., Al-Lozi, E., & 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, 43–54.
   Web of Science ®Google Scholar
• Al-Rahmi, W. M., Yahaya, N., Alamri, M. M., Alyoussef, I. Y., Al-Rahmi, A. M., & Kamin, Y. B. (2019). Integrating innovation diffusion theory with technology acceptance model: Supporting students’ attitude towards using a massive open online courses (MOOCs) systems. Interactive Learning Environments, 1–13. https://doi.org/10.1080/10494820.2019.1629599.
   Google Scholar
• Al-Somali, S. A., Gholami, R., & Clegg, B. (2009). An investigation into the acceptance of online banking in Saudi Arabia. Technovation, 29, 130–141.
   Web of Science ®Google Scholar
• Alolah, T., Stewart, R. A., Panuwatwanich, K., & Mohamed, S. (2014). Determining the causal relationships among balanced scorecard perspectives on school safety performance: Case of Saudi Arabia. Accident Analysis & Prevention, 68, 57–74.
   PubMed Web of Science ®Google Scholar
• Anderson, E. W., & Sullivan, M. W. (1993). The antecedents and consequences of customer satisfaction for firms. Marketing Science, 12, 125–143.
   Web of Science ®Google Scholar
• Angehrn, A. (1997). Designing mature Internet business strategies: The ICDT model. European Management Journal, 15, 361–369.
   Google Scholar
• Aparicio, M., Bacao, F., & Oliveira, T. (2017). Grit in the path to e-learning success. Computers in Human Behavior, 66, 388–399.
   Web of Science ®Google Scholar
• Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25, 351–370.
   Web of Science ®Google Scholar
• Bhattacherjee, A., & Lin, C.-P. (2015). A unified model of IT continuance: Three complementary perspectives and crossover effects. European Journal of Information Systems, 24, 364–373.
   Web of Science ®Google Scholar
• Bhattacherjee, A., Perols, J., & Sanford, C. (2008). Information technology continuance: A theoretic extension and empirical test. Journal of Computer Information Systems, 49, 17–26.
   Web of Science ®Google Scholar
• Bøe, T., Gulbrandsen, B., & 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, 375–384.
   Web of Science ®Google Scholar
• Brown, S. A., Venkatesh, V., & Goyal, S. (2012). Expectation confirmation in technology use. Information Systems Research, 23, 474–487.
   Web of Science ®Google Scholar
• Cebi, S. (2013). Determining importance degrees of website design parameters based on interactions and types of websites. Decision Support Systems, 54, 1030–1043.
   Web of Science ®Google Scholar
• Chang, I.-C., Liu, C.-C., & Chen, K. (2014). The effects of hedonic/utilitarian expectations and social influence on continuance intention to play online games. Internet Research, 24, 21–45.
   Web of Science ®Google Scholar
• Chen, S.-C., Yen, D. C., & Hwang, M. I. (2012). Factors influencing the continuance intention to the usage of Web 2.0: An empirical study. Computers in Human Behavior, 28, 933–941.
   Web of Science ®Google Scholar
• Cheng, M., & Yuen, A. H. K. (2018). Student continuance of learning management system use: A longitudinal exploration. Computers & Education, 120, 241–253.
   Web of Science ®Google Scholar
• Cheon, J., Lee, S., Crooks, S. M., & Song, J. (2012). An investigation of mobile learning readiness in higher education based on the theory of planned behavior. Computers & Education, 59, 1054–1064.
   Web of Science ®Google Scholar
• Childers, T. L., Carr, C. L., Peck, J., & Carson, S. (2001). Hedonic and utilitarian motivations for online retail shopping behavior. Journal of Retailing, 77, 511–535.
   Web of Science ®Google Scholar
• Chiu, C.-M., & Wang, E. T. (2008). Understanding Web-based learning continuance intention: The role of subjective task value. Information & Management, 45, 194–201.
   Web of Science ®Google Scholar
• Choi, G., Nam, C., & Kim, S. (2019). The impacts of technology platform openness on application developers’ intention to continuously use a platform: From an ecosystem perspective. Telecommunications Policy, 43, 140–153.
   Web of Science ®Google Scholar
• Cidral, W. A., Oliveira, T., Di Felice, M., & Aparicio, M. (2018). E-learning success determinants: Brazilian empirical study. Computers & Education, 122, 273–290.
   Web of Science ®Google Scholar
• Cigdem, H., & Topcu, A. (2015). Predictors of instructors’ behavioral intention to use learning management system: A Turkish vocational college example. Computers in Human Behavior, 52, 22–28.
   Web of Science ®Google Scholar
• Cohen, J. (1992). Quantitative methods in psychology: A power primer. Psychological Bulletin, 112, 1155–1159.
   Google Scholar
• Dağhan, G., & Akkoyunlu, B. (2016). Modeling the continuance usage intention of online learning environments. Computers in Human Behavior, 60, 198–211.
   Web of Science ®Google Scholar
• Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13, 319–340.
   Web of Science ®Google Scholar
• Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace1. Journal of Applied Social Psychology, 22, 1111–1132.
   Web of Science ®Google Scholar
• Duan, Y., He, Q., Feng, W., Li, D., & Fu, Z. (2010). A study on e-learning take-up intention from an innovation adoption perspective: A case in China. Computers & Education, 55, 237–246.
   Web of Science ®Google Scholar
• Eveleth, D. M., Baker-Eveleth, L. J., & Stone, R. W. (2015). Potential applicants’ expectation-confirmation and intentions. Computers in Human Behavior, 44, 183–190.
   Web of Science ®Google Scholar
• Fang, X., & Holsapple, C. W. (2011). Impacts of navigation structure, task complexity, and users’ domain knowledge on Web site usability—An empirical study. Information Systems Frontiers, 13, 453–469.
   Web of Science ®Google Scholar
• Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. NY, USA: Addison-Wesley.
   Google Scholar
• Fornell, C., & Larcker, D. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18, 39–50.
   Web of Science ®Google Scholar
• Hair, J. J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM). Thousand Oaks, California, USA: Sage Publications.
   Google Scholar
• Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. (2012). An assessment of the use of partial least squares structural equation modeling in marketing research. Journal of the Academy of Marketing Science, 40, 414–433.
   Web of Science ®Google Scholar
• Hassan, M. A., Habiba, U., Majeed, F., & Shoaib, M. (2019). Adaptive gamification in e-learning based on students’ learning styles. Interactive Learning Environments, 1–21. https://doi.org/10.1080/10494820.2019.1588745.
   Google Scholar
• Hassanzadeh, A., Kanaani, F., & Elahi, S. (2012). A model for measuring e-learning systems success in universities. Expert Systems with Applications, 39, 10959–10966.
   Web of Science ®Google Scholar
• Henseler, J., Dijkstra, T. K., Sarstedt, M., Ringle, C. M., Diamantopoulos, A., Straub, D. W., … Calantone, R. J. (2014). Common beliefs and reality about PLS: Comments on Rönkkö and Evermann (2013). Organizational Research Methods, 17, 182–209.
   Web of Science ®Google Scholar
• Henseler, J., Hubona, G., & Ray, P. A. (2016). Using PLS path modeling in new technology research: Updated guidelines. Industrial Management & Data Systems, 116, 2–20.
   Web of Science ®Google Scholar
• Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43, 115–135.
   Web of Science ®Google Scholar
• Horvat, A., Dobrota, M., Krsmanovic, M., & Cudanov, M. (2015). Student perception of Moodle learning management system: A satisfaction and significance analysis. Interactive Learning Environments, 23, 515–527.
   Web of Science ®Google Scholar
• Hsu, M.-H., Ju, T. L., Yen, C.-H., & Chang, C.-M. (2007). Knowledge sharing behavior in virtual communities: The relationship between trust, self-efficacy, and outcome expectations. International Journal of Human-Computer Studies, 65, 153–169.
   Web of Science ®Google Scholar
• Hu, L.-T., & Bentler, P. M. (1998). Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. Psychological Methods, 3, 424–453.
   Web of Science ®Google Scholar
• Hubalovsky, S., Hubalovska, M., & Musilek, M. (2019). Assessment of the influence of adaptive e-learning on learning effectiveness of primary school pupils. Computers in Human Behavior, 92, 691–705.
   Web of Science ®Google Scholar
• Huizingh, E. K. (2000). The content and design of web sites: An empirical study. Information & Management, 37, 123–134.
   Web of Science ®Google Scholar
• Hung, M.-C., Chang, I.-C., & Hwang, H.-G. (2011). Exploring academic teachers’ continuance toward the web-based learning system: The role of causal attributions. Computers & Education, 57, 1530–1543.
   Web of Science ®Google Scholar
• Islam, A. (2012). The role of perceived system quality as educators’ motivation to continue e-learning system use. AIS Transactions on Human-Computer Interaction, 4, 25–43.
   Google Scholar
• Jahanmir, S. F., & Cavadas, J. (2018). Factors affecting late adoption of digital innovations. Journal of Business Research, 88, 337–343.
   Web of Science ®Google Scholar
• Joo, Y. J., Park, S., & Shin, E. K. (2017). Students’ expectation, satisfaction, and continuance intention to use digital textbooks. Computers in Human Behavior, 69, 83–90.
   Web of Science ®Google Scholar
• Joo, Y. J., So, H.-J., & Kim, N. H. (2018). Examination of relationships among students’ self-determination, technology acceptance, satisfaction, and continuance intention to use K-MOOCs. Computers & Education, 122, 260–272.
   Web of Science ®Google Scholar
• Kang, Y. S., Hong, S., & Lee, H. (2009). Exploring continued online service usage behavior: The roles of self-image congruity and regret. Computers in Human Behavior, 25, 111–122.
   Web of Science ®Google Scholar
• 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, 7033–7039.
   Web of Science ®Google Scholar
• Kline, R. B. (2015). Principles and practice of structural equation modeling. NY, USA: Guilford publications.
   Google Scholar
• Kwon, O., & Wen, Y. (2010). An empirical study of the factors affecting social network service use. Computers in Human Behavior, 26, 254–263.
   Web of Science ®Google Scholar
• Lai, H.-M., Hsiao, Y.-L., & Hsieh, P.-J. (2018). The role of motivation, ability, and opportunity in university teachers’ continuance use intention for flipped teaching. Computers & Education, 124, 37–50.
   Web of Science ®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, 506–516.
   Web of Science ®Google Scholar
• Lee, Y.-H., Hsieh, Y.-C., & Ma, C.-Y. (2011). A model of organizational employees’ e-learning systems acceptance. Knowledge-Based Systems, 24, 355–366.
   Web of Science ®Google Scholar
• Lee, Y., & Kozar, K. A. (2012). Understanding of website usability: Specifying and measuring constructs and their relationships. Decision Support Systems, 52, 450–463.
   Web of Science ®Google Scholar
• Lew, S.-L., Lau, S.-H., & Leow, M.-C. (2019). Usability factors predicting continuance of intention to use cloud e-learning application. Heliyon, 5, e01788.
   PubMed Web of Science ®Google Scholar
• Li, C.-Y., & Fang, Y.-H. (2019). Predicting continuance intention toward mobile branded apps through satisfaction and attachment. Telematics and Informatics, 43, 101248.
   Web of Science ®Google Scholar
• Liao, C., Chen, J.-L., & 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, 2804–2822.
   Web of Science ®Google Scholar
• Lin, K.-M. (2011). e-Learning continuance intention: Moderating effects of user e-learning experience. Computers & Education, 56, 515–526.
   Web of Science ®Google Scholar
• Lin, T.-C., Wu, S., Hsu, J. S.-C., & Chou, Y.-C. (2012). The integration of value-based adoption and expectation–confirmation models: An example of IPTV continuance intention. Decision Support Systems, 54, 63–75.
   Web of Science ®Google Scholar
• Lin, C. S., Wu, S., & Tsai, R. J. (2005). Integrating perceived playfulness into expectation-confirmation model for web portal context. Information & Management, 42, 683–693.
   Web of Science ®Google Scholar
• Malhotra, N. K., Schaller, T. K., & Patil, A. (2017). Common method variance in advertising research: When to be concerned and how to control for it. Journal of Advertising, 46, 193–212.
   Web of Science ®Google Scholar
• Mcgill, T. J., & Klobas, J. E. (2009). A task–technology fit view of learning management system impact. Computers & Education, 52, 496–508.
   Web of Science ®Google Scholar
• Mohammadi, H. (2015). Investigating users’ perspectives on e-learning: An integration of TAM and IS success model. Computers in Human Behavior, 45, 359–374.
   Web of Science ®Google Scholar
• Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2, 192–222.
   Web of Science ®Google Scholar
• Mouakket, S. (2015). Factors influencing continuance intention to use social network sites: The Facebook case. Computers in Human Behavior, 53, 102–110.
   Web of Science ®Google Scholar
• Moustakis, V., Tsironis, L., & Litos, C. (2006). A model of web site quality assessment. Quality Management Journal, 13, 22–37.
   Google Scholar
• Ngai, E. W., Poon, J., & Chan, Y. (2007). Empirical examination of the adoption of WebCT using TAM. Computers & Education, 48, 250–267.
   Web of Science ®Google Scholar
• Nurakun Kyzy, Z., Ismailova, R., & Dündar, H. (2018). Learning management system implementation: A case study in the Kyrgyz Republic. Interactive Learning Environments, 26, 1010–1022.
   Web of Science ®Google Scholar
• Oghuma, A. P., Libaque-Saenz, C. F., Wong, S. F., & Chang, Y. (2016). An expectation-confirmation model of continuance intention to use mobile instant messaging. Telematics and Informatics, 33, 34–47.
   Web of Science ®Google Scholar
• Oliver, R. L. (1980). A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of Marketing Research, 17, 460–469.
   Web of Science ®Google Scholar
• Ozkan, S., & Koseler, R. (2009). Multi-dimensional students’ evaluation of e-learning systems in the higher education context: An empirical investigation. Computers & Education, 53, 1285–1296.
   Web of Science ®Google Scholar
• Peng, C., OuYang, Z., & Liu, Y. (2019). Understanding bike sharing use over time by employing extended technology continuance theory. Transportation Research Part A: Policy and Practice, 124, 433–443.
   Web of Science ®Google Scholar
• Perdue, R. R. (2002). Internet site evaluations: The influence of behavioral experience, existing images, and selected website characteristics. Journal of Travel & Tourism Marketing, 11, 21–38.
   Google Scholar
• Podsakoff, P. M., Mackenzie, S. B., & Podsakoff, N. P. (2012). Sources of method bias in social science research and recommendations on how to control it. Annual Review of Psychology, 63, 539–569.
   PubMed Web of Science ®Google Scholar
• Ramakrishnan, T., Prybutok, V., & Peak, D. A. (2014). The moderating effect of gender on academic website impression. Computers in Human Behavior, 35, 315–319.
   Web of Science ®Google Scholar
• Ramírez-Correa, P. E., Rondan-Cataluña, F. J., Arenas-Gaitán, J., & Alfaro-Perez, J. L. (2017). Moderating effect of learning styles on a learning management system’s success. Telematics and Informatics, 34, 272–286.
   Web of Science ®Google Scholar
• Richter, N. F., Cepeda, G., Roldán, J. L., & Ringle, C. M. (2016). European management research using partial least squares structural equation modeling (PLS-SEM). European Management Journal, 34, 589–597.
   Web of Science ®Google Scholar
• Ringle, C. M., Wende, S., & Becker, J.-M. (2015). . SmartPLS 3. Boenningstedt: SmartPLS GmbH. Retrieved from www.smartpls.com
   Google Scholar
• Sabi, H. M., Uzoka, F.-M. E., Langmia, K., & Njeh, F. N. (2016). Conceptualizing a model for adoption of cloud computing in education. International Journal of Information Management, 36, 183–191.
   Web of Science ®Google Scholar
• Samir, M., De Fatima Marin, H., Sonia, E. B., De Lira, A. C. O., De Quirós, F. G. B., & Patrice, D. (2020). External validation of the unified model of information systems continuance (UMISC): An international comparison. International Journal of Medical Informatics, 134, 103927.
   PubMed Web of Science ®Google Scholar
• Shi, A., Wang, Y., & Ding, N. (2019). The effect of game–based immersive virtual reality learning environment on learning outcomes: Designing an intrinsic integrated educational game for pre-class learning. Interactive Learning Environments, 1–14. https://doi.org/10.1080/10494820.2019.1681467.
   Google Scholar
• Siamagka, N.-T., Christodoulides, G., Michaelidou, N., & Valvi, A. (2015). Determinants of social media adoption by B2B organizations. Industrial Marketing Management, 51, 89–99.
   Web of Science ®Google Scholar
• Stone, R. W., & Baker-Eveleth, L. (2013). Students’ expectation, confirmation, and continuance intention to use electronic textbooks. Computers in Human Behavior, 29, 984–990.
   Web of Science ®Google Scholar
• Streukens, S., & Leroi-Werelds, S. (2016). Bootstrapping and PLS-SEM: A step-by-step guide to get more out of your bootstrap results. European Management Journal, 34, 618–632.
   Web of Science ®Google Scholar
• Thong, J. Y., 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, 799–810.
   Web of Science ®Google Scholar
• Tran, L. T. T., Pham, L. M. T., & Le, L. T. (2019). E-satisfaction and continuance intention: The moderator role of online ratings. International Journal of Hospitality Management, 77, 311–322.
   Web of Science ®Google Scholar
• Tsai, Y.-H., Lin, C.-H., Hong, J.-C., & Tai, K.-H. (2018). The effects of metacognition on online learning interest and continuance to learn with MOOCs. Computers & Education, 121, 18–29.
   Web of Science ®Google Scholar
• Van der Heijden, H. (2003). Factors influencing the usage of websites: The case of a generic portal in The Netherlands. Information & Management, 40, 541–549.
   Web of Science ®Google Scholar
• Van Oorschot, J. A., Hofman, E., & Halman, J. I. (2018). A bibliometric review of the innovation adoption literature. Technological Forecasting and Social Change, 134, 1–21.
   Web of Science ®Google Scholar
• Van Raaij, E. M., & Schepers, J. J. (2008). The acceptance and use of a virtual learning environment in China. Computers & Education, 50, 838–852.
   Web of Science ®Google Scholar
• Venkatesh, V., & 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, 24, 115–139.
   Web of Science ®Google Scholar
• Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27, 425–478.
   Web of Science ®Google Scholar
• Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36, 157–178.
   Web of Science ®Google Scholar
• Wang, W.-T., Ou, W.-M., & Chen, W.-Y. (2019). The impact of inertia and user satisfaction on the continuance intentions to use mobile communication applications: A mobile service quality perspective. International Journal of Information Management, 44, 178–193.
   Web of Science ®Google Scholar
• Wang, W.-T., & Wang, C.-C. (2009). An empirical study of instructor adoption of web-based learning systems. Computers & Education, 53, 761–774.
   Web of Science ®Google Scholar
• 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, 1792–1808.
   Web of Science ®Google Scholar
• Weaver, D., Spratt, C., & Nair, C. S. (2008). Academic and student use of a learning management system: Implications for quality. Australasian Journal of Educational Technology, 24, 30–41.
   Web of Science ®Google Scholar
• Wells, J. D., Valacich, J. S., & Hess, T. J. (2011). What signal are you sending? How website quality influences perceptions of product quality and purchase intentions. MIS Quarterly, 35, 373–396.
   Web of Science ®Google Scholar
• Wen, C., Prybutok, V. R., & Xu, C. (2011). An integrated model for customer online repurchase intention. Journal of Computer Information Systems, 52, 14–23.
   Web of Science ®Google Scholar
• Yoon, C., & Rolland, E. (2015). Understanding continuance use in social networking services. Journal of Computer Information Systems, 55, 1–8.
   Web of Science ®Google Scholar
• Zhang, X., De Pablos, P. O., Wang, X., Wang, W., Sun, Y., & She, J. (2014). Understanding the users’ continuous adoption of 3D social virtual world in China: A comparative case study. Computers in Human Behavior, 35, 578–585.
   Web of Science ®Google Scholar
• Zhang, X., Keeling, K. B., & Pavur, R. J. (2000). Information quality of commercial web site home pages: an explorative analysis. Proceedings of the twenty first international conference on Information systems, Association for Information Systems, 164–175.
   Google Scholar
• Zhou, T. (2011). Understanding online community user participation: A social influence perspective. Internet Research, 21, 67–81.
   Web of Science ®Google Scholar