Teachers Continuance Intention to Use E-Learning Platforms for Blended Teaching in The Post-Epidemic Era
Abstract
Background and Aim: During the COVID-19 pandemic, there was a rapid transition to online teaching, which has led to questions about the future of e-learning platforms for blended learning after the pandemic. It is crucial to understand what influences teachers’ intentions to continue using these platforms, both for technology developers and educators. However, there is a lack of high-quality empirical studies on teachers’ perspectives regarding the continued use of blended learning technologies. This study aims to address this gap by examining the factors that affect teachers’ intentions to continue using these technologies.
Materials and Methods: In this study, survey data from 306 respondents who come from A university in Jilin province, China, were analyzed using structural equation modeling to validate theoretical constructs and hypotheses.
Results: Confirmation positively influences both perceived usefulness (p <.001) and satisfaction (p <.001). Additionally, perceived usefulness significantly impacts satisfaction (p <.001) and attitude (p <.001). Moreover, perceived ease of use positively affects perceived usefulness (p <.001) and attitude (p <.001). Furthermore, satisfaction (p <.001), attitude (p <.001), and compatibility (p <.001) directly and positively influence continuance intention. The perceived usefulness of university teachers in using the Chaoxing platform for blended teaching is related to their intentions of continuous use, but it is not significant (p > 0.05). However, perceived usefulness can indirectly affect teachers’ continued intentions by affecting their satisfaction and attitude toward using the Chaoxing platform for blended teaching.
Conclusion: The satisfaction, attitude, and compatibility of university teachers are important factors influencing their continuance intention to use the Chaoxing platform for blended teaching. While the perceived usefulness is linked to teachers’ intentions for continued use, this correlation is not found to be statistically significant. However, perceived usefulness can indirectly impact teachers’ continuance intentions to use the Chaoxing platform by influencing their satisfaction and attitude. Additionally, while e-learning platforms like Chaoxing offer flexibility and a wide range of resources that enhance the teaching and learning experience, they face technical issues and reduce face-to-face interaction. Addressing these factors can help optimize the effectiveness of such platforms in supporting blended teaching.
Article Details
How to Cite
Section
Articles
Copyright & License
Copyright (c) 2025 International Journal of Sociologies and Anthropologies Science Reviews

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Copyright on any article in the International Journal of Sociologies and Anthropologies Science Reviews is retained by the author(s) under the under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. Permission to use text, content, images, etc. of publication. Any user to read, download, copy, distribute, print, search, or link to the full texts of articles, crawl them for indexing, pass them as data to software, or use them for any other lawful purpose. But do not use it for commercial use or with the intent to benefit any business.
References
Agarwal, R., & Prasad, J. (1997). The role of innovation characteristics and perceived voluntariness in the acceptance of information technologies. Decision Sciences, 28(3), 557-582. https://doi.org/10.1111/j.1540-5915.1997.tb01322.x
Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50(2), 179-211. https://doi.org/10.1016/0749-5978(91)90020-T
Alraimi, K. M., Zo, H., & Ciganek, A. P. (2015). Understanding the MOOCs continuance: The role of openness and reputation. Computers & Education, 80, 28-38. https://doi.org/10.1016/j.compedu.2014.08.006
Altbach, P., & de Wit, H. (2020). Post-pandemic outlook for higher education is bleakest for the poorest. International Higher Education, (102), 3-5. https://ejournals.bc.edu/index.php/ihe/article/view/14583
Azhari, B., & Fajri, I. (2022). Distance learning during the COVID-19 pandemic: School closure in Indonesia. International Journal of Mathematical Education in Science and Technology, 53(7), 1934-1954. https://doi.org/10.1080/0020739X.2021.1875072
Barnes, S. J., & Böhringer, M. (2011). Modeling uses continuance behavior in microblogging services: the case of Twitter. Journal of Computer Information Systems, 51(4), 1-10. https://doi.org/10.1080/08874417.2011.11645496
Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 351-370. https://doi.org/10.2307/3250921
Cao, J. (2022, March 29). My country ranks first in the world in terms of number of MOOCs and number of students. Ministry of Education of China. http://www.moe.gov.cn/fbh/live/2022/54324/mtbd/202203/t20220329_611860.html
Carter, L., & Campbell, R. (2011). The impact of trust and relative advantage on internet voting diffusion. Journal of theoretical and applied electronic commerce research, 6(3), 28-42. https://doi.org/10.4067/S0718-18762011000300004
Chang, C. C., Liang, C., & Chiu, Y. C. (2020). Direct or indirect effects from “perceived characteristic of innovation” to “intention to pay”: mediation of continuance intention to use e-learning. Journal of Computers in Education, 7, 511-530. https://doi.org/10.1007/s40692-020-00165-6
Chen, S. C., Liu, M. L., & Lin, C. P. (2013). Integrating technology readiness into the expectation–confirmation model: An empirical study of mobile services. Cyberpsychology, Behavior, and Social Networking, 16(8), 604-612. https://doi.org/10.1089/cyber.2012.0606
Cheng, S. I., Chen, S. C., & Yen, D. C. (2015). Continuance intention of E-portfolio system: A confirmatory and multigroup invariance analysis of technology acceptance model. Computer Standards & Interfaces, 42, 17-23. https://doi.org/10.1016/j.csi.2015.03.002
Daneji, A. A., Ayub, A. F. M., & Khambari, M. N. M. (2019). The effects of perceived usefulness, confirmation, and satisfaction on continuance intention in using massive open online course (MOOC). Knowledge Management & E-Learning, 11(2), 201-214. http://www.kmel-journal.org/ojs/index.php/online-publication
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 319-340. https://doi.org/10.2307/249008
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982-1003. https://doi.org/10.1287/mnsc.35.8.982
Espino-Díaz, L., Fernandez-Caminero, G., Hernandez-Lloret, C. M., Gonzalez-Gonzalez, H., & Alvarez-Castillo, J. L. (2020). Analyzing the impact of COVID-19 on education professionals. Toward a paradigm shift: ICT and neuroeducation as a binomial of action. Sustainability, 12(14), 5646. https://doi.org/10.3390/su12145646
Fornell, C., & Larcker, D. F. (1981). Structural Equation Models with Unobservable Variables and Measurement Error: Algebra and Statistics. Journal of Marketing Research, 18, 382-388.
http://dx.doi.org/10.2307/3150980
Friesen, N. (2012). Report: defining blended learning. Retrieved from http://learningspaces.org/papers/Defining_Blen ded_Learning_NF.pdf
Graham, C. R., Bonk, C. J., & Graham, C. R. (2006). Handbook of blended learning: Global Perspectives, local designs. San Francisco: Pfeiffer.
Hair, J. F., Black, W. C., Babin, B. J. & Anderson, R. E. (2010). Multivariate data analysis. Pearson, NJ: Pearson Education Inc
Hair, J. F., Hollingsworth, C., Randolph, A., & Chong, A. (2017). An updated and expanded assessment of PLS-SEM in information systems research. Industrial Management & Data Systems, 117(3), 442–458. https://doi.org/10.1108/IMDS-04-2016-0130
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(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. New Challenges to International Marketing, 20, 277–319. https://doi.org/10.1108/S1474 7979(2009)0000020014
Ho, C. H. (2010). Continuance intention of e-learning platform: Toward an integrated model. International Journal of Electronic Business Management, 8(3), 206.
Hong, S., Thong, J. Y. L., & Tam, K. Y. (2006). Understanding continued information technology usage behavior: A comparison of three models in the context of mobile internet. Decision Support Systems, 42(3), 1819–1834. https://doi.org/10.1016/j.dss.2006.03.009
Hong, S.-J., Thong, J., & Tam, K. (2006). Understanding Continued Information Technology Usage Behavior: A Comparison of Three Models in the Context of Mobile Internet. Decision Support Systems, 42, 1819-1834. http://dx.doi.org/10.1016/j.dss.2006.03.009
Huang, Y. M. (2016). The factors that predispose students to continuously use cloud services: Social and technological perspectives. Computers & Education, 97, 86-96. https://doi.org/10.1016/j.compedu.2016.02.016
Isaac, O., Aldholay, A., Abdullah, Z., & Ramayah, T. (2019). Online learning usage within Yemeni higher education: The role of compatibility and task-technology fit as mediating variables in the IS success model. Computers & Education, 136, 113-129. https://doi.org/10.1016/j.compedu.2019.02.012
Kaleta R., Skibba K., Joosten T. (2007). Discovering, designing, and delivering hybrid courses. In Picciano A. G., Dziuban C. D. (Eds.), Blended learning: Research perspectives (pp. 111–143). Sloan Consortium.
Karahanna, E., Straub, D. W., & Chervany, N. L. (1999). Information technology adoption across time: A cross-sectional comparison of pre-adoption and post-adoption beliefs. MIS Quarterly, 183-213. https://doi.org/10.2307/249751
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), 7033-7039. https://doi.org/10.1016/j.eswa.2010.03.015
Lee, D. Y., & Lehto, M. R. (2013). User acceptance of YouTube for procedural learning: An extension of the Technology Acceptance Model. Computers & Education, 61, 193-208. https://doi.org/10.1016/j.compedu.2012.10.001
Li, X., & Heng, Q. (2021). Design of mobile learning resources based on new blended learning: a case study of superstar learning app. In 2021 IEEE 3rd International Conference on Computer Science and Educational Informatization (CSEI), 333-338. IEEE. https://doi.org/10.1109/CSEI51395.2021.9477709
Liao, C., Palvia, P., & Chen, J. L. (2009). Information technology adoption behavior life cycle: Toward a Technology Continuance Theory (TCT). International Journal of Information Management, 29(4), 309-320. https://doi.org/10.1016/j.ijinfomgt.2009.03.004
Liao, H. L., & Lu, H. P. (2008). The role of experience and innovation characteristics in the adoption and continued use of e-learning websites. Computers & Education, 51(4), 1405-1416. https://doi.org/10.1016/j.compedu.2007.11.006
Malaquias, R. F., Malaquias, F. F., & Hwang, Y. (2018). Understanding technology acceptance features in learning through a serious game. Computers in Human Behavior, 87, 395-402. https://doi.org/10.1016/j.chb.2018.06.008
Mouakket, S. (2015). Factors influencing continuance intention to use social network sites: The Facebook case. Computers in Human Behavior, 53, 102-110. https://doi.org/10.1016/j.chb.2015.06.045
Oliver, M., & Trigwell, K. (2005). Can ‘blended learning’be redeemed?. E-learning and Digital Media, 2(1), 17-26. https://doi.org/10.2304/elea.2005.2.1.17
Pelgrum, W. J. (2001). Obstacles to the integration of ICT in education: results from a worldwide educational assessment. Computers & education, 37(2), 163-178. https://doi.org/10.1016/S0360-1315(01)00045-8
Polit, D. F., & Beck, C. T. (2006). The content validity index: are you sure you know what’s being reported? Critique and recommendations. Research in nursing & health, 29(5), 489-497. https://doi.org/10.1002/nur.20147
Roca, J. C., Chiu, C. M., & Martínez, 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. https://doi.org/10.1016/j.ijhcs.2006.01.003
Rogers, E. (1995). Diffusion of innovations. (Fourth Paperback ed.). New York, NY: Free Press.
Rogers, E. M. (2003). Diffusion of innovations. (5th ed.). New York, NY: Free Press.
Sharma, S., & Saini, J. R. (2022). On the role of teachers’ acceptance, continuance intention and self-efficacy in the use of digital technologies in teaching practices. Journal of Further and Higher Education, 46(6), 721-736. https://doi.org/10.1080/0309877X.2021.1998395
Stacey, E., & Gerbic, P. (Eds.). (2009). Effective blended learning practices: Evidence-based perspectives in ICT-facilitated education: Evidence-Based Perspectives in ICT-Facilitated Education. IGI Global.
Stone, R. W., & Baker-Eveleth, L. (2013). Students’ expectation, confirmation, and continuance intention to use electronic textbooks. Computers in Human Behavior, 29(3), 984-990. https://doi.org/10.1016/j.chb.2012.12.007
Susanto, A., Chang, Y., & Ha, Y. (2016). Determinants of continuance intention to use the smartphone banking services: An extension to the expectation-confirmation model. Industrial Management & Data Systems, 116(3), 508-525. https://doi.org/10.1108/IMDS-05-2015-0195
Taber, K. S. (2018). The use of Cronbach’s alpha when developing and reporting research instruments in science education. Research in science education, 48, 1273-1296. DOI 10.1007/s11165-016-9602-2
Taylor, S., & Todd, P. (1995). Assessing IT usage: The role of prior experience. MIS Quarterly, 561-570. https://doi.org/10.2307/249633
Teo, T. (2011). Factors influencing teachers’ intention to use technology: Model development and test. Computers & Education, 57(4), 2432-2440. https://doi.org/10.1016/j.compedu.2011.06.008
Travagli, F. (2012). Smartphone buying behavior: The chasm between early and late Adopters. J Sci Strateg Mark Creat.
Wu, B., & Chen, X. (2017). Continuance intention to use MOOCs: Integrating the technology acceptance model (TAM) and task technology fit (TTF) model. Computers in human behavior, 67, 221-232. https://doi.org/10.1016/j.chb.2016.10.028
Wu, B., & Zhang, C. (2014). Empirical study on continuance intentions towards E-Learning 2.0 systems. Behaviour & Information Technology, 33(10), 1027-1038. https://doi.org/10.1080/0144929X.2014.934291
Yi, Chen. (2021). Most Common Types of Online English Teaching During Covid-19 Pandemic in China—An Introduction to Fanya and Chaoxing Platform. Sino-US English Teaching, 18(4), 79-85. https://doi:10.17265/1539-8072/2021.04.001