Factors Impacting Chinese College Students’ Perceived Usefulness and Continuance Intention Toward an Online Learning Platform in Yibin, China
Abstract
This study investigates the factors influencing Chinese college students’ perceived usefulness and continuance intention regarding an online learning platform in Yibin, China. Based on the Expectation Confirmation Model (ECM) and Technology Acceptance Model (TAM), a conceptual framework was developed with five independent variables (Information Quality (IQ), System Quality (SQ), Perceived Ease of Use (PEU), Social Influence (SI), and Interactivity (INT)), one mediating variable - Perceived Usefulness (PU), and one dependent variable - Continuance Intention (CI)). A structured questionnaire was distributed to 500 senior undergraduate students from four majors at Sichuan University of Science & Engineering (Yibin campus). Empirical findings indicate that all five independent variables—IQ, SQ, PEU, SI, and INT—positively and significantly affect PU. Furthermore, both PU and PEU have significant direct impacts on CI. PU plays a key mediating role, linking system and social factors to usage behavior. These observations reaffirm prior scholarly work in technology adoption and on learning, supporting the premise that students’ judgments of content quality, platform usability, social environment, and interactive features critically shape their evaluation of usefulness, which in turn influences their CI to use online learning platforms. The findings offer practical insights for improving online learning platforms through enhanced quality, interactivity, and social engagement features.
References
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
Alami, Y., & El Idrissi, I. (2022). Students’ adoption of e-learning: Evidence from a Moroccan business school in the COVID-19 era. Arab Gulf Journal of Scientific Research, 40(1), 54-78. https://doi.org/10.1108/AGJSR-05-2022-0052
Aldholay, A., Abdullah, Z., Isaac, O., & Mutahar, A. M. (2019). Perspective of Yemeni students on use of online learning: Extending the information systems success model with transformational leadership and compatibility. Information Technology & People, 33(1), 106-128. https://doi.org/10.1108/ITP-02-2018-0095
Al-Fraihat, D., Joy, M., Masa’deh, R., & Sinclair, J. (2020). Evaluating E-learning systems success: An empirical study. Computers in Human Behavior, 102, 67-86. https://doi.org/10.1016/j.chb.2019.08.004
Al-Mamary, Y. H., & Shamsuddin, H. (2015). Testing of the technology acceptance model in context of Yemen. Mediterranean Journal of Social Sciences, 6(4). https://www.tandfonline.com/doi/abs/10.1080/01621459.1938.10502329
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
Bajaj, P., Khan, A., Tabash, M. I., & Anagreh, S. (2021). Teachers’ intention to continue the use of online teaching tools post Covid-19. Cogent Education, 8(1), 2002130. https://doi.org/10.1080/2331186X.2021.2002130
Barreda, A. A., Bilgihan, A., Nusair, K., & Okumus, F. (2016). Online branding: Development of hotel branding through interactivity theory. Tourism Management, 57, 180-192. https://doi.org/10.1016/j.tourman.2016.06.007
Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238-246. https://doi.org/10.1037/0033-2909.107.2.238
Bhattacherjee, A. (2001). Understanding Information Systems Continuance: An Expectation-Confirmation Model. MIS Quarterly, 25(3), 351-370. https://doi.org/10.2307/3250921
Calisir, F., Altin Gumussoy, C., 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 & Service Industries, 24(5), 515-531. https://doi.org/10.1002/hfm.20548
Chang, C. (2013). Exploring the determinants of e‐learning systems continuance intention in academic libraries. Library Management, 34(1/2), 40-55. https://doi.org/10.1108/01435121311298261
Chauhan, S., Goyal, S., Bhardwaj, A. K., & Sergi, B. S. (2022). Examining continuance intention in business schools with digital classroom methods during COVID-19: A comparative study of India and Italy. Behaviour & Information Technology, 41(8), 1596-1619. https://doi.org/10.1080/0144929X.2021.1892191
Chen, Q., Chen, H.-M., & Kazman, R. (2007). Investigating antecedents of technology acceptance of initial eCRM users beyond generation X and the role of self-construal. Electronic Commerce Research, 7(3), 315-339.
https://doi.org/10.1007/s10660-007-9009-2
Cheng, B., Wang, M., Moormann, J., Olaniran, B. A., & Chen, N.-S. (2012). The effects of organizational learning environment factors on e-learning acceptance. Computers & Education, 58(3), 885-899. https://doi.org/10.1016/j.compedu.2011.10.014
Cheng, Y.-M. (2012). The effects of information systems quality on nurses’ acceptance of the electronic learning system. Journal of Nursing Research, 20(1), 19. https://doi.org/10.1097/JNR.0b013e31824777aa
Cheng, Y.-M. (2014). Extending the expectation-confirmation model with quality and flow to explore nurses’ continued blended e-learning intention. Information Technology & People, 27(3), 230-258. https://doi.org/10.1108/ITP-01-2013-0024
Cheng, Y.-M. (2020). Students’ satisfaction and continuance intention of the cloud-based e-learning system: Roles of interactivity and course quality factors. Education + Training, 62(9), 1037-1059. https://doi.org/10.1108/ET-10-2019-0245
Cheng, Y.-M. (2021). Investigating medical professionals’ continuance intention of the cloud-based e-learning system: An extension of expectation-confirmation model with flow theory. Journal of Enterprise Information Management, 34(4), 1169-1202. https://doi.org/10.1108/JEIM-12-2019-0401
Cho, V., Cheng, T. C. E., & Lai, W. M. J. (2009). The role of perceived user-interface design in continued usage intention of self-paced e-learning tools. Computers & Education, 53(2), 216-227. https://doi.org/10.1016/j.compedu.2009.01.014
Choi, D. H., Kim, J., & Kim, S. H. (2007). ERP training with a web-based electronic learning system: The flow theory perspective. International Journal of Human-Computer Studies, 65(3), 223-243. https://doi.org/10.1016/j.ijhcs.2006.10.002
Claar, C., Dias, L. P., & Shields, R. (2014). Student acceptance of learning management systems: A study on demographics. Issues In Information Systems, 15(1). https://doi.org/10.48009/1_iis_2014_409-417
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319. 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
DeLone, W. H., & McLean, E. R. (1992). Information systems success: The quest for the dependent variable. Information Systems Research, 3(1), 60-95. https://doi.org/10.1287/isre.3.1.60
Dorobăţ, I., Corbea, A. M. I., & Muntean, M. (2019). Integrating student trust in a conceptual model for assessing learning management system success in higher education: An empirical analysis. IEEE Access, 7, 69202-69214.
https://doi.org/10.1109/ACCESS.2019.2919100
Elsotouhy, M. M., Mobarak, A. M. A., Dakrory, M. I., Ghonim, M. A., & Khashan, M. A. (2023). Integrating ISS and SOR models to investigate the determinants of continuance intention toward using m-payment for donations (Sadaqah): The moderating role of Islamic religiosity. International Journal of Bank Marketing, 41(7), 1640-1670. https://doi.org/10.1108/IJBM-11-2022-0498
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50. https://doi.org/10.1177/002224378101800104
Foroughi, B., Iranmanesh, M., & Hyun, S. S. (2019). Understanding the determinants of mobile banking continuance usage intention. Journal of Enterprise Information Management, 32(6), 1015-1033. https://doi.org/10.1108/JEIM-10-2018-0237
Ghosh, V., Bharadwaja, M., & Mukherjee, H. (2023). Examining online learning platform characteristics and employee engagement relationship during Covid-19. VINE Journal of Information and Knowledge Management Systems, 53(2), 335-357. https://doi.org/10.1108/VJIKMS-05-2022-0154
Guo, Q., Zeng, Q., & Zhang, L. (2023). What social factors influence learners’ continuous intention in online learning? A social presence perspective. Information Technology & People, 36(3), 1076-1094. https://doi.org/10.1108/ITP-02-2021-0151
Gupta, A., Dhiman, N., Yousaf, A., & Arora, N. (2021). Social comparison and continuance intention of smart fitness wearables: An extended expectation confirmation theory perspective. Behaviour & Information Technology, 40(13), 1341-1354. https://doi.org/10.1080/0144929X.2020.1748715
Hadji, B., & Degoulet, P. (2016). Information system end-user satisfaction and continuance intention: A unified modeling approach. Journal of Biomedical Informatics, 61, 185-193. https://doi.org/10.1016/j.jbi.2016.03.021
Hair, J. F., Gabriel, M., & Patel, V. (2014). AMOS Covariance-Based Structural Equation Modeling (CB-SEM): Guidelines on Its Application as a Marketing Research Tool (SSRN Scholarly Paper 2676480). https://papers.ssrn.com/abstract=2676480
Hair, J. F., Money, A. H., Samouel, P., & Page, M. (2007). Research methods for business. Education + Training, 49(4), 336-337. https://doi.org/10.1108/et.2007.49.4.336.2
Hellier, P. K., Geursen, G. M., Carr, R. A., & Rickard, J. A. (2003). Customer repurchase intention: A general structural equation model. European Journal of Marketing, 37(11/12), 1762-1800. https://doi.org/10.1108/03090560310495456
Huang, J., Lin, Y., & Chuang, S. (2007). Elucidating user behavior of mobile learning: A perspective of the extended technology acceptance model. The Electronic Library, 25(5), 585-598. https://doi.org/10.1108/02640470710829569
Ifinedo, P. (2017). Examining students’ intention to continue using blogs for learning: Perspectives from technology acceptance, motivational, and social-cognitive frameworks. Computers in Human Behavior, 72, 189-199.
https://doi.org/10.1016/j.chb.2016.12.049
Jeong, H. (2011). An investigation of user perceptions and behavioral intentions towards the e-library. Library Collections, Acquisitions, and Technical Services, 35(2), 45-60. https://doi.org/10.1016/j.lcats.2011.03.018
Karaali, D., Gumussoy, C. A., & Calisir, F. (2011). Factors affecting the intention to use a web-based learning system among blue-collar workers in the automotive industry. Computers in Human Behavior, 27(1), 343-354. https://doi.org/10.1016/j.chb.2010.08.012
Karahanna, E., & Straub, D. W. (1999). The psychological origins of perceived usefulness and ease-of-use. Information & Management, 35(4), 237-250. https://doi.org/10.1016/S0378-7206(98)00096-2
Kim, K.-H., Kim, K.-J., Lee, D.-H., & Kim, M.-G. (2019). Identification of critical quality dimensions for continuance intention in mHealth services: Case study of onecare service. International Journal of Information Management, 46, 187-197.
https://doi.org/10.1016/j.ijinfomgt.2018.12.008
Kline, R. B. (2023). Principles and Practice of Structural Equation Modeling. Guilford Publications.
Kuo, Y.-C., Walker, A. E., Schroder, K. E. E., & Belland, B. R. (2014). Interaction, Internet self-efficacy, and self-regulated learning as predictors of student satisfaction in online education courses. The Internet and Higher Education, 20, 35-50. https://doi.org/10.1016/j.iheduc.2013.10.001
Lee, B.-C., Yoon, J.-O., & Lee, I. (2009). Learners’ acceptance of e-learning in South Korea: Theories and results. Computers & Education, 53(4), 1320-1329. https://doi.org/10.1016/j.compedu.2009.06.014
Lee, H., Kim, J., & Kim, J. (2007). Determinants of success for application service provider: An empirical test in small businesses. International Journal of Human-Computer Studies, 65(9), 796-815. https://doi.org/10.1016/j.ijhcs.2007.04.004
Lee, Y. (2006). An empirical investigation into factors influencing the adoption of an e‐learning system. Online Information Review, 30(5), 517-541. https://doi.org/10.1108/14684520610706406
Lee, Y.-H., Hsieh, Y.-C., & Chen, Y.-H. (2013). An investigation of employees’ use of e-learning systems: Applying the technology acceptance model. Behaviour & Information Technology, 32(2), 173-189. https://doi.org/10.1080/0144929X.2011.577190
Lin, H.-C., Chiu, Y.-H., Chen, Y. J., Wuang, Y.-P., Chen, C.-P., Wang, C.-C., Huang, C.-L., Wu, T.-M., & Ho, W.-H. (2017). Continued use of an interactive computer game-based visual perception learning system in children with developmental delay. International Journal of Medical Informatics, 107, 76-87. https://doi.org/10.1016/j.ijmedinf.2017.09.003
Liu, Y., & Shrum, L. J. (2002). What is interactivity and is it always such a good thing? Implications of definition, person, and situation for the influence of interactivity on advertising effectiveness. Journal of Advertising, 31(4), 53-64.
https://doi.org/10.1080/00913367.2002.10673685
Lu, J. (2014). Are personal innovativeness and social influence critical to continue with mobile commerce? Internet Research, 24(2), 134-159. https://doi.org/10.1108/IntR-05-2012-0100
Lwoga, E. (2014). Critical success factors for adoption of web-based learning management systems in Tanzania. International Journal of Education and Development Using ICT, 10(1). https://www.learntechlib.org/p/147447/
Lwoga, E. T., & Komba, M. (2015). Antecedents of continued usage intentions of web-based learning management system in Tanzania. Education + Training, 57(7), 738-756. https://doi.org/10.1108/ET-02-2014-0014
Madani, H., Adhikari, A., & Hodgdon, C. (2023). Understanding faculty acceptance of online teaching during the COVID-19 pandemic: A Saudi Arabian case study. Journal of International Education in Business, 16(2), 152-166. https://doi.org/10.1108/JIEB-12-2021-0109
Mailizar, M., Almanthari, A., & Maulina, S. (2021). Examining teachers’ behavioral intention to use e-learning in teaching of mathematics: An extended TAM model. Contemporary Educational Technology, 13(2), 298. https://doi.org/10.30935/cedtech/9709
Marandu, E. E., Mathew, I. R., Svotwa, T. D., Machera, R. P., & Jaiyeoba, O. (2023). Predicting students’ intention to continue online learning post-COVID-19 pandemic: Extension of the unified theory of acceptance and usage technology. Journal of Applied Research in Higher Education, 15(3), 681-697. https://doi.org/10.1108/JARHE-02-2022-0061
McKinney, V., Yoon, K., & Zahedi, F. M. (2002). The measurement of web-customer satisfaction: An expectation and disconfirmation approach. Information Systems Research, 13(3), 296-315. https://doi.org/10.1287/isre.13.3.296.76
Nikou, S. A. (2021). Web-based videoconferencing for teaching online: Continuance intention to use in the post-COVID-19 period. Interaction Design and Architecture.
Nikou, S. A., & Economides, A. A. (2017). Mobile-based assessment: Investigating the factors that influence behavioral intention to use. Computers & Education, 109, 56-73. https://doi.org/10.1016/j.compedu.2017.02.005
Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill.
Ong, C.-S., & Lai, J.-Y. (2006). Gender differences in perceptions and relationships among dominants of e-learning acceptance. Computers in Human Behavior, 22(5), 816-829. https://doi.org/10.1016/j.chb.2004.03.006
Paechter, M., Maier, B., & Macher, D. (2010). Students’ expectations of, and experiences in e-learning: Their relation to learning achievements and course satisfaction. Computers & Education, 54(1), 222-229. https://doi.org/10.1016/j.compedu.2009.08.005
Palloff, R. M., & Pratt, K. (1999). Building Learning Communities in Cyberspace: Effective Strategies for the Online Classroom. Jossey-Bass Higher and Adult Education Series. Jossey-Bass Publishers, San Francisco, CA.
Peck, D. (2025, January 3). Online Learning Statistics: The Ultimate List in 2025. https://www.devlinpeck.com/content/online-learning-statistics
Pedroso, R., Zanetello, L., Guimarães, L., Pettenon, M., Gonçalves, V., Scherer, J., Kessler, F., & Pechansky, F. (2016). Confirmatory factor analysis (CFA) of the Crack Use Relapse Scale (CURS). Archives of Clinical Psychiatry (São Paulo), 43(3), 37-40. https://doi.org/10.1590/0101-60830000000081
Pituch, K. A., & Lee, Y. (2006). The influence of system characteristics on e-learning use. Computers & Education, 47(2), 222-244. https://doi.org/10.1016/j.compedu.2004.10.007
Qasim, A., & Abu-Shanab, E. (2016). Drivers of mobile payment acceptance: The impact of network externalities. Information Systems Frontiers, 18(5), 1021-1034. https://doi.org/10.1007/s10796-015-9598-6
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
Rui-Hsin, K., & Lin, C.-T. (2018). The usage intention of e-learning for police education and training. Policing: An International Journal, 41(1), 98-112. https://doi.org/10.1108/PIJPSM-10-2016-0157
Seddon, P. B. (1997). A respecification and extension of the DeLone and McLean Model of IS Success. Information Systems Research, 8(3), 240-253. https://doi.org/10.1287/isre.8.3.240
Shah, J., & Khanna, M. (2023). Determining the post-adoptive intention of millennials for MOOCs: An information systems perspective. Information Discovery and Delivery, 52(2), 243-260. https://doi.org/10.1108/IDD-11-2022-0109
Shao, Z. (2018). Examining the impact mechanism of social psychological motivations on individuals’ continuance intention of MOOCs: The moderating effect of gender. Internet Research, 28(1), 232-250. https://doi.org/10.1108/IntR-11-2016-0335
Sharma, G. P., Verma, R. C., & Pathare, P. (2005). Mathematical modeling of infrared radiation thin layer drying of onion slices. Journal of Food Engineering, 71(3), 282-286. https://doi.org/10.1016/j.jfoodeng.2005.02.010
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
Sica, C., & Ghisi, M. (2007). The Italian versions of the Beck Anxiety Inventory and the Beck Depression Inventory-II: Psychometric properties and discriminant power. Leading-Edge Psychological Tests and Testing Research, 27-50.
Steuer, J. (1992). Defining Virtual Reality: Dimensions determining telepresence. Journal of Communication, 42(4), 73-93. https://doi.org/10.1111/j.1460-2466.1992.tb00812.x
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. https://doi.org/10.1016/j.compedu.2006.11.007
Thong, J. Y. L., 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(9), 799-810. https://doi.org/10.1016/j.ijhcs.2006.05.001
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the Technology Acceptance Model: Four longitudinal field studies. Management Science, 46(2), 186-204. https://doi.org/10.1287/mnsc.46.2.186.11926
Williams, F., Rice, R. E., & Rogers, E. M. (1988). Research Methods and the New Media. Simon and Schuster.
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
Wu, J. H., & Wang, S. C. (2005). What drives mobile commerce? An empirical evaluation of the revised technology acceptance model. Information & Management, 42(5), 719-729.
Xu, W., Zhang, N., & Wang, M. (2023). The impact of interaction on continuous use in online learning platforms: A metaverse perspective. Internet Research. https://doi.org/10.1108/INTR-08-2022-0600
Yakubu, N., & Dasuki, S. (2018). Assessing elearning systems success in Nigeria: An application of the DeLone and McLean Information Systems Success Model. Journal of Information Technology Education: Research, 17, 183-203. https://doi.org/10.28945/4077
Zhang, L. (2023). A Study of the Factors Influencing University Students’ Willingness to Continue Learning Online. Guangdong Polytechnic Normal University.
Zheng, Y., Zhao, K., & Stylianou, A. (2013). The impacts of information quality and system quality on users’ continuance intention in information-exchange virtual communities: An empirical investigation. Decision Support Systems, 56, 513-524.
https://doi.org/10.1016/j.dss.2012.11.008
Zhu, J., Zhao, H., Wang, X., Yang, L., Qin, Z., & Geng, J. (2022). Effects of online learning on college students in Eastern China: A structural equation model. Frontiers in Public Health, 10, 853928. https://doi.org/10.3389/fpubh.2022.853928
