Factors Impacting Satisfaction and Continuance Intention of MOOCs Learning among Medical Students in Chengdu, China
Keywords:
MOOCs learning, Satisfaction, Continuance Intention, Medical students, ChinaAbstract
MOOCs, which stand for Massive Open Online Courses, have become an important tool for promoting educational reform in local medical colleges. This article constructed a conceptual framework using the expectation confirmation model (ECM), technology acceptance model (TAM), and unified theory of acceptance and use of technology (UTAUT), to investigate the factors impacting satisfaction and continuance intention of MOOCs learning among medical students. Structural equation modeling is used to evaluate the model using data from a survey with 500 medical students in Chengdu, China. The results showed that the proposed theoretical model can explain the causal relationship between factors very well. Task-technology fit, perceived usefulness, and facilitating conditions are important determinants of students’ satisfaction, furthermore, satisfaction plays a vital role in motiving or influencing medical students’ continuance intention of MOOCs learning. It is recommended that the MOOC platform should focus on improving task-technology fit, perceived usefulness, facilitating conditions and satisfaction toward medical students’ MOOCs learning, the education institutions ought to strengthen medical students’ interaction by establishing various online learning communities in MOOCs. The findings provide a reference model for future research toward impacting factors of MOOCs learning and contribute to improving the teaching management of MOOCs in local medical colleges.
References
Adamson, I. and Shine, J. (2003). Extending the new technology acceptance model to measure the end user information systems satisfaction in a mandatory environment: a bank’s treasury. Technology Analysis and Strategic Management, 15(4), 441-455.
Agudo-Peregrina, Á. F., Hernández-García, Á., & Pascual-Miguel, F. J. (2014). Behavioral intention, use behavior and the acceptance of electronic learning systems: Differences between higher education and lifelong learning. Computers in Human Behavior, 34, 301-314.
Ainur, A. K., Sayang, M. D., Jannoo, Z., & Yap, B. W. (2017). Sample Size and Non-Normality Effects on Goodness of Fit Measures in Structural Equation Models. Pertanika Journal of Science & Technology, 25(2),575-586.
Alalwan, A. A. (2020). Mobile food ordering apps: An empirical study of the factors affecting customer e-satisfaction and continued intention to reuse. International Journal of Information Management, 50, 28-44.
Aldholay, A., Isaac, O., Abdullah, Z., Abdulsalam, R., & Al-Shibami, A. H. (2018). An extension of Delone and McLean IS success model with self-efficacy: Online learning usage in Yemen. The International Journal of Information and Learning Technology, 35(4), 285-304.
Alfany, Z., Saufi, A., & Mulyono, L. E. H. (2019). The impact of social influence, self-efficacy, perceived enjoyment, and individual mobility on attitude toward use and intention to use mobile payment of OVO. Global Journal of Management and Business Research, 19(7), 1-8.
Al-Mamary, Y. H., & Shamsuddin, A. (2015). The impact of top management support, training, and perceived usefulness on technology acceptance. Mediterranean Journal of Social Sciences, 6(6), S4.
Amin, M., Rezaei, S., & Abolghasemi, M. (2014). User satisfaction with mobile websites: the impact of perceived usefulness (PU), perceived ease of use (PEOU) and trust. Nankai Business Review International, 5(3), 258-274.
Ammenwerth, E., Iller, C., & Mahler, C. (2006). IT-adoption and the interaction of task, technology and individuals: a fit framework and a case study. BMC medical informatics and decision making, 6, 1-13.
Aslam, W., Ham, M., & Arif, I. (2017). Consumer behavioral intentions towards mobile payment services: An empirical analysis in Pakistan. Trziste= Market, 29(2), 161-176.
Attuquayefio, S., & Addo, H. (2014). Using the UTAUT model to analyze students’ ICT adoption. International Journal of Education and Development using ICT, 10(3), 75-86.
Awang, Z. (2012). Research methodology and data analysis second edition. UiTM Press.
Baabdullah, A. M., Alalwan, A. A., Rana, N. P., Kizgin, H., & Patil, P. (2019). Consumer use of mobile banking (M-Banking) in Saudi Arabia: Towards an integrated model. International journal of information management, 44, 38-52.
Bandura, A. (1986). Social Foundations of Thought and Action: A Social Cognitive Theory. Englewood Cliffs, NJ: Prentice Hall.
Bandura, A. (1991). Social cognitive theory of self-regulation. Organizational behavior and human decision processes, 50(2), 248-287.
Bederson, B. B., Russell, D. M., & Klemmer, S. (2015). Introduction to online learning at scale. ACM Transactions on Computer-Human Interaction (TOCHI), 22(2), 1-3.
Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological bulletin, 107(2), 238.
Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS quarterly, 25(3), 351-370.
Brown, T. A. (2015). Confirmatory factor analysis for applied research. Guilford publications.
Chan, F. K., Thong, J. Y., Venkatesh, V., Brown, S. A., Hu, P. J., & Tam, K. Y. (2010). Modeling citizen satisfaction with mandatory adoption of an e-government technology. Journal of the association for information systems, 11(10), 519-549.
Chen, C. C., Lee, C. H., & Hsiao, K. L. (2018). Comparing the determinants of non-MOOC and MOOC continuance intention in Taiwan: Effects of interactivity and openness. Library Hi Tech, 36(4), 705-719.
Chen, L., Zhang, J., Zhu, Y., Shan, J., & Zeng, L. (2023). Exploration and practice of humanistic education for medical students based on volunteerism. Medical Education Online, 28(1), 2182691.
Chen, S. C., Li, S. H., Liu, S. C., Yen, D. C., & Ruangkanjanases, A. (2021). Assessing determinants of continuance intention towards personal cloud services: Extending utaut2 with technology readiness. Symmetry, 13(3), 467.
Cheng, S., Liu, L., & Li, K. (2020). Explaining the factors influencing the individuals’ continuance intention to seek information on Weibo during rainstorm disasters. International journal of environmental research and public health, 17(17), 6072.
Chuleeporn, C. (2014). Students' Perceptions of Cloud Computing. Issues in Information Systems, 15(1), 312-322.
Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS quarterly, 19(2), 189-211.
Compeau, D., Higgins, C. A., & Huff, S. (1999). Social cognitive theory and individual reactions to computing technology: A longitudinal study. MIS quarterly, 23(2), 145-158.
Creswell, J. W. (2003). Research design: Qualitative, quantitative and mixed methods approaches (2nd ed., pp. 1-16). London, UK.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 13(3), 319-340.
Dečman, M. (2015). Modeling the acceptance of e-learning in mandatory environments of higher education: The influence of previous education and gender. Computers in human behavior, 49, 272-281.
Du, B. (2023). Research on the factors influencing the learner satisfaction of MOOCs. Education and Information Technologies, 28(2), 1935-1955.
Etikan, I., Musa, S. A. & Alkassim, R. S. (2015). Comparison of convenience sampling and purposive sampling. American Journal of Theoretical and Applied Statistics. 5(1), 1-4.
Federici, R. A., & Skaalvik, E. M. (2012). Principal self-efficacy: Relations with burnout, job satisfaction and motivation to quit. Social Psychology of Education, 15, 295-320.
Fianu, E., Blewett, C., Ampong, G. O. A., & Ofori, K. S. (2018). Factors affecting MOOC usage by students in selected Ghanaian universities. Education Sciences, 8(2), 70.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 18(1), 39-50.
Fuller, R. M., & Dennis, A. R. (2009). Does fit matter? The impact of task-technology fit and appropriation on team performance in repeated tasks. Information Systems Research, 20(1), 2-17.
Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. MIS quarterly, 19(2), 213-236.
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.
Hair, J. F., Anderson, R. E., Babin, B. J., & Black, W. C. (2010). Multivariate data analysis: A global perspective (7th Ed.). Upper Saddle River, NJ: Pearson.
Hair, J. F., Money, A. H., Samouel, P., & Page, M. (2007). Research methods for business. Education+ Training, 49(4), 336-337.
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.
Hong, J. C., Hwang, M. Y., Szeto, E., Tsai, C. R., Kuo, Y. C., & Hsu, W. Y. (2016). Internet cognitive failure relevant to self-efficacy, learning interest, and satisfaction with social media learning. Computers in Human Behavior, 55, 214-222.
Howard, M. C., & Rose, J. C. (2019). Refining and extending task–technology fit theory: Creation of two task–technology fit scales and empirical clarification of the construct. Information & Management, 56(6), 103134.
Huang, L. C., Shiau, W. L., & Lin, Y. H. (2017). What factors satisfy e-book store customers? Development of a model to evaluate e-book user behavior and satisfaction. Internet Research, 27(3), 563-585.
Ifeanyi, I. P., & Chukwuere, J. E. (2018). The impact of using smartphones on the academic performance of undergraduate students. Knowledge Management & E-Learning, 10(3), 290-308.
Isaac, O., Abdullah, Z., Ramayah, T., & Mutahar, A. M. (2017). Internet usage, user satisfaction, task-technology fit, and performance impact among public sector employees in Yemen. The International Journal of Information and Learning Technology, 34(3), 210-241.
Ives, B., Olson, M. H., & Baroudi, J. J. (1983). The measurement of user information satisfaction. Communications of the ACM, 26(10), 785-793.
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.
Kaium, M. A., Bao, Y., Alam, M. Z., & Hoque, M. R. (2020). Understanding continuance usage intention of mHealth in a developing country: An empirical investigation. International Journal of Pharmaceutical and Healthcare Marketing, 14(2), 251-272.
Kim, J. A. (2006). Toward an understanding of Web-based subscription database acceptance. Journal of the American Society for Information Science and Technology, 57(13), 1715-1728.
Koh, C. E., Prybutok, V. R., & Ryan, S. D. (2010). A model for mandatory use of software technologies: An integrative approach by applying multiple levels of abstraction of informing science. Informing Science, 13, 177.
Lee, T., & Jun, J. (2007). Contextual perceived value? Investigating the role of contextual marketing for customer relationship management in a mobile commerce context. Business Process Management Journal, 13(6), 798-814.
Leontyev, A., & Baranov, D. (2013). Massive open online courses in chemistry: A comparative overview of platforms and features. J. Chem. Educ. 90(11), 1533-1539.
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(6), 2804-2822.
Liaw, S. S. (2008). Investigating students’ perceived satisfaction, behavioral intention, and effectiveness of e-learning: A case study of the Blackboard system. Computers & education, 51(2), 864-873.
Lin, W. S. (2012). Perceived fit and satisfaction on web learning performance: IS continuance intention and task-technology fit perspectives. International Journal of Human-Computer Studies, 70(7), 498-507.
Lin, W. S., & Wang, C. H. (2012). Antecedences to continued intentions of adopting e-learning system in blended learning instruction: A contingency framework based on models of information system success and task-technology fit. Computers & Education, 58(1), 88-99.
López-Nicolás, C., Molina-Castillo, F. J., & Bouwman, H. (2008). An assessment of advanced mobile services acceptance: Contributions from TAM and diffusion theory models. Information & management, 45(6), 359-364.
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.
Milic N. (2008). Sample Size Determination. In Encyclopedia of Public Health (pp. 155-192). Springer. https://doi.org/10.1007/978-1-4020-5614-7_3090Dordrecht.
Nikou, S. A., & Economides, A. A. (2017). Mobile-based assessment: Investigating the factors that influence behavioral intention to use. Computers & Education, 109, 56-73.
Oh, S. H., Kim, Y. M., Lee, C. W., Shim, G. Y., Park, M. S., & Jung, H. S. (2009). Consumer adoption of virtual stores in Korea: Focusing on the role of trust and playfulness. Psychology & Marketing, 26(7), 652-668.
Park, E., Kim, K. J., & Del Pobil, A. P. (2013). An examination of psychological factors affecting drivers’ perceptions and attitudes toward car navigation systems. In IT Convergence and Security 2012 (pp. 555-562). Springer Netherlands.
Park, J. H. (2014). The effects of personalization on user continuance in social networking sites. Information processing & management, 50(3), 462-475.
Pedroso, R., Zanetello, L., Guimarães, L., Pettenon, M., Gonçalves, V., Scherer, J., ... & Pechansky, F. (2016). Confirmatory factor analysis (CFA) of the crack use relapse scale (CURS). Archives of Clinical Psychiatry (São Paulo), 43, 37-40.
Prior, D. D., Mazanov, J., Meacheam, D., Heaslip, G., & Hanson, J. (2016). Attitude, digital literacy and self-efficacy: Flow-on effects for online learning behavior. The Internet and Higher Education, 29, 91-97.
Ramadhan, A., Hidayanto, A. N., Salsabila, G. A., Wulandari, I., Jaury, J. A., & Anjani, N. N. (2022). The effect of usability on the intention to use the e-learning system in a sustainable way: A case study at Universitas Indonesia. Education and Information Technologies, 27, 1489-1522.
Rekha, I. S., Shetty, J., & Basri, S. (2023). Students’ continuance intention to use MOOCs: empirical evidence from India. Education and Information Technologies, 28(4), 4265-4286.
Revels, J., Tojib, D., & Tsarenko, Y. (2010). Understanding consumer intention to use mobile services. Australasian Marketing Journal, 18(2), 74-80.
Rezaei, S., & Amin, M. (2013). Exploring online repurchase behavioural intention of university students in Malaysia. Journal for Global Business Advancement, 6(2), 92-119.
Robles-Flores, J. A., & Roussinov, D. (2012). Examining question-answering technology from the task technology fit perspective. Communications of the Association for Information Systems, 30, 439-454.
Sayegh, A. J., Ahmad, S. Z., AlFaqeeh, K. M., & Singh, S. K. (2023). Factors affecting e-government adoption in the UAE public sector organisations: the knowledge management perspective. Journal of Knowledge Management, 27(3), 717-737.
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.
Shankar, A., & Datta, B. (2018). Factors affecting mobile payment adoption intention: An Indian perspective. Global Business Review, 19(3_suppl), S72-S89.
Sharma, S., Mukherjee, S., Kumar, A., & Dillon, W. R. (2005). A simulation study to investigate the use of cutoff values for assessing model fit in covariance structure models. Journal of business research, 58(7), 935-943.
Shen, D., Cho, M. H., Tsai, C. L., & Marra, R. (2013). Unpacking online learning experiences: Online learning self-efficacy and learning satisfaction. The Internet and Higher Education, 19, 10-17.
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.
Singh, A., & Sharma, A. (2021). Acceptance of MOOCs as an alternative for internship for management students during COVID-19 pandemic: An Indian perspective. International Journal of Educational Management, 35(6), 1231-1244.
Soper, D. (2006). Calculator: A-Priori Sample Size for Structural Equation Models. Retrieved from https://www.danielsoper.com/statcalc/calculator.aspx?id=89
Su, P., Wang, L., & Yan, J. (2018). How users’ Internet experience affects the adoption of mobile payment: a mediation model. Technology Analysis & Strategic Management, 30(2), 186-197.
Sun, Y., Fang, Y., Lim, K. H., & Straub, D. (2012). User satisfaction with information technology service delivery: A social capital perspective. Information Systems Research, 23(4), 1195-1211.
Sweeney, J. C., & Ingram, D. (2001). A comparison of traditional and web-based tutorials in marketing education: An exploratory study. Journal of Marketing Education, 23(1), 55-62.
Teo, T., & Wong, S. L. (2013). Modeling key drivers of e-learning satisfaction among student teachers. Journal of educational computing research, 48(1), 71-95.
Tu, C. C., Fang, K., & Lin, C. Y. (2012). Perceived Ease of Use, Trust, and Satisfaction as Determinants of Loyalty in e-Auction Marketplace. Journal of Computers, 7(3), 645-652.
Venkatesh, V., Morris, M.G., Davis, G.B., & Davis, F. (2003). User acceptance of information technology: Toward a unified view. Management Information System Quarterly, 27(3), 425-478.
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(1), 157-178.
Venkatesh, V., Thong, J. Y., Chan, F. K., Hu, P. J. H., & Brown, S. A. (2011). Extending the two-stage information systems continuance model: Incorporating UTAUT predictors and the role of context. Information systems journal, 21(6), 527-555.
Wan, L., Xie, S., & Shu, A. (2020). Toward an understanding of university students’ continued intention to use MOOCs: When UTAUT model meets TTF model. Sage Open, 10(3), 1-15.
Wilson, N., Keni, K., & Tan, P. H. P. (2021). The role of perceived usefulness and perceived ease-of-use toward satisfaction and trust which influence computer consumers' loyalty in China. Gadjah Mada International Journal of Business, 23(3), 262-294.
Wu, B., & Zhang, C. (2014). Empirical study on continuance intentions towards E-Learning 2.0 systems. Behaviour & Information Technology, 33(10), 1027-1038.
Wu, J. H., & Wang, Y. M. (2006). Measuring KMS success: A respecification of the DeLone and McLean's model. Information & management, 43(6), 728-739.
Xiong, J., Choi, H. S., Chen, C., & Tang, Y. (2020). ENHANCING LOYALTY TO MOBILE PAYMENT SERVICES: AN EMPIRICAL STUDY. Issues in Information Systems, 21(2), 30-42.
Yang, H. H., & Su, C. H. (2017). Learner behaviour in a MOOC practice-oriented course: In empirical study integrating TAM and TPB. International Review of Research in Open and Distributed Learning, 18(5), 35-63.
Yang, M., Shao, Z., Liu, Q., & Liu, C. (2017). Understanding the quality factors that influence the continuance intention of students toward participation in MOOCs. Educational Technology Research and Development, 65, 1195-1214.
Ye, Q., Luo, Y., Chen, G., Guo, X., Wei, Q., & Tan, S. (2019). Users intention for continuous usage of mobile news apps: The roles of quality, switching costs, and personalization. Journal of Systems Science and Systems Engineering, 28, 91-109.
Zhao, X., Mattila, A. S., & Eva Tao, L. S. (2008). The role of post-training self-efficacy in customers' use of self-service technologies. International Journal of Service Industry Management, 19(4), 492-505.
Zhou, T., Lu, Y., & Wang, B. (2010). Integrating TTF and UTAUT to explain mobile banking user adoption. Computers in human behavior, 26(4), 760-767.
Zhu, H., Xu, J., Wang, P., Bian, J., Zhao, Z., Liu, H., & Ji, L. (2023). The irreplaceable role of medical massive open online courses in China during the COVID-19 pandemic. BMC Medical Education, 23(1), 1-11.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Local Administration Journal
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
The copyright of all articles published in the Local Administration Journalis owned by the College of Local Administration, Khon Kaen University.