Factors Impacting Vocational Education' Satisfaction, Learning Engagement, and Continuance Intention of MOOCs

Zhu Chenjie

doi:10.14456/shserj.2025.87

Authors

Zhu Chenjie

DOI:

https://doi.org/10.14456/shserj.2025.87

CITATION

DOI: 10.14456/shserj.2025.87

Published: 2025-09-29

Keywords:

MOOCs, Vocational education, Satisfaction, Learning engagement, Continuance intention

Abstract

Purpose: This study aims to enhance vocational school students' satisfaction, learning engagement, and intention to use MOOCs in Hangzhou, China. Research design, data, and methodology: The quantitative method (N=550) was used to distribute questionnaires to first-year students and collect sample data. The validity and reliability of the questionnaire were tested by project-objective consistency test and pilot test before delivery. Confirmatory factor analysis (CFA) and structural equation model (SEM) were used to analyze the data, verify the model's goodness of fit, the structure's validity, and research hypothesis testing. Results: The research results show that the Perceived Usefulness, Satisfaction, and Learning Engagement of conceptual models have a significant impact on Continuance interaction. Course material developers, course teachers, and senior managers of higher education institutions, when comprehensively evaluating the existing or upcoming MOOC platforms, should ensure that the human-machine interaction, human-machine system interaction, human-machine message interaction, and flow experience attributes are reasonable and practical and that students can indeed improve the efficiency of learning using the system. To further enhance students' satisfaction in using MOOCs and further Continuance Intention to Use MOOCs learning. Conclusions: MOOC platform managers should explicitly link the use of the platform to learner activities and positive learning outcomes.

Author Biography

Zhu Chenjie

School of Tourism and Culinary Arts, Zhengjiang Business College, China.

References

Agag, G., & El-Masry, A. A. (2016). Understanding consumer intention to participate in online travel community and effects on consumer intention to purchase travel online and OM: An integration of innovation diffusion theory and TAM with trust. Computers in Human Behavior, 60, 97-111. https://doi.org/10.1016/j.chb.2016.02.038

Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior a Human Decision Processes, 50(2), 179-211. https://doi.org/10.1016/0749-5978(91)90020-t

Al-Mamary, Y. H., & Shamsuddin, A. (2015). Testing of the technology acceptance model in context of yemen. Mediterranean Journal of Social Sciences, 6(4), 268-273. https://doi.org/10.5901/mjss.2015.v6n4s1p268

Al-Omairi, L., Al-Samarraie, H., Alzahrani, A. I., & Alalwan, N. (2021). Students' intention to adopt e-government learning services: A developing country perspective. Library Hi Tech, 39(1), 308-334. https://doi.org/10. 1108/LHT-02-2020-0034

Alraimi, K. M., Zo, H., & Ciganek, A. P. (2015). Understanding the MOOCs continuance: the role of openness and reputation. Computers and Education, 80(1), 28-38.

https://doi.org/10.1016/j.compedu.2014.08.006

Al-Sabawy, A. Y., Cater-Steel, A., & Soar, J. (2011)., Measuring e-learning system success (research in progress). Proceedings of the 15th Pacific Asia Conference on Information Systems (PACIS 2011), Queensl and University of Technology, 1-15.

Amoroso, D. L., & Chen, Y. A. (2017)., Constructs affecting continuance intention in consumers with mobile financial apps: a dual factor approach. Journal of Information Technology Management, 28(3). https://doi.org:10.3390/su12166641

Arteaga Sanchez, R., Duarte Hueros, A., & García Ordaz, M. (2013). E‐learning and the University of Huelva: a study of WebCT and the technological acceptance model. Campus- Wide Information Systems, 30(2), 135-160. https://doi.org/10. 1108/10650741311306318

Awang, Z. (2012). Structural equation modeling using AMOS graphic (1st ed.). Penerbit Universiti Teknologi MARA.

Badia, A., Garcia, C., & Meneses, J. (2014). Factors influencing university instructors’ adoption of the conception of online teaching as a medium to promote learners’ collaboration in virtual learning environments. Procedia - Social and Behavioral Sciences, 141, 369-374.

https://doi.org/10.1016/j.sbspro.2014.05.065

Bansal, H. S., Taylor, S. F., & James, Y. S. (2005). Migrating to new service providers: toward a unifying framework of consumers’ switching behaviors. Journal of the Academy of Marketing Science, 33(1), 96-115. https://doi.org/10.1177/0092070304267928

Barbour, M. K. (2010). Researching K-12 online learning: What do we know and what should we examine?. Distance Learning, 7(2), 7-12.

Benjangjaru, B., & Vongurai, R. (2018). Behavioral intention of Bangkokians to adopt mobile payment services by type of users. AU-GSB e-Journal, 11(1), 34-46.

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

Bharatia, P., & Chaudhury, A. (2004). An empirical investigation of decision-making satisfaction in web-based decision support systems. Decision Support Systems, 37(2), 187-197.

https://doi.org/10.1016/s0167-9236(03)00006-x

Bhattacherjee, A. (2000). Acceptance of e-commerce services: the case of electronic brokerages. IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans, 30(4), 411-420. https://doi.org/10.1109/3468.852435

Bhattacherjee, A. (2001). Understanding information systems continuance: an expectation confirmation model. MIS Quarterly, 12(3), 351-370. https://doi.org/10.2307/325092

Byrne, B. M. (2010). Structural equation modeling with AMOS: Basic concepts, applications, and programming (2nd ed.). Routledge Taylor and Francis Group.

Camarero, C., Rodríguez, J., & San José, R. (2012). An exploratory study of online forums as a collaborative learning tool. Online Information Review, 36(4), 568-586.

https://doi.org/10.1108/14684521211254077.

Cao, M., Zhang, Q., & Seydel, J. (2005). B2C e-commerce web site quality: an empirical examination. Industrial Management and Data Systems, 105(5), 645-661.

https://doi.org/10.1108/02635570510600000

Chang, Y. P., & Zhu, D. H. (2012). The role of perceived social capital and flow experience in building users’ continuance intention to social networking sites in China. Computers in Human Behavior, 28(3), 995-1001.

Chawla, D., & Joshi, H. (2019). Consumer attitude and intention to adopt mobile wallet in India- A empirical study. International Journal of Bank Marketing, 37(7), 1590-1618.

https://doi.org/10.1108/ijbm-09-2018-0256

Chen, C., Lee, C., & Hsiao, K. (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, C. C., Lee, C. H., & Hsiao, K. L. (2017). Comparing the determinants of non-MOOC and MOOC continuance intention in Taiwan Effects of interactivity and openness. Library Hi Tech, 11(2), 178-191. https://doi.org/10.1108/lht-11-2016-0129

Cheng, Y. (2022). Which quality determinants cause MOOCs continuance intention? A hybrid extending the expectation-confirmation model with learning engagement and information systems success. Library Hi Tech, 41(6), 1748-1780.

https://doi.org/10.1108/lht-11-2021-0391

Cheng, Y. M. (2012). The effects of information systems quality on nurses’ acceptance of the electronic learning system. Journal of Nursing Research, 20(1), 19-30.

https://doi.org/10.1097/jnr.0b013e31824777aa

Cheng, Y. M. (2014). What drives nurses’ blended e-learning continuance intention? Educational Technology and Society, 17(4), 203-215.

Cheng, Y. M. (2021). Can gamification and interface design aesthetics lead to MOOCs’ success?. Education+Training, 63(9), 1346-1375. https://doi.org/10.1108/et-09-2020-0278

Chiu, C.-M., Sun, S.-Y., Sun, P.-C., & Ju, T. L. (2007). An empirical analysis of the antecedents of web-based learning continuance. Computers & Education, 49(4), 1224-1245. https://doi.org/10.1016/j.compedu.2006.01.010

Cronin, J. J., Jr., Brady, M. K., & Hult, G. T. M. (2000). Assessing the effects of quality, value, and customer satisfaction on consumer behavioral intentions in service environments. Journal of Retailing, 76(2), 193-218. https://doi.org/10.1016/s0022-4359(00)00028-2

Danaher, P. J., & Rust, T. R. (1996). Indirect financial benefits from service quality. Quality Management Journal, 3(2), 63-75. https://doi.org/10.1080/10686967.1996.11918728

Davis, F., Bagozzi, R., & Warshaw, P. (1989). User acceptance of computer technology: a comparison of two theoretical models. Management Science, 35(8), 982-1003.

https://doi.org/10.1287/mnsc35.8.982

DeLone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: A ten-year update. Journal of Management Information Systems, 19(4), 9-30. https://doi.org/10.1080/07421222.2003.11045748

Drennan, J., Kennedy, J., & Pisarski, A. (2005). Factors Affecting Student Attitudes Toward Flexible Online Learning in Management Education. The Journal of Educational Research, 98(6), 331–338.https://doi.org/10.3200/JOER.98.6.331-338

Eom, S. B., Wen, H. J., & Ashill, N. (2006). The determinants of students’ perceived learning outcomes and satisfaction in university online education: an empirical investigation. Decision Sciences Journal of Innovative Education, 4(2), 215-235. https://doi.org/10.1111/j.1540-4609.2006.00114.x

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

Gefen, D., & Heart, T. H. (2006). On the need to include national culture as a central issue in e-commerce trust beliefs. Journal of Global Information Management, 14(4), 1-30.

https://doi.org/10.4018/jgim.2006100101

Goel, L., Johnson, N. A., Junglas, I., & Ives, B. (2013). How cues of what can be done in a virtual world influence learning: an affordance perspective. Information and Management, 50(5), 197-206. https://doi.org/10.1016/j.im.2013.01.003

Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate data analysis (1st ed.). Prentice-Hall.

Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis (6th ed.). Pearson Prentice Hall.

Harder, D. A. M., Frijlingh, M., Ravesloot, C. J., Oosterbaan, A. E., & Gijp, V. D. A. (2016). The importance of Human-Computer interaction in radiology E-Learning. Journal of Digital Imaging, 29, 195-205. https://doi.org/10.1007/s10278-015-9828-y

Harris, L. C., & Goode, M. M. H. (2004). Online servicescapes, trust, and purchase intentions. Journal of Services Marketing, 24(3), 230-243. https://doi.org/10.1108/08876041011040631

Harris, M. A., Brett, C. E., Johnson, W., & Deary, I. J. (2016). Personality stability from age 14 to age 77 years.Psychology and Aging, 31(8), 862-874. https://doi.org/10.1037/pag0000133

Hodges, C. B., Moore, S. L., Lockee, B. B., Trust, T., & Bond, M. A. (2020). The Difference Between Emergency Remote Teaching and Online Learning (1st ed.). Educational Review.

Hollender, N., Hofmann, C., Deneke, M., & Schmitz, B. (2010). Integrating cognitive load theory and concepts of Human-Computer interaction. Computers in Human Behavior, 26(6), 1278-1288. https://doi.org/10.1016/j.chb.2010.05.031

Horzum, M. B. (2015). Interaction, structure, social presence, and satisfaction in online learning. Eurasia Journal of Mathematics, Science and Technology Education, 11(3), 505-512.

https://doi.org/10.12973/eurasia.2014.1324a

Hossain, M. N., Talukder, M. S., Khayer, A., & Bao, Y. (2021). Investigating the factors driving adult learners' continuous intention to use M-learning application: a fuzzy-set analysis. Journal of Research in Innovative Teaching and Learning, 14(2), 245-270. https://doi.org/10.1108/JRIT-09-2019-0071

Hsiao, C. H., Chang, J. J., & Tang, K. Y. (2016). Exploring the influential factors in continuance usage of mobile social Apps: satisfaction, habit, and customer value perspectives. Telematics and Informatics, 33(2), 342-355. https://doi.org/10.1016/j.tele.2015.08.014

Hsu, H. Y., Kwok, O., Lin, J. H., & Acosta, S. (2015). Detecting mis specified multilevel structural equation models with common fit indices: a monte carlo study. Multivariate Behavioral Research, 50(2), 197-215.

Islam, A. K. M. N., & Azad, N. (2015). Satisfaction and continuance with a learning management system. International Journal of Information and Learning Technology, 32(2), 109-123. https://doi.org/10.1108/ijilt-09-2014-0020

Islam, A. N. (2013). Investigating e-learning system usage outcomes in the university context. Computers and Education, 69, 387-399. https://doi.org/10.1016/j.compedu.2013.07.037

Jaiyeoba, O. O., & Iloanya, J. (2019). E-learning in tertiary institutions in Botswana: apathy to adoption. International Journal of Information and Learning Technology, 36(2), 157- 168. https://doi.org/10.1108/IJILT-05-2018-0058

Joo, Y. J., Joung, S., & Kim, E. K. (2013). Structural relationships among e-learners’ sense of presence, usage, flow, satisfaction, and persistence. Educational Technology and Society, 16(2), 310-324. https://doi.org/10.1080/10494820.2012.745421.

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 KMOOCs. Computers and Education, 122(7), 260-272. https://doi.org/10.1016/j.compedu.2018.01.003

Kim, E., & Tadisina, S. (2007). A model of customers' trust in e-businesses: Micro-level inter- party trust formation. Journal of Computer Information Systems, 48(1), 88- 104.

Kim, T. G., Lee, J. H., & Law, R. (2008). An empirical examination of the acceptance behavior of hotel front office systems: an extended technology acceptance model. Tourism Management, 29(3), 500-513. https://doi.org/10.1016/j.tourman.2007.05.016

Lam, P., Mcnaught, C., Lee, J., & Chan, M. (2014). Disciplinary difference in students’ use of technology, experience in using eLearning strategies and perceptions towards elearning. Computers and Education, 73, 111-120. https://doi.org/10.1016/j.compedu.2013.12.015

Lao, M., & Pupat, N. (2020). Comparison of factors influencing Chinese people in Bangkok intention to use two online payment applications in Thailand. AU-GSB e-Journal, 12(2), 74- 81.

Lau, S.-H., & Woods, P. C. (2008). An investigation of user perceptions and attitudes towards learning objects. British Journal of Educational Technology, 39(4), 685-699.

https://doi.org/10.1111/j.1467-8535.2007.00770.x

Lee, D., Moon, J., Kim, Y.-J., & Yi, M. Y. (2015). Antecedents and consequences of mobile phone usability: Linking simplicity and interactivity to satisfaction, trust, and brand loyalty. Information and Management, 52(3), 295-304. https://doi.org/10.1016/j.im.2014.12.001

Lee, M. (2009). Factors influencing the adoption of Internet banking: An integration of TAM and TPB with perceived risk and perceived benefit. Electronic Commerce Research and Applications, 8(3), 130-141. https://doi.org/10. 1016/j.elerap.2008. 11.006

Lee, M. C. (2010). Explaining and predicting users’ continuance intention toward e-learning: an extension of the expectation–confirmation model. Computers and Education, 54(2), 506-516. https://doi.org/10.1016/j.compedu.2009.09.002

Lei, P. W., & Wu, Q. (2007). Introduction to structural equation modeling: Issues and practical considerations. Educational Measurement: Issues and Practice, 26(3), 33-43.

https://doi.org/10.1111/j.1745-3992.2007.00099.x

Leong, P. (2011). Role of social presence and cognitive absorption in online learning environments. Distance Education, 32(1), 5-28. https://doi.org/10.1080/01587919.2011.565495

Lin, C. C., Liu, G. Z., & Wang, T. I. (2017). Development and usability test of an E-Learning tool for engineering graduates to develop academic writing in English: a case study. Educational Technology and Society, 20, 148-161.

Lin, H. F. (2003). Technological acceptance model in a training context: The role of trainee’s motivation. Journal of Educational Technology & Society, 6(3), 50-60.

Lin, H.-F. (2007). The role of online and offline features in sustaining virtual communities: an empirical study. Internet Research, 17(2), 119-138. https://doi.org/10.1108/10662240710736997

Liu, S. H., Liao, H. L., & Pratt, J. A. (2009). Impact of media richness and flow on e-learning technology acceptance. Computers and Education, 52(3), 599-607. https://doi.org/10.1016/j.compedu.2008.11.002

Lwoga, E. T., & Komba, M. (2015). Antecedents of continued usage intentions of web-based learning management system in Tanzania. Education + Training, 57, 738-756.

https://doi.org/10.1108/et-02-2014-0014

Materia, F. T., Miller, E. A., Runion, M. C., Chesnut, R. P., Irvin, J. B., Richardson, C. B., & Perkins, D. F. (2016). Let us get technical: enhancing program evaluation through the use and integration of internet and mobile technologies. Evaluation and Program Planning, 56, 31-42.

https://doi.org/10.1016/j.evalprogplan.2016.03.004

McLean, G. (2018). Examining the determinants and outcomes of mobile app engagement a longitudinal perspective. Computers in Human Behavior, 84, 392-403.

https://doi.org/10.1016/j.chb.2018.03.015

Mulik, S., Srivastava, M., Yajnik, N., & Taras, V. (2019). Antecedents and outcomes of flow experience of MOOC users. Journal of International Education in Business, 13(1), 1-19. https://doi.org/10.1108/jieb-10-2018-0049

Oliver, R. L. (1980). A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of Marketing Research, 17(4), 460-469. https://doi.org/10.1177/002224378001700405

Oliver, R. L., & Winer, R. S. (1987). A framework for the formation and structure of consumer expectations: Review and propositions. Journal of Economic Psychology, 8(4), 469-499. https://doi.org/10.1016/0167-4870(87)90037-7

Omar, E. N. M. M. A., Abdalrahim, A., Drewish, A., Saeed, Y. M., & Abdalbagi, Y. M. (2015). Test of information technology (it)-Self efficacy and online learning interaction components on student retention: a study of synchronous learning environment. International Conference On E-Learning Proceedings, 165-173.

Pappano, L. (2012). The year of the MOOC (1st ed.). The New York Times.

Park, J. H. (2014). The effects of personalization on user continuance in social networking sites. Information Processing and Management, 50(3), 462-475. https://doi.org/10.1016/j.ipm.2014.02.002

Pedroso, R., Zanetello, L., Guimaraes, L., Pettenon, M., Goncalves, V., Scherer, J., Kessler, F., & Pechansky, F. (2016). Confirmatory factor analysis (CFA) of the crack use relapse scale (CURS). Archives of Clinical Psychiatry, 43(3), 37-40. https://doi.org/10.1590/0101-60830000000081

Perkowitz, M., & Etzioni, O. (1999). Towards adaptive web sites: conceptual framework and case study. Computer Networks, 31(11/16), 1245-1258. https://doi.org/10.1016/s1389-1286(99)00017-1

Perry, A. (2017). Factors comprehensively influencing acceptance of 3D-printed apparel. Journal of Fashion Marketing and Management, 21(2), 219-234. https://doi.org/10.1108/jfmm-03-2016-0028

Rai, A., Lang, S. S., & Welker, R. B. (2002). Assessing the validity of IS success models: An empirical test and theoretical analysis. Information Systems Research, 13(1), 50-69.

https://doi.org/10.1287/isre.13.1.50.96

Ranganathan, C., & Ganapathy, S. (2002). Key dimensions of business-to-consumer web sites. Information and Management, 39(6), 457-465. https://doi.org/10.1016/s0378-7206(01)00112-4

Rose, J., Jones, E., & Smith, R. (2012). The impact of social media on student engagement in higher education. Journal of Technology and Education, 3(1), 45-58.

Rossin, D., Decker, R., & Mathews, S. (2009). The impact of classroom engagement on student performance: A multi-disciplinary approach. Journal of Educational Psychology, 101(2), 309-325.

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

Saeed, K. A., & Abdinnour-Helm, S. (2008). Examining the effects of information system characteristics and perceived usefulness on post adoption usage of information systems. Information & Management, 45(6), 376-386. https://doi.org/10.1016/j.im.2008.06.002

Sementelli, A. J., & Garrett, T. M. (2015). MOOCs: meaningful learning tools for public administration education or academic simulacra?. Education Training, 57(4), 461-470.

https://doi.org/10.1108/et-03-2014-0031

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

Shiau, W. L., & Luo, M. M. (2013). Continuance intention of blog users: the impact of perceived enjoyment, habit, user involvement and blogging time. Behavior and Information Technology, 32(6), 570-583. https://doi.org/10.1080/0144929x.2012.671851

Shyu, H. Y. C., & Chou, Y. H. (2008). Investigation of E-Learning content instructional design principles in the affective domain, Imsci 2nd International Multi-Conference on Society. Cybernetic and Informatics, 1, 233-233.

Sica, C., & Ghisi, M. (2007). The Italian versions of the Beck Anxiety Inventory and the Beck Depression Inventory-II: Psychometric properties and discriminant power. In M. A. Lange (Ed.), Leading-edge psychological tests and testing research (pp. 27-50). Nova Science Publishers.

Soper, D. (2006). Calculator: A-priori sample size for structural equation models. Daniel Soper.

https://www.danielsoper.com/statcalc/calculator.aspx?id=89

Spector, J. M. (2014). Remarks on MOOCS and mini-MOOCS. Educational Technology Research and Development, 62(3), 385-392. https://doi.org/10.1007/s11423-014-9339-4

Tarhini, A., Masa’deh, R., Al-Busaidi, K. A., Mohammed, A. B., & Maqableh, M. (2017). Factors influencing students’ adoption of e-learning: a structural equation modeling approach. Journal of International Education in Business, 10(2), 164-182. https://doi.org/10.1108/JIEB-09-2016-0032

Ullman, J. B., & Bentler, P. M. (2013). Structural equation modeling (2nd ed.). Handbook of Psychology.

UNESCO. (2020a). Distance learning solutions. https://en.unesco.org/covid19/educationresponse/solutions.

UNESCO. (2020b). 1.37 billion students now home as COVID-19 school closures expand, ministers scale up multimedia approaches to ensure learning continuity. https://en.unesco.org/news/137-billion-students-now-home-covid-19-school-closures-expand-ministers-scalemultimedia.

Wanichbancha, K. (2014). Structural Equation Modeling (SEM) with AMOS (2nd ed.). Samlada.

Wegmann, S. J., & Thompson, K. (2014). Scope-ing out interactions in blended environments (2nd ed.). Routlege.

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, J. H., & Wang, Y. M. (2006). Measuring KMS success: A respecification of the DeLone and McLean’s model. Information and Management, 43(6), 728-739. https://doi.org/10.1016/j.im.2006.05.002

Xue, J., Liang, X., Xie, T., & Wang, H. (2020). See now, act now: how to interact with customers to enhance social commerce engagement?. Information and Management, 57(6), 103324. https://doi.org/10.1016/j.im.2020.103324

You, J. W. (2015). Examining the effect of academic procrastination on achievement using lms data in E-Learning. Educational Technology and Society, 18, 64-74.