A Study on The Influencing Factors of Students’ Behavioral Intention and Usage Behavior of Massive Open Online Courses in Dazhou, China

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Junxing Zhao


Purpose: Massive Open Online Courses (MOOCs) play an important role in adult higher education. This study aims to explore the factors that influence the students’ behavior intention and usage behavior of Massive Open Online Courses (MOOCs). The proposed conceptual framework includes perceived usefulness, perceived ease of use, subjective norms, performance expectation, intrinsic motivation, behavioral intention, and user behavior. Research design, data, and methodology: Using a quantitative method (n=500), questionnaires were distributed to adult students in higher education in Dazhou City. The study employed purposive, stratified random and convenience sampling to distribute online and offline questionnaire for the data collection. Structural equation modeling and confirmatory factor analysis were used for to analyze the data and interpret the results. Results: The results show that perceived usefulness, perceived ease of use, subjective norm, performance expectation, and intrinsic motivation have significant influence on behavioral intention, in which performance expectation has the strongest impact and perceived ease of use has the weakest impact. Additionally, behavioral intention significantly influences usage behavior. Conclusions: Six hypotheses were proved to be consistent with the research objectives. Therefore, it is suggested that colleges and universities enhance the performance expectation of adult students in MOOCs teaching to obtain better teaching results.


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Zhao, J. (2024). A Study on The Influencing Factors of Students’ Behavioral Intention and Usage Behavior of Massive Open Online Courses in Dazhou, China. AU-GSB E-JOURNAL, 17(1), 148-159. https://doi.org/10.14456/augsbejr.2024.15
Author Biography

Junxing Zhao

Sichuan University of Arts and Science, China.


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

Awang, Z. (2012). A Handbook on SEM Structural Equation Modelling: SEM Using AMOS Graphic (5th ed.). Kota Baru: Universiti Teknologi Mara Kelantan.

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., & Premkumar, G. (2004). Understanding changes in belief and attitude toward information technology usage: a theoretical model and longitudinal test. MIS Quarterly, 28, 229-254. https://doi.org/10.2307/25148634

Chen, H. H., & Fang, X. J. (2019). Methods for Social Investigation (1st ed.). University of Science and Technology of China Press.

Chen, X. X. (2019). Research on factors affecting the use of Doctoral dissertation [Unpublished Master’s Thesis]. Jiangxi Agricultural University

Cho, V., Cheng, T., & Lai, W. (2009). The role of perceived user-interface design in continued usage intention of self-paced e-learning tools. Computers & Education, 53(2), 216–227.

Davis, F. D. (1986). A technology acceptance model for empirically testing new end-user information systems: theory and results. Massachusetts Institute of Technology, 8(1), 1-10.

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

Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computer in the workplace. Journal of Applied Social Psychology, 22(14), 1109-1130. https://doi.org/10.1111/j.1559-1816.1992.tb00945.x

Davis, F. D., & Venkatesh, V. (1996). A critical assessment of potential measurement biases in the technology acceptance model: three experiments. International Journal of Human Computer Studies, 45(1), 19-45. https://doi.org/10.1006/ijhc.1996.0040

Davis, H. C., Dickens, K., Leon Urrutia, M., Sanchéz, V., Del Mar, M., & White, S. (2014). MOOCs for Universities and Learners An analysis of motivating factors (4th ed.). International Conference on Computer Supported Education.

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), 9-3.

Downes, S. (2008). Places to go: Connectivism & connective knowledge. Innovate: Journal of Online Education, 5(1), 1-6.

Faria, S. C., Provete, D. B., Thurman, C. L., & McNamara, J. C. (2017). Phylogenetic patterns and the adaptive evolution of osmoregulation in fiddler crabs (Brachyura, Uca). PLoS ONE, 12(2), e0171870.

Fishbein, M. A., & Ajzen, I. (1975). Belief, attitude, intention, and behaviour: An introduction to theory and research (1st ed.). Reading, MA: Addison-Wesley

Fisser, P., Rosenberg, J., Teske, J., Koehler, M., Mishra, P., & Voogt, J. (2015). Technological Pedagogical Content Knowledge (TPACK): Revision and Rethinking. Journal of Turkish Science Education, 18(4), 589-604.

Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: algebra and statistics. Journal of Marketing Research, 18(3), 382-388. https://doi.org/10.1177/002224378101800313

Gao, F. (2011). A study on the influencing factors of network teaching by teachers in vocational colleges - A case study of a vocational and technical college. Distance Education in China, 1(9), 32-38.

Gefen, D., & Straub, D. W. (2000). The relative importance of perceived ease of use in IS adoption: A study of E-commerce adoption. Journal of the Association for Information Systems, 1(1), 1-30. https://doi.org/10.17705/1jais.00008

Guriting, P., & Ndubisi, N. O. (2006). Borneo online banking: evaluating customer perceptions and behavioral intention. Management research news, 29(1/2), 6-15. https://doi.org/10.1108/01409170610645402

Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate data analysis (5th ed.). Prentice-Hall.

Hooper, D., Coughlan, J., & Mullen, M. R. (2008). Structural equation modelling: Guidelines for determining model fit. Electronic Journal of Business Research Methods, 6(1), 53-60.

Hsieh, P. A., & Wang, W. (2007). Explaining employees' extended use of complex information systems. European Journal of Information Systems, 16(3), 216-227. https://doi.org/10.1057/palgrave.ejis.3000663

Hu, A. A., Jiang, J., & Huang, L. (2007). Research on implementation model of ERP system based on information technology user acceptance theory. Science and Management of Science and Technology, 1(8), 20-26.

Hung, H., & Cho, V. (2010). Continued usage of e-learning communication tools: a study from the learners' perspective in Hong Kong. International Journal of Training and Development, 12(3),171-187.

Idorenyin, I. T. U., & Donyaprueth, K. (2019). Justifying students’ performance, A comparative study of both ICT students’ and instructors’ perspective. Interactive Technology and Smart Education, 16(1),18-35.

Kempf, D. S., & Smith, R. E. (1998). Consumer processing of product trial and the influence of prior advertising: A structural modeling approach. Journal of Marketing Research, 35(3), 325-338. https://doi.org/10.1177/002224379803500304

Kitcharoen, K., & Vongurai, R. (2021). Factors Influencing Customer Attitude and Behavioral Intention Towards Consuming Dietary Supplements. AU-GSB E-JOURNAL, 13(2), 94-109.

Kline, R. B. (2015). Principles and practice of structural equation modeling (5th ed.). Guilford publications.

Kolowich, S. (2013). The professors who make the MOOCs. The Chronicle of Higher Education, 59(28), 20-23.

Koutromanos, G., Styliaras, G., & Christodoulou, S. (2015). Student and in-service teachers' acceptance of spatial hypermedia in their teaching: the case of hypersea. Education and Information Technologies, 20(3), 559-578. https://doi.org/10.1007/s10639-013-9302-8

Latham, G. P., & Locke, E. A. (1991). Self-regulation through goal setting. Organizational behavior and human decision processes, 50(2), 212-247. https://doi.org/10.1016/0749-5978(91)90021-k

Latunde, Y. (2016). Research in Parental Involvement: Methods and Strategies for Education and Psychology (1st ed.). Palgrave Macmillan.

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

Lewis, C. C., Fretwell, C. E., Jim, R., & Parham, J. B. (2013). Faculty use of established and emerging technologies in higher education: a unified theory of acceptance and use of technology perspective. International Journal of Higher Education, 2(2), 22-34. https://doi.org/10.5430/ijhe.v2n2p22

Li, Y., Wu, S., & Liao, Q. (2016). Research on influencing factors and moderating effects of teachers' use of information technology - based on UTAUT model. China Audio-visual Education, 1(10), 31-38.

Lindsey, A. P., King, E. B., Hebl, M., & Levine, N. (2014). The Impact of Method, Motivation, and Empathy on Diversity Training Effectiveness. Journal of Business and Psychology 30(3), 1-10.

Locke, E. A., & Latham, G. P. (2006). New directions in goal-setting theory[J]. Current Directions in Psychological Science, 15(5), 265-268. https://doi.org/10.1111/j.1467-8721.2006.00449.x

Luo, C. (2018). A study on the influencing factors of adult higher education students' willingness to use MOOC [Unpublished Master's Thesis]. Shenzhen University.

Merat, N., Dziennus, M., & Schieben, A. (2016). What influences the decision to use automated public transport? Using UTAUT to understand public acceptance of automated road transport systems. Transportation Research Part F: Traffic Psychology & Behaviour, 50, 55–64.

Nielsen, J. (1993). Iterative user-interface design. Computer, 26(11), 32-41. https://doi.org/10.1109/2.241424

Osatuyi, B., & Turel, O. (2020). Conceptualization and validation of system use reduction as a self-regulatory IS use behavior. European Journal of Information Systems, 29(1), 44-64. https://doi.org/10.1080/0960085x.2019.1709575

Pedersen, P. E. (2005). Adoption of mobile internet services: an exploratory study of mobile commerce early adopters. Journal of Organizational Computing, 15(3), 203-222. https://doi.org/10.1207/s15327744joce1503_2

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

Peterson, R. A., & Kim, Y. (2013). On the relationship between coefficient alpha and composite reliability. Journal of Applied Psychology, 98(1), 194–198. https://doi.org/10.1037/a0030767

Piret, J., & Boivin, G. (2019). Clinical development of letermovir and maribavir: Overview of human cytomegalovirus drug resistance. Antiviral Research, 163, 91-105. https://doi.org/10.1016/j.antiviral.2019.01.011

Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. Am Psychol, 55(1), 68-78. https://doi.org/10.1037/0003-066x.55.1.68

Sarkar, S., & Khare, A. (2017). Moderating effect of price perception on factors affecting attitude towards online shopping. Journal of Marketing Analytics, 5(2), 68-80. https://doi.org/10.1057/s41270-017-0018-2

Shang, F., & Fu, S. (2015). "MOOCs" and the transformation of adult education in China. Adult Education in China, 1(11),8-10.

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

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.

Sun, J. J. (2021). Study on influencing factors of online learning behavior intention of secondary vocational students in post-epidemic era. Int. J. Environ. Res. Public Health, 18(3), 1168.

Timothy, B., & Maitreesh, G. (2018). Prosocial motivation and incentives. Annual Review of Economics, 10(1), 411-438.

Venkatesh, V., & Davis, F. D. A. (2000). Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies[J]. Management Science, 2(2), 186-204. https://doi.org/10.1287/mnsc.

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: toward a unified view. MIS Quarterly, 27(3), 425-478. https://doi.org/10.2307/30036540

Wang, C., Horby, P. W., Hayden, F. G., & Gao, G. F. (2020). A novel coronavirus outbreak of global health concern. Lancet, 395(10223), 470–473.

Wang, Y., & Yan, Q. (2016). Study on the influencing factors of students' willingness to learn MOOC based on UTAUT model. Journal of Beijing University of Posts and Telecommunications (Social Science Edition), 1(2), 96-103.

Wang, Y. D., & Emurian, H. H. (2005). An overview of online trust: concepts, elements, and implications. Computers in Human Behavior, 21(1), 105-125. https://doi.org/10.1016/j.chb.2003.11.008

Williams, B., Brown, T., & Onsman, A. (2010). Exploratory factor analysis: A five-step guide for novices. Australasian Journal of Paramedicine, 8(3), 1-13. https://doi.org/10.33151/ajp.8.3.93

Wolfsfeld, G. (1993). Journalism, publicity, and the lost art of argument. Political Communication, 10(3), 325-326. https://doi.org/10.1080/10584609.1993.9962993

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

Zhang, C. (2015). An empirical study on the factors influencing the acceptance of flipped classroom teaching mode among full-time university teachers. Journal of South China Normal University (Social Science Edition), 1(4), 57-62.