Factors Influencing Behavioral Intention Towards MOOC Platform of Jingdezhen Vocational University of Art Students Majoring in Art and Design in Jiangxi Province, China
DOI:
https://doi.org/10.14456/abacodijournal.2023.32Abstract
The objective of this investigation was to analyze the behavioral intention of students at the Jingdezhen Vocational University of Art who majoring in art and design majors to use the MOOC platform. It was carried out by the researchers using quantitative research techniques. Based on the Theory of Reason and Action (TRA), the Technology Acceptance Model (TAM), and the Unified Theory of Technology Acceptance and Use (UTAUT), this study develops a conceptual framework. Seven potential variables were chosen to assess the validity of the research tool using project-goal consistency and passed the internal consistency test: self-efficacy, perceived ease of use, perceived usefulness, attitude, performance expectancy, subjective norm, and behavioral intention. The reliability was evaluated by Cronbach α coefficient through the pilot test. In addition, the sampling strategy was multi-stage sampling. In the course of the study, a face-to-face questionnaire was distributed to 500 professional undergraduates majoring in art and design with MOOC platform experience at the School of Ceramic Art and Design and the School of Digital Art of Jingdezhen Art Vocational University. As statistical analysis tools, confirmatory factor analysis and structural equation modeling were applied in this research to advance the influence on data, matrix accuracy, basic variables, hypothetical support, and path coefficients. The results revealed that all the hypotheses are suggested, and the subjective norm was the most influential factor that affected art and design majors' behavioral intention to use the MOOC platform.
References
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211.
Alasmari, T., & Zhang, K. (2019). Mobile learning technology acceptance in Saudi Arabian higher education: An extended framework and a mixed-method study. Edu.Inf. Technol, 24(3), 2127-2144.
Bandura, A. (1977). Self-efficacy-toward a unifying theory of behavioral change. Psychological Review, 84(2),191-215.
Batara, E., Nurmandi, A., Warsito, T., & Pribadi, U. (2017). Are government employees adopting local e-government transformation? The need for having the right attitude, facilitating conditions and performance expectations. Transforming Government: People, Process and Policy, 11(4), 612-638.
Chen, L. (2018). Education informatization 2.0: Trends and directions of Internet promoting education reform [in Chinese]. Distance Education in China, (9), 6-8.
Cheung, R., & Vogel, D. (2013). Predicting user acceptance of collaborative technologies: An extension of the technology acceptance model for e-learning. Computers & Education, 63,160-175.
Cooper, D. R., & Schindler, P. S. (2011). Business Research Methods (11th ed.). Sage Publications.
Davis, F. (1989). Perceived Usefulness, Perceived Ease of Use, And User Acceptance of Information Technology. MIS Quarterly, 13(3), 319-340.
Elkaseh, A., Wong, K., & Fung, C. (2016). Perceived Ease of Use and Perceived Usefulness of Social Media for e-Learning in Libyan Higher Education: A Structural Equation Modeling Analysis. International Journal of Information and Education Technology, 6(3), 192-199.
Fishbein, M., & Ajzen, I. (1975). Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research (1st ed.). Addison-Wesley.
Fokides, E. (2017). Greek Pre-service Teachers’ Intentions to Use Computers as In-service Teachers. Contemporary Educational Technology, 8(1), 56-75.
Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (2010). Multivariate Data Analysis (6th ed.). Prentice Hall.
Hasan, B. (2007). Examining the effects of computer self-efficacy and system complexity on technology acceptance. Information Resources Management Journal, 20(3), 76-88. https://doi.org/10.34105/j.kmel.2019.11.006
Hu, L.-T., & Bentler, P. M. (1999). Cutoff Criteria for Fit Indexes in Covariance Structure Analysis: Conventional Criteria Versus New Alternatives. Structural Equation Modeling, 6(1), 1-55.
Hu, J., & Zhang, H. (2016). Chinese students’ behavior intention to use mobile library apps and effects of education level and discipline. Library Hi Tech, 34(4), 639-656.
Huang, G., & Yuan, X. (2020). New edition Tutorial of Education Statistics and Measurement Evaluation. East China Normal University Press.
Huang, J. H., Lin, Y. R., & Chuang, S. T. (2007). Elucidating user behavior of mobile learning:a perspective of the extended technology acceptance model. The Electronic Library, 25(5), 585-598.
Ibrahim, R., Leng, N. S., Yusoff, R. C. M., Samy, G. N., Masrom, S., & Rizman, Z. I. (2017). E-learning acceptance based on technology acceptance model (TAM). Journal of Fundamental and Applied Sciences, 9(4S), 871-889.
Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). The Guilford Press. https://doi.org/10.1038/156278a0
Kumar, J. A., Bervell, B., Annamalai, A., & Osman, S. (2020). Behavioral Intention to Use Mobile Learning: Evaluating the Role of Self-Efficacy, Subjective Norm, and WhatsApp Use Habit. IEEE Access, 8, 20858-208074.
Luo, Y., Zhou, G., Li, J., & Xiao, X. (2018). Study on MOOC scoring algorithm based on Chinese University MOOC learning behavior data. Heliyon, 4(11), E00960.
Mtebe, J. S., & Raisamo, A. (2014). Investigating students’ behavioral intention to adopt and use mobile learning in higher education in East Africa. International Journal of Education and Development using Information and Communication Technology (IJEDICT), 10(3), 4-20.
Park, E., & Kim, K. J. (2014). An integrated adoption model of mobile cloud services: exploration of key determinants and extension of technology acceptance model. Telematics and Informatics, 31(3), 376-385.
Park, S. Y., Nam, M. W., & Cha, S. B. (2012). University students’ behavioral intention to use mobile learning: Evaluating the technology acceptance model. Brit. J. Educ. Technol., 43(4), 592-605.
Punniyamoorthy, M., & Asumptha, J. A. (2019). A study on knowledge sharing behavior among academicians in India. Knowledge Management & E-Learning, 11(1), 95-113.
Qin, C. X., Liu, Y. X., Mou, J., & Chen, J. P. (2019). User adoption of a hybrid social tagging approach in an online knowledge community. Aslib Journal of Information Management, 71(2), 155-175.
Quan, Y. (2015). MOOC: Challenges or Opportunities for Traditional Classes in China’s Higher Education. International Journal of Arts and Commerce, 4(4), 156-160.
Rui-Hsin, K., & Lin, C. (2018). The Usage Intention of E-Learning for Police Education and Training. Policing an International Journal of Police Strategies and Management, 41(1), 98-112.
Salloum, S. A., & Shaalan, K. (2019). Factors Affecting Students’ Acceptance of E-Learning System in Higher Education Using UTAUT and Structural Equation Modeling Approaches. Springer International Publishing, 469-480.
https://doi.org/10.1007/978-3-319-99010-1_43
Schumacker, R. E., & Lomax, R. G. (2010). A beginner’s guide to structural equation modeling (3rd ed.). Routledge.
Shao, Z. (2018). Examining the impact mechanism of social psychological motivations on individuals’ continuance intention of MOOCs. Internet Research, 28(1), 232-250.
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.
Slatten, L. A. D. (2012). Something old and something new: using the technology acceptance model to evaluate nonprofit certification. International Journal of Organization Theory and Behavior, 15(3), 423-449.
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.
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.
Wang, C. S., Jeng, Y. L., & Huang, Y. M. (2017). What influences teachers to continue using cloud services? The role of facilitating conditions and social influence. The Electronic Library, 35(3), 520-533.
Wu, B., & Chen, X. (2016). Continuance intention to use MOOCs: Integrating the technology acceptance model (TAM) and task technology fit (TTF) model. Computers in Human Behavior. http://dx.doi.org/10.1016/j.chb.2016.10.028.
Wut, T. M., & Lee, S. W. (2021). Factors affecting students’online behavioral intention in using discussion forum. Interactive Technology and Smart Education, 2021(6), 1741-5659.
Yan, S., & Yang, Y. (2021). Education Informatization 2.0 in China: Motivation, Framework and Vision. ECNU Review of Education, 4(2), 410-428.
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.
Yau, K. Y., & Ho, T. C. (2015). Influence of Subjective Norm on Behavioral Intention in Using E-Learning: An Empirical Study in Hong Kong Higher Education. Proceedings of the International Multi Conference of Engineers and Computer Scientists, 2015(2), 18-20.
Yip, K. H. T., Lo, P., Ho, K. K. W., & Chiu, D. K. W. (2020). Adoption of mobile library apps as learning tools in higher education: a tale between Hong Kong and Japan. Online Information Review, 45(2), 389-405.
