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http://dx.doi.org/10.5392/IJoC.2022.18.2.058

Research on Influencing Factors of Continuous Learning Willingness in Online Art Education Based on the UTAUT Model  

Wang, Youwang (Arts Management, Dept. of Business, Gachon University)
Fang, Xiuqing (Arts Management, Dept. of Business, Gachon University)
Publication Information
International Journal of Contents / v.18, no.2, 2022 , pp. 58-67 More about this Journal
Abstract
As the Internet rapidly evolves, online learning has emerged as the third largest scenario in the field of education. Online education, different from the two traditional learning scenarios of the school and society, is characterized with broader learning types and higher freedom. In today's post-pandemic era, art education, which relies on face-to-face teaching, is of particular significance to expand online education methods. Based on the UTAUT model, this paper posits seven hypotheses about the willingness to continue learning in online art education. After collecting valid data through a questionnaire, a detailed empirical analysis was conducted via SPSS and AMOS. The results of empirical analysis show that less than half of the respondents had experienced the online art education, mirroring that this is a market worth developing. Based on the findings, learning habit does not significantly impact art learners' willingness to continue learning online. This result and other verified hypotheses are detailed in the discussion part of this paper. This study proves that UTAUT can better explain user behavior than the traditional information system model prior to the improvement, and also has strong explanatory power in the field of art education. The conclusion also posits some operational suggestions from the perspective of practitioners in this field, thereby providing a theoretical basis for art education practitioners.
Keywords
UTAUT; Online Education; Path Analysis; Art Education; SEM;
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1 2020 China Music Industry Development General Report, 2020. [Online] Available: https://www.sohu.com/a/437722006_152615
2 Opinions on further reducing the burden of homework and off-campus training for students in compulsory education. (2021). [Online] Available: http://www.moe.gov.cn/jyb_xxgk/moe_1777/moe_1778/202107/t20210724_546576.html
3 W. Su, X. H. Bi, and L. Wang, "Research on User Acceptance Model of Internet of Things Based on UTAUT Theory" Information Science, vol. 31, no. 5, pp. 128-132, 2013, doi: http://qikan.cqvip.com/Qikan/Article/Detail?id=45533582.
4 V. Venkatesh, M. G. Morris, and G. B. Davis, "User Acceptance of Information Technology: Toward a Unified View," MIS Quarterly, vol. 27, no. 3, pp. 425-478, 2003, doi: https://doi.org/10.2307/30036540.   DOI
5 S. F. Verkijika, "Factors influencing the adoption of mobile commerce applications in Cameroon," Telematics and Informatics, vol. 35, no. 6, pp. 1665-1674, 2018, doi: https://doi.org/10.1016/j.tele.2018.04.012.   DOI
6 C. L. Hsu and J. C. C. Lin, "What drives purchase intention for paid mobile apps?: An expectation confirmation model with perceived value," Electronic Commerce Research and Applications, vol. 14, no. 1, pp. 46-57, 2015, doi: https://doi.org/10.1016/j.elerap.2014.11.003.   DOI
7 C. C. Aggarwal and S. Y. Philip, "On the network effect in Web 2.0 applications," Electronic Commerce Research and Applications, vol. 11, no. 2, pp. 142-151, 2012, doi: https://doi.org/10.1016/j.elerap.2011.11.001.   DOI
8 G. F. Yang, "Factors Affecting the Continued Use of MOOC User Behavior," Open Education Research, vol. 22, no. 1, pp. 100-111, 2016, doi: http://en.cnki.com.cn/Article_en/CJFDTOTAL-JFJJ201601013.htm.
9 J. T. Marchewka and K. Kostiwa, "An application of the UTAUT model for understanding student perceptions using course management software," Communications of the IIMA, vol. 7, no. 2, pp. 93-104, 2007, doi: https://scholarworks.lib.csusb.edu/ciima/vol7/iss2/10.
10 C. M. Chao, "Factors determining the behavioral intention to use mobile learning: An application and extension of the UTAUT model," Frontiers in psychology, vol. 10, pp. 1652, 2019, doi: https://doi.org/10.3389/fpsyg.2019.01652.   DOI
11 M. D. Williams, N. P. Rana, and Y. K. Dwivedi, "The unified theory of acceptance and use of technology (UTAUT): a literature review," Journal of enterprise information management, vol. 28, no. 3, pp. 443-488, 2015, doi: https://doi.org/10.1108/JEIM-09-2014-0088.   DOI
12 R. Eisinga, M. Te Grotenhuis, and B. Pelzer, "The reliability of a two-item scale: Pearson, Cronbach, or SpearmanBrown?," International journal of public health, vol. 58, no. 4, pp. 637-642, 2013, doi: https://link.springer.com/article/10.1007/s00038-012-0416-3.   DOI
13 M. L. Wu, Questionnaire statistical analysis practice: SPSS operation and application, Chongqing University Press, Chongqing, 2010.
14 Y. I. Jung, "Effects of MOOC: Based Learning on Students' Competence in English and Satisfaction," journal of linguistics science, vol. 82, 2017, doi: https://doi.org/10.21296/jls.2017.09.82.219.   DOI
15 N. A. Albelbisi and F. D. Yusop, "Factors Influencing Learners' Self: Regulated Learning Skills in a Massive Open Online Course (MOOC) Environment," Turkish Online Journal of Distance Education, vol. 20, no. 3, pp. 1-16, 2019, doi: https://doi.org/10.17718/tojde.598191.   DOI
16 M. K. Lee and E. Turban, "A trust model for consumer internet shopping," International Journal of electronic commerce, vol. 6, no. 1, pp. 75-91, 2001, doi: https://doi.org/10.1080/10864415.2001.11044227.   DOI
17 J. F. Hair, W. C. Black, B. J. Babin, R. E. Anderson, and R. L. Tatham, Multivariate data analysis, Prentice-Hall International, New York, 2006.
18 A. Y. S. Ali, A.M. Dahie, and A. A. Ali, "Teacher motivation and school performance, the mediating effect of job satisfaction: Survey from Secondary schools in Mogadishu," International Journal of Education and Social Science, vol. 3, no. 1, pp. 24-38, dol: https://www.semanticscholar.org/paper/Teacher-Motivation-and-SchoolPerformance-%2C-the-of-Ali/31816c35b99c9511314a45b9d0ec259b9aecb4ee.
19 V. Venkatesh, J. Y. Thong, and X. Xu, "Unified theory of acceptance and use of technology: A synthesis and the road ahead," Journal of the association for Information Systems, vol. 17, no. 5, pp. 328-376, 2016, doi: https://ssrn.com/abstract=2800121.   DOI
20 H. Jeong, "A Study on the Structural Relationships among Variables Affecting Adult Learners' Intention to Take MOOC: Based on Social Influence, Self Efficacy, Expectation-Confirmation, Task-Technology Fit, and Satisfaction," Journal of Lifelong Learning Society, vol. 14, no. 4, pp. 203-229, 2018, doi: http://journal.kci.go.kr/jlls/archive/articleView?artiId=ART002408033.   DOI
21 T. Oliveira, M. Thomas, and G. Baptista, et al., "Mobile Payment: Understanding the Determinants of Customer Adoption and Intention to Recommend the Technology," Computers in Human Behavior, vol. 61, pp. 404-414, 2016, doi: https://doi.org/10.1016/j.chb.2016.03.030.   DOI
22 E. Yeo, J. Kim, and S. Han, "Applying MOOC-Based Flipped Learning to Business Education: Analysis of Academic Achievement and Student Satisfaction," Korea business review, vol. 19, no. 4, pp. 181-202, 2015, doi: https://doi.org/10.17287/kbr.2015.19.4.181.   DOI
23 Data analysis of the industry concerning online education, 2020. [Online] Available: https://www.iimedia.cn/c1061/75791.html
24 D. Chalmers, N. G. MacKenzie, and S. Carter, "Artificial Intelligence and Entrepreneurship: Implications for Venture Creation in the Fourth Industrial Revolution," Entrepreneurship Theory and Practice, vol. 45, no. 5, pp. 1028-1053, July. 2021, doi: https://doi.org/10.1177%2F1042258720934581.   DOI
25 B. A. John, "The Fourth Industrial Revolution and education," South African Journal of Science, vol. 114, no. 5-6, 2018, doi: https://dx.doi.org/10.17159/sajs.2018/a0271   DOI