Investigation on Satisfaction and Performance of Online Education Among Fine Arts Major Undergraduates in Chengdu Public Universities
Main Article Content
Abstract
Purpose: This research investigates factors affecting satisfaction and performance of online education among undergraduate fine art students in three public universities in Chengdu, China. The variables include perceived usefulness, perceived ease of use, self-efficacy, task-technology fit, compatibility, satisfaction and performance. Research design, data, and methods: Through a quantitative research approach, questionnaires were distributed via online and offline channels to 500 target respondents. Judgmental, quota and convenience samplings were used to collect the data. The data previously examined by Item Objective Congruence (IOC) Index to confirm content validity, and by Cronbach’s Alpha coefficient value to approve constructs’ reliability in a pilot test of 30 participants. Statistical analysis involves confirmatory factor analysis (CFA) and structural equation model (SEM), including the test of factor loadings, validity, reliability and goodness of fit model. Results: The results showed that perceived ease of use significant affected satisfaction and perceived usefulness. The relationship between self-efficacy, perceived ease of use and perceived usefulness was supported. Compatibility and task-technology fit significantly affected student satisfaction. Furthermore, satisfaction is a predictor of performance. Conclusion: For online education providers, the system should be designed to be easy, useful, self-control, compatibility and task-fit to gain higher student satisfaction and performance.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
The submitting author warrants that the submission is original and that she/he is the author of the submission together with the named co-authors; to the extend the submission incorporates text passages, figures, data, or other material from the work of others, the submitting author has obtained any necessary permission.
Articles in this journal are published under the Creative Commons Attribution License (CC-BY What does this mean?). This is to get more legal certainty about what readers can do with published articles, and thus a wider dissemination and archiving, which in turn makes publishing with this journal more valuable for you, the authors.
References
Aldholay, A., Isaac, O., & Abdullah, Z. (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.
Allen, E., & Seaman, J. (2014). Grade change: Tracking online education in the United States. Babson Survey Research Group Report. http://sloanconsortium.org/publications/survey/grade-change-2013
Anderson, E. W., & Sullivan, M. W. (1993). The Antecedents and Consequences of Customer Satisfaction for Firms. Marketing Science, 12(2), 125-143.
Arbuckle, J. J. (1995). AMOS user's guide. Small Waters.
Bagozzi, R., & Yi, Y. (1988) On the Evaluation of Structural Equation Models. Journal of the Academy of Marketing Sciences, 16, 74-94. http://dx.doi.org/10.1007/BF02723327
Bali, S., & Liu, M. C. (2018). students’ perceptions toward online learning and face-to-face learning courses. Journal of Physics: Conference Series, 1108(1), 1-7.
Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory (1st ed.). Prentice-Hall.
Bhattacherjee, A. (2001). Understanding information systems continuance: an expectation–confirmation model. MIS Quarterly, 25(3), 351-370.
Browne, W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen & J. S. Long (Eds.), Testing structural equation models. Sage.
Chen, D. Q., Preston, D. S., & Swink, M. (2015). How the use of big data analytics affects value creation in supply chain management. Journal of Management Information Systems, Taylor & Francis, 32(4), 4-39.
Cheng, Y. M. (2019). How does task-technology fit influence cloud-based e-learning continuance and impact?. International Journal of Web Information Systems, 15(2), 215-235.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.
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.
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/Spring, 19(4), 9-30.
DeLone, W. H., & McLean, E. R. (2016). Information systems success measurement. Foundations and Trends® in Information Systems, 2(1), 1-116.
Filippini, R., Forza, C., & Vinelli, A. (1998). Trade-off and Compatibility Between Performance: Definitions and Empirical Evidence. International Journal of Production Research, 36(12), 3379-3406.
Fokides, E. (2017). Greek Pre-service Teachers’ Intentions to Use Computers as In-service Teachers. Contemporary Educational Technology, 8(1), 56-75.
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.
Glowalla, P., & Sunyaev, A. (2014). ERP system fit–an explorative task and data quality perspective. Journal of Enterprise Information Management, 27(5), 668-686.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate Data Analysis (7th ed.). Pearson.
Hair, J. F., Black, W. C., Babin, B. J., Andeirson, R. E., & Tatham, R. L. (2006). Multivariate Data Analysis (6th ed.). Pearson Prentice Hall.
Hok, T., Daengdej, J., & Vongurai, R. (2021). Determinants of Student Satisfaction on Continuing Education Intention: A Case Study of Private University in Cambodia. AU-GSB E-JOURNAL, 14(2), 40-50. https://doi.org/10.14456/augsbejr.2021.13
Huang, H., & Liaw, S. (2018). An Analysis of Learners’ Intentions Toward Virtual Reality Learning Based on Constructivist and Technology Acceptance Approaches. International Review of Research in Open and Distributed Learning, 19(1), 91-115.
Huang, J., & Duangekanong, S. (2022). Factors Impacting the Usage Intention of Learning Management System in Higher Education. AU-GSB E-JOURNAL, 15(1), 41-51. https://doi.org/10.14456/augsbejr.2022.59
Igbaria, M., Guimaraes, T., & Davis, G. B. (1995). Testing the determinants of microcomputer usage via a structural equation model. Journal of Management Information Systems, 11(4), 87-114.
Isaac, O., Abdullah, Z., Ramayah, T., Mutahar, A.M., & Alrajawy, I. (2017). Towards a better understanding of Internet technology usage by Yemeni employees in the public sector: an extension of the task-technology fit (TTF) model. Research Journal of Applied Sciences, 12(2), 205-223.
Isaac, O., Masoud, Y., Samad, S., & Abdullah, Z. (2016). The mediating effect of strategic implementation between strategy formulation and organizational performance within government institutions in Yemen. Research Journal of Applied Sciences, 11(10), 1002-1013.
Islam, A. Y. M. A., Mok, M. M. C., Qian, X., & Leng, C. H. (2018). Factors influencing students’ satisfaction in using wireless internet in higher education: Cross-validation of TSM. The Electronic Library, 36(1), 2-20.
Islam, Z. M., Hasan, I., Ahmed, S. U., & Ahmed, S. M. (2011). Organizational culture and knowledge sharing: empirical evidence from service organizations. African Journal of Business Management, 5(14), 5900-5909. https://ssrn.com/abstract=1989270
Jarupathirun, S., & Zahedi, F. M. (2007). Exploring the influence of perceptual factors in the success of web-based spatial DSS. Decision Support Systems, 43(3), 933-951.
Larsen, T. J., Sørebø, A. M., & Sørebø, Ø. (2009). The role of task-technology fit as users ‘motivation to continue information system use. Computers in Human Behavior, 25(3), 778-784.
Lee, M. K., Cheung, C. M., & Chen, Z. (2005). Acceptance of Internet-based learning medium: the role of extrinsic and intrinsic motivation. Information and Management, 42(8), 1095-1104.
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.
Lu, H.-P., & Yang, Y.-W. (2014). Toward an understanding of the behavioral intention to use a social networking site: an extension of task-technology fit to social-technology fit. Computers in Human Behavior, 34, 323-332.
Malhotra, N., Hall, J., Shaw, M., & Oppenheim, P. (2004). Essentials of Marketing Research, An Applied Orientation (1st ed.). Pearson Education Australia.
Montesdioca, G. P. Z., & Maçada, A. C. G. (2014). Measuring user satisfaction with information security practices. Computers & Security, 48, 267-280.
Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2(3), 192-222.
Nagy, J. (2018). Evaluation of Online Video Usage and Learning Satisfaction: An Extension of the Technology Acceptance Mode. International Review of Research in Open and Distributed Learning, 19(1), 160-185.
Norzaidi, M. D., Chong, S. C., Murali, R., & Salwani, M. I. (2007). Intranet usage and managers ‘performance in the port industry. Industrial Management & Data Systems, 107(8), 1227-1250.
Nunnally, J. C., & Bernstein, I. H. (1994) The Assessment of Reliability. Psychometric Theory, 3, 248-292.
Roca, J. C., Chiu, C. M., & Martinez, F. J. (2006). Understanding e-learning continuance intention: an extension of the technology acceptance model. International Journal of Human Computer Studies, 64(8),683-696.
Rogers, E. M. (2003). Diffusion of Innovations (5th ed.). Free press.
Shereen, M. A., Khan, S. M., Kazmi, A., Bashir, N., & Siddique, R. (2020). COVID-19 infection: origin, transmission, and characteristics of human coronaviruses. Journal of Advanced Research, 24, 91-98.
Soper, D. S. (2022, May 24). A-priori Sample Size Calculator for Structural Equation Models. Danielsoper. www.danielsoper.com/statcalc/default.aspx
Venkatesh, V., & Davis, F. D. (1996). A Model of the Antecedents of Perceived Ease of Use: Development and Test. Decision Sciences, 27(3), 451-481.
Vululleh, P. (2018). Determinants of Students’ E-Learning Acceptance in Developing Countries: An Approach Based on Structural Equation Modeling (SEM). International Journal of Education and Development Using Information and Communication Technology, 14(1), 141-151.
Wang, D. D., Wang, H. B., Zhang, W., Wang, H. R., & Shen, X. P. (2020). Online teaching during the “School is Out, but Class is on” period: Based on 33,240 online questionnaire surveys across China. Best Evidence in Chinese Education, 6(1), 753- 767.
Wang, Q. (2006). Quality assurance- best practices for assessing online programs. International Journal on E-Learning, 5(2), 265-274.
Wang, Z. L. (2020). How should education be transformed in the post-epidemic era?. Electrochemical Education Research, 41(4), 13-20.
Yüce, A., Abubakar, A. M., & İlkan, M. (2019). Intelligent tutoring systems and learning performance: Applying task-technology fit and IS success model. Online Information Review, 43(4), 600–616.
Zhang, M., & Du, H. (2020). Analysis on the Actuality and Research Tendency of Hybrid Education. The Chinese Journal of ICT in Education, 1(1), 84 - 85.