Factors Affecting Students’ Continuous Intention to Use Online Art Education Software in Chengdu, China
DOI:
https://doi.org/10.14456/shserj.2023.33Keywords:
Online Art Education, Self-Efficacy, Continuous Intention to Use, Technology Adoption, TAMAbstract
Purpose: This study aims to explore the analysis of factors influencing the continuous use of online art education software by private art education institutions in Chengdu, Sichuan Province, China. The conceptual framework is based on TAM, UTAUT, and IS success model, indicating the relationship between self-efficacy, perceived ease of use, perceived usefulness, attitude, satisfaction, information quality, and continuous intention to use. Research design, data, and methodology: The researchers used quantitative methods (n=500) to distribute questionnaires to students at three private fine arts institutions. Confirmatory factor analysis (CFA) and a structural equation model (SEM) were used for data analysis, including model fitting, reliability, and validity of the structure. Results: The results indicate that satisfaction and attitude are significant factors affecting the continuous use intention of online art education software. Perceived ease of use has the most significant effect on perceived usefulness. Among them, the perceived usefulness and perceived ease of use significantly affect the attitude. Furthermore, information quality significantly impacts students’ satisfaction with using online art education software. However, self-efficacy has no significant effect on perceived ease of use. Conclusions: Therefore, this study suggests that educators should create a more suitable learning platform for combining technology and art in the course design and teaching of online art education software.
References
Agrebi, S., & Jallais, J. (2015). Explain the intention to use smartphones for mobile shopping. Journal of Retailing and Consumer Services, 22(6), 16-23. https://doi.org/10.1016/j.jretconser.2014.09.003
Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191-215.
https://doi.org/10.1037/0033-295x.84.2.191
Bashir, I., & Madhavaiah, C. (2015). Consumer attitude and behavioural intention towards Internet banking adoption in India. Journal of Indian Business Research, 7(1), 67-102. https://doi.org/10.1108/jibr-02-2014-0013
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), 187-97.
Bhattacherjee, A. (2001). Understanding Information Systems Continuance: An Expectation-Confirmation Model. MIS Quarterly, 25(3), 351. https://doi.org/10.2307/3250921
Chen, H. R., & Huang, H. L. (2010). User acceptance of mobile knowledge management learning system: Design and analysis. Educational Technology & Society, 13(3), 70-77.
Compeau, D., & Higgins, C. (1999). Attribution Analysis of Computer Self-Efficacy. AIS Electronic Library, 10(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
Didyasarin, H., Vongurai, R., & Inthawadee, S. (2017). The Factors Impact Attitude Toward Using and Customer Satisfaction with Elderly Health Care Mobile Application Services: A Case Study of People in Bangkok Metropolitan, Thailand. AU-GSB E-Journal, 10(1), 167.
Diener, E., Emmons, R. A., Larsen, R. J., & Griffin, S. (1985). The Satisfaction with Life Scale. Journal of Personality Assessment, 49(1), 71-75. https://doi.org/10.1207/s15327752jpa4901_13
Fokides, E. (2017). Pre-Service Teachers' Intention to Use MUVEs as Practitioners-A Structural Equation Modeling Approach. Journal of Information Technology Education: Research, 16(1), 047-068. https://doi.org/10.28945/3645
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
Gbenga, O., Olanrewaju, B. R., Henry, A. A., & Iyabo, O. F. (2015). Irrigation and Income-Poverty Alleviation: An Assessment Study of Kampe Irrigation Dam in Kogi State, Nigeria. Journal of Agricultural Sciences, 10(2), 76.
https://doi.org/10.4038/jas.v10i2.8053
Giles, M. (2019). The influence of paired grouping on teacher candidates' attitude towards technology use and integration. Technology, Pedagogy and Education, 28(3), 363-380. https://doi.org/10.1080/1475939x.2019.1621772
Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate data analysis (1st ed). Prentice-Hall.
Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (2010). Multivariate data analysis (7th ed.). Prentice Hall
Hopwood, C. J., & Donnellan, M. B. (2010). How Should the Internal Structure of Personality Inventories Be Evaluated?. Personality and Social Psychology Review, 14(3), 332-346. https://doi.org/10.1177/1088868310361240
Hsu, C.-L., & Lin, J. C.-C. (2020). Understanding continuance intention to use online to offline (O2O) apps. Electronic Markets, 30(4), 883-897. https://doi.org/10.1007/s12525-019-00354-x
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
Ifinedo, P. (2006). Acceptance and continuance intention of web-based learning technologies (WLT) use among university students in a Baltic country. The Electronic Journal of Information Systems in Developing Countries, 23(6), 1-20.
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 K-MOOCs. Computers & Education, 122, 260-272.
https://doi.org/10.1016/j.compedu.2018.01.003
Kroesen, M., Handy, S., & Chorus, C. (2017). Do attitudes cause behavior or vice versa? An alternative conceptualization of the attitude-behavior relationship in travel behavior modeling. Transportation Research Part A: Policy and Practice, 101, 190-202. https://doi.org/10.1016/j.tra.2017.05.013
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.
Lee, Y. W., Strong, D. M., Kahn, B. K., & Wang, R. Y. (2002). AIMQ: a methodology for information quality assessment. Information & Management, 40(2), 133-146. https://doi.org/10.1016/s0378-7206(02)00043-5
Lee, Y.-C. (2006). An empirical investigation into factors influencing the adoption of an e-learning system. Online Information Review, 30(5), 517-541. https://doi.org/10.1108/14684520610706406
Lewis, W., Agarwal, R., & Sambamurthy, V. (2003). Sources of influence on beliefs about information technology use: an empirical study of knowledge workers. MIS Quarterly 27(4), 657-678.
Li, Y., Duan, Y., Fu, Z., & Alford, P. (2012). An empirical study on behavioural intention to reuse elearning systems in rural China. British Journal of Educational Technology, 43(6), 933-948.
Lin, K. M., Chen, N.-S., & Fang, K. (2011). Understanding e-learning continuance intention: A negative critical incidents perspective. Behaviour & Information Technology, 30(1), 77-89.
Lin, W.-S., & Wang, C.-H. (2012). Antecedences to continued intentions of adopting e-learning system in blended learning instruction: A contingency framework based on models of information system success and task-technology fit. Computers & Education, 58(1), 88-99.
Meuter, M. L., Bitner, M. J., Ostrom, A. L., & Brown, S. W. (2005). Choosing among alternative service delivery modes: an investigation of customer trial of self-service technologies. Journal of Marketing, 69(2), 61-83.
Nelson, H. E. (2005). Cognitive-Behavioural Therapy with delusions and hallucinations (2nd ed.). A Practice Manual
Nunnally, J. C. (1978). Psychometric theory (2nd ed.). McGraw-Hill.
O’Rourke, N., & Hatcher, L. (2013). A step-by-step approach to using SAS for factor analysis and structural equation modeling (1st ed.). SAS Institute.
Oliver, R. L. (1980). A Cognitive Model of the Antecedents and Consequences of Satisfaction Decisions. Journal of Marketing Research, 17(4), 460. https://doi.org/10.2307/3150499
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
Petter, S., & Mclean, E. R. (2008). A meta-analytic assessment of the DeLone and McLean IS success model: an examination of IS success at the individual level. Information & Management, 46(3), 159-166. https://doi.org/10.1016/j.im.2008.12.006
Rust, R. T., & Oliver, R. L. (1994). Service Quality: Insights and Managerial Implications from the Frontier. In R. T. Rust & R. L. Oliver (Eds.), Service Quality: New Directions in Theory and Practice (pp. 1-19). Sage Publications. http://dx.doi.org/10.4135/9781452229102.n1
Salkind, N. J. (2010). Encyclopedia of research design (1st ed.). Sage. https://dx.doi.org/10.4135/9781412961288
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 (1st ed.). In M. A. Lange (Ed.), Leading-edge psychological tests and testing research (pp. 27-50). Nova Science Publishers.
Straub, D. W. (1989). Validating Instruments in MIS Research. MIS Quarterly, 13(2), 147. https://doi.org/10.2307/248922
Suki, N. M. (2011). Exploring the relationship between perceived usefulness, perceived ease of use, perceived enjoyment, attitude, and subscribers' intention towards using 3G mobile services. Journal of Information Technology Management, 22(1), 1-7.
Sumak, B., Hericko, M., & Pusnik, M. (2011). A meta-analysis of e-learning technology acceptance: The role of user types and e-learning technology types. Computers in Human Behavior, 27(6), 2067-2077.
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204.
Wang, Y., Zhonglin, W., Wei, L., & Fang, J. (2022). Methodological research and model development on structural equation models in China’s mainland from 2001 to 2020. Advances in Psychological Science, 30(8), 1715-1733.
Watjatrakul, B. (2016). Online learning adoption: effects of neuroticism, openness to experience, and perceived values. Interactive Technology and Smart Education, 13(3), 229-243. https://doi.org/10.1108/ITSE-06-2016-0017
Wheaton, B., Muthen, B., Alwin, D. F., & Summers, G. F. (1977). Assessing Reliability and Stability in Panel Models. Sociological Methodology, 8, 84. https://doi.org/10.2307/270754
Wu, B., & Zhang, C. (2014). Empirical study on continuance intentions towards e-learning 2.0 systems. Behaviour & Information Technology, 33(10), 1027-1038.
Wu, J.-H., & Wang, S.-C. (2005). What drives mobile commerce? Information & Management, 42(5), 719-729. https://doi.org/10.1016/j.im.2004.07.001
Zhen, S. G. (2018). Examining the impact mechanism of social psychological motivations on individuals’ continuance intention of MOOC: The moderating effect of gender. Internet Research, 28(1), 232-250.
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