INFLUENCING FACTORS OF ARTISTIC POSTGRADUATES' BEHAVIOR OF COMPREHENSIVE MATERIALS ART CREATION IN CHENGDU
Keywords:
Comprehensive Materials Art, Art Education, Higher Education, Artistic Postgraduates’ BehaviorAbstract
This study explores the factors influencing the artistic creation behavior of comprehensive materials among postgraduate students majoring in art in Chengdu, China. This paper assumes that students' final creative behavior is determined by Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Social Influences (SI), Subjective Norms (SN), Attitude Toward Using (ATU), and Behavioral Intention (BI). The determinants of this study are taken from three core theories; namely, planned behavior Theory (TPB), Technology acceptance model (TAM) and flow theory. The researchers used quantitative methods for the analysis and used judgmental and quota sampling as a convenient sampling tool to identified 500 data of the postgraduates at five target universities. In this paper, structural equation model (SEM) and confirmatory factor analysis (CFA) are used to analyze the model fitting, the reliability and validity of variables to ensure the rationality of the hypothesis. The preliminary results show that attitude toward using has the strongest positive and significant influence on behavioral intention, followed by social influence and subject norms. The direct relationship between perceived ease of use and behavioral intention is not significant while the results show that perceived usefulness has a great influence on individual's attitude toward the use of comprehensive materials. Finally, behavioral intention determines the actual behavior of using comprehensive materials by art majors in Chengdu, China. Analysis results show that the comprehensive material widely used in the artistic creation of students, weather students use comprehensive materials to create was mainly affected by PU and SN, in addition, social influence, attitude toward using and behavior intention in different extent, also affect the use of the behavior, so the professional art universities should attach importance to the comprehensive materials course education settings, and improve students' effective cognition of comprehensive materials art.
References
Abd Rahman, A., Asrarhaghighi, E., & Abd Rahman, S. (2015). Consumers and halal cosmetic products: knowledge, religiosity, attitude, and intention. Journal of Islamic Marketing, 6(1), 148-163.
Afendi, N.A., Azizan, F.L., & Darami, A.I. (2014). Determinants of halal purchase intention: case in Perlis. International Journal of Business and Social Research, 4(5), 118-123.
Ajzen, I. (1991), “The theory of planned behavior”, Organizational Behavior and Human Decision Processes, Vol. 50 No. 2, pp. 179-211.
Ajzen, I., & Driver, B. L. (1992). Application of the Theory of Planned Behavior in leisure choice. Journal of Leisure Research, 24(3), 207–224. https://doi.org/10.1080/00222216.1992.11969889
Ajzen, I., & Fishbein, M. (1980), Understanding Attitudes and Predicting Social Behavior, Prentice-Hall.
Alam, M. S., & Uddin, M. A. (2019). Adoption and Implementation of Enterprise Resource Planning (ERP): An Empirical Study. J. Manag. Res, 6, 84–116.
Al-Mamary, Y.H., & Shamsuddin, A. (2015) Testing of The Technology Acceptance Model in Context of Yemen, Mediterranean Journal of Social Sciences, 6(4).
Armitage, C.J. and Conner, M. (2001). Efficacy of the theory of planned behaviour: a meta-analytic review. British Journal of Social Psychology, 40(4), 471-499. Behavior. Springer.
Bashir I., & Madhavaiah C. (2015). Consumer attitude and behavioural intention towards internet banking adoption in India. Journal of Indian Business Research, 2015;7(1):67-102.
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., & Sanford, C. (2006), “Influence processes for information technology acceptance: an elaboration likelihood model”, MIS Quarterly, Vol. 30 No. 4, pp. 805-825.
Chao, C. (2019). Factors Determining the Behavioral Intention to Use Mobile Learning: An Application and Extension of the UTAUT Model. Frontiers in Psychology, 10, https://doi.org/10.3389/fpsyg.2019.01652
Chauhan, S. (2015). Acceptance Of Mobile Money by Poor Citizens of India: Integrating Trust into The Technology Acceptance Model. Info, 17(3), 58-68.
Chen, S. (2005). Case teaching plan of comprehensive painting basic course. China Fine Arts Publishing House, Vol.12.
Cialdini, R.B., & Trost, M.R. (1998), “Social influence: social norms, conformity and compliance”, in Gilbert, D.T., Fiske, S.T. and Lindzey, G. (Eds), The Handbook of Social Psychology, McGrawHill, New York, NY, pp. 151-192.
Clark, R. L., & Ivankova, N. V. (2017). Mixed methods research. The Journal of Positive Psychology, 12(3), 305-306.
Csikszentmihalyi, M. (1975), Beyond Boredom and Anxiety, Jossey-Bass, San Francisco, CA.
Dalati, S., & Marx Gómez, J. (2018). Surveys and questionnaires. In J. Marx Gómez, & S. Mouselli (Eds.), Modernizing the academic teaching and research environment (pp.175- 186). Springer.
Darroch, J. (2003). Developing a measure of knowledge management behaviors and practices. Journal of knowledge management.
Davis, F.D. (1993). User acceptance of information technology: system characteristics, user perceptions and behavioral impacts. International Journal of Man-Machine Studies, 38(3), 475-487.
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.
Deci, E. L., & Ryan, R. M. (1985). Intrinsic Motivation and Self-Determination in Human.
Festinger, L. (1950). Informal social communication. Psychological Review, 57(5), 271.
Fishbein, M., & Ajzen, I. (1975), Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research, Addison-Wesley, Reading, MA.
Fishbein, M., & Ajzen, I. (2010), Predicting and Changing Behavior. The Reasoned Action Approach, Taylor & Francis, New York, NY.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18, 39–50.
Gefen, D., Karahanna, E., & Straub, D. (2003). Trust and TAM in online shopping: an integrated model. MIS Quarterly, 27(1), 51-90.
Geng, H. (2005). The Practical Expression of Comprehensive Materials in the Process of Painting Art Creation. Journal of The Institution of Engineers (India): Series D.
Hao, S., Dennen, P., & Mei, L. (2017). Influential Factors for Mobile Learning Acceptance Among Chinese Users. Educational Technology Research and Development, 65(1), 101-123.
Hart, M., & Henriques, V. (2006). On The Influence of Facilitating Conditions on DSS.
Hayashi, A., Chen, C., Ryan, T. and Wu, J. (2004), “The role of social presence and moderating role of computer self efficacy in predicting the continuance usage of e-learning systems”, Journal of Information Systems Educations, Vol. 15 No. 2, pp. 139-54.
Herbart, J. (1806) Allgemeine Pädagogik aus dem Zweck der Erziehung abgeleitet. bey Johann Friedrich Röwer Ort: Göttingen.
Herzog, W., & Boomsma, A. (2009). Small-sample robust estimators of noncentrality-based and incremental model fit. Structural Equation Modeling: A Multidisciplinary Journal, 16(1), 1-27.
Hogg, M.A., & Vaughan, G.M. (2005), Introduction to Social Psychology, 4th ed., Pearson Prectice Hall, Australia.
Islam, J.U., Rahman, Z., & Hollebeek, L.D. (2018). Consumer engagement in online brand communities: a solicitation of congruity theory. Internet Research, 28(1), 23-45.
Jaruwanakul, T. (2021). Key Influencers of Innovative Work Behavior in Leading Thai Property Developers. AU-GSB e-Journal, 14(1), 61-70.
Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34(2), 183–202. https://doi.org/10.1007/BF02289343.
Kanchanapibul, M., Lacka, E., Wang, X., & Chan, H.K. (2014). An empirical investigation of green purchase behaviour among the young generation. Journal of Cleaner Production, 66, 528-536.
Kanchanapibul, M., Lacka, E., Wang, X., & Chan, H.K. (2014). An empirical investigation of green purchase behaviour among the young generation. Journal of Cleaner Production, 66, 528-536.
Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). The Guilford Press. https://doi.org/10.1038/156278a0.
Kruger, M. (2009). Western painting materials and Techniques Manual. Anhui Fine Arts Publishing House, Vol.8.
Lau, S.-H., & Woods, P.C. (2008). An investigation of user perceptions and attitudes towards learning objects. British Journal of Educational Technology, 39(4), 685-699.
Lee, B.-C., Yoon, J.-O., & Lee, I. (2009). Learners’ acceptance of e-learning in South Korea: theories and results. Computers & Education, 53(4), 1320-9.
Lin, J., & Lu, H. (2000), “Towards an understanding of the behavioral intention to use a web site”, International Journal of Information Management, Vol. 20 No. 3, pp. 197-208.
Malhotra, N., Hall, J., Shaw, M., & Oppenheim, P. (2004). Essentials of Marketing Research, An Applied Orientation: Pearson Education Australia.
Mazman, S., Usluel, Y., & Çevik, V. (2009). Social Influence in The Adoption Process and Usage of Innovation: Gender Differences. International Journal of Behavioral, Cognitive, Educational and Psychological Sciences, 1(4), 229-232.
Neo, M., Park, H., Lee, M.-J., Soh, J.-Y., & Oh, J.-Y. (2015). Technology Acceptance of Healthcare E-Learning Modules: A Study of Korean and Malaysian Students' Perceptions. Turkish Online Journal of Educational Technology-TOJET, 14(2), 181-194.
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). New York: Nova
Suki, N. M. (2011), “Users’ behavior towards ubiquitous m-learning”, Turkish Online Journal of Distance Education, Vol. 12 No. 3, pp. 118-129. Awang, Z. 2012. Structural equation modeling using AMOS graphic. Penerbit Universiti Teknologi MARA
Toft, M.B., Schuitema, G. and Thøgersen, J. (2014), “Responsible technology acceptance: model development and application to consumer acceptance of Smart Grid technology”, Applied Energy, Vol. 134, pp. 392-400.
Triandis, H. C. (1980). Values, Attitudes, and Interpersonal Behavior. In Page, M.M. (Ed.), Nebraska Symposium on Motivation, 1979: Beliefs, Attitudes, and Values (pp. 195-259). University of Nebraska Press.
Venkatesh, V. (2000). Determinants of Perceived Ease of Use: Integrating Perceived Behavioral Control, Computer Anxiety and Enjoyment into the Technology Acceptance Model. Information Systems Research, 11(4), 342-365.
Venkatesh, V., & Davis F.D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204. https://doi.org/10.1287/mnsc.46.2.186.11926
Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Quarterly, 36(1), 157-178.
Wu, B., & Zhang, C. (2014), “Empirical study on continuance intentions towards e-learning 2.0 systems”, Behaviour & Information Technology, Vol. 33 No. 10, pp. 1027-1038.
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
Wu, J., & Liu, D. (2007). The effects of trust and enjoyment on intention to play online games. Journal of Electronic Commerce Research, 8(2), 128–140.
