Analysis on Influencing Factors of Art Application Behavior of Comprehensive Materials among Art Undergraduates in Chengdu Colleges

Main Article Content

Xueyong Zhang

Abstract

Purpose: The study examines the affecting factors of the undergraduate students in art major using comprehensive materials for creation in Chengdu. Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Social Influences (SI), Subjective Norms (SN), Attitude Toward Using (ATU), Behavioral Intention (BI), all these variables have a direct or indirect effect on Behavior. Data and methodology: The determinants of the study are adapted from theory of planned behavior (TPB), technology acceptance model (TAM) and flow theory. This study uses the index of the item-objective congruence (IOC) to measure the validity of content. IOC was evaluated by three experts and analysis based on the item's objective suitability index of the project. Reliability of each measurement item was ensured by conducting a preliminary test. Confirmatory factor analysis (CFA) and the structural equation model (SEM) were used to measure and test the questionnaire data. Results: The results show that PU is the direct factor affects art majors' attitude toward using comprehensive materials, while PEOU has no significant impact on it. Students’ attitude toward using comprehensive materials, SI and SN has been proved to have impact on BI, and behavioral intention directly affects the final behavior. Major findings: The data shows that the artistic creation of comprehensive materials is widely applied in universities. Students' use of comprehensive materials is mainly influenced by the PU of it, and students’ ATU, BI are the biggest factors influencing their final behavior.

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How to Cite
Zhang, X. (2022). Analysis on Influencing Factors of Art Application Behavior of Comprehensive Materials among Art Undergraduates in Chengdu Colleges . AU-GSB E-JOURNAL, 15(2), 139-149. https://doi.org/10.14456/augsbejr.2022.79
Section
Articles
Author Biography

Xueyong Zhang

Ph.D. Graduate, School of Business and Advanced Technology Management, Assumption University, Bangkok, Thailand.

References

Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 2(50), 179-211.

Ajzen, I., & Fi M Parenthetical Shbein, M. (1980). Understanding Attitudes and Predicting Social Behavior. Prentice-Hall.

Al-Debei, M. M., Al-Lozi, E., & Papazafeiropoulou, A. (2013). Why people keep coming back to Facebook: explaining and predicting continuance participation from an extended theory of planned behaviour perspective. Decision Support Systems, 55(1), 43-54.

Al-Mamary, Y. H., & Shamsuddin, A. (2015). Testing of The Technology Acceptance Model in Context of Yemen. Mediterranean Journal of Social Sciences, 6(4).

Amos, C., Holmes, G., & Strutton, D. (2008). Exploring the relationship between celebrity endorser effects and advertising effectiveness: a quantitative synthesis of effect size. International Journal of Advertising, Routledge, 27(2), 209-234.

Attuquayefio, S., & Addo, H. (2014). Using the UTAUT model to analyze students’ ICT adoption. International Journal of Education and Development using Information and Communication Technology (IJEDICT), 10(3), 75-86.

Awang, Z. (2012). Structural equation modeling using AMOS graphic. Penerbit Universiti Teknologi MARA.

Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74-94.

Bardhoshi, G., & Erford, B. (2017). Processes and Procedures for Estimating Score Reliability and Precision. Measurement and Evaluation in Counseling and Development, 50(4), 256-263. https://doi.org/10.1080/07481756.2017.1388680

Bashir, I., & Madhavaiah, C. (2015). Consumer attitude and behavioural intention towards internet banking adoption in India. Journal of Indian Business Research, 7(1), 282-286.

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, 30(4), 805-825.

Bollen, K. A. (1989). Structural Equations with Latent Variables. John Wiley & Sons. Chaka, G., & Govender, I. (2017). Students’ Perceptions and Readiness Towards Mobile Learning in Colleges of Education: A Nigerian Perspective. South African Journal of Education, 37(1), 1-12.

Chao, C. (2019). Factors Determining the Behavioral Intention to Use Mobile Learning: An Application and Extension of the UTAUT Model. Frontiers in Psychology. https://doi.org/10.3389/fpsyg.2019.01652

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.

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.

Fishbein, M., & Ajzen, I. (2005). Theory-based behavior change interventions: comments on hobbis and sutton. Journal of Health Psychology, 10(1), 27-31.

Fishbein, M., & Ajzen, I. (1975). Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research. Addison-Wesley.

Fokides, E. (2017). Greek Pre-service Teachers’ Intentions to Use Computers as In-service Teachers. Contemporary Educational Technology, 8(1), 56-75.

Gruzd, A., Staves, K., & Wilk, A. (2012). Connected scholars: Examining the role of social media in research practices of faculty using the UTAUT model. Computers in Human Behavior, 28(6), 2340-2350. https://doi.org/10.1016/j.chb.2012.07.004

Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (2010). Multivariate Data Analysis (6th ed.). Prentice Hall.

Hair, J. F., Marko, S., Lucas, H., & Volker, K. (2014). Partial least squares structural equation modeling (PLS-SEM): An emerging tool for business research. European Business Review, 26(2), 106-121. https://doi.org/10.1108/EBR-10-2013-0128

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.

Harun, Z., Hassan, Z. P., Ismail, M. H., Awang, K., & Awang, W. (2016). The Confirmatory Factor Analysis (CFA) on GST Compliance Research Model in Malaysia. Imperial Journal of Interdisciplinary Research (IJIR), 2(6).

Hayashi, A., Chen, C., Ryan, T., & 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, 15(2), 139-54.

Hong, J. Y., Suh, E. H., & Kim, S. J. (2009). Context-aware systems: a literature review and classification. Expert Systems with Applications, 36(4), 8509-8522.

Kim, H., & Kwahk, K. (2007). Comparing the usage behavior and the continuance intention of mobile internet services. In Eighth World Congress on the Management of eBusiness.

La Nasa, J., Biale, G., & Sabatini, F. (2019). Synthetic materials in art: a new comprehensive approach for the characterization of multi-material artworks by analytical pyrolysis. Heritage Science, 7(1), 1-14. https://doi.org/10.1109/WCMEB.2007.98

Lee, B.-C., Yoon, J.-O., & Lee, I. (2009). Learners’ acceptance of e-learning in South Korea: theories and results. Computers & Education, 53(4), 1320-1329.

Lin, F. T., Wu, H. Y., & Tran, T. (2015). Internet banking adoption in a developing country: an empirical study in Vietnam. Information Systems and e-Business Management, 13(2), 267-287.

Liu, S. H., Liao, H. L., & Pratt, J. A. (2009). Impact of media richness and flow on e-learning technology acceptance. Computer & Education, 53(3), 599-607.

Lodico, G., Spaulding, T., & Voegtle, H. (2006). Method in Educational Research: From Theory to Practice (1st ed.). Jossey-Bass Press.

Nuttavuthisit, K., & Thøgersen, J. (2017). The importance of consumer trust for the emergence of a market for green products: the case of organic food. Journal of Business Ethics, 140(2), 323-337.

Olasina, G. (2019). Human and social factors affecting the decision of students to accept e- learning. Interactive Learning Environments, 27(3), 363-376.

Ozgen, O., & Kurt, S. D. K. (2013). Purchasing behavior of Islamic brands: experimental research [Paper presentation]. The 42nd Annual Conference of EMAC European Marketing Academy, Istanbul.

Pastorella, F., Borges, J. G., & De Meo, I. (2016). Usefulness and perceived usefulness of decision support systems (DSSs) in participatory forest planning: the final users’ point of view. iForest-Biogeosciences and Forestry, 9(3), 422-429.

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.

Savaş, P., & Deniz, K.-P. (2019). Preservice Teachers’ Intention to Recycle and Recycling Behavior: The Role of Recycling Opportunities. International Electronic Journal of Environmental Education, 9(1), 33-45.

Schierz, P. G., Schilke, O., & Wirtz, B. W. (2010). Understanding consumer acceptance of mobile payment services: an empirical analysis. Electronic Commerce Research and Applications, 9(3), 209-216.

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, 165, 27-50.

Sung, H.-N., Jeong, D.-Y., Jeong, Y.-S., & Shin, J.-I. (2015). The Relationship among Self-Efficacy, Social Influence, Performance Expectancy, Effort Expectancy, and Behavioral Intention in Mobile Learning Service. International Journal of U- and e- Service, Science and Technology, 8(9), 197-206. https://doi.org/10.14257/ijunesst.2015.8.9.21

Trotter, R. T. II. (2012). Qualitative research sample design and sample size: Resolving and unresolved issues and inferential imperatives. Preventive medicine, 55(5), 398-400.

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., Morris, M. G., Hall, M., Davis, G. B., Davis, F. D., & Walton, S. M. (2003). User Acceptance of Information Technology: Toward A Unified View 1. MIS Quarterly, 27(3), 425-478.

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.

Weathington, B. L., & Bechtel, A. R. (2012). Alternative sources of information and the selection decision making process. Journal of Behavioral and Applied Management, 13(2), 108-120.

Williams, B., Brown, T., & Onsman, A. (2010). Exploratory factor analysis: A five-step guide novices. Australasian Journal of Paramedicine, 8(3), 1-13. https://doi.org/10.33151/ajp.8.3.93

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

Yadav, R., Chauhan, V., & Pathak, G. V. (2015). Intention to adopt internet banking in an emerging economy: a perspective of Indian youth. International Journal of Bank Marketing, 33(4), 530-544. https://doi: 10.1108/IJBM-06-2014-0075

Yan, C. (2020). On the Feasibility of Incorporating Comprehensive Materials in the Teaching of Watercolour Painting in Chinese Universities. Contemporary Education Research, 1.

Yang, H. H., Yu, J. C., Yang, H. J., & Tsai, H. Y. (2007). Attitude, subjective norm and intention toward using the statistical software. Proceedings of the 6th WSEAS International Conference on Applied Computer Science, 15(17), 338-343.

Zhao, L. (2009). Comprehensive material art experiment. Wuhan University of Technology Press.