Predicting Factors of Undergraduate Art Students’ Behavioral Intention to Use Online Education in Chengdu, China

Main Article Content

Linhan Geng

Abstract

Purpose:  This study aims to explore the factors affecting online education behavior intention of fine arts students in three target universities in Chengdu, China. The conceptual framework proposes a causal relationship between perceived usefulness, perceived ease of use, attitude, facilitation condition, social impact, effort expectation, and behavioral intention. Research design, data, and methodology: The researchers used quantitative assessment techniques to conduct a statistical survey of 500 samples and identified undergraduate students at three target universities in Chengdu. The quantitative approach is used to distribute questionnaire to obtain survey data. The sampling techniques are purposive, quota, and convenience sampling. Confirmatory factor analysis (CFA) and structural equation model (SEM) were used for quantitative analysis, including model goodness of fit, correlation validity, and reliability test of each component. Results: Most variables had a significant effect on related latent variables, except that social influence had no significant effect on behavioral intention. In addition, perceived usefulness had the greatest effect on behavioral intention. Conclusions: Seven hypotheses were proved to achieve the research objectives. Therefore, the suggestion is to promote these aspects in the whole online education process to improve the online education behavior intention of fine arts students in Chengdu's target university. 

Downloads

Download data is not yet available.

Article Details

How to Cite
Geng, L. (2025). Predicting Factors of Undergraduate Art Students’ Behavioral Intention to Use Online Education in Chengdu, China. AU-GSB E-JOURNAL, 18(1), 12-22. https://doi.org/10.14456/augsbejr.2025.2
Section
Articles
Author Biography

Linhan Geng

School of Fine Arts and Design, Chengdu University, China.

References

Ajzen, I. (1991). The Theory of Planned Behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211. https://doi.org/10.1016/0749-5978(91)90020-t

Al-Mamary, Y. H., & Shamsuddin, A. (2015). Testing of the technology acceptance model in context of yemen. Mediterranean Journal of Social Sciences, 6(4), 268-273. https://doi.org/10.5901/mjss.2015.v6n4s1p268

Arbaugh, J. B., & Duray, R. (2002). Technological and structural characteristics, student learning and satisfaction with web-based courses an exploratory study of two on-line MBA programs. Management learning, 33(3), 331-347. https://doi.org/10.1177/1350507602333003

Arbuckle, J. L., & Wothke, W. (2008). AMOS 18.0 update to the AMOS user’s guide. Amos Development Corporation.

Arslan, K. (2022). Information technology (IT) teacher candidates’ attitudes towards and opinions on online testing during COVID-19 pandemic. International Online Journal of Education and Teaching (IOJET), 9(1), 176-193.

Asadi, S., Nilashi, M., Husin, A. R. C., & Yadegaridehkordi, E. (2016). Customers Perspectives on Adoption of Cloud Computing in Banking Sector. Information Technology and Management, 18(4), 305-330. https://doi.org/10.1007/s10799-016-0270-8

Awang, Z. (2012). Structural equation modeling using AMOS graphic (1st ed.). Penerbit Universiti Teknologi MARA.

Bajat, A. (2018). Lean management and innovation performance. Management Research Review, 42(2), 239-262.

Bardakcı, S. (2019). Exploring High School Students’ Educational Use of YouTube. International Review of Research in Open and Distributed Learning, 20(2), 260-278. https://doi.org/10.19173/irrodl.v20i2.4074

Bashir, I., & Madhavaiah, C. (2015). Consumer Attitude and Behavioral 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

Brown, T. (2015). Confirmatory Factor Analysis for Applied Research (1st ed.). The Guilford Press.

Byrne, B. M. (2010). Structural Equation Modeling with AMOS: Basic Concepts, Applications, and Programming (2nd ed.). Routledge Taylor & Francis Group.

Celik, V., & Yesilyurt, E. (2013). Attitudes To Technology, Perceived Computer Self-Efficacy, and Computer Anxiety as Predictors of Computer Supported Education. Computers & Education, 60(1), 148-158. https://doi.org/10.1016/j.compedu.2012.06.008

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. https://doi.org/10.15700/saje.v37n1a1282

Chang, C., Yan, C., & Tseng, J. (2012). Perceived Convenience in An Extended Technology Acceptance Model: Mobile Technology and English Learning for College Students. Australasian Journal of Educational Technology, 28(5). 809-826. https://doi.org/10.14742/ajet.818

Chao, C. M. (2019). Factors determining the behavioral intention to use mobile learning: An application and extension of the UTAUT model. Frontiers in Psychology, 10, 1–14. 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. https://doi.org/10.1108/info-02-2015-0018

Davis, F. (1989). Perceived Usefulness, Perceived Ease of Use, And User Acceptance of Information Technology. MIS Quarterly, 13(3), 319-340. https://doi.org/10.2307/249008

Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: a comparison of two theoretical models. Management Science, 25(8), 982- 1003. https://doi.org/10.1287/ mnsc.35.8.982

Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and Intrinsic Motivation to Use Computers in the Workplace. Journal of Applied Social Psychology, 22(14), 111-1132. https://doi.org/10. 1111/j.1559-1816.1992.tb00945.x

Eagly, A. H., & Chaiken, S. (1993). The psychology of attitudes (1st ed.). Harcourt Brace Jovanovich College Publishers.

Elkaseh, A., Wong, K., & Fung, C. (2016). Perceived Ease of Use and Perceived Usefulness of social media for e-Learning in Libyan Higher Education: A Structural Equation Modeling Analysis. International Journal of Information and Education Technology, 6(3), 192-199. https://doi.org/10.7763/ijiet.2016.v6.683

Engelbrecht, E. (2005). Adapting to changing expectations: Post-graduate students’ experience of an e-learning tax program. Computers and Education, 45(2), 217–229. https://doi.org/10.1016/j.compe du.2004.08.001

Fang, Z. Y. (2015). A Comparative study of Online learning and Blended Learning: A Case study of modern Educational Technology [Published Master's thesis]. China National Knowledge Infrastructure.

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

Fokides, E. (2017). Greek Pre-service Teachers’ Intentions to Use Computers as In-service Teachers. Contemporary Educational Technology, 8(1), 56-75. https://doi.org/10.30935/cedtech/6187

Golnaz, R., Zainulabidin, M., Mad-Nasir, S., & Eddie Chiew, F. C. (2010). Non-Muslim Perception Awareness of Halal Principle and Related Food Products in Malaysia. International Food Research Journal, 17(3), 667-674.

Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). Prentice-Hall.

Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2016). Multivariate Data Analysis (7th ed.). Pearson. https://doi.org/10.1016/j.ijpharm.2011.02.019

Hang, H. (2021, October 14). 2020 China Online Education Industry Research Report. Zhi Hu. https://zhuanlan.zhihu.com/p/421332167

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. https://doi.org/10.19173/irrodl.v19i1.2503

Humida, T., Al Mamun, M. H., & Keikhosrokiani, P. (2021). Predicting behavioral intention to use E-lEarning system: A case-study in Begum Rokeya University, Rangpur, Bangladesh. Education and Information Technologies, 27(2), 2241-2265. https://doi.org/10.1007/s10639-021-10707-9

Isaac, R., Zerbe, W., & Pitt, D. (2001). Leadership and Motivation: The Effective Application of Expectancy Theory. Journal of Managerial Issues, 13(2), 212.

Joo, Y. J., Joung, S., Shin, E. K., Lim, E., & Choi, M. (2014). Factors Influencing Actual Use of Mobile Learning Connected with E-Learning. In D. C. Wyld & J. Zizka, (Eds), Computer Science & Information Technology (pp. 169–176). Third International Conference on Advanced Information Technologies & Applications (ICAITA-2014). https://doi.org/10.5121/csit.2014.41116

Klem, L. (2000). Structural equation modeling. In L. G. Grimm & P. R. Yarnold (Eds.), Reading and understanding MORE multivariate statistics (pp. 227-260). American Psychological Association.

Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). The Guilford Press.

Lewis-Beck, M., Bryman, A., & Liao, T. (2004). Encyclopedia of Social Science Research Methods (1st ed.). Saga Publications.

Li, A., Islam, A. Y., & Gu, X. (2021). Factors engaging college students in online learning: An investigation of learning stickiness. SAGE Open, 11(4), 215824402110591. https://doi.org/10.1177/21582440211059181

Liestiawati, F., & Agustina, P. (2018). The Influence of UTAUT Factors on E-retention with E-satisfaction as. Mediating Variable in E-learning. Hasanuddin Economics and Business Review, 2(1), 19. https://doi.org/10.26487/hebr.v2i1.1465

Malhotra, N., Hall, J., Shaw, M., & Oppenheim, P. (2004). Essentials of Marketing Research, An Applied Orientation (1st ed.). Pearson Education Australia.

Meng, X. Y., & Wang, C. (2021). Research of the Online Teaching Mode of Art Majors of Undergraduate Colleges. Heilongjang Science, 1(2), 80-81.

Min, Y., Huang, J., Varghese, M. M., & Jaruwanakul, T. (2022). Analysis of Factors Affecting Art Major Students’ Behavioral Intention of Online Education in Public Universities in Chengdu. AU-GSB E-JOURNAL, 15(2), 150-158. https://doi.org/10.14456/augsbejr.2022.80

Moore, G. C., & Benbasat, I. (1996). Integrating diffusion of innovations and theory of reasoned action models to predict utilization of information technology by end-users. Diffusion and Adoption of Information Technology, 1(2), 132-146. https://doi.org/10.1007/978-0-387-34982-4_10

Mtebe, J., & Raisamo, R. (2014). Challenges And Instructors’ Intention to Adopt and Use Open Educational Resources in Higher Education in Tanzania. International Review of Research in Open and Distance Learning, 15(1). 249-271. https://doi.org/10.19173/irrodl.v15i1.1687

Nagy, J. (2018). Evaluation of Online Video Usage and Learning Perceived satisfaction: An Extension of the Technology Acceptance Mode. International Review of Research in Open and Distributed Learning, 19(1), 160- 185. https://doi.org/10.19173/irrodl.v19i1.2886

Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill.

Pedroso, R., Zanetello, L., Guimaraes, L., Pettenon, M., Goncalves, V., Scherer, J., Kessler, F., & Pechansky, F. (2016). Confirmatory factor analysis (CFA) of the crack use relapse scale (CURS). Archives of Clinical Psychiatry, 43(3), 37-40. https://doi.org/10.1590/0101-60830000000081

Perry, A. (2017). Factors comprehensively influencing acceptance of 3D-printed apparel. Journal of Fashion Marketing and Management, 21(2), 219-234. https://doi.org/10.1108/jfmm-03-2016-0028

Pituch, K. A., & Lee, Y. K. (2006). The influence of system characteristics on e-learning use. Computers & Education, 47(2), 222–244. https://doi.org/10.1016/j.compedu.2004.10.007

Qin, C., Liu, Y., Mou, J., & Chen, J. (2019). User Adoption of a Hybrid Social Tagging Approach in an Online Knowledge Community. Aslib Journal of Information Management, 71(2), 155- 175. https://doi.org/10.1108/AJIM-09-2018-0212.

Salloum, S. A., & Shaalan, K. (2018, October). Factors Influence Students’ Acceptance of E-Learning System in Higher Education Using UTAUT and Structural Equation Modeling 142 Approaches [Paper presentation]. International Conference on Advanced Intelligent Systems and Informatics, Cairo, Egypt.

Shao, C. (2020). An empirical study on the identification of driving factors of satisfaction with online learning based on TAM. 5th International Conference on Economics, Management, Law and Education (EMLE 2019), (110), 1067-1073. https://doi.org/10.2991/aebmr.k.191225.205

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. In M.A. Lange (Ed.), Leading - Edge Psychological Tests and Testing Research (pp. 27-50). Nova

Ssekakubo, G., Suleman, H., & Marsden, G. (2011). Issues of Adoption: Have E-Learning Management Systems Fulfilled their Potential in Developing Countries. In Proceedings of the South African Institute of Computer Scientists and Information Technologists Conference on Knowledge, Innovation and Leadership in a Diverse, Multidisciplinary Environment, 23, 1-238. https://doi.org/10.1145/2072221.2072248.

Taherdoost, H. (2017). Determining sample size; How to calculate survey sample size. International Journal of Economics and Management Systems, 2, 237-239. https://doi.org/10.1093/acprof:oso/9780195315493.001.0001

Taylor, P., & Todd, A. (1995). Assessing IT Usage: The Role of Prior Experience. MIS Quarterly, 19(4), 561-570. https://doi.org/10.2307/249633

Teo, T. (2009). Modelling technology acceptance in education: A study of pre-service teachers. Computers & Education, 52(2), 302–312. https://doi.org/10.1016/j.compedu.2008.08.006

Teo, T., & Noyes, J. (2014). Explaining the Intention to Use Technology among Pre-Service Teachers: A Multi-Group Analysis of the Unified Theory of Acceptance and Use of Technology. Interactive Learning Environments, 22(1), 51-66. https://doi.org/10.1080/10494820.2011.641674

Truong, Y., & McColl, R. (2011). Intrinsic Motivations, Self-Esteem, and Luxury Goods Consumption. Journal of Retailing and Consumer Services, 18(6), 555–561. https://doi.org/10.1016/j.jretconser.2011.08.004.

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. https://doi.org/10.1287/isre.11.4.342.11872

Venkatesh, V., & Bala, H. (2008). Technology Acceptance Model 3 and a Research Agenda on Interventions'. Decision Sciences, 39(2), 273-315. https://doi.org/10.1111/j.1540-5915.2008.00192.x

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. https://doi.org/10.2307/30036540

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. https://doi.org/10.2307/41410412

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.

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

Yu, C., Chao, C., Chang, C., Chen, R., Chen, P., & Liu, Y. (2021). Exploring behavioral intention to use a mobile health education website: An extension of the UTAUT 2 model. SAGE Open, 11(4). https://doi.org/10.1177/21582440211055721