PREDICTORS OF STUDENTS’ BEHAVIORAL INTENTION OF ONLINE ART APPRECIATION COURSES IN PUBLIC UNIVERSITIES IN CHENGDU, CHINA
Keywords:
Online Education, Perceived Satisfaction, Effort Expectancy, Attitude Towards Use, Behavioral IntentionAbstract
The purpose of this study is to explore the key predictors of the behavior intention of online art appreciation courses among undergraduates in three public universities in Chengdu, China. Perceived ease of use, perceived usefulness, performance expectancy, perceived satisfaction, effort expectancy, attitude towards use and behavioral intention were associated in a research framework. The researchers employed a quantitative method to distribute surveys to 498 participants. The sampling techniques were judgmental, quota and convenience samplings. Item Objective Congruence (IOC) Index and Cronbach’s Alpha were approved before the data collection. Confirmatory Factor Analysis (CFA) and Structural Equation Model (SEM) were utilized for quantitative analysis, which include goodness of model fits, validity, and reliability tests. Most hypotheses were supported, except the relationship between effort expectancy and behavioral intention. Therefore, education managers are recommended to identify the main contributors to design effective online learning system and contents, so as to improve students' behavioral intention.
References
Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50(2), 179-211.
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.
Awang, Z. (2012). Structural equation modeling using AMOS graphic. Penerbit Universiti Teknologi MARA.
Bagozzi, R., & Yi, Y. (1988). On The Evaluation of Structural Equation Models. Journal of the Academy of Marketing Science, 16(1), 74-94. https://doi.org/10.1007/BF02723327
Bahjat, A. (2018), Attitudes Towards Using Mobile Applications in Teaching Mathematics in Open Learning Systems. International Journal of E-Learning & Distance Education, 33(1), 2-16.
Bardakci, S. (2019). Exploring high school students' educational use of YouTube. International Review of Research in Open and Distance Learning, 20(2), 260-278.
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
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.
Chauhan, S. (2015). Acceptance Of Mobile Money by Poor Citizens of India: Integrating Trust into The Technology Acceptance Model. Info, 17(3), 58-68.
Cigdem, H., & Öztürk, M. (2016). Factors Affecting Students' Behavioral Intention to Use LMS in Turkish Post-Secondary Vocational School. International Review of Research in Open and Distance Learning, 17(3), 277-295. https://doi.org/10.19173/irrodl.v17i3.2253
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. DOI: 10.1287/mnsc.35.8.982
Diep, N. A., Cocquyt, C., Zhu, C., & Vanwing, T. (2016). Predicting adult learners’ online participation: Effects of altruism, performance expectancy, and social capital. Computers and Education, 101, 84–101. https://doi.org/10.1016/j.compedu.2016.06.002
Drury, H., Kay, J., & Losberg, W. (2003). Student Perceived satisfaction with groupwork in undergraduate computer science: do things get better? In Proceedings of the fifth Australasian conference on Computing Education-Volume 20 (pp. 77-85).
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.
Fokides, E. (2017). Pre-Service Teachers’ Intention to Use MUVEs as Practitioners – A Structural Equation Modeling Approach. Journal of Information Technology Education: Research. 2016(1), 47-68. https://doi.org/10.28945/3645
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.
Gu, X., & Li, L. (2022). China’s Experience of Online Education during the COVID-19 Pandemic: Policies, Lessons and Challenges. V. Dennen, C. Dickson-Deane, X. Ge, D. Ifenthaler, S. Murthy & J. C. Richardson (Eds.), Global Perspectives on Educational Innovations for Emergency Situations, Educational Communications and Technology: Issues and Innovations. Springer. https://doi.org/10.1007/978-3-030-99634-5_28
Hair, J. F., Black., W. C., Babin., B. J., Anderson., R. E., & Tatham., R. L. (2006). Multivariant Data Analysis. Pearson International Edition.
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2009). Multivariant Data Analysis. Pearson International Edition.
Hochberg, J. M. (2006) Online Distance Education Pedagogy: Emulating the Practice of Global Business. Distance Education, 27(1), 129-133.
Isaac, R., Zerbe, W., & Pitt, D. (2001). Leadership and Motivation: The Effective Application of Expectancy Theory. Journal of Managerial Issues, 13(2), 212.
Israel, G. D. (1992). Determining Sample Size. University of Florida.
Jaruwanakul, T. (2021). Key Influencers of Innovative Work Behavior in Leading Thai Property Developers. AU-GSB e-Journal, 14(1), 61-70.
Kitcharoen, K., & Vongurai, R. (2021). Factors Influencing Customer Attitude And Behavioral Intention Towards Consuming Dietary Supplements. AU-GSB E-JOURNAL, 13(2), 94-109.
Lee, G. G., & Lin, H. F. (2005) Customer Perceptions of E-Service Quality in Online Shopping. International Journal of Retail & Distribution Management, 33, 161-176.
https://doi.org/10.1108/09590550510581485
Liestiawati, F. & Agustina, P. (2018). The Influence of UTATU factors on E-Retention with E-Perceived satisfaction as Mediating Variable in E-Learning. Hasanuddin Economics and Business Review, 2(19). https://doi.org/10.26487/hebr.v2i1.1465.
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.
Neo, M., Park, H., Lee, M., Soh, J., & Oh, J. (2015). Technology Acceptance of Healthcare E-Learning Modules: A Study of Korean and Malaysian Students’ Perceptions. The Turkish Online Journal of Educational Technology, 14(2), 181-194.
Onaolapo, S., & Oyewole, O. (2018). Performance Expectancy, Effort Expectancy, And Facilitating Conditions as Factors Influencing Smart Phones Use for Mobile Learning by Postgraduate Students of The University of Ibadan, Nigeria. Interdisciplinary Journal of E-Skills and Lifelong Learning, 14(1), 96-115. https://doi.org/10.28945/4085
Oliver, R. L. (1999). Whence consumer loyalty? Journal of marketing, 63(4), 33-44.
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.
Rui-Hsin, K., & Lin, C. (2018). The Usage Intention of E-Learning for Police Education and Training. Policing An International Journal of Police Strategies and Management, 41(1), 98-112.
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.
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.
Teo, T. (2009). Modelling Technology Acceptance in Education: A Study of Pre-Service Teachers. Computers & Education, 52(1), 302-312.
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.
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. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
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
Downloads
Published
Issue
Section
License
Copyright (c) 2023 Suvarnabhumi Institute of Technology
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
บทความที่ได้รับการตีพิมพ์เป็นลิขสิทธิ์ของวารสารวิชาการ สถาบันเทคโนโลยีแห่งสุวรรณภูมิ
ข้อความที่ปรากฏในบทความแต่ละเรื่องในวารสารวิชาการเล่มนี้เป็นความคิดเห็นส่วนตัวของผู้เขียนแต่ละท่านไม่เกี่ยวข้องกับสถาบันเทคโนโลยีแห่งสุวรรณภูมิ และคณาจารย์ท่านอื่นๆในสถาบันฯ แต่อย่างใด ความรับผิดชอบองค์ประกอบทั้งหมดของบทความแต่ละเรื่องเป็นของผู้เขียนแต่ละท่าน หากมีความผิดพลาดใดๆ ผู้เขียนแต่ละท่านจะรับผิดชอบบทความของตนเองแต่ผู้เดียว
2.jpg)
