Factors Affecting the Behavioral Intention of Public Universities Art Design Major Students by Using Blended Learning in Chengdu





Blended Learning; , Art design; , Behavioral Intention; , Attitudes


Background and Aim: This paper mainly reacts to the important factors affecting the willingness of undergraduate art and design students to participate in blended learning in three public universities in Chengdu. The study investigated latent variables including Perceived Ease of Use (PEOU), Perceived Usefulness (PU), Attitude (ATT), Effort Expectancy (EE), Social Influence (SI), Facilitating Conditions (FC), and Behavioral Intention (BI). The goal of this paper is to determine the extent to which each variable influences the target population's participation in blended learning activities.

Materials and Methods: In this paper, the characteristics of anchors are classified into three dimensions: attitudes, behavioral intentions, etc., and the relationship between blended learning and students is discussed. In this paper, 488 data were collected through questionnaires and statistically analyzed, and the hypothesis was tested using SPSS and AMOS software.

Results: The results of the statistical analysis confirmed all the hypotheses, with effort expectancy exhibiting the most pronounced and significant direct impact on behavioral intention.

Conclusion: For art and design students to fully appreciate and acknowledge the efficacy of blended learning, college administrators and instructional staff must allocate adequate attention to the factors that wield substantial influence over instructional behavioral intentions. Moreover, they should contemplate prospective instructional modifications or reforms guided by the outcomes of this study.


Ajmal, Fouzia, S., & Muhammad, H. (2021). Critical Review on Flipped Classroom Model versus Traditional Lecture Method. International Journal of Education and Practice, 9(1), 128-140.

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

Ajzen, I., & Fishbein, M. (1975). A Bayesian analysis of attribution processes. Psychological Bulletin, 82(2), 261–277.

Al-Azawei, A., & Lundqvist, K. (2016). Investigating the Effect of Learning Styles in a Blended e-Learning System: An Extension of the Technology Acceptance Model (TAM), Australasian Journal of Educational Technology, 33(2), 1-23.

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 Behavior Perspective. Decision Support Systems, 55(1), 43-54.

Alshare, K., & Lane. (2011). Predicting Student-Perceived Learning Outcomes and Satisfaction in ERP Courses: An Empirical Investigation. Communications of the Association for Information Systems, 28(1), 571-584.

Armitage, C. J., & Conner, M. (2001). Efficacy of the Theory of Planned Behavior: A Meta-Analytic Review. British Journal of Social Psychology,40(4), 471-499.

ASKCI. (2022). Forecast And Analysis of Market Size of China's Online Education Industry and Its Segments in 2022, ASKCI. https://baijiahao.baidu.com/s?id=1721314902891572188&wfr=spider&for=pc

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

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

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.

Cheng, T.C.E., Lam, D.Y.C., & Yeung, A.C.L. (2006), Adoption of Internet Banking: An Empirical Study in Hong Kong, Decision Support Systems, 42(3), 1558-1572.

Chiou, J.S., & Shen, C.C. (2012), The Antecedents of Online Financial Service Acceptance: The Impact of Physical Banking Services on Internet Banking Acceptance, Behavior and Information Technology, 31 (9), 859-871.

Cigdem, H., & Ozturk, M. (2016). Factors Affecting Students' Behavioral Intention to Use LMS at a Turkish Post-Secondary Vocational School. International Review of Research in Open and Distributed Learning, 17(3), 276-295.

Cigdem, H., & Topcu, A. (2015). Predictors of instructors’ behavioral intention to use learning management system: A Turkish vocational college example. Computers in Human Behavior, 52,22–28. doi 10.1016/j.chb.2015.05.049

Davis, F.D. (1985). A technology acceptance model for empirically testing new end-user information systems: Theory and results. Doctoral dissertation, Massachusetts Institute of Technology, Sloan School of Management, Massachusetts Institute of Technology.

Davis, F.D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.

Davis, R.D., Bagozzi, R.P., & Warshaw, R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace. Journal of Applied Social Psychology, 22(14), 1111-1132. DOI: 10.1111/j.1559-1816.1992.tb00945.x.

Der Heijden, H.V. (2004). User Acceptance of Hedonic Information Systems. Management Information Systems Quarterly. 28, 695-704. https://doi.org/10.2307/25148660

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.

Festinger, L. (1950). Informal Social Communication. Psychological Review, 57(5), 271-282.

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

Folkes, V.S. (1984), Consumer Reactions to Product Failure: An Attributional Approach, Journal of Consumer Research, 10(4), 398–409. https://doi.org/10.1086/208978

Fornell, C., & Larcker, D.F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 24(4), 337-346.

Gangwar, H., Date, H., & Ramaswany, R. (2015). Understanding Determinants of Cloud Computing Adoption Using an Integrated TAM-TOE Model. Journal of Enterprise Information Management, 28(1), 107-130.

Ghalandari, K. (2012). The Effect of Performance Expectancy, Effort Expectancy, Social Influence and Facilitating Conditions on Acceptance of E-Banking Services in Iran: The Moderating Role of Age and Gender. Middle East Journal of Scientific Research, 12(6), 801-807.

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., Arthur, H.M., &Samouel, M. (2007). Research Methods for Business. Chichester: John Wiley and Sons.

Harvey, G., Loftus‐Hills, A., Rycroft‐Malone, J., Titchen, A., Kitson, A., McCormack, B., & Seers, K. (2002). Getting evidence into practice: the role and function of facilitation. Journal of Advanced Nursing, 37(6), 577-588.

Heijden, H., Verhagen, T., & Creemers, M. (2003). Understanding online purchase intentions: contributions from technology and trust perspectives. European Journal of Information Systems. 12(1), 41–48.

Hill, R. (1998). What sample size is “enough” in internet survey research? Interpersonal Computing and Technology. An Electronic Journal for the 21st Century, 6(3-4). Retrieved July 12, 2008, from http://www.emoderators.com/ipct-j/1998/n3-4/hill.html

Hoi, V. (2020). Understanding Higher Education Learners' Acceptance and Use of Mobile Devices for Language Learning: A Rasch-Based Path Modeling Approach. Computers & Education, 146(1), 155-171.

Islam, J., Rahman, Z., & Hollebeek, L. (2018). Consumer Engagement in Online Brand Communities: A Solicitation of Congruity Theory. Internet Research, 28(1), 23-45.

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.

Kelman, H. (1958). Compliance, Identification, and Internalization: Three Processes of Attitude Change. Journal of Conflict Resolution, 2(1), 51-60.

King, W.R., & He, J. (2006) A Meta-Analysis of the Technology Acceptance Model. Information and Management, 43, 740-755. http://dx.doi.org/10.1016/j.im.2006.05.003

Klobas, J.E. (1995). Beyond information quality: fitness for purpose and electronic information resource use. Journal of Information Science, 21(2), 95-11

Lan, W., & Luo, J. (2022). Current Situation and Problems of Postgraduate Education: An Analysis Based on the Survey Data of the Satisfaction with National Postgraduate Education in 2021. Journal of Graduate Education, 68(2), 72-80

Lee, D.S. (2009). Training, wages, and sample selection: Estimating sharp bounds on treatment effects. Review of Economic Studies. 76, 1071–1102.

Lin, H.-F. (2007) Knowledge Sharing and Firm Innovation Capability: An Empirical Study. International Journal of Manpower, 28, 315-332. https://doi.org/10.1108/01437720710755272

Lin, H-F. (2007). The role of online and offline features in sustaining virtual communities: an empirical study. Internet Research, 17(2), 119-138.

Marchewka, J.T., Liu, C., & Kostiwa, K. (2007). An Application of the UTAUT Model for Understanding Student Perceptions Using Course Management Software. Communications of the IIMA. 7 (2), 10. DOI: https://doi.org/10.58729/1941-6687.1038

Moon, J. W., & Kim, Y. G. (2001). Extending the TAM for a World-Wide-Web context. Information & Management, 38(4), 217-230.

Mulyanengsih, R., & Wibowo, F.C. (2021). E-learning in Sains Learning: A-Review of Literature. Journal of Physics: Conference Series. 1, 012042. IOP Publishing.

Nagy, J. (2018). Evaluation of Online Video Usage and Learning 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.

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.

Ozgen, O., & Kurt, S.D.K. (2013). Purchasing Behavior of Islamic Brands: An Experimental Research. Paper presented at the 42nd Annual Conference of EMAC European Marketing Academy, Istanbul.

Pavlou, A. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce, 7(3), 101-134

Percy, T., & Belle, J.P.V. (2012). Exploring the Barriers and Enablers to the Use of Open Educational Resources by University Academics in Africa. Open-Source Systems: Long-Term Sustainability, 112-128.

Rashwan, N. (2021). Some Notes on The Sample Size Determination. Tanta University Press.

Rienties, B., & Toetenel, L. (2016). The impact of learning design on student behavior, satisfaction, and performance: A cross-institutional comparison across 151 modules. Computers in Human Behavior, 60, 333-341.

Salkind, N.J. (2017). Tests & measurement for people who (think they) hate tests & measurement. Sage Publications.

Schmitt, N., & Stults, D.M. (1986). Methodology review: Analysis of Multitrait-Multimethod Matrices. Applied Psychological Measurement, 10(1), 1-22.

Simeonov, T. (2015). Blending Educational Communication: A Bulgarian Case. Rhetoric and Communications E-Journal, ISSN, 1314-4464.

Srinivasan, R., Lilien, G. L., & Rangaswamy, A. (2002). Technological Opportunism and Radical Technology Adoption: An Application to E-Business. J. Mark.

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. 231-238.

Su, L.Y., Lin, W., & Liu, J. (2022). Research status and development trend analysis of blended teaching based on CNKI-related papers. Journal of Guangxi Open University. (04),48-55.

Teo, T. (2012). Examining the Intention to Use Technology among Preservice Teachers: An Integration of the Technology Acceptance Model and Theory of Planned Behavior. Interactive Learning Environments, 20 (1), 3-18.

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.

Truong, Y., & McColl, R. (2011). Intrinsic motivations, self-esteem, and luxury goods consumption. Journal of retailing and consumer services, 18(6), 555-561.

Valiathan: (2002). Blended learning models. Learning circuits, 3(8), 50-59.

Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46, 186-204. doi:10.1287/mnsc.

Venkatesh, V., Ganster, D.C., Schuetz, S. W., & Sykes, T. A. (2021). Risks and rewards of conscientiousness during the COVID-19 pandemic. Journal of Applied Psychology, 106(5), 643–656. https://doi.org/10.1037/apl0000919

Venkatesh, V., Morris, M.G., Davis, G.B., & Davis F.D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478.

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.

Warshaw: R., & Davis, F. D. (1985). Disentangling behavioral intention and behavioral expectation. Journal of Experimental Social Psychology, 21(3), 213–228. https://doi.org/10.1016/0022-1031(85)90017-4

Wu, C. & Wu: (2019). Investigating User Continuance Intention Toward Library Self-Service Technology the Case of Self-Issue and Return Systems in The Public Context. Library Self-Service Technology, 37(3), 401-417.

Wu, X., & Gao, Y. (2011). Applying The Extended Technology Acceptance Model to The Use Of Clickers In Student Learning: Some Evidence From Macroeconomics Classes. American Journal of Business Education (AJBE), 4(7), 43–50. https://doi.org/10.19030/ajbe.v4i7.4674

Yang, Y., & Pan, J. (2021). In-depth Teaching of Ideological and Political Courses in Higher Vocational Colleges Under the Blended Education Content - A Case Study of Sichuan Polytechnic of Engineering. Journal of Civil Aviation Flight University of China, 32(3), 38-42.

Young, J. R. (2002). 'Hybrid' teaching seeks to end the divide between traditional and online instruction. Chronicle of Higher Education, pp. A33.

Yousafzai, S.Y., Foxall, G.R., & Pallister, J.G. (2007). Technology acceptance: a meta‐analysis of the TAM: Part 1. Journal of modeling in management, 2(3), 251-280.

Yuen, A.H., & Ma, W.W. (2008). Exploring teacher acceptance of e‐learning technology. Asia‐Pacific Journal of Teacher Education, 36(3), 229-243.

Yulihasri, Islam, M. A., & Daud, K. A. K. (2011). Factors that influence customers' buying intention on shopping online. International Journal of Marketing Studies, 3(1), 128-139.

Zhang, Q., & Wang, A. (2014). The Design of New Blended Learning Model Based on Flipped Classroom. Modern Educational Technology, 24 (1), 27-32.

Zhang, Y., & Deng, J. (2021). The market size of online education reached 68.06 billion in Q1 2020, iResearch. Retrieved from: https://report.iresearch.cn/content/2020/04/322437.shtml




How to Cite

Li, X., & Phongsatha, S. (2023). Factors Affecting the Behavioral Intention of Public Universities Art Design Major Students by Using Blended Learning in Chengdu. International Journal of Sociologies and Anthropologies Science Reviews, 3(5), 119–138. https://doi.org/10.60027/ijsasr.2023.3314