Determinants of Behavioral Intention to Use Hybrid Education Among Painting Students in Public Universities in Chengdu, China
DOI:
https://doi.org/10.14456/shserj.2023.2Keywords:
Hybrid Education, Performance Expectancy, Social Influence, Facilitating conditions, Behavioral IntentionAbstract
Purpose: The purpose of this study is to examining determinants of behavioral intention to use hybrid education among undergraduate students, majoring in painting at three public universities in Chengdu, China. Key variables are perceived ease of use (PEOU), perceived usefulness (PU), perceived satisfaction (PS), social influence (SI), performance expectancy (PE), facilitating conditions (FC), and behavioral intention (BI). Research design, data, and methods: The researchers used quantitative method by distributing questionnaire to 500 participants via offline and online channels. The sampling techniques involve judgmental, quota and convenience samplings. The content validity was approved by three experts, applying Item Objective Congruence (IOC) Index. All constructs were reserved by Cronbach’s Alpha coefficient values by pilot testing of 30 participants. Afterwards, Confirmatory Factor analysis (CFA) and Structural Equation Model (SEM) were executed in the data analysis, including goodness-of-fit, validities, and reliabilities. Results: All latent variables had a significant influence on behavioral intention. In addition, perceived ease of use had the strongest significant influence on perceived usefulness. Conclusion: Future researchers are recommended to extend the research model in considering to more variables in technology adoption theories in different region. Universities could improve hybrid education system to uplift students' engagement and learning performance.
References
Adedoja, G., Adelore, O., Egbokhare, F., & Oluleye, A. (2013). Learners ’ Acceptance of the Use of Mobile Phones to Deliver Tutorials in a Distance Learning Context: A Case Study at the University of Ibadan. The African Journal of Information Systems, 5(3), 80-93.
Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50(2), 179-211.
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.
Appleton-Knapp, S. K., & Krentler, K. A. (2006). Measuring student expectations and their effects on satisfaction: The importance of managing student expectations. Journal of Marketing Education, 28(3), 254-264.
Arbaugh., & Ben, J. (2002). Managing the on-line classroom: A study of technological and behavioral characteristics of web-based MBA courses. The Journal of High Technology Management Research, 13(2), 203-223.
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.
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
Cigdem, H., & Öztürk, M. (2016). Factors Affecting Students’ Behavioral Intention to Use LMS at a Turkish Post-Secondary Vocational School. The International Review of Research in Open and Distributed Learning, 17(3), 277-295.
https://doi.org/10.19173/irrodl.v17i3.2253
Clark-Carter, D. (2018). Quantitative Psychological Research: The Complete Student's Companion (4th ed.). Routledge Press.
Collis, B., & Moonen, J. (2002). Flexible Learning in a Digital World. Open Learning: The Journal of Open, Distance and e-Learning, 17(3), 217-230.
Damnjanovic, V., Jednak, S., & Mijatovic, I. (2015). Factors affecting the effectiveness and use of Moodle: students' perception. Interactive Learning Environments, 23(4), 496-514.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.
Elkaseh, A. M., Wong, K. W., & Fung, C. 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
Fishbein, M., & Ajzen, I. (1977). Belief, attitude, intention, and behavior: An introduction to theory and research. Philosophy and Rhetoric, 10(2), 130-132.
Fornell, C., & Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18(1), 39-50.
Francisco, G., Jorge, G., Oscar Martin, R., & Miquel Angel Motero, A. (2012). Gender differences in E-learning Satisfaction. Computers & Education, 58(1), 283-290.
Frye, N. E., & Dornisch, M. M. (2015, February 27). Till graduation do us part? Predicting intention to graduate among college students [Paper Presentation]. The annual conference of the Society for Personality and Social Psychology, Long Beach, CA.
Hair, J., Black, W., Babin, B., Anderson, R., & Tatham, R. (2006). Multivariate Data Analysis (6th ed.). Pearson Education.
https://doi.org/10.1080/0268051022000048228
Huang, J., & Duangekanong, S. (2022). Factors Impacting the Usage Intention of Learning Management System in Higher Education. AU-GSB E-JOURNAL, 15(1), 41-51.
https://doi.org/10.14456/augsbejr.2022.59
Israel, D. (1992). Determining Sample Size. University of Florida Cooperative Extension Service.
Jaruwanakul, T. (2021). Key Influencers of Innovative Work Behavior in Leading Thai Property Developers. AU-GSB e-Journal, 14(1), 61-70.
Lee, M. C. (2009). Factors influencing the adoption of internet banking: An integration of TAM and TPB with perceived risk and perceived benefit. Electronic commerce research and applications, 8(3), 130-141.
Lee, M. K., Cheung, C. M., & Chen, Z. (2005). Acceptance of Internet-based learning medium: the role of extrinsic and intrinsic motivation. Information & management, 42(8), 1095-1104.
Lodico, G., Spaulding, T., & Voegtle, H. (2006). Method in Educational Research: From Theory to Practice (1st ed.). Jossey-Bass. Press.
Malhotra, N., Kim, S., & Agarwal, J. (2004). Internet Users' Information Privacy Concerns (IUIPC): The Construct, the Scale, and a Causal Model. Information Systems Research. 15(4), 336-355. https://doi.org/10.1287/isre.1040.0032
Miller, M. D., Ranier, R. K., & Corley, J. K. (2003). Predictors of engagement and participation in an on-line course. Online Journal of Distance Learning Administration, 6(1), 1-13.
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.
Netemeyer, R. G., & Bearden, W. O. (1992). A comparative analysis of two models of behavioral intention. Journal of the Academy of Marketing Science, 20(1), 49-59.
Ngai, E. W., Poon, J., & Chan, Y. H. (2007). Empirical examination of the adoption of WebCT using TAM. Computers & Education, 48(2), 250-267.
Osguthorpe, R. T., & Graham, C. R. (2003). Hybrid education environments: Definitions and directions. Quarterly review of distance education, 4(3), 227-233.
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.
Sahin, I., & Shelley, M. (2008). Considering students' perceptions: The distance education student satisfaction model. Journal of Educational Technology & Society, 11(3), 216-223.
Salkind, J. (2017). Exploring Research (9th ed.). Pearson Press.
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.
Shin, W. S., & Kang, M. (2015). The use of a mobile learning management system at an online university and its effect on learning satisfaction and achievement. International Review of Research in Open and Distributed Learning, 16(3), 110-130.
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.
Tanasapsakul, W., & Vongurai, R. (2018). An investigation of the factors influencing consumers of different generation’s behavioral intention towards franchised Japanese restaurants in Bangkok, Thailand. AU-GSB E-JOURNAL, 11(2), 40-53.
Teo, T. (2009). Modelling Technology Acceptance in Education: A Study of Pre-Service Teachers. Computers & Education, 52(1), 302-312.
Teo, T. (2011). Factors influencing teachers’ intention to use technology: Model development and test. Computers & Education, 57(4), 2432-2440.
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.
Thong, J. Y., Hong, W., & Tam, K. Y. (2002). Understanding user acceptance of digital libraries: what are the roles of interface characteristics, organizational context, and individual differences?. International journal of human-computer studies, 57(3), 215-242.
Tsai, P. S., Tsai, C. C., & Hwang, G. H. (2010). Elementary school students' attitudes and self-efficacy of using PDAs in a ubiquitous learning context. Australasian Journal of Educational Technology, 26(3), 297-308.
Valerand, R. J., Deshaies, P., Cuerrier, J.-P., Pelletier, L. G., & Mongeau, C. (1992). Ajzen and Fishbein's theory of reasoned action as applied to moral behavior: A confirmatory analysis. Journal of personality and social psychology, 62(1), 98.
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., 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.
Venkatesh, V., Thong, J. Y., & Xu, X. (2016). Unified theory of acceptance and use of technology: A synthesis and the road ahead. Journal of the association for Information Systems, 17(5), 328-376.
Vermeir, I., & Verbeke, W. (2006). Sustainable Food Consumption: Exploring the Consumer “Attitude–Behavioral Intention” Gap. Journal of Agricultural and Environmental Ethics, 19(2), 169-194.
Voß, H., Scholz-Kreisel, P., Richter, C., Ringel, F., Singer, S., & Renovanz, M. (2021). Development Of Screening Questions for Doctor–Patient Consultation Assessing the Quality of Life and Psychosocial Burden of Glioma Patients: An Explorative Study. Quality of Life Research, 4(1), 1513-1522.
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 ICT, 14(1), 141-151.
Wang, Y. S., Wang, Y. M., Lin, H. H., & Tang, T. I. (2003). Determinants of user acceptance of internet banking: An empirical study. International Journal of Service Industry Management, 14(5), 501-519.
https://doi.org/10.1108/09564230310500192
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
Yang, Y., & Wang, X. (2019). Modeling the intention to use machine translation for student translators: An extension of Technology Acceptance Model. Computers & Education, 133, 116-126
Young, I. M. (2002). Inclusion and democracy. (1st ed.). Oxford University press on demand.
Zarei, N., & Kaur, H. (2019). Lecturers’ perceptions towards hybrid teaching approach. An International Peer-Reviewed English Journal, 2(3), 25-29.
Zhang, Y., & Lin, C.-H. (2020). Student interaction and the role of the teacher in a state virtual high school: what predicts online learning satisfaction? Technology, Pedagogy and Education, 29(1), 57-71.
