• JUELIANG HUANG Hunan Women's University


blended learning; UTAUT2; CUCEI; structural equation modeling; early childhood education


The mixed-method research approach was applied to explore early childhood major students' levels of acceptance and attitudes towards blended learning based on the Unified Theory of Acceptance and Use of Technology 2 (UTAUT 2) and College and University Classroom Environment Inventory (CUCEI). Three hundred sixty-three undergraduate early childhood major students in China participated in the study. The Structural Equation Modeling (SEM) analysis was used to determine which variables in the UTAUT2 and CUCEI significantly impacted blended learning acceptance. The interview data supplemented the findings of the quantitative data. It was found that social influence and classroom environment significantly impacted blended learning acceptance. Results also indicated that blended learning is more likely to be accepted and used because of the ease of its use and the convenience it offers, as well as promoting a better social and classroom environment. However, the blended learning acceptance was not correlated with performance expectancy, effort expectancy, hedonic motivation, facilitating condition, and price value. Based on these findings, the researcher provided suggestions for decision-makers regarding using and accepting blended learning in their institutions. Administrators and educators can use this study's findings to guide their implementation and improvement of blended learning.

Author Biography


Asst. Prof. Thanawan Phongsatha, Ph.D.
Program Director of Ph.D. in Teaching and Technology Program Graduate School of Business and Advanced Technology Management (GSBATM)
Assumption University of Thailand
Tel: 083-269-3545


Abdullah, F., & Ward, R. (2016). Developing a general extended technology acceptance model for e-Learning (GETAMEL) by analyzing commonly used external factors. Computers in Human Behavior, 56, 238–256.

Al-Gahtani, S. S. (2016). An empirical investigation of e-learning acceptance and assimilation: A structural equation model. Applied Computing and Informatics, 12, 27–50.

Alayyar, G., Fisser, P., & Voogt, J. (2012). Developing technological pedagogical content knowledge in pre-service science teachers: Support from blended learning. Australasian Journal of Educational Technology, 28(8), 1298-1316.

Asare, A., Yun-Fei, S., & Adjei-Budu, K. (2016). Adoption of e-learning in higher education: Expansion of UTAUT model. European Academic Research, 3, 13236-13259.

Baumgartner, H., & Homburg, C. (1996). Applications of StructuralEquation Modeling in Marketing and Consumer Research: a review. International Journal of Research in Marketing 13(2), 139-161.

Berrett, D. (2012) How 'flipping' the classroom can improve the traditional lecture. The Chronicle of Higher Education 58(25), 1–6.

Browne, M.W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In: Bollen, K.A. and Long, J. S. (Eds.) Testing structural equation models, Beverly Hills, CA: Sage

Byrne, B. M. &Campbell, T. L. (1999). Cross-cultural comparisons and the presumption of equivalent measurement and theoretical structure: A look beneath the surface. Journal of Cross-Cultural Psychology, 30, 557-576.

Carter, H., Drury, J., Rubin, G. J., Williams, R., & Amlôt, R. (2015). Applying crowd psychology to develop recommendations for the management of mass decontamination. Health security, 13(1), 45-53.

Collis, B., Margaryan, A., & Amory, M. (2005). Multiple perspectives on blended learning design. Journal of Learning Design, 1(1), 12-21.

Doll, W.J., Xia, W., Torkzadeh, G.: A confirmatory factor analysis of the end-user computing satisfaction instrument. MIS Quarterly 18(4), 357–369 (1994)

Dziuban, C., Graham, C. R., Moskal, P. D., Norberg, A., & Sicilia, N. (2018). Blended learning: the new normal and emerging technologies. International journal of educational technology in Higher education, 15(1), 1-16.

Farquhar, S., & Gibbons, A. (2019). Early childhood education policy pathways: A learning story. In Luetjens J., Mintrom M., & Hart P. (Eds.), Successful Public Policy: Lessons from Australia and New Zealand (pp. 453-476). Acton ACT, Australia: ANU Press.

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.

Fraser, B. (1994). Research on classroom and school climate. In D. Gabel (Ed.)

Handbook of research on science teaching and learning. New York, New York: Macmillan.

Garner, R., & Rouse, E. (2016). Social presence–connecting pre-service teachers as learners using a blended learning model. Student Success, 7(1), 25-36.

Garson, G. D. (2006). Structural equation modeling. North Carolina: G. David Garson and Statistical Associates Publishing.

Hrastinski, S. (2019). What do we mean by blended learning? TechTrends, 63(5), 564-569.

Hu, L.T. and Bentler, P.M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural equation modeling: a multidisciplinary journal, 6(1), 1-55.

Kline, T. (2005). Psychological testing: A practical approach to design and evaluation. Sage.

Kristanto, A. (2017). The Development of Instructional Materials E-Learning Based on Blended Learning. International Education Studies, 10(7), 10-17.

Lomonosova, N. V., & Zolkina, A. V. (2018). Digital learning resources: Enhancing efficiency within blended higher education. Вестник Новосибирского государственного педагогического университета, 8(6), 121-137.

Marpinjun, S., Rengganis, N., Andri Riyanto, Y., & Yuni Dhamayanti, F. (2018). Feminists' strategic role in early childhood education. Feminism and the politics of childhood or foes.

McMullen, M. & Alat, K. (2002). Education matters in the nurturing of the beliefs of preschool caregivers and teachers. Early Childhood Research & Practice, 4(2). Retrieved from http://ecrp.uiuc.edu/v4n2/mcMullen.html-29.

Olatubosun, O., Olusoga, F., & Samuel, O. (2015). Adoption of e-learning technology in Nigerian tertiary institution of learning. British Journal of Applied Science & Technology, 10(2), 1-15. doi:10.9734/BJAST/2015/18434

Oliveira, T., Faria, M., Thomas, M. A., & Popovic, A. (2014). Extending the understanding of mobile banking adoption: When UTAUT meets TTF and ITM. International Journal of Information Management, 34(5), 689-703.

Stein, J., & Graham, C. R. (2014). Essentials for blended learning: A standards-based guide. Routledge.

Treagust, D. F., & Fraser, B. J. (1986). Validation and Application of the College and University Classroom Environment Inventory (CUCEI). Annual Metting of American Educational Research Association.

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 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, 157-178.

Wilson, S. G. (2013) The flipped class a method to address the challenges of an undergraduate statistics course. Teaching of Psychology 40(3): 193–199.

Xia, X. Y., & Hui, Z. (2017). "Yi shijian wei quxiang" de meiguo xueqian jiaoshi jiaoyu kecheng--yi alabama daxue ertong zaoqi jiaoyu zhuanye weili. [American preschool teacher education curriculum with practice orientation--Take the University of Alabama's major in an early childhood education as an example]. Waiguo zhongxiaoxue jiaoyu(8)

Yang, C. M., Nay, S., & Hoyle, R. H. (2010). Three approaches to using lengthy ordinal scales in structural equation models: Parceling, latent scoring, and shortening scales. Applied Psychological Measurement, 34, 122–142.

Yang, H. H., Feng, L., & MacLeod, J. (2019). Understanding college students' acceptance of cloud classrooms in flipped instruction: integrating UTAUT and connected classroom climate. Journal of Educational Computing Research, 56(8), 1258-1276.

Yeop, M. A., Yaakob, M. F. M., Wong, K. T., Don, Y., & Zain, F. M. (2019). Implementation of ICT Policy (Blended Learning Approach): Investigating Factors of Behavioural Intention and Use Behaviour. International Journal of Instruction, 12(1), 767-782.




How to Cite

HUANG, J., & PHONGSATHA, T. (2022). FACTORS INFLUENCING THE ACCEPTANCE OF BLENDED LEARNING BY EARLY CHILDHOOD UNDERGRADUATE STUDENTS. Scholar: Human Sciences, 14(2), 678. Retrieved from http://www.assumptionjournal.au.edu/index.php/Scholar/article/view/5701