Test of Gender Invariance of the E-learning Acceptance Scale of College Students during the COVID-19 Pandemic

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Akadet Kedcham
Tatchapong Sattabut


This research aimed to 1) assess the reliability, construct validity of the E-learning acceptance scale and 2) test gender invariance of the E-learning acceptance scale. The sample consisted of 400 students of the Faculty of Management Science of Bansomdejchaopraya Rajabhat University; power analysis for structural equation modeling was used to determine the sample size. Stratified random sampling based on gender using secondary dataset of e-learning effectiveness evaluation was used. Frequency, percentage, the mean and standard deviation, Pearson’s correlation coefficient and multi-group confirmatory factor analysis were used to analyze the data.
The results yielded that 1) the E-learning acceptance scale was composed of 3 constructs: perceived ease of use, perceived usefulness and intention to use e-learning, with high reliability of 0.91, 0.92 and 0.93 accordingly. The confirmatory factor analysis demonstrated that the measurement model labeled a high quality of construct validity (gif.latex?\chi2=32.93, df=24, p-value=0.11, CFI=.99, RMSEA=0.03), and 2) the E-learning acceptance scale featured strict gender invariance.


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Aguilera-Hermida, A. P. (2020). College students’ use and acceptance of emergency online learning due to Covid-19. International Journal of Educational Research Open, 1, Article 100011. https://doi.org/10.1016/j.ijedro.2020.100011

Al-Azawei, A., Parslow, P., & Lundqvist, K. (2017). 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-Okaily, M., Alqudah, H., Matar, A., Lutfi, A. A., & Taamneh, A. (2020). Impact of Covid-19 pandemic on acceptance of elearning system in Jordan: A case of transforming the traditional education systems. Humanities and social Sciences Review, 6(4), 840-851.

Boomsma, A., & Hoogland, J. J. (2001). The robustness of LISREL modeling revisited. Structural equation models: Present & future. A Festschrift in honor of Karl Jöreskog, 2(3), 139-168.

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.

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

Hair, J. F., Black, W. C., Babin, B.J., & Anderson, R. E. (2010). Multivariate Data Analysis (7th ed.). Pearson Education.

Hirschfeld, G., & Von Brachel, R. (2014). Multiple-Group confirmatory factor analysis in R-A tutorial in measurement invariance with continuous and ordinal indicators. Practical Assessment, Research & Evaluation, 19, Article 7. https://doi.org/10.7275/qazy-2946

Kline, R. B. (2011). Methodology in the Social Sciences.Principles and practice of structural equation modeling (3rd ed.). Guilford Press.

Milfont, T. L., & Fischer, R. (2010). Testing measurement invariance across groups: Applications in cross-cultural research. International Journal of psychological research, 3(1), 111-130.

Ong, C.S. and Lai, J.Y. (2006) Gender Differences in Perceptions and Relationships among Dominants of E-Learning Acceptance. Computers in Human Behavior, 22, 816-829. http://dx.doi.org/10.1016/j.chb.2004.03.006

Preacher, K. J., & Coffman, D. L. (2006). Computing power and minimum sample size for RMSEA [Computer software]. http://quantpsy.org

Schreiber, J. B. (2017). Update to core reporting practices in structural equation modeling. Research in Social and Administrative Pharmacy. 13(3), 634-643.

Sukendro, S., Habibi, A., Khaeruddin, K., Indrayana, B., Syahruddin, S., Makadada, F. A., & Hakim, H. (2020). Using an extended Technology Acceptance Model to understand students’ use of e-learning during Covid-19: Indonesian sport science education context. Heliyon, 6(11), Article e05410. https://doi.org/10.1016/j.heliyon.2020.e05410

Tarhini, A., Hone, K., & Liu, X. (2014). Measuring the moderating effect of gender and age on e-learning acceptance in England: A structural equation modeling approach for an extended technology acceptance model. Journal of Educational Computing Research, 51(2), 163-184.

UNESCO, (2020, May 3). COVID-19 Impact on Education. https://en.unesco.org/covid19/educationresponse.

WHO, (2020, October 12). Coronavirus disease (COVID-19). https://www.who.int/emergencies/diseases/novel-coronavirus-2019/question-and-answers-hub/q-a-detail/coronavirus-disease-covid-19

Zhu, N., Zhang, D., Wang, W., Li, X., Yang, B., Song, J., Zhao, X., Huang, B., Shi, W., Lu, R., Niu, P., Zhan, F., Ma, X., Wang, D., Xu, W., Wu, G., Gao, G.F., & Tan, W. (2020). A novel coronavirus from patients with pneumonia in China, 2019. New England journal of medicine, 382(8), 727-733.