Salarzadeh Jenatabadi Hashem, Moghavvemi Sedigheh, Wan Mohamed Radzi Che Wan Jasimah Bt, Babashamsi Parastoo, Arashi Mohammad
Department of Science and Technology Studies, University of Malaya, Kuala Lumpur, Malaysia.
Department of Operation and Management Information System, University of Malaya, Kuala Lumpur, Malaysia.
PLoS One. 2017 Sep 8;12(9):e0182311. doi: 10.1371/journal.pone.0182311. eCollection 2017.
Learning is an intentional activity, with several factors affecting students' intention to use new learning technology. Researchers have investigated technology acceptance in different contexts by developing various theories/models and testing them by a number of means. Although most theories/models developed have been examined through regression or structural equation modeling, Bayesian analysis offers more accurate data analysis results. To address this gap, the unified theory of acceptance and technology use in the context of e-learning via Facebook are re-examined in this study using Bayesian analysis. The data (S1 Data) were collected from 170 students enrolled in a business statistics course at University of Malaya, Malaysia, and tested with the maximum likelihood and Bayesian approaches. The difference between the two methods' results indicates that performance expectancy and hedonic motivation are the strongest factors influencing the intention to use e-learning via Facebook. The Bayesian estimation model exhibited better data fit than the maximum likelihood estimator model. The results of the Bayesian and maximum likelihood estimator approaches are compared and the reasons for the result discrepancy are deliberated.
学习是一种有目的的活动，有几个因素会影响学生使用新学习技术的意愿。研究人员通过开发各种理论/模型并采用多种方法对其进行测试，研究了不同背景下的技术接受情况。尽管大多数已开发的理论/模型都是通过回归或结构方程建模进行检验的，但贝叶斯分析能提供更准确的数据分析结果。为了填补这一空白，本研究使用贝叶斯分析重新审视了在通过脸书进行电子学习的背景下的技术接受与使用统一理论。数据（S1数据）收集自马来西亚马来亚大学修读商业统计学课程的170名学生，并采用最大似然法和贝叶斯方法进行测试。两种方法结果的差异表明，绩效期望和享乐动机是影响通过脸书进行电子学习意愿的最强因素。贝叶斯估计模型比最大似然估计模型表现出更好的数据拟合度。对贝叶斯方法和最大似然估计方法的结果进行了比较，并探讨了结果差异的原因。
