COVID-19推广后网络学习后接受的实证研究--基于多元分析方法的混合SEM-ANN
The empirical study of e-learning post-acceptance after the spread of COVID-19: A multi-analytical approach based hybrid SEM-ANN
摘要：
对疫苗接种的恐惧导致人群排斥的原因有几个。学生们对疫苗的有效性提出了质疑，这反过来又导致了疫苗接种的犹豫。学生的看法受到疫苗接种犹豫的影响，这影响了对电子学习平台的接受。因此，本研究旨在探讨后接受的电子学习平台的概念模型的基础上，采用不同的变量。各变量对网络学习平台的后接受度有显著的影响。在当前的研究中使用的混合模型，其中技术接受模型（TAM）的决定因素与其他外部因素，如对疫苗接种的恐惧，感知常规使用，感知享受，感知临界质量和自我效率，这是直接联系到接受后的电子学习平台。早期关于这一主题的研究的重点一直是在各种环境和国家接受电子学习的意义。然而，在这项研究中，在海湾地区的电子学习平台的新传播使用使用的混合概念模型进行了检查。以往的实证研究主要采用结构方程模型（SEM）分析，而本研究采用了基于深度学习的SEM和人工神经网络（ANN）的混合分析方法。在本研究中，还使用了重要性-性能图分析（IPMA），以确定每个因素的显着性和性能。所提出的模型得到了数据分析结果的支持。
英文摘要：
There are several reasons why the fear of vaccination has caused population rejection. Questions have been raised by students regarding the effectiveness of vaccines, which in turn has led to vaccination hesitancy. Students perceptions are influenced by vaccination hesitancy, which affects the acceptance of e-learning platforms. Hence, this research aimed to examine the post-acceptance of e-learning platforms on the basis of a conceptual model that employs different variables. Distinct contribution is made by every variable to the post-acceptance of e-learning platforms. A hybrid model was used in the current study in which technology acceptance model (TAM) determinants were employed along with other external factors such as fear of vaccination, perceived routine use, perceived enjoyment, perceived critical mass, and self-efficiency which are directly linked to post-acceptance of e-learning platforms. The focus of earlier studies on this topic has been on the significance of e-learning acceptance in various environments and countries. However, in this study, the newly-spread use of e-learning platforms in the gulf area was examined using a hybrid conceptual model. The empirical studies carried out in the past mainly used structural equation modelling (SEM) analysis; however, this study used an evolving hybrid analysis approach, in which SEM and the artificial neural network (ANN) that are based on deep learning were employed. The importance-performance map analysis (IPMA) was also used in this study to determine the significance and performance of each factor. The proposed model is backed by the findings of data analysis.
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