[论文解读] The empirical study of e-learning post-acceptance after the spread of COVID-19: A multi-analytical approach based hybrid SEM-ANN

Ashraf Elnagar, Imad Afyouni+3 more|arXiv (Cornell University)|Nov 26, 2021

Organizational and Employee Performance被引用 8

一句话总结

本研究采用混合SEM-ANN模型，调查新冠疫情后海湾地区电子学习平台的后采纳行为，整合了技术接受模型（TAM）与疫苗接种恐惧、感知乐趣和自我效能感等外部因素。多分析方法，包括IPMA，显示感知自我效能感和感知乐趣是持续电子学习采纳的最重要预测因子。

ABSTRACT

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.

研究动机与目标

• 调查新冠疫情导致快速转变后，海湾地区电子学习平台的后采纳行为。
• 识别并评估外部因素（如疫苗接种恐惧、感知常规使用、乐趣、临界规模和自我效能感）对电子学习采纳的影响，超越传统TAM因素。
• 开发并验证一种结合结构方程模型（SEM）与人工神经网络（ANN）的混合概念模型，以提高预测准确性。
• 使用重要性-绩效地图分析（IPMA）对各因素的重要性与表现水平进行排序。

提出的方法

• 开发一种混合概念模型，将技术接受模型（TAM）与外部变量（疫苗接种恐惧、感知常规使用、感知乐趣、感知临界规模和自我效能感）相结合。
• 从海湾地区的电子学习用户中收集实证数据，以检验所提出的模型。
• 应用结构方程模型（SEM）评估潜在变量之间的测量关系与结构关系。
• 采用基于深度学习的人工神经网络（ANN），以在非线性SEM假设之外提升预测准确性。
• 进行重要性-绩效地图分析（IPMA），以评估各预测变量的相对重要性与表现水平。
• 通过数据驱动的性能指标及与传统SEM的对比分析，验证混合SEM-ANN模型。

实验结果

研究问题

• RQ1 新冠疫情爆发后，影响海湾地区电子学习平台后采纳的关键决定因素是什么？
• RQ2 外部因素（如疫苗接种恐惧、感知乐趣和自我效能感）如何在核心TAM构念之外影响电子学习的持续采纳？
• RQ3 混合SEM-ANN模型在预测电子学习后采纳方面，相较于传统SEM的优越程度如何？
• RQ4 根据IPMA结果，哪些因素在重要性与表现水平上均处于高水平，从而成为机构干预的优先领域？

主要发现

• 感知自我效能感在SEM模型中表现出较高的标准化路径系数，是电子学习后采纳的最重要预测因子。
• 感知乐趣是第二重要的影响因素，表明用户体验与参与度对平台的持续使用至关重要。
• 尽管存在疫苗接种恐惧，但其对电子学习采纳的影响相对较低，表明在海湾地区该因素并非主要障碍。
• 混合SEM-ANN模型在预测准确性方面优于单独使用SEM，经验证其RMSE更低、R平方值更高。
• IPMA结果表明，'自我效能感'和'感知乐趣'是两个在重要性与表现水平上均处于高水平的因素，应作为机构干预的优先领域。
• 感知临界规模和感知常规使用的影响中等，表明社会因素与习惯性因素在长期采纳中起支持性但次要的作用。