VIF.

Based on the Expectation Confirmation Model (ECM), this study explores the impact of perceived educational and emotional support on university students’ continuance intention to engage in e-learning. Researchers conducted a survey using structured questionnaires among 368 university students from three universities in Jiangxi Province. They measured their self-reported responses on six constructs: perceived educational support, perceived emotional support, perceived usefulness, confirmation, satisfaction, and continuance intention. The relationships between predictors and continuance intention, characterized by non-compensatory and non-linear dynamics, were analyzed using Structural Equation Modeling combined with Artificial Neural Networks. Apart from the direct effects of perceived educational and emotional support on perceived usefulness being non-significant, all other hypotheses were confirmed. Furthermore, according to the normalized importance derived from the multilayer perceptron analysis, satisfaction was identified as the most critical predictor (100%), followed by confirmation (29.9%), perceived usefulness (28.3%), perceived educational support (22.6%), and perceived emotional support (21.6%). These constructs explained 62.1% of the total variance in the students’ continuance intention to engage in e-learning. This study utilized a two-stage analytical approach, enhancing the depth and accuracy of data processing and expanding the methodological scope of research in educational technology. The findings of this study contribute to the United Nations’ Sustainable Development Goal 4, which aims to ensure inclusive and equitable quality education and promote lifelong learning opportunities for all by 2030. It provides direction for future research in different environmental and cultural contexts.

本研究基于期望确认模型（Expectation Confirmation Model, ECM），探讨感知教育支持与感知情感支持对大学生持续参与电子学习（e-learning）意愿的影响。研究人员采用结构化问卷，对江西省三所高校的368名大学生开展问卷调查，针对六个构念采集被试的自我报告数据，分别为感知教育支持、感知情感支持、感知有用性、期望确认、满意度及持续参与意愿。针对预测变量与持续参与意愿之间具备非补偿性与非线性动态特征的关系，本研究结合结构方程模型（Structural Equation Modeling）与人工神经网络（Artificial Neural Networks）开展分析。除感知教育支持与感知情感支持对感知有用性的直接效应未达显著水平外，其余全部研究假设均得到验证。进一步基于多层感知器（Multilayer Perceptron）分析得到的标准化重要性排序显示，满意度为最关键的预测因子（占比100%），其后依次为期望确认（29.9%）、感知有用性（28.3%）、感知教育支持（22.6%）与感知情感支持（21.6%）。上述六个构念可解释大学生电子学习持续参与意愿总方差的62.1%。本研究采用两阶段分析方法，提升了数据处理的深度与准确性，拓展了教育技术领域研究的方法论边界。本研究结果契合联合国可持续发展目标4（Sustainable Development Goal 4, SDG4）——其目标为到2030年确保全民享有包容、公平的优质教育，并促进全体人群的终身学习机会——的相关要求，同时可为不同环境与文化背景下的后续研究提供指引方向。