人工智能与中国MOOC融合背景下用户技术接受度研究— 基于UTAUT模型实证分析

Abstract

本文通过中国人工智能MOOC远程教育相关先行研究及概念，对用户技术接受度进行了实证研究，并结合用户所在地区的不同发展程度，进一步探讨了地区发展程度对其技术接受度的潜在影响。基于UTAUT理论模型进行研究模型构建，将绩效期望、努力期望、社会影响、促进条件和拟人化交互作为自变量，来研究对用户技术使用意愿和使用态度的影响。此外，根据“数字鸿沟”理论，本文将所在地作为调节变量，采用多群组调节效应分析，进一步探讨由于用户所在地发展程度不同，是否对用户技术接受度产生显著的调节作用。使用SPSS、AMOS软件进行样本数据分析并构建结构方程模型。结果显示，绩效期望、努力期望、社会影响、促进条件和拟人化交互对技术使用意愿与态度，皆呈现出显著的正向关系。由于用户所在地发展程度的不同，在发达地区与欠发达地区之间，社会影响对行为意图的路径关系中存在显著差异。整体来看，用户能够对人工智能MOOC技术产生较高的技术接受度，而欠发达地区仅在社群影响方面对用户技术接受度存在着一定的抑制性。

This study conducts an empirical research on user technology acceptance based on prior studies and concepts related to Artificial Intelligence (AI) and MOOCs in China. It further explores the potential impact of regional development levels on users’ technology acceptance by considering the varying levels of development in different regions. The research model is constructed based on the UTAUT theoretical model, with performance expectancy, effort expectancy, social influence, facilitating conditions, and anthropomorphic interaction as independent variables to study their impact on users’ technology usage intentions and attitudes. Additionally, based on the “digital divide” theory, the study treats the location of users as a moderating variable and employs multi-group moderating effect analysis to examine whether the development levels of different regions significantly moderate users’ technology acceptance. SPSS and AMOS software are used to analyze sample data and construct the structural equation model. The results show that the independent variables exhibit a positive relationship with both technology usage intentions and attitudes. However, there is a significant difference in the path relationship of social influence on behavioral intention between developed and underdeveloped regions. Overall, users demonstrate a high level of technology acceptance towards AI MOOCs, whereas in underdeveloped regions, social influence is the only factor that significantly inhibits users’ technology acceptance.