期望理论
调解
适度
心理学
分层抽样
高等教育
样品(材料)
技术接受模型
知识共享
知识管理
数学教育
应用心理学
社会心理学
计算机科学
社会学
政治学
社会科学
统计
化学
数学
色谱法
可用性
人机交互
法学
作者
Cong Doanh Duong,Trong Nghia Vu,Thi Viet Nga Ngo
标识
DOI:10.1016/j.ijme.2023.100883
摘要
Generative artificial intelligence (AI), such as ChatGPT, has taken the world by storm, especially in the education sector, because of its capacity to produce responses that are contextually relevant and appear to imitate human language. This has increased concerns from both scholars and practitioners regarding the potential impacts of ChatGPT on students' learning. However, research on higher education students' adoption of ChatGPT is still scant. Drawing on the modified technology acceptance model (TAM) and a sample of 1389 higher education students recruited in 11 universities in Vietnam with a stratified random sampling approach, the findings of this study indicated that effort expectancy not only directly affected students' actual usage of ChatGPT, but also serially indirectly increased their actual use of ChatGPT through performance expectancy and intentions to use ChatGPT. Additionally, knowledge sharing was found to significantly increase higher education students’ transformation from having the intention to use ChatGPT to actual users of ChatGPT. The theoretical and managerial implications of this are discussed in this paper in order to gain benefits and manage the potential threats from this new technology.
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