感知有用性,信任和行为意图:对大学生用户采用人工智能行为进行的一项研究,基于技术接受模型生成了基于技术接受模型的新闻
1School of Digital Communication, Guangzhou Huashang College, Guangzhou, China.
Big data
|February 9, 2026
概括
信任是影响大学生采用人工智能产生的新闻的最关键因素,超过了感知到的有用性. 这项研究将大数据与技术接受模型 (TAM) 整合在一起,以了解AI新闻的接受程度.
科学领域:
- 信息科学 信息科学 信息科学
- 人与计算机的交互
- 媒体研究 媒体研究
背景情况:
- 传统的技术接受模型 (TAM) 往往忽略了技术采用中的心理因素.
- 人工智能产生的新闻给用户带来了与信任和可信度相关的独特挑战.
- 大数据方法提供了新的方法来捕捉用户行为超出自我报告的数据.
研究的目的:
- 确定影响大学生采用人工智能产生的新闻的关键因素.
- 通过整合大数据和信任来扩展技术接受模型 (TAM).
- 开发和测试一种结合技术认知和心理情绪的双路径理论模型.
主要方法:
- 利用了一种双路径理论模型,将TAM与信任,大数据和心理情绪集成在一起.
- 通过对300名大学生进行调查收集数据,并分析实时行为和非结构化文本数据.
- 采用结构方程建模用于数据分析.
主要成果:
- 信任对使用人工智能新闻的意愿产生了最强的直接积极影响 (β = 0.49,p < 0.001),超过感知有用性 (β = 0.35,p < 0.001).
- 感知到的易用性通过增强信任和感知到的有用性来间接影响使用意愿.
- 在人工智能新闻的高风险感知环境中,信任成为比认知因素更重要的心理机制.
结论:
- 信任对于大学生接受人工智能产生的新闻至关重要,尤其是在高风险环境中.
- 这项研究扩大了TAM对新兴技术 (如AI新闻) 的适用性.
- 研究结果为人工智能开发人员提供了实际见解,以提高系统的可信度,并为教育工作者促进算法素养.
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