在人工智能背景下,技术接受模型和采用者类型分析
Fabio Ibrahim1, Johann-Christoph Münscher1, Monika Daseking1
1Faculty of Humanities and Social Sciences, Helmut-Schmidt-University/University of the Armed Forces, Hamburg, Germany.
Frontiers in artificial intelligence
|January 31, 2025
概括
这项研究发现,感知到的有用性和人工智能思维方式是人工智能采用的关键驱动因素. 确定了四个不同的采用者群体,从早期采用者到落后者,为技术传播提供了洞察力.
科学领域:
- 心理学 心理学 心理学
- 计算机科学 计算机科学
- 社会学 社会学 社会学
背景情况:
- 人工智能 (AI) 的采用正在各个领域迅速扩大.
- 了解用户接受因素对于有效的AI集成至关重要.
- 现有的模型需要扩展以捕捉AI特定的细微差别.
研究的目的:
- 验证人工智能的扩展技术接受模型 (TAM),结合人格特征和人工智能思维方式.
- 用人口统计,态度和使用数据将AI采用者分为不同的类别.
- 为人工智能扩散模式和用户细分提供见解.
主要方法:
- 通过对TAM,五大个性特征和AI思维方式的验证尺度对1007个人进行了调查.
- 使用回归分析来测试扩展的TAM.
- 利用k-原型集群来识别AI采用者部分.
主要成果:
- 感知有用性 (β=0.34) 和人工智能思维增长 (β=0.28) 显著预测了人工智能采用态度.
- "开放"的个性特征对被感知的易用性产生了积极的影响 (β=0.15).
- 出现了四个采用者集群:早期采用者 (n=218),早期多数 (n=331),后期多数 (n=293) 和落后者 (n=165).
结论:
- 感知到的有用性和人工智能思维方式是人工智能接受的关键决定因素.
- 人工智能采用者细分与创新扩散理论保持一致,揭示了不同的用户群体.
- 调查结果为有针对性的AI实施策略和未来研究提供了信息.
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