一项关于影响音乐爱好者的行为意图使用基于扩展技术接受模型的生成人工智能用于音乐作曲的因素的研究
Yongxin Zhou1, Yuhui Wang2, Jiayu Su3
1China Conservatory of Music, Beijing, China.
Scientific reports
|December 30, 2025
概括
音乐爱好者 音乐爱好者
科学领域:
- 音乐 技术 音乐 技术
- 人工智能的人工智能
- 人与计算机的交互
背景情况:
- 生成型人工智能 (GenAI) 越来越多地用于创意领域.
- 了解用户对GenAI在音乐创作中的接受度对于采用至关重要.
- 现有的技术接受模型 (TAM) 需要对GenAI音乐工具进行验证.
研究的目的:
- 调查音乐爱好者使用GenAI进行音乐创作的意图.
- 确定影响GenAI音乐工具接受度的因素.
- 适应和测试用于音乐中的GenAI的技术接受模型 (TAM).
主要方法:
- 一项使用基于共差的结构方程建模 (SEM) 的定量研究.
- 通过调查问卷从429名音乐爱好者收集的数据.
- 对修改后的TAM进行分析,包括感知到的AI风险,信任,内容质量,社会影响和感知到的乐趣.
主要成果:
- 人工智能风险的感知会对使用意图和易用性产生负面影响.
- 感知到的信任和内容质量会对易用性和有用性产生积极影响.
- 内容质量直接影响使用意图;感知到的享受和社会影响不会直接影响使用意图.
- 人口统计学 (性别,年龄,教育) 并没有显著改变GenAI工具的评估.
结论:
- 原来的TAM结构在很大程度上是适合GenAI音乐工具的.
- 内容质量是GenAI在音乐创作中采用的关键驱动因素.
- 解决被认为的AI风险和提高被认为的有用性对于促进在音乐创作中使用GenAI至关重要.
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