分析AIGC工具在时装设计中的采用:一个S-O-R框架,整合任务技术合适
Mengyun Yang1,2, Jiabing Jin3
1Faculty of Creative Arts and Design, UCSI University, Kuala Lumpur, Malaysia.
PloS one
|October 27, 2025
概括
这项研究揭示了服装设计师如何采用人工智能生成内容 (AIGC) 工具. 感知到的内容质量和个性化适应对采用有积极的影响,而感知到的技术风险则阻碍了采用,强调了任务技术适应的重要性.
科学领域:
- 服装设计技术 服装设计技术
- 人与计算机的交互
- 技术的心理学 技术的采用
背景情况:
- 人工智能产生的内容 (AIGC) 工具在服装设计中越来越普遍.
- 了解设计者采用和潜在的心理机制至关重要.
研究的目的:
- 模拟服装设计师采用AIGC工具的采用意图,使用刺激-有机体-反应 (S-O-R) 理论.
- 调查外部刺激的影响 (感知内容质量,个性化适合,行业压力,感知技术风险) 由心理状态 (自我效能,创新性,任务技术适合) 介导.
主要方法:
- 使用刺激-生物体-反应 (S-O-R) 理论作为理论框架.
- 收集了来自267名中国时装设计师的问卷数据.
- 使用的部分最小平方结构方程建模 (PLS-SEM) 用于经验分析.
主要成果:
- 感知到的内容质量和个性化的适合性对自我效能和任务技术适合性产生了积极的影响.
- 感知到的技术风险对这些心理机制产生了负面影响.
- 自我有效性,创新性和任务技术的合适性积极影响了AIGC采用意图,任务技术的合适性是最重要的因素.
- 行业压力对采用意向没有显著影响.
结论:
- S-O-R理论适用于理解服装设计中的创意技术采用.
- 任务技术合适性和心理认知因素是采用AIGC工具的关键驱动因素.
- 结果为优化AIGC工具和指导设计师提供理论支持和实际见解.
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