算法意识对接受基于技术接受模型的个性化社交媒体内容建议的影响
1School of Literature and Media, Jingchu University of Technology, Jingmen 448000, China.
Acta psychologica
|August 9, 2025
概括
了解算法是如何工作的,会增加人们对算法的感知效用,但也会增加人们对算法的怀疑. 透明的,用户控制的推系统是保持对社交媒体平台的参与和信任的关键.
科学领域:
- 人与计算机的交互
- 社交计算社会计算
- 信息科学 信息科学 信息科学
背景情况:
- 人们仍然担心数字平台的个性化偏见和算法不透明.
- 用户往往没有意识到内容推算法是如何工作的,这会影响信任和代理.
- 本研究调查了算法逻辑理解,偏见感知和透明度识别对用户信任,感知有用性和行为意图在扩展技术接受模型 (TAM) 内的影响.
研究的目的:
- 检查用户对推算法的认识与他们对有用性,易用性和信任的看法之间的关系.
- 调查感知到的有用性,易用性和对算法意识和行为意图之间的关系的信任的调解作用.
- 探索数字素养,先前经验和隐私问题等个体差异如何调节算法意识对用户感知和意图的影响.
主要方法:
- 在Twitter (X),TikTok,YouTube和Facebook上对1200名用户进行了横截面调查,根据算法意识水平分层 (低,中等,高).
- 利用验证和自定义的尺度来评估TAM构造和算法特定的感知,通过Python中的结构方程建模 (SEM) 进行分析.
- 进行了40次半结构面试,以收集对用户体验和抵抗策略的定性见解,并使用NVivo进行了主题编码.
主要成果:
- 更高的算法意识与感知到的有用性 (r=0.62),易用性 (r=0.55),信任 (r=0.48) 和行为意图 (r=0.50) 有正相关.
- SEM证实了意识对有用性 (β=0.63),易用性 (β=0.58) 和信任 (β=0.51) 的直接积极影响,这些共同介导了与行为意图的联系 (间接影响=0.29-0.35).
- 数字素养,以前的经验和隐私问题显著缓解了这些关系 (ΔR2=0.05-0.07),具有很好的模型匹配指数 (CFI=0.98,TLI=0.97,RMSEA=0.04,SRMR=0.05).
结论:
- 算法意识提高了感知效用,但也可以增加用户对推系统的怀疑.
- 透明和用户可控的推系统对于维持用户参与和保持信任至关重要.
- 调查结果为开发更透明,以用户为中心,根据个人需求量身定制的推系统提供了基础.
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