在推的新闻应用程序中对用户持续使用意图的情绪影响:在认知-影响-结合框架内研究算法欣赏和疲劳
Ke Zhang1, Yuchen Xie1, Yutong He1
1School of Communication, Soochow University, Suzhou, Jiangsu, China.
Acta psychologica
|April 13, 2025
概括
这项研究发现,算法欣赏和疲劳显著影响用户继续使用新闻应用程序的意图. 解决这些情绪可以提高用户体验和平台的成功.
科学领域:
- 人与计算机的交互
- 信息科学 信息科学 信息科学
- 心理学 心理学 心理学
背景情况:
- 了解推的新闻应用程序中的用户行为对于平台的成功至关重要.
- 认知-影响-结合模型为分析用户意图提供了一个框架.
- 对算法的情绪反应显著影响用户与新闻应用程序的互动.
研究的目的:
- 调查算法欣赏和算法疲劳在用户感知和推新闻应用程序的持续意图之间的关系中扮演的调解角色.
- 检查隐私问题对这些关系的温和影响.
- 为增强用户体验和新闻推平台的持续意图提供见解.
主要方法:
- 使用了认知-影响-结合模型.
- 从中国主要推的新闻应用TouTiao的用户那里收集了1014份有效的问卷.
- 采用调解和调节分析来测试提出的假设.
主要成果:
- 算法欣赏调解了感知到的新闻价值,个性化和可解释性对延续意图的积极影响.
- 算法疲劳介绍了感知信息缩小,内容质量低下和信息过载对延续意向的负面影响.
- 隐私问题缓和了算法欣赏和疲劳对延续意向的影响.
结论:
- 情绪反应,特别是算法欣赏和疲劳,是推新闻应用程序中用户持续意图的关键驱动因素.
- 平台应该专注于管理与算法相关的情绪,以改善用户体验.
- 平台目标与用户隐私之间的平衡对于持续的参与至关重要.
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