分析十万条AI产品评论，发现4个交互维度显著影响用户满意度。
Human-AI Interaction and User Satisfaction: Empirical Evidence from Online Reviews of AI Products
- 基于行业指南提取7个HAI维度，从评论中挖掘用户情感。
- 适应性、可定制性、容错和安全性的正面评价与满意度正相关。
- 技术背景不同用户关注点不同，但影响满意度的机制一致。
人机交互（HAI）指南与设计原则在产业界和学术界日益重要，旨在指导开发符合用户需求与期望的AI系统。然而，关于HAI原则如何实际影响用户满意度的大规模实证证据仍有限。本研究通过分析来自G2平台的10万余条AI相关产品的用户评论，基于广泛采用的行业指南，识别出七个核心HAI维度，并考察其在评论中的覆盖度与情感倾向。研究发现，适应性、可定制性、错误恢复和安全性四个维度的情感倾向与整体用户满意度呈正相关。此外，不同职业背景用户的关注点存在差异：具有技术背景的用户更关注可靠性等系统性特征，而非技术用户则更重视可定制性和反馈等交互性功能。有趣的是，工作角色并未调节HAI情感与满意度之间的关系，表明一旦用户感知到某维度，其对满意度的影响在各类角色间保持一致。
原文摘要 · Abstract (English)
Human-AI Interaction (HAI) guidelines and design principles have become increasingly important in both industry and academia to guide the development of AI systems that align with user needs and expectations. However, large-scale empirical evidence on how HAI principles shape user satisfaction in practice remains limited. This study addresses that gap by analyzing over 100,000 user reviews of AI-related products from G2, a leading review platform for business software and services. Based on widely adopted industry guidelines, we identify seven core HAI dimensions and examine their coverage and sentiment within the reviews. We find that the sentiment on four HAI dimensions-adaptability, customization, error recovery, and security-is positively associated with overall user satisfaction. Moreover, we show that engagement with HAI dimensions varies by professional background: Users with technical job roles are more likely to discuss system-focused aspects, such as reliability, while non-technical users emphasize interaction-focused features like customization and feedback. Interestingly, the relationship between HAI sentiment and overall satisfaction is not moderated by job role, suggesting that once an HAI dimension has been identified by users, its effect on satisfaction is consistent across job roles.
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