AI 中文总结
本研究针对情感支持大语言模型，开发验证了心理测量量表，通过结构方程模型与多组分析发现不同社会人口群体对AI的信任影响因素及采纳逻辑存在差异，为情感支持AI的公平设计提供了理论支撑。
AI 中文摘要
信任AI提供情感支持并非普遍现象，它受用户身份、来源地及价值观的影响。然而该领域研究缺乏用于评估情感AI情境下用户感知的有效心理测量工具，以及关于信任形成如何在不同用户群体间变化的大规模证据。为填补这些空白，我们开发并验证了包含7个维度的心理测量量表，测试了将系统属性作为信任与感知益处的中介变量，关联到实际系统使用的结构方程模型（SEM），并基于来自7个国家的1343名活跃用户，针对5个社会人口统计学维度（性别、年龄、教育程度、社会经济地位、跨国区域）进行了多组分析（MGA）。我们发现，用户将共情与拟人化感知为统一的“类人性”维度，隐私性、个性化和类人性会推动信任，而感知到的偏见则会降低信任。值得注意的是，不同群体的采纳逻辑存在差异：隐私对女性信任的影响大于男性；英语圈（英国、美国）用户对类人性的积极反应强于欧洲用户；受教育程度高、收入高的用户需要信任才能使用，而老年人和低社会经济地位群体则完全绕过信任，依赖感知到的实际益处（如全天候可用、无评判性支持）。我们的发现拓展了技术接受理论，并为情感支持AI的公平设计提供了依据。
英文摘要
Trust in AI for emotional support is not universal; it is shaped by who users are, where they come from, and what they value. Yet research in this area lacks validated psychometric instruments for assessing user perceptions in affective AI contexts and large-scale evidence on how trust formation varies across user segments. To address these gaps, we develop and validate a seven-construct psychometric scale, test a Structural Equation Model (SEM) linking system attributes to Trust and Perceived Benefits as mediators of Actual System Use, and conduct a Multi-Group Analysis (MGA) across five sociodemographic dimensions (gender, age, education, socioeconomic status, cross-national region), drawing on 1,343 active users from seven countries. We find that users experience empathy and anthropomorphism as a unified "Humanlikeness" construct, and that Privacy, Personalization, and Humanlikeness drive Trust while Perceived Bias degrades it. Notably, adoption logic diverges across groups: Privacy shapes women's trust more than men's, Anglosphere (UK, USA) users respond more positively to Humanlikeness than Europeans, and educated and higher-income users require Trust to engage, whereas older adults and lower socioeconomic groups bypass it entirely, relying on perceived practical benefits (e.g., 24/7 availability, non-judgmental support). Our findings extend technology acceptance theory and inform the equitable design of emotional support AI.
