摘要：生成式 AI（GenAI）应用已迅速获得消费者采用，但针对用户感知质量、信任及采纳障碍的大规模研究仍然较少。我们对六款主要 GenAI 应用——ChatGPT、Gemini、Microsoft Copilot、Claude、DeepSeek 和 Perplexity——的应用商店评论进行了首批跨应用分析之一，数据包括来自 Google Play 和 Apple App Store 的 17,012 条英文评论。我们结合 BERTopic 主题建模与 RoBERTa 情感分类，并使用经过 Bonferroni 校正的卡方检验、Kruskal-Wallis 检验和多项 Logistic 回归来评估应用间差异。两项组件均采用分层抽取的 300 条评论样本，并与人工编码结果进行验证。结果显示，负面情绪主要集中于广告（91%）、身份验证（89%）、服务器可靠性（83%）和订阅定价（73%）问题。不同应用的情感存在显著差异，其中 Claude 的负面情绪比例最高（47.7%），同时其用户群体表现出强烈热情，这表明存在统计显著的两极分化。尽管各应用的评论数量并不均等，这些发现依然稳健。作为探索性观察，一部分 DeepSeek 评论对其中国背景提出了地缘政治和数据隐私担忧；此外，研究提出的 Trust Friction Score 将各应用特定的信任和可用性障碍概括为可解释的维度。本研究为理解消费者生成式 AI 应用中的用户信任、可用性及采纳障碍提供了经过验证、可付诸行动的证据。

Abstract:Generative AI (GenAI) applications have achieved rapid consumer adoption, yet little large-scale research examines user-perceived quality, trust, and adoption barriers. We present one of the first cross-application analyses of app store reviews for six major GenAI applications (ChatGPT, Gemini, Microsoft Copilot, Claude, DeepSeek, and Perplexity), comprising 17,012 English-language reviews from Google Play and the Apple App Store. We combine BERTopic topic modeling with RoBERTa sentiment classification and evaluate cross-application differences using chi-square, Kruskal-Wallis, and multinomial logistic regression with Bonferroni correction. Both components are validated against human coding using a stratified sample of 300 reviews. Results show that negative sentiment concentrates in advertising (91%), authentication (89%), server reliability (83%), and subscription pricing (73%). Sentiment differs significantly across applications, with Claude exhibiting the highest negative sentiment (47.7%) alongside a strongly enthusiastic user base, indicating statistically significant polarization. These findings are robust despite unequal review counts across applications. As exploratory observations, a subset of DeepSeek reviews raised geopolitical and data privacy concerns related to its Chinese origin, while a proposed Trust Friction Score summarizes application-specific trust and usability barriers into interpretable dimensions. The study provides validated and actionable evidence on user trust, usability, and adoption barriers in consumer generative AI applications.