Online peer-to-peer platforms have been widely utilized to facilitate sharing vast amounts of user-generated content (UGC) in the hospitality industry. However, research on the cultural impacts of the broad spectrum of UGC remains nascent and limited. This study therefore elaborates how the hospitality service dimensions that are reflected in UGC both predict service evaluation and are contingent on a reviewer’s prevailing culture. We analyzed a usable sample of 9,257 hotel reviews from 148 countries with latent Dirichlet allocation and aspect-based sentiment analysis algorithms to identify the top service features and corresponding polarity scores. Machine learning identified three dimensions of hospitality service quality: adaptability, reliable delivery, and tangibles. These three dimensions predict customers’ overall hotel ratings, and the magnitude and direction of their effects depend on alignments of cultural orientations. Following Hofstede’s cultural dimensions, reliable delivery and tangibles align with individualism and uncertainty avoidance cultures. Adaptability aligns with power distance, masculinity, long-term orientation, and indulgence cultures.

在线点对点平台已被广泛用于促进酒店业共享大量用户生成内容 (UGC)。然而，关于广泛的 UGC 的文化影响的研究仍然是新生的和有限的。因此，本研究详细阐述了 UGC 中反映的酒店服务维度如何预测服务评估并取决于评论者的主流文化。我们使用潜在 Dirichlet 分配和基于方面的情感分析算法分析了来自 148 个国家/地区的 9,257 条酒店评论的可用样本，以确定顶级服务特征和相应的极性分数。机器学习确定了酒店服务质量的三个维度：适应性、可靠交付和有形。这三个维度预测客户的整体酒店评分，其影响的大小和方向取决于文化取向的一致性。遵循 Hofstede 的文化维度，可靠的交付和有形的东西与个人主义和避免不确定性的文化相一致。适应性与权力距离、阳刚之气、长期定位和放纵文化相一致。