Renting a property via a peer-to-peer platform involves a variety of risks. Humans inherently, subconsciously use facial cues as important shortcuts in making assessments about other persons. On property sharing platforms, such as Airbnb, facial cues can be used in a similar fashion alongside reputational information. According to Dangerous Decisions Theory (DDT), intuitive evaluations of trustworthiness based on faces can bias subsequent assessment of an individual, requiring further information sources to make a more balanced assessment. In this study we apply DDT to demonstrate that evaluations based on perceived facial trustworthiness are overvalued; when combined with reputational measures, such as ‘super host’ status, such assessments are diminished. The study is based on deep learning to classify host faces for a large data set of online accommodation (n = 78,386). The research demonstrates that facial trust cues in online platforms should be treated with caution and must be combined with more objective measures of reputation in order to reduce the effects of overvaluation. The paper concludes with implications for practice and future research.

通过点对点平台租用财产涉及多种风险。人类天生就潜意识地将面部提示作为评估他人的重要捷径。在诸如Airbnb之类的财产共享平台上，面部表情可以与声誉信息一起以类似的方式使用。根据危险决策理论（DDT），基于面孔的直观可信度评估可能会使对个人的后续评估产生偏差，需要更多的信息来源来进行更加平衡的评估。在这项研究中，我们使用滴滴涕来证明基于感知到的面部信任度的评估被高估了。当结合声誉措施（例如“超级主人”身份）时，此类评估就会减少。该研究基于深度学习，对大型在线住宿数据集（n = 78,386）中的主人面部进行分类。研究表明，应谨慎对待在线平台中的面部信任提示，并且必须将其与声誉的更客观衡量指标相结合，以减少高估的影响。本文的结论对实践和未来研究具有启示意义。