Understanding the overvaluation of facial trustworthiness in Airbnb host images

Stuart J. Barnes

International Journal of Information Management, 2021, vol. 56, issue C

Abstract: 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.

Keywords: Host; Face; Perceived trustworthiness; Dangerous decisions theory; Deep learning (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ininma:v:56:y:2021:i:c:s026840122031464x

DOI: 10.1016/j.ijinfomgt.2020.102265

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