在线评论中的偏见和互惠:来自Airbnb现场实验的证据

Andrey Fradkin, Elena Grewal, David Holtz, Matthew Pearson
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引用次数: 197

摘要

消费者用评论和其他评价来决定购买什么商品,公司用评论和其他评价来选择与谁交易、雇佣谁或提拔谁。然而,由于潜在的审稿人不会因为提交审稿而得到补偿,并且可能有理由在他们的审稿中省略相关信息,审稿可能是有偏见的。我们使用Airbnb的设置来研究评论行为的决定因素,评论有偏见的程度,以及声誉系统设计的变化是否可以减少这种偏见。我们发现Airbnb上的评论通常都是信息丰富的,97%的客人私下表示有积极的体验。通过两个旨在减少偏见的现场实验,我们发现非审稿人往往比审稿人有更差的体验,并且在网站上发生了战略性评论行为,尽管战略行为的总体效应相对较小。我们使用定量练习来显示我们记录的偏见机制将负面文本和非推荐的评论率降低了0.86个百分点。最后,我们讨论了在线市场如何设计更具信息性的评论系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bias and Reciprocity in Online Reviews: Evidence From Field Experiments on Airbnb
Reviews and other evaluations are used by consumers to decide what goods to buy and by firms to choose whom to trade with, hire, or promote. However, because potential reviewers are not compensated for submitting reviews and may have reasons to omit relevant information in their reviews, reviews may be biased. We use the setting of Airbnb to study the determinants of reviewing behavior, the extent to which reviews are biased, and whether changes in the design of reputation systems can reduce that bias. We find that reviews on Airbnb are generally informative and 97% of guests privately report having positive experiences. Using two field experiments intended to reduce bias, we show that non-reviewers tend to have worse experiences than reviewers and that strategic reviewing behavior occurred on the site, although the aggregate effect of the strategic behavior was relatively small. We use a quantitative exercise to show that the mechanisms for bias that we document decrease the rate of reviews with negative text and a non-recommendation by just .86 percentage points. Lastly, we discuss how online marketplaces can design more informative review systems.
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