| Abstract: |
Online ratings turn a long review history into one public number. This makes
sellers easier to compare, but it also gives them a single clear target to
manipulate. We study a low-quality seller who can add fake reviews carrying
different scores, buyers who infer quality from the displayed average, and a
platform that can target particular scores for enforcement. The model
separates the rating buyers see from the hidden mix of reviews used to produce
it, and the two need not move together. An almost-perfect rating can be less
credible than a slightly lower one when low-quality sellers are especially
likely to manufacture the top of the scale, so a seller whose buyers become
more valuable may display less and sell more. At a fixed displayed rating,
targeted enforcement can redirect fake reviews toward other scores rather than
eliminate manipulation; buyers do not see this substitution because the
displayed average is unchanged. Raw ratings therefore provide only a partial
picture of credibility and enforcement, and buyer-oriented ranking should
account for what a rating conveys, not only its numerical level. |