Reputation in Online Service Marketplaces: Empirical Evidence from a Transactional Dataset

Abstract

Online service marketplaces allow service buyers to post their project requests and service providers to bid for them. In order to reduce the transactional risks, marketplaces typically track and publish previous seller performance. By analyzing a detailed transactional dataset with more than 1,800,000 bids corresponding to 270,000 projects posted between 2001 and 2010 in a leading online intermediary for software development services, we empirically study the effects of the reputation system on market outcomes. We consider both a structured measure summarized in a numerical reputation score and a unstructured measure based on the verbal praise left by previous buyers, which we encode using text mining techniques. We find that buyers trade off reputation (both structured and unstructured) and price and are willing to accept higher bids posted by more reputable bidders. Sellers also respond to changes in their own reputation through three different channels. They increase their bids with their reputation score (price effect), but primarily use a superior reputation to increase their probability of being selected (volume effect) as opposed to increasing their bid prices. Negative shocks in seller reputation are associated to an increase in the probability of seller exit (exit effect), but this effect is moderated by the investment that the seller has made in the site. We conclude that participants in this market are very responsive to the numerical reputation score and also to the unstructured reputational information, which behaves in a similar way to the structured numerical reputation score, but provides complementary information.