The dissertation is comprised of six studies. I attempt to determine the cause of this positive ratings bias, and provide solutions to fix the bias. It suggests that peer-to-peer ratings may be biased, and may not reflect a provider’s true quality. However, nearly all peer-to-peer ratings are five-stars, which makes it difficult for consumers to distinguish between providers. Online ratings and reviews are extremely important for consumers of peer-to-peer services because they establish trust with unknown (and mostly non-professional) providers. The peer-to-peer sharing economy is growing quickly behind platforms such as Airbnb and Uber that help people rent or share their skills and belongings with other consumers. I demonstrate that platforms can attenuate the positive bias by making ratings anonymous, by clearly defining service standards, and by increasing perceived controllability by providers for expectations and performance failure. Negative ratings for peer providers may result only if consumers believe that a provider caused and controlled a negative outcome, which suggests a lack of integrity (Study 6). Standards of evaluation are relatively unclear for peer-to-peer services (making it more difficult to identify performance failure), and social norms of gratitude and empathy motivate consumers to forgive peer providers for unreliable service (Studies 4 and 5).
#GOMEZ PEER TRUST WORTHY DRIVER#
This elevates trust as an important driver of ratings at the expense of satisfaction, because satisfaction is more subjective and more difficult to justify (Study 3).Ĭonsumers may give peer providers positive ratings even if performance is worse than expected. Contextual factors in peer-to-peer networks cause consumers to feel that their ratings are more important to peer providers, and that they may need to justify ratings. When uncertainty and risk are high, a provider demonstrates that they can be trusted by meeting a consumer’s prior expectations (Study 2). I demonstrate that the process works differently for peer-to-peer services a consumer’s determination of whether a provider met expectations has an effect on ratings beyond the effect of satisfaction (Study 1). Consumers compare a provider’s performance against prior expectations the resultant satisfaction or dissatisfaction leads to online ratings. Research on service evaluation is often informed by the expectancy disconfirmation process (Oliver, 1980, 2010). This trust evaluation, in concert with network and social factors, contributes to the bias. I address this gap by demonstrating that consumers evaluate peer-peer experiences based on trust. Despite these concerns, little progress has been made to demonstrate the cause of the bias or how it can be fixed. However, there is a large positive bias in the ratings, making differentiation difficult, and causing some consumers to lose trust. Consumers exchange with non-professional providers with whom they have no past history, and must rely on ratings and reviews for choice selection. Transactions in the peer-to-peer sharing economy carry high risk and uncertainty.