Tradeoff Between Distributed Social Learning and Herding Effect in Online Rating Systems

Abstract

We investigated how social diffusion increased client participation in an online rating system and, in turn, how this herding effect may affect the metrics of client feedback over the course of years. In a field study, we set up a transparent feedback system for university services: During the process of making service requests, clients were presented with short-term trends of client satisfaction with relevant service outcomes. Deploying this feedback system initially increased satisfaction moderately. Thereafter, mean satisfaction levels remained stable between 50% and 60%. Interestingly, at the individual client level, satisfaction increased significantly with experience despite the lack of any global trend across all users. These conflicting results can be explained at the social network level: If satisfied clients attracted new clients with more negative attitudes (a herding effect), then the net increase in service clients may dampen changes in global trends at the individual level. Three observations support this hypothesis: first, the number of service clients providing feedback increased monotonically over time. Second, spatial analysis of service requests showed a pattern of expansion from floor to floor. Finally, satisfaction increased over iterations only in clients who scored below average.