Maintaining vs. milking good reputation when customer feedback is inaccurate

Behnud Mir Djawadi, René Fahr, Claus-Jochen Haake and Sonja Recker

PLOS ONE, 2018, vol. 13, issue 11, 1-23

Abstract: In Internet transactions, customers and service providers often interact once and anonymously. To prevent deceptive behavior a reputation system is particularly important to reduce information asymmetries about the quality of the offered product or service. In this study we examine the effectiveness of a reputation system to reduce information asymmetries when customers may make mistakes in judging the provided service quality. In our model, a service provider makes strategic quality choices and short-lived customers are asked to evaluate the observed quality by providing ratings to a reputation system. The customer is not able to always evaluate the service quality correctly and possibly submits an erroneous rating according to a predefined probability. Considering reputation profiles of the last three sales, within the theoretical model we derive that the service provider’s dichotomous quality decisions are independent of the reputation profile and depend only on the probabilities of receiving positive and negative ratings when providing low or high quality. Thus, a service provider optimally either maintains a good reputation or completely refrains from any reputation building process. However, when mapping our theoretical model to an experimental design we find that a significant share of subjects in the role of the service provider deviates from optimal behavior and chooses actions which are conditional on the current reputation profile. With respect to these individual quality choices we see that subjects use milking strategies which means that they exploit a good reputation. In particular, if the sales price is high, low quality is delivered until the price drops below a certain threshold, and then high quality is chosen until the price increases again.

Date: 2018
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Working Paper: Maintaing vs. Milking Good Reputation when Customer Feedback is Inaccurate (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0207172

DOI: 10.1371/journal.pone.0207172

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