16 Pages Posted: 14 Nov 2001
Date Written: October 2001
This paper introduces a model for analyzing marketplaces, such as eBay, which rely on binary reputation mechanisms for quality signaling and quality control. In our model sellers keep their actual quality private and choose what quality to advertise. The reputation mechanism is primarily used to induce sellers to advertise truthfully. Buyers base their ratings on the difference between expected and actual quality. Furthermore, raters are lenient and do not post negative ratings unless transactions end up exceptionally bad. It is shown that, in such a setting, the fairness of the market outcome is determined by the relationship between rating leniency and corresponding strictness when assessing a seller's feedback profile. If buyers judge sellers too strictly (relative to how leniently they rate) then, at steady state, sellers will be forced to understate their true quality. On the other hand, if buyers judge too leniently then sellers can get away with consistently overstating their true quality. An optimal judgment rule, which results in outcomes where, at steady state, buyers accurately predict the true quality of sellers, is theoretically possible to derive for all leniency levels. Furthermore, if buyers judge sellers using that rule, then the more lenient buyers are when rating sellers, the more likely it is that sellers will find it optimal to settle down to steady-state quality levels, as opposed to oscillating between good quality and bad quality. However, it is argued that this optimal rule depends on several parameters, which are difficult to estimate from the information that eBay currently makes available to its members. It is therefore questionable to what extent unsophisticated buyers are currently using eBay feedback information in an optimal way.
Suggested Citation: Suggested Citation
Dellarocas, Chrysanthos, Analyzing the Economic Efficiency of eBay-like Online Reputation Reporting Mechanisms (October 2001). MIT Sloan Working Paper No. 4181-01. Available at SSRN: https://ssrn.com/abstract=289968 or http://dx.doi.org/10.2139/ssrn.289968