Optimal Windows for Aggregating Ratings in Electronic Marketplaces

Christina Aperjis () and Ramesh Johari ()
Additional contact information
Christina Aperjis: Social Computing Lab, Hewlett-Packard Laboratories, Palo Alto, California 94304
Ramesh Johari: Department of Management Science and Engineering, Stanford University, Stanford, California 94305

Management Science, 2010, vol. 56, issue 5, 864-880

Abstract: Aseller in an online marketplace with an effective reputation mechanism should expect that dishonest behavior results in higher payments now whereas honest behavior results in a better reputation--and thus higher payments--in the future. We study the Window Aggregation Mechanism, a widely used class of mechanisms that shows the average value of the seller's ratings within some fixed window of past transactions. We suggest approaches for choosing the window size that maximizes the range of parameters for which it is optimal for the seller to be truthful. We show that mechanisms that use information from a larger number of past transactions tend to provide incentives for patient sellers to be more truthful but for higher-quality sellers to be less truthful.

Keywords: reputation mechanisms; ratings; online markets (search for similar items in EconPapers)
Date: 2010
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

Downloads: (external link)
http://dx.doi.org/10.1287/mnsc.1090.1145 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:56:y:2010:i:5:p:864-880

Access Statistics for this article

More articles in Management Science from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().