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Optimal windows for aggregating ratings in electronic marketplaces / Christina Aperjis in Management science, Vol. 56 N° 5 (Mai 2010) 

Public ISBD [article]  in Management science > Vol. 56 N° 5 (Mai 2010) . - pp. 864-880 Titre : Optimal windows for aggregating ratings in electronic marketplaces Type de document : texte imprimé Auteurs : Christina Aperjis, Auteur ; Ramesh Johari, Auteur Année de publication : 2010 Article en page(s) : pp. 864-880 Note générale : Management Langues : Anglais (eng) Mots-clés : Reputation  mechanisms  Ratings  Online  markets Index. décimale : 658 Organisation des entreprises. Techniques du commerce Résumé : 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. DEWEY : 658 ISSN : 0025-1909 En ligne : http://mansci.journal.informs.org/content/56/5.toc [article] Optimal windows for aggregating ratings in electronic marketplaces [texte imprimé] / Christina Aperjis, Auteur ; Ramesh Johari, Auteur . - 2010 . - pp. 864-880. Management Langues : Anglais (eng) in Management science > Vol. 56 N° 5 (Mai 2010) . - pp. 864-880 Mots-clés : Reputation  mechanisms  Ratings  Online  markets Index. décimale : 658 Organisation des entreprises. Techniques du commerce Résumé : 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. DEWEY : 658 ISSN : 0025-1909 En ligne : http://mansci.journal.informs.org/content/56/5.toc