Technical opinion: Online auctions hidden metrics

Paulo Goes, Yanbin Tu, Y. Alex Tung

Research output: Contribution to journalArticle

3 Scopus citations

Abstract

Three auction metrics, namely, auction listing errors, seller frustration, and auction fraud, which do not typically appear on the radar screen of most auction research has been reported. The auction literature provides supporting evidence for better performance by experienced sellers in terms of their positive feedback ratings. Listing errors refer to seller mistakes in setting parameters for an auction and can potentially lead to major frustration of sellers and/or buyers. Online auction fraud poses great concerns to market participants simply because most online transactions are based on trust. Tutorials and online recommendation systems should be in place to minimize listing errors and help sellers make the right selection of auction parameters that can enhance the probability of achieving successful transactions. A more careful investigation of these metrics can lead to a win-win-win situation for the auction house, sellers, as well as buyers.

Original languageEnglish (US)
Pages (from-to)147-149
Number of pages3
JournalCommunications of the ACM
Volume52
Issue number4
DOIs
StatePublished - Apr 1 2009

ASJC Scopus subject areas

  • Computer Science(all)

Fingerprint Dive into the research topics of 'Technical opinion: Online auctions hidden metrics'. Together they form a unique fingerprint.

  • Cite this