Online auction segmentation and effective selling strategy: Trust and information asymmetry perspectives

Yanbin Tu, Y. Alex Tung, Paulo Goes

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Based on the theory of online trust and information asymmetry, we empirically find structural differences in auction success and price determinants between new and experienced sellers, and between new and used items in online auctions. We classify auction listings into four segments ((new sellers, experienced sellers) × (new items, used items)) and find that sellers in these four segments behave significantly differently. We also discover that, given the same product condition, experienced sellers with unsuccessful auctions can more likely transition to successful auctions (via re-listing) than new sellers with unsuccessful auctions. In addition, trust enhancing strategies are found to be relatively more important than transaction enhancing strategies for auction success. The auction segmentation knowledge attained in this study not only provides the online auction house with solid guidance to customize its services for different groups of market participants, it also helps sellers better position themselves and buyers more intelligently select auction items to bid in online marketplaces.

Original languageEnglish (US)
Pages (from-to)189-211
Number of pages23
JournalJournal of Electronic Commerce Research
Issue number3
StatePublished - 2017


  • Auction segmentation
  • Information asymmetry
  • Online trust
  • Selling strategy
  • Transaction enhancing strategies
  • Trust enhancing strategies

ASJC Scopus subject areas

  • Economics, Econometrics and Finance(all)
  • Computer Science Applications


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