Exploring decision rules for sellers in business-to-consumer (B2C) internet auctions

Jeff Baker, Jaeki Song

Research output: Contribution to journalArticle

2 Scopus citations

Abstract

The recent growth of business-to-consumer (B2C) Internet auctions challenges researchers to develop empirically-sound explanations of critical factors that allow merchants to earn price premiums in these auctions. The absence of a comprehensive model of Internet auctions leads us to conduct an exploratory study to elucidate and rank critical factors that lead to price premiums in Internet auctions. We employ Classification and Regression Trees (CART), a decision-tree induction technique, to analyze data collected in a field study of eBay auctions. Our analysis yields decision trees that visually depict noteworthy factors that may lead to price premiums and that indicate the relative importance of these factors. We find shipping cost, reputation initial bid rice, and auction ending time as the factors most predictive of price premiums in B2C Internet auctions.

Original languageEnglish
Pages (from-to)1-21
Number of pages21
JournalInternational Journal of e-Business Research
Volume4
Issue number1
DOIs
StatePublished - 2008

Keywords

  • B2C e-commerce
  • Data mining
  • Decision support
  • Electronic markets
  • Internet commerce
  • Internet economy
  • Online auctions

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