Effective selling strategies for online auctions on eBay: A comprehensive approach with CART model

Yanbin Tu, Y. Alex Tung, Paulo Goes

Research output: Contribution to journalArticlepeer-review


Most existing studies on selling strategies in online auctions do not distinguish auction heterogeneity when providing operational selling recommendations. They also tend to assume single objective for sellers. In this study, we incorporate seller and product heterogeneity into our analytical framework and implement data mining analysis in four auction segments. We use classification and regression tree (CART) to identify the critical factors along with their sequences for auction success and prices. We find different determinants for auction success and ending prices in these four auction segments. The classification and regression trees provide operational choices for sellers to build the most effective selling strategies. We propose that, by using expected auction prices with the classification and regression trees, sellers can integrate auction success and prices as multiple objectives in their selling strategies. Overall, this study contributes to the literature by providing an innovative methodology for effective selling recommendations, which can potentially lead to significant and smooth growth of the online auction market.

Original languageEnglish (US)
Pages (from-to)125-151
Number of pages27
JournalInternational Journal of Business Information Systems
Issue number2
StatePublished - 2019


  • Classification
  • Data mining
  • Electronic marketplaces
  • Online auctions
  • Regression
  • Selling strategies

ASJC Scopus subject areas

  • Management Information Systems
  • Information Systems and Management
  • Management of Technology and Innovation


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