A seller's perspective characterization methodology for online auctions

Online auction services have reached great popularity and revenue over the last years. A key component for this success is the seller. Few studies proposed analyzing how the seller and the auction configuration affect the negotiation results. In this work we propose a methodology to characterize online auctions by the seller's perspective. This methodology is based on: (1) recognizing the characteristics of the variables related to the auction results and (2) capturing the correlation among these variables to identify seller profiles and selling strategies. We applied our methodology to a real case study, using an eBay dataset, to validate two hypotheses about sellers and their practices. These results are useful to understand the complex mechanisms that guide ending prices, success (or failure), and the attraction of bids in online auctions, which can support decision strategies for buyers and sellers.

[1]  Ali Hortaçsu,et al.  Winner's Curse, Reserve Prices and Endogenous Entry: Empirical Insights from Ebay Auctions , 2003 .

[2]  Paul Resnick,et al.  The value of reputation on eBay: A controlled experiment , 2002 .

[3]  David Lucking-Reiley,et al.  Using field experiments to test equivalence between auction formats: Magic on the internet , 1999 .

[4]  David H. Reiley,et al.  Pennies from Ebay: The Determinants of Price in Online Auctions , 2000 .

[5]  R. Becherer,et al.  Characteristics and internet marketing strategies of online auction sellers , 2004 .

[6]  Yves Lechevallier,et al.  Pre-Processing and Clustering Complex Data in E-Commerce Domain , 2005 .

[7]  Hans-Peter Kriegel,et al.  Hierarchical density-based clustering of uncertain data , 2005, Fifth IEEE International Conference on Data Mining (ICDM'05).

[8]  Elke Achtert,et al.  Hierarchical Density-Based Clustering for Multi-Represented Objects , 2005 .

[9]  Paul A. Pavlou,et al.  Evidence of the Effect of Trust Building Technology in Electronic Markets: Price Premiums and Buyer Behavior , 2002, MIS Q..

[10]  K. Reynolds,et al.  Determinants of internet auction success and closing price: An exploratory study , 2003 .

[11]  Theodore Johnson,et al.  Exploratory Data Mining and Data Cleaning , 2003 .

[12]  Charles R. Plott,et al.  Rational Choice in Experimental Markets , 1986 .

[13]  Daniel A. Menascé,et al.  Scaling for E-Business: Technologies, Models, Performance, and Capacity Planning , 2000 .

[14]  Paul Resnick,et al.  Trust among strangers in internet transactions: Empirical analysis of eBay' s reputation system , 2002, The Economics of the Internet and E-commerce.

[15]  Yishay Mansour,et al.  An Information-Theoretic Analysis of Hard and Soft Assignment Methods for Clustering , 1997, UAI.

[16]  Steven T. Anderson,et al.  Seller Strategies on eBay , 2004 .

[17]  Ron Kohavi,et al.  Feature Selection for Knowledge Discovery and Data Mining , 1998 .

[18]  Alok Gupta,et al.  Insights and analyses of online auctions , 2001, CACM.

[19]  Raj Jain,et al.  The art of computer systems performance analysis - techniques for experimental design, measurement, simulation, and modeling , 1991, Wiley professional computing.

[20]  Hans-Hermann Bock.  Data mining tasks and methods: Classification: the goal of classification , 2002 .

[21]  Vincent Kanade,et al.  Clustering Algorithms , 2021, Wireless RF Energy Transfer in the Massive IoT Era.