Consumer-to-Consumer Internet Auction Models

Internet auctions have become an increasingly common method for exchanging goods and services across the world both among consumers themselves, as well as between businesses and consumers. These Internet auction mechanisms have the scope of incorporating procedures of much greater complexity and variety, and they exhibit characteristics and properties that are quite distinct from conventional auctions. In this paper, the authors provide an experimental study of the performance characteristics and operational behaviour of a number of online auction models, including the fixed time forward auctions, the Vickrey auctions, and models with soft close variable auction times. These online auction models are studied through systematic simulation experiments, based on a series of operational assumptions, which characterize the arrival rate of bids, as well as the distribution from which the private values of buyers are sampled. Suggestions for efficient online auction design and procedures for improving auction performance are given, and the behaviour of the average auction income and average auction duration are quantified and compared.

[1]  William Vickrey,et al.  Counterspeculation, Auctions, And Competitive Sealed Tenders , 1961 .

[2]  Roger B. Myerson,et al.  Optimal Auction Design , 1981, Math. Oper. Res..

[3]  Paul R. Milgrom,et al.  A theory of auctions and competitive bidding , 1982 .

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

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

[6]  Alvin E. Roth,et al.  The Timing of Bids in Internet Auctions: Market Design, Bidder Behavior, and Artificial Agents , 2001, AI Mag..

[7]  Alok Gupta,et al.  User heterogeneity and its impact on electronic auction market design: an empirical exploration , 2004 .

[8]  Alvin E. Roth,et al.  Late and Multiple Bidding in Second Price Internet Auctions: Theory and Evidence Concerning Different Rules for Ending an Auction , 2003, Games Econ. Behav..

[9]  Wolfgang Jank,et al.  A family of growth models for representing the price process in online auctions , 2007, ICEC.

[10]  A. Kwasnica,et al.  Time is money: The effect of clock speed on seller’s revenue in Dutch auctions , 2003 .

[11]  Wenyan Hu,et al.  Online auctions efficiency: a survey of ebay auctions , 2008, WWW.

[12]  S. Reddy,et al.  An Analysis of Price Dynamics, Bidder Networks, and Market Structure in Online Art Auctions , 2008 .

[13]  Wolfgang Jank,et al.  Explaining and Forecasting Online Auction Prices and Their Dynamics Using Functional Data Analysis , 2008 .

[14]  Sandy D. Jap,et al.  Competition between auctions , 2008 .

[15]  Chrysanthos Dellarocas,et al.  The Sound of Silence in Online Feedback: Estimating Trading Risks in the Presence of Reporting Bias , 2006, Manag. Sci..

[16]  Wolfgang Jank,et al.  Consumer surplus in online auctions , 2008 .

[17]  Roumen Vragov,et al.  Operational efficiency of decentralized Internet auction mechanisms , 2010, Electron. Commer. Res. Appl..

[18]  Xian-Sheng Hua,et al.  Online Multimedia Advertising: Techniques and Technologies , 2010 .

[19]  Wolfgang Jank,et al.  Forecasting Online Auctions using Dynamic Models , 2010, Data Mining for Business Applications.

[20]  Mark Klein,et al.  Auctions and bidding: A guide for computer scientists , 2011, CSUR.

[21]  Xian-Sheng Hua,et al.  In-Image Advertising , 2011 .

[22]  Sadiq Shahbaz Ali,et al.  ICT Infrastructure Framework for Microfinance Institutions and Banks in Pakistan: An Optimized Approach , 2013, Int. J. Online Mark..

[23]  Edwin Agwu,et al.  Cyber Criminals on the Internet Super Highways: A Technical Investigation of Different Shades and Colours Within the Nigerian Cyber Space , 2013, Int. J. Online Mark..

[24]  International Journal of Online Marketing , 2022 .