Elsevier

Decision Support Systems

Volume 44, Issue 3, February 2008, Pages 641-656
Decision Support Systems

Price formation and its dynamics in online auctions

https://doi.org/10.1016/j.dss.2007.09.004Get rights and content

Abstract

This research uses functional data modeling to study the price formation process in online auctions. It conceptualizes the price evolution and its first and second derivatives (velocity and acceleration respectively) as the primary objects of interest. Together these three functional objects permit us to talk about the dynamics of an auction, and how the influence of different factors vary throughout the auction. For instance, we find that the incremental impact of an additional bidder's arrival on the rate of price increase is smaller towards the end of the auction. Our analysis suggests that “stakes” do matter and that the rate of price increase is faster for more expensive items, especially at the start and the end of an auction. We observe that higher seller ratings (which correlate with experience) positively influence the price dynamics, but the effect is weaker in auctions with longer durations. Interestingly, we find that the price level is negatively related to auction duration when the seller has low rating whereas in auctions with high-rated sellers longer auctions achieve higher price levels throughout the auction, and especially at the start and end. Our methodological contributions include the introduction of functional data analysis as a useful toolkit for exploring the structural characteristics of electronic markets.

Section snippets

Introduction and background

Auctions have long served as operationally simple mechanisms that coordinate privately held information quickly and accurately to achieve efficient exchange. In doing so, they play a critical role in informing us about the underlying price formation process. Nowhere is this more evident than on eBay, perhaps the world's largest online auction house, where over to a billion items were listed for sale in 2006. The focus of this research is to shed new light on the dynamics of the price formation

Functional models for online auctions

Functional data analysis models are becoming increasingly popular, in particular in statistics and related disciplines. Functional models distinguish themselves from traditional statistical models in that the input and/or the output variables (i.e., x and/or y) can be a functional object rather than simply a data vector. The process of functional auction modeling begins by representing the price path in a single auction by a continuous curve. After the underlying curve is estimated, or

Explanatory variable selection

We elaborate on five sets of explanatory variables, relying heavily on the online auction literature from Economics and Information Systems (IS).

  • a)

    Seller's mechanism design choices: sellers, who strive to maximize their revenues, can be expected to strategize on eBay by choosing the appropriate combination of starting price level, auction duration, and the usage of a hidden reserve price. Starting price can be interpreted as an open reserve price and prior research has contrasted the comparative

Data description

The data used in our analysis consist of a random sample of 1009 auctions that took place on one single day on eBay's auction page. To obtain this sample, we undertook a “title and description”; advanced search of eBay auctions using the phrase “May-13-04.” This returned approximately 10,000 eBay listings with this test string anywhere in the HTML text. Subsequently, after the last of these auctions closed, we obtained the auction information by parsing the HTML pages of only competitive

Conclusion

In this paper, using a random sample of 1009 eBay auctions, we demonstrate how functional data modeling can be applied to understand the process of price formation and its dynamics in online auctions on eBay. We believe that such an understanding enhances researchers' ability to examine the drivers of the price formation process of online auctions. It facilitates new conversations regarding the temporal nature of the various effects and their interactions, and provides important clues to

Dr. Ravi Bapna is the Executive Director of the Centre for Information Technology and the Networked Economy (CITNE) at the Indian School of Business, as well as a tenured Associate Professor in the Information Systems area. He teaches in the ISB's PGP and Executive Education programmes. Prior to joining the ISB, Professor Bapna was an Associate Professor and Ackerman Scholar in the Operations and Information Management Department at the School of Business, University of Connecticut. His

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    Dr. Ravi Bapna is the Executive Director of the Centre for Information Technology and the Networked Economy (CITNE) at the Indian School of Business, as well as a tenured Associate Professor in the Information Systems area. He teaches in the ISB's PGP and Executive Education programmes. Prior to joining the ISB, Professor Bapna was an Associate Professor and Ackerman Scholar in the Operations and Information Management Department at the School of Business, University of Connecticut. His research interests are in the areas of Online Auctions, E-market Design, Grid Computing, and the Economics of Information Systems. His research has been extensively published in a wide array of journals such as Management Science, Informs Journal on Computing, Statistical Science, Information Systems Research, Journal of Retailing, MIS Quarterly, Decision Sciences, CACM, Naval Research Logistics, DSS, EJOR and ITM. He was awarded the prestigious Ackerman Scholar Award for outstanding research at the University of Connecticut. He contributes regularly to popular press outlets such as India Knowledge @ Wharton, The Economic Times and Business Today. Professor Bapna has been invited to present his research at the Federal Trade Commission, Washington DC, Telecom Regulatory Authority of India (TRAI), Carnegie Mellon University, New York University, University of Maryland, IIM-Calcutta, National Chengchi University, Taiwan, University of Washington, National University of Singapore and University of Minnesota, among others. He serves on the editorial board of the flagship journals MIS Quarterly, as well as for the journal Production and Operations Management. He also serves as ad-hoc AE for Management Science. He regularly serves on programme committees of major international IS conferences and workshops, and is the Co-Chair of the First International Symposium of Information Systems to be held at the ISB. He is a member of the Nasscom Innovation Institute, a think-tank to foster innovation in the Indian IT/ITeS industry. Professor Bapna completed his Bachelor's degree in Computer Engineering from the Manipal Institute of Technology and received his doctorate degree from the University of Connecticut, where his thesis was in the area of Information Systems.

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    Wolfgang Jank is an Associate Professor in the Smith School of Business at the University of Maryland. He joined the University of Maryland in August 2001. He is interested in data-driven problems and challenges in electronic commerce, marketing, operations management and aviation. His methodological interests focus on stochastic estimation and optimization, functional data analysis, information visualization and methods for spatial and temporal data. Wolfgang Jank received his Master’s degree in Mathematics from the Technical University of Aachen. He received his Ph.D. in Statistics from the University of Florida. He is a member of the American Statistical Society, the Institute of Mathematical Statistics, the European Network for Business and Industrial Statistics, the Association for Computing Machinery and INFORMS. He is past president of the University of Florida's chapter of the statistical honor society Mu Sigma Rho.

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    Galit Shmueli is an Associate Professor of Statistics at the Robert H. Smith School of Business, University of Maryland, College Park. She holds an M.Sc. and Ph.D. in Statistics from the Israel Institute of Technology. Her research focuses on statistical methods for modern data structures with applications to eCommerce and biosurveillance.

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    Bapna gratefully acknowledges the support of the Treibeck Electronic Commerce Initiative (TECI), UConn. Jank and Shmueli's research was partially funded by the NSF grant DMI-0205489. They gratefully acknowledge support by the Center of Electronic Markets and Enterprises, University of Maryland.

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