Online Auctions

The economic literature on online auctions is rapidly growing because of the enormous amount of freely available field data. Moreover, numerous innovations in auction-design features on platforms such as eBay have created excellent research opportunities. In this article, we survey the theoretical, empirical, and experimental research on bidder strategies (including the timing of bids and winner's-curse effects) and seller strategies (including reserve-price policies and the use of buy-now options) in online auctions, as well as some of the literature dealing with online-auction design (including stopping rules and multi-object pricing rules).

Country-Specific Effects of Reputation and Information: A Comparison of Online Auctions in Germany, the UK, and the US

Empirical studies on the effect of sellers’ reputation on closing prices in online auctions present mixed results. A large number of studies addresses reputational effects in one country, especially in the US. Only a small number of cross-country studies inspect the moderating role of institutional frameworks on bidder behavior. The purpose of this paper is to examine if country-specific differences in the formal and informal institutional framework influence the effects of reputation and information signals on final prices in online auctions. From the perspective of the New Institutional Economics, management decisions and individuals’ characteristics are affected by the institutional framework, which consists of cultural aspects as well as a set of social and legal rules and regulations. Therefore, bidders that are influenced by one institutional framework have different preferences, expectations, and perceptions about reputation and information in online auctions than individuals socialized by another institutional framework. In order to examine the effects of reputation and information on prices as well as to asses cross-country similarities and differences in these effects, a sample of 6,166 homogenous online auctions, conducted on the respective eBay websites in Germany, the UK, and the US, is analyzed. The results suggest that either the effects of reputation and product information variables vary significantly across countries or that different variables have an impact on prices in different countries. It can be concluded that country-specific institutional frameworks influence bidder behavior in international online auction markets.reputation, information, online auctions, cross-country studies

Statistical properties of online auctions

We characterize the statistical properties of a large number of online auctions run on eBay. Both stationary and dynamic properties, like distributions of prices, number of bids etc., as well as relations between these quantities are studied. The analysis of the data reveals surprisingly simple distributions and relations, typically of power-law form. Based on these findings we introduce a simple method to identify suspicious auctions that could be influenced by a form of fraud known as shill bidding. Furthermore the influence of bidding strategies is discussed. The results indicate that the observed behavior is related to a mixture of agents using a variety of strategies.Comment: 9 pages, 4 figures, to be published in Int. J. Mod. Phys.

The Timing of Bid Placement and Extent of Multiple Bidding: An Empirical Investigation Using eBay Online Auctions

Online auctions are fast gaining popularity in today's electronic commerce. Relative to offline auctions, there is a greater degree of multiple bidding and late bidding in online auctions, an empirical finding by some recent research. These two behaviors (multiple bidding and late bidding) are of ``strategic'' importance to online auctions and hence important to investigate. In this article we empirically measure the distribution of bid timings and the extent of multiple bidding in a large set of online auctions, using bidder experience as a mediating variable. We use data from the popular auction site \url{www.eBay.com} to investigate more than 10,000 auctions from 15 consumer product categories. We estimate the distribution of late bidding and multiple bidding, which allows us to place these product categories along a continuum of these metrics (the extent of late bidding and the extent of multiple bidding). Interestingly, the results of the analysis distinguish most of the product categories from one another with respect to these metrics, implying that product categories, after controlling for bidder experience, differ in the extent of multiple bidding and late bidding observed in them. We also find a nonmonotonic impact of bidder experience on the timing of bid placements. Experienced bidders are ``more'' active either toward the close of auction or toward the start of auction. The impact of experience on the extent of multiple bidding, though, is monotonic across the auction interval; more experienced bidders tend to indulge ``less'' in multiple bidding.Comment: Published at http://dx.doi.org/10.1214/088342306000000123 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

E-loyalty networks in online auctions

Creating a loyal customer base is one of the most important, and at the same time, most difficult tasks a company faces. Creating loyalty online (e-loyalty) is especially difficult since customers can ``switch'' to a competitor with the click of a mouse. In this paper we investigate e-loyalty in online auctions. Using a unique data set of over 30,000 auctions from one of the main consumer-to-consumer online auction houses, we propose a novel measure of e-loyalty via the associated network of transactions between bidders and sellers. Using a bipartite network of bidder and seller nodes, two nodes are linked when a bidder purchases from a seller and the number of repeat-purchases determines the strength of that link. We employ ideas from functional principal component analysis to derive, from this network, the loyalty distribution which measures the perceived loyalty of every individual seller, and associated loyalty scores which summarize this distribution in a parsimonious way. We then investigate the effect of loyalty on the outcome of an auction. In doing so, we are confronted with several statistical challenges in that standard statistical models lead to a misrepresentation of the data and a violation of the model assumptions. The reason is that loyalty networks result in an extreme clustering of the data, with few high-volume sellers accounting for most of the individual transactions. We investigate several remedies to the clustering problem and conclude that loyalty networks consist of very distinct segments that can best be understood individually.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS310 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Trust and Experience in Online Auctions

This paper aims to shed light on the complexities and difficulties in predicting the effects of trust and the experience of online auction participants on bid levels in online auctions. To provide some insights into learning by bidders, a field study was conducted first to examine auction and bidder characteristics from eBay auctions of rare coins. We proposed that such learning is partly because of institutional-based trust. Data were then gathered from 453 participants in an online experiment and survey, and a structural equation model was used to analyze the results. This paper reveals that experience has a nonmonotonic effect on the levels of online auction bids. Contrary to previous research on traditional auctions, as online auction bidders gain more experience, their level of institutional-based trust increases and leads to higher bid levels. Data also show that both a bidder’s selling and bidding experiences increase bid levels, with the selling experience having a somewhat stronger effect. This paper offers an in-depth study that examines the effects of experience and learning and bid levels in online auctions. We postulate this learning is because of institutional-based trust. Although personal trust in sellers has received a significant amount of research attention, this paper addresses an important gap in the literature by focusing on institutional-based trust

Impact of Consumer Characteristics and Hedonic Shopping Motivations on Online Auctions

Online auctions present unique characteristics in the consumer decision making process that raise new issues related to consumer shopping behaviors in auction-based purchases. The present research examined the relationship between hedonic shopping motivations and shopping values in online auctions and found that the hedonic shopping motivations are important predictors of shopping values in online auctions. This research also defined consumer characteristics that influence hedonic shopping motivations. Hedonic shopping motivations combined with consumer characteristics are critical factors of consumer shopping evaluation in the online auction environment. The results of this study also revealed that consumers’ shopping evaluation (i.e., shopping value) positively influence their preferences for online auctions. Preferences are important factor to form behavioral intentions in online auctions. The primary contribution of this dissertation is that it provides an empirically tested theoretical foundation on the components of consumer characteristics, hedonic shopping motivations, and shopping values in online auction environment. Contrary to previous studies that focused on utilitarian benefits of online shopping, this study focused on hedonic aspects of shopping which may explain the success of online auctions in the current retail market