Sustainable Reputations with Rating Systems

In a product choice game played between a long lived seller and an infnite sequence of buyers, we assume that buyers cannot observe past signals. To facilitate the analysis of applications such as online auctions (e.g. eBay), online shopping search engines (e.g. BizRate.com) and consumer reports, we assume that a central mechanism observes all past signals, and makes public announcements every period. The set of announcements and the mapping from observed signals to the set of announcements is called a rating system. We show that, absent reputation effects, information censoring cannot improve attainable payoffs. However, if there is an initial probability that the seller is a commitment type that plays a particular strategy every period, then there exists a finite rating system and an equilibrium of the resulting game such that, the expected present discounted payoff of the seller is almost his Stackelberg payoff after every history. This is in contrast to Cripps, Mailath and Samuelson (2004), where it is shown that reputation effects do not last forever in such games if buyers can observe all past signals. We also construct .nite rating systems that increase payoffs of almost all buyers, while decreasing the seller’s payoff.Reputations, Rating Systems, Online Reputation Mechanisms, Disappearing Reputations, Permanent Reputations. JEL Classification Numbers: D82

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

Jump bidding does not reduce prices: Field-experimental evidence from online auctions

One feature of online auctions that has attracted much interest is jump bidding, whereby a bidder raises the price by more than what is needed to become the highest bidder. The effects of jump bidding on the final selling price are unknown because past observational studies could not separate bidder interest from bidder behavior. Our study involves an in vivo experiment during live auctions on a large online auction platform. We intervened early in auctions at low, non-competitive price levels, either through jump bidding or through incremental bidding, and randomly varied the magnitude of our intervention. In contrast to leading theories in the auction literature, which predict a negative effect of jump bidding on the final selling price, we find that our jump bidding intervention has no effect on the final selling price

Information Disclosure in Open Non-Binding Procurement Auctions: an Empirical Study

The outcome of non-binding reverse auctions critically depends on how information is distributed during the bidding process. We use data from a large European procurement platform to study the impact of different information structures, specifically the availability of quality information to the bidders, on buyers' welfare and turnover of the platform. First we show that on the procurement platform considered bidders indeed are aware of their rivals' characteristics and the buyers preferences over those non-price characteristics. In a counterfactual analysis we then analyze the reduction of non-price information available to the bidders. As we find, platform turnovers in the period considered would decrease by around 30%, and the buyers' welfare would increase by the monetary equivalent of around 45% of turnover of the platform

Determinants and Effects of Reserve Prices in Hattrick Auctions

We use a unique hand collected data set of 6,258 auctions from the online football manager game Hattrick to study determinants and effects of reserve prices. We find that chosen reserve prices exhibit both very sophisticated and suboptimal behavior by the sellers. On the one hand, reserve prices are adjusted remarkably nuanced to the resulting sales price pattern. However, reserve prices are too clustered at zero and at multiples of e 50,000 as to be consistent with fully rational behavior. We recover the value distribution and simulate the loss in expected revenue from suboptimal reserve prices. Finally, we find evidence for the sunk cost fallacy as there is a substantial positive effect on the reserve price when the player has been acquired previously

Smoothing sparse and unevenly sampled curves using semiparametric mixed models: An application to online auctions

Functional data analysis can be challenging when the functional objects are sampled only very sparsely and unevenly. Most approaches rely on smoothing to recover the underlying functional object from the data which can be difficult if the data is irregularly distributed. In this paper we present a new approach that can overcome this challenge. The approach is based on the ideas of mixed models. Specifically, we propose a semiparametric mixed model with boosting to recover the functional object. While the model can handle sparse and unevenly distributed data, it also results in conceptually more meaningful functional objects. In particular, we motivate our method within the framework of eBay's online auctions. Online auctions produce monotonic increasing price curves that are often correlated across two auctions. The semiparametric mixed model accounts for this correlation in a parsimonious way. It also estimates the underlying increasing trend from the data without imposing model-constraints. Our application shows that the resulting functional objects are conceptually more appealing. Moreover, when used to forecast the outcome of an online auction, our approach also results in more accurate price predictions compared to standard approaches. We illustrate our model on a set of 183 closed auctions for Palm M515 personal digital assistants

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.

Functional Data Analysis in Electronic Commerce Research

This paper describes opportunities and challenges of using functional data analysis (FDA) for the exploration and analysis of data originating from electronic commerce (eCommerce). We discuss the special data structures that arise in the online environment and why FDA is a natural approach for representing and analyzing such data. The paper reviews several FDA methods and motivates their usefulness in eCommerce research by providing a glimpse into new domain insights that they allow. We argue that the wedding of eCommerce with FDA leads to innovations both in statistical methodology, due to the challenges and complications that arise in eCommerce data, and in online research, by being able to ask (and subsequently answer) new research questions that classical statistical methods are not able to address, and also by expanding on research questions beyond the ones traditionally asked in the offline environment. We describe several applications originating from online transactions which are new to the statistics literature, and point out statistical challenges accompanied by some solutions. We also discuss some promising future directions for joint research efforts between researchers in eCommerce and statistics.Comment: Published at http://dx.doi.org/10.1214/088342306000000132 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Choosing between Auctions and Negotiations in Online B2B Markets for IT Services: The Effect of Prior Relationships and Performance

The choice of contract allocation mechanism in procurement affects such aspects of transactions as information exchange between buyer and supplier, supplier competition, pricing and, eventually, performance. In this study we investigate the buyer’s choice between reverse auctions and bilateral negotiations as an allocation mechanism for IT services contracts. Prior studies into allocation mechanism choice focused on factors pertaining to discrete exchange situation, such as con-tract complexity or availability of suppliers. We broaden the research by focusing on buyers’ past exchange relationships with vendors. Based on the literature on the economics of contracting and agency theory, we hypothesize that prior re-peat interaction with vendors favors the use of negotiations over auctions in the next transaction, while the need to explore the marketplace due to buyer’s inexperience or dissatisfaction with vendor’s performance in the most recent project leads to the use of auctions instead of negotiations. We find support for these hypotheses in a longitudinal dataset of 2,081 IT projects realized by 91 repeat buyers at a leading online services marketplace over a period of eight years. Taken together, the results show that analyzing B2B auctions and negotiations should move beyond analyzing discrete instances and instead analyze them in the context of the individual firm’s history and supplier strategy.outsourcing;IT services;online marketplace;reverse auctions