Auctions on the Internet provide a new source of data on how bidding is influenced by the detailed rules of the auction. Here we study the second-price auctions run by eBay and Amazon, in which a bidder submits a reservation price and has this (maximum) price used to bid for him by proxy. That is, a bidder can submit his reservation price (called a proxy bid) early in the auction and have the resulting bid register as the minimum increment above the previous high bid. As subsequent reservation prices are submitted, the bid rises by the minimum increment until the second-highest submitted reservation price is exceeded. Hence, an early bid with a reservation price higher than any other submitted during the auction will win the auction and pay only the minimum increment

A great deal of late bidding has been observed on intemet auctions such as eBay, which employ a second price auction with a fixed deadline. Much less late bidding has been observed on intemet auctions such as those run by Amazon, which employ similar auction rules, but use an ending role that automatically extends the auction if necessary after the scheduled close until ten minutes have passed without a bid. This paper reports an experiment that allows us to examine the effect of the different ending rules under controlled conditions, without the other differences between intemet auction houses that prevent unambiguous interpretation of the field data. We find that the difference in auction ending rules is sufficient by itself to produce the differences in late bidding observed in the field data. The experimental data also allow us to examine how individuals bid in relation to their private values, and how this behavior is shaped by the different opportunities for learning provided in the auction conditions.

marketing departments around the country, where I presented an early version of this paper in

indebted to the editor and three anonymous Journal of Marketing Research referees for their insightful and valuable comments on previous versions of this article. The authors would like to thank Vrinda Kadiyali, Vithala Rao, and

Recently fixed pricing and auctions have been brought together in a new pricing format that offers bidders the option of prematurely ending an auction at a fixed price. eBay calls this a “Buy-it-Now ” auction, uBid calls this “UBuy it ” and Yahoo auctions refer to this as a “Buy Now”. The growing popularity of auctions presents an interesting pricing decision for managers: whether to sell at a fixed price, sell in a regular auction, or to sell through a buy-it-now auction. By posting a fixed-price at an auction, the seller provides customers a convenient option to buy the item directly without bidding. However, in doing so, the seller implicitly imposes a maximum bidding level that could potentially limit revenue. This paper studies this new pricing format and answers the following research questions: why is fixed price used at traditional auctions, will buy-it-now increase the seller’s profit, how is an optimal price determined, and how is the buy-it-now decision influenced by key factors such as the customer’s cost of participating in the auction, the seller’s reserve price, and the number of potential customers. Our results show that buy-it-now auctions can increase both customers ’ utility and sellers ’ profit under certain conditions. We empirically test the predictions of our theoretical model using data collected from eBay. 1

Past research has explored how external reference prices affect consumer perceptions (i.e., their "internal" reference price) and consequently the price they are willing to pay for a product or service. Historically, research has examined the effects of exposure to prices for the same product, the same brand, or at the very least products within the same category as price cues. This research explores the effects of incidental prices on the consumer's willingness to pay. We define incidental prices as prices advertised, offered or paid for unrelated goods -- goods that are neither viewed by the seller nor the buyer as an indicator of what may or should be paid for the item in question. More specifically, we examine how the prices for products that the buyer encountered unintentionally can affect the buyer's willingness to pay for the product they intend to buy, or the focal good. This research demonstrates how incidental prices can act as an anchor that serves to shift an individual's reservation price, or the upper bound of what they are willing to pay, often unbeknownst to the buyer. Unlike the external reference prices studied thus far, an incidental price is neither intended nor presumed to affect consumer decision-making. KEY WORDS: Pricing, Reference Price, Anchoring, Auctions, Decision Making, Willingness-to-Pay, Consumer Behavior, Incidental Price Imagine you are at a classic car auction attempting to buy a classic 1966 Chevy Corvette Roadster. The car on the block before the Corvette upon which you intend to bid is a 1933 Duesenberg J Murphy Roadster. You have absolutely no interest in that car and do not intend to bid on it. The Duesenberg sells for $675,000 to a collector whom you've never met. The next car on the block is the '66 Corvette. Its blue book value...

Abstract – In traditional auctions, it is often assumed that bidders are a homogenous group. However, since most online bidders are average consumers instead of professional bidders, we suspect that online bidders are a heterogeneous group. The purpose of this paper is to explore the types of online bidders based on their real bidding behaviors in a popular auction site. A cluster analysis is employed and four types of online bidders are finally identified in a private value auction, namely, goal-driven bidders, experiential bidders, playful bidders, and opportunistic bidders. The profile and performance of each group are also discussed.

Electronic commerce continues to grow at an impressive pace despite widely publicized failures by prominent online retailers. According to the Department of

Past research has explored how external reference prices affect consumer perceptions (i.e., their “internal ” reference price) and consequently the price they are willing to pay for a product or service. Historically, research has examined the effects of exposure to prices for the same product, the same brand, or at the very least products within the same category as price cues. This research explores the effects of incidental prices on the consumer’s willingness to pay. We define incidental prices as prices advertised, offered, or paid for unrelated goods – goods that are neither viewed by the seller nor the buyer as an indicator of what may or should be paid for the item in question. More specifically, we examine how the prices for products that the buyer encountered unintentionally can affect the buyer’s willingness to pay for the product they intend to buy, or the focal good. This research demonstrates how incidental prices can act as an anchor that serves to shift an individual’s reservation price, or the upper bound of what they are willing to pay, often unbeknownst to the buyer. Unlike the external reference prices studied thus far, an incidental price is neither intended nor presumed to affect consumer decision-making.

The popularity of online auctions is a relatively recent phenomenon. eBay, the leading online auctioneer, is one of the most popular websites and has become synonymous for trading online. Wine is a good well suited for online trading and there is a huge market for it on eBay's websites in France and Germany. Buying and selling Bordeaux reds is especially popular on eBay in Germany where about 2,000 offers of single bottles or bundles are available at any given time. In Germany, many wine enthusiasts and traders have discovered online auctions to trade wine and availability of Bordeaux is even larger than on the French site. In this paper, I analyze the factors that determine online auction prices for top range Bordeaux sold on the German eBay site. I have collected a data set reporting about 2,200 transactions. I evaluate the impact of eBay specific data such as auction length, weekday of weekend auction ending, buyer experience, buyer interest (number of bids), seller reputation and experience, initial price, private auctions impact (bidder identity remains undisclosed), picture effects or whether relevant quality information is given or not. Many sellers report Robert Parker's point rating when listing their Bordeaux. Thus, I use Parker's rating and other quality signals such as the 1855 classification, brands, regional origin, and wine age as well as the impact of eBay specific data to build a hedonic pricing model. This application is unique because eBay auction data for wine has not been evaluated in the literature. Very few studies have used data collected directly from internet auction sites to analyze customer bidding