{'fraction_non_alphanumeric': 0.02860047886122864, 'fraction_numerical': 0.01425924103775132, 'mean_word_length': 4.857684918347743, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Internet Auctions

Internet auctions are common in nearly all consumer categories. Hence, it is not surprising that a great deal of research has emerged on the topic in recent years. New design and format considerations and a wealth of available data from various platforms provide new questions and promising research opportunities for marketing researchers. This monograph begins with the introduction of the basic settings, concepts, and processes that are the building blocks of auction research. It then focuses on the transition from pre-Internet auction research to more recent topics. Special attention is given to research opportunities as well as to experimental methods that can provide both laboratory and field data to answer important questions. The survey reviews recent empirical and theoretical works on Internet auctions with a focus on Internet auction design, formats, and features that are currently debated in the marketing literature. Some of these issues are extensions of general auction topics, but the findings can be quite different in Internet environments. We touch on new design features that are particularly relevant to Internet auctions such as feedback ratings, buy-it-now options, and different closing rules. We also look at strategic and behavioral models that are shaping marketing research on Internet auctions. Particular emphasis is given to behaviors that are relevant in offline environments but take on new meanings and forms in Internet auction environments.


Introduction
Online consumer auctions represent hundreds of thousands of retailers, over half a million storefronts, millions of individual sellers, tens of millions of active buyers, hundreds of millions of items sold weekly, and tens of billions of dollars in annual sales (see Park and Wang, 2009, for some of the eBay figures). In addition to their spectacular reach into all consumer segments, online auctions have greatly increased the variety of goods and services that can be bought and sold using auctions, expanded the ways in which auctions can be conducted, and created altogether new uses for auctions (Pinker et al., 2003). As a result, there has been hectic research activity devoted to analyzing data from online auctions and building theoretical models of their design and use. It is not surprising that online auctions garner a great deal of attention in many academic fields, including marketing. What may be surprising, however, is how many puzzles remain unresolved in this area. The field of auctions has been an active field for nearly half a century and yet the wealth of knowledge it has generated does not begin to scratch the surface of questions being asked. Online auctions have led to the creation of many new auction design features relative to traditional auctions, such as proxy bidding machines, feedback mechanisms, and buy-now prices. Given the increased importance of Internet auctions for retailers, there is an increased need to study these new features (Cheema et al., 2005;Haruvy et al., 2008). In addition to numerous new features, online auctions operate in a unique environment that allows bidders easy search and access to information and at the same time requires unparalleled levels of trust (and mistrust).
Hence many of the questions we ask relate to search and trust. Here is a sample of questions we touch on in this monograph: Why do buyers often pay a price in an online auction that exceeds the listed price by other popular online retailers (like Amazon) or in another concurrent auction on the same site? What motivates buyers to provide feedback on sellers for the benefit of other buyers, to trust feedback by other buyers, and to trust the seller based on such feedback? Why do sellers, who are allowed to list their items for up to seven days on eBay and are not being charged for the duration of the auction, often choose durations shorter than seven days? Why do sellers often provide a buyit-now option which caps the maximum possible price they can obtain?
Before addressing these interesting questions and others, it is worthwhile addressing perhaps the biggest puzzle relating to online auctions. Specifically, how did we get to the point where online auctions are a major component of the online retail sector? How did a platform, seemingly intended for collectors, come to dominate online retailing?
The rapid growth of online consumer auctions is often attributed to the ability of the Internet to bring together buyers and sellers from geographically dispersed markets as well as to the relatively low cost of search on the Internet (Bajari and Hortaçsu, 2004;Klein and O'Keefe, 1999;Pinker et al., 2003). This has enabled active markets whose existence, scope, and size would be limited without the ability of buyers and sellers to find each other and easily transact. Collectibles, used items, and novelties are some examples of such markets, but even traditional consumer goods such as books, CDs, toys, baby products, and appliances can be found in Internet auctions due to the ease of search and easy access.
An equally important factor in the success of Internet auctions is the efficiency they bring to markets, allowing buyers and sellers to bypass a number of intermediaries that might otherwise be crucial. Bajari and Hortaçsu (2004) argue that online auction sites substitute for more traditional market intermediaries such as specialty dealers. According to Pinker et al. (2003), the easy access to information on electronic auction sites allows sellers to base pricing decisions on past data and to choose design attributes.
One should also not dismiss the role that promotion played in the dissemination of online consumer auctions in a short span of a few years to about one-third of online users. In the early days of online auctions, eBay spent a great deal of resources on identifying heavily searched products (e.g., Furby) and ensuring that search keywords would direct the searcher to the eBay site. eBay also entered into expensive yet critical alliances, such as a key alliance with America Online, that directed traffic to its site. These efforts involved a great deal of resources, and should not be ignored in any analysis on the diffusion of online consumer auctions.
Research in the field of Internet auctions moves at a lightning speed compared to other empirical and theoretical research. Some reasons include data availability that is truly unparalleled, a nearly infinite number of possible format and design choices, and the rapid evolution of the auction platforms themselves. Moreover, some of the questions that were of greatest interest a few years ago are of lesser interest and importance today and some of the questions that are of importance today were not yet conceived then. Auction platforms popular when many past articles were written (Amazon, Yahoo, Freemarkets) no longer operate, or are much more limited in scope. The closing rules employed by these auction platforms are likewise no longer of interest. Feedback systems have changed. Fraud detection systems are far more evolved and effective. And the list goes on.
Our expertise is on auction field experiments, where we believe a great deal of insight lies. Hence, the focus and organization of the present survey is markedly different from previous reviews and is more suitable as a comprehensive guide of the type of research that is pursued in field experiments, with a somewhat different focus and set of tools. Accordingly, the background assumed here is very different, with somewhat less focus on economic theory and greater focus on managerial questions.

Background
Markets can generally be described as two-sided exchanges, where multiple buyers and multiple sellers interact and price is determined at the intersection of the demand and supply curves. An auction, in contrast, is a one-sided exchange, where only one side (say buyers) enters offers and an allocation rule determines the allocation of the goods being auctioned to the bidders based on some ranking of their bids. This rule also determines the price to be paid.
In commonly studied auction formats, one item is being auctioned off. In online consumer auctions, the buyer with the highest bid for the item is declared the winner and this buyer pays his own bid or in some cases (e.g., eBay), the second-highest bid.

The Four Standard Auction Formats
There are four auction formats which are known in the auction literature as the "standard auction types" (e.g., Klemperer, 1999). These are the English auction, the Dutch auction, first-price sealed-bid, and second-price sealed-bid formats. They are all revenue-equivalent under mild assumptions which we will list shortly.
The best known of the standard auction formats is the English auction, also known as the ascending bid format. In this format, buyers enter bids dynamically and incrementally over the course of the auction, where each bid must exceed the previously highest observed bid. The winner is the bidder with the highest bid at the end of the auction and this bidder pays his own bid. Every bidder has a dominant (best) strategy of bidding incrementally up to his true valuation for the item. As long as bidders bid incrementally, all bidders, except the winning bidder, will have a last bid on record that is within a welldefined distance from their true valuations. The winning bid will be one bidding increment above the second-highest bid. The assumption about final bids representing individual valuations is a commonly used assumption in empirical research with observed bids -eBay in particular. Haile and Tamer (2003) challenge that assumption as unreasonable. In contrast, they make two simple and less restrictive assumptions regarding the relationship between bids and valuations: (1) A bidder never bids more than his own valuation, and (2) a bidder will not let another bidder win at a price lower than his own valuation. These two assumptions are generally noncontroversial in the empirical literature and imply that observed bids provide an upper and a lower bound on each bidder's valuation.
The Dutch auction is the other dynamic format in the group of four standard formats. In that format, prices begin at some high initial price and decline gradually over time by some increment. The auction stops when any bidder claims the item at the current price. The bidder then pays that price.
Sealed bid formats are formats where each bidder makes a single bid which is not shown to other bidders. They are commonly used as an alternative to dynamic formats like the English auction and the Dutch auction. Two common sealed bid formats are the first-price sealedbid and second-price sealed-bid formats. In the first-price sealed-bid format, each bidder privately enters a single bid and the bidder with the highest bid wins and pays his own bid.
The second-price sealed-bid format is a format wherein the highest bidder wins but pays only the second-highest bid. This format is theoretically very appealing, making it a common benchmark in auction research. In this format, it is easy to see that the dominant (best) strategy for any bidder is to bid his true valuation for the item. To illustrate, stating a price below the true valuation will result in the possibility of losing the item to a competing bid that is lower than one's own valuation. Stating a price above the true value will result in the possibility of winning the item at a price above one's true valuation. Thus, there is no advantage in misrepresenting one's valuation since the price paid is not one's stated price. This dominance of truthful revelation is critical in almost any theoretical or empirical analysis of second price auctions because it allows for the characterization of bidder valuations and consequently revenues.

Revenue Equivalence
The different auction formats reviewed above are all efficient and revenue-equivalent under certain restrictive assumptions which are detailed in Milgrom (1989). This property is generally known as the revenue equivalence theorem (Vickrey, 1961).
The revenue equivalence theorem states that if buyers are all riskneutral and have independent private values drawn from a continuous distribution, then any mechanism in which (i) the prize always goes to the buyer with the highest valuation and (ii) any bidder with the lowest feasible valuation expects zero surplus (which can often be managed through the auctioneer selecting an appropriate reserve price), yields the same expected revenue and results in each bidder making the same expected payment as a function of her valuation.
The three key terms which are critical in that theorem are (1) risk neutrality, (2) independent private values, and (3) reserve price. They are worth defining at this stage because they will repeat often in the discussions to follow.
A bidder is risk-neutral if he is indifferent between a lottery and the expected value of that lottery. A bidder is risk averse if he prefers a certain payoff to a lottery which yields the same expected payoff. If the bidder prefers the lottery to the lottery's expected value, then he is risk loving. An auction is a lottery because its outcome is probabilistic. That is, each bidder has some probability between 0 and 1 of winning. Sometimes bidders behave as risk lovers and take risks in auctions (sniping, to be discussed, is one such risk). Overbidding in some consumer auctions (specifically first-price sealed-bid) is attributed to risk aversion manifested by the bidder willingness to sacrifice some expected payoff for the sake of reducing the probability of losing the auction. When bidders are risk averse they should theoretically overbid in some auction formats (like first-price sealed-bid) but not in others (like second-price sealed-bid or English auction).
Bidders are said to have independent private values when other bidders' information or values for the object are irrelevant to them. This assumption is typically reasonable for goods that are used for private consumption and have no resale value. The revenue equivalence and many other nice properties of auctions break down when bidders' values are not independent and private.
There are other situations where the assumption of independent private values is not reasonable because, for example, of resale opportunities. Collector's items, like coins, stamps, sports memorabilia, antiques, and collectible figurines, are commonly traded in online consumer auctions. While each collector may have a different willingness to pay for an item, the values that collectors have depend on or are related to the values other collectors have -in other words, they have affiliated values.
In some instances, buyers all have the exact same value for an item but information regarding that value is dispersed among bidders. Such instances are often called common values.
A reserve price is a minimum price the seller allows bidders to submit. Bidders with low valuations will thus be excluded by the reserve price. The revenue equivalence critically depends on the property that the bidder with the lowest possible valuation (the lowest end of the valuation support) obtains no surplus and this can often be managed with the imposition of an appropriate reserve price.

Proxy Bidding
Unlike other auctions, online consumer auctions are computerized so they generally involve the existence of a computerized agent that can bid on one's behalf. eBay uses a proxy bidding system. Under proxy bidding, each bidder enters a maximum bid in a text box labeled "Your maximum bid." As subsequent maximum bids are submitted by other bidders, the bidder's bid rises by the minimum increment until reaching the maximum bid. Hence, a bidder could enter his willingness to pay for the item as the maximum bid and let the proxy bidding take place. 1 When all bids have been submitted, the bidder with the highest maximum bid wins the item and pays the second highest maximum bid, plus a bid increment. Bidders can revise their maximum bids upward anytime before the closing of the auction. Bidding strategy can be quite flexible. Bidders can state their highest willingness to pay early on and let the proxy bidding feature of eBay bid incrementally for them, or they can bid incrementally themselves. They can bid continually or intermittently, and they can bid actively or rely on automated reminders when they are outbid.
The bid screen on eBay contains a box like the one shown in Figure 2.1.
In that example, the current high bid is $27 and the current bid increment is $1. Note that the system requests the bidder's maximum bid. Suppose that the currently winning bidder had entered a maximum bid of $31. His current bid is showing at $27 which means that the previous high bidder had put in a maximum bid of $26. First, let's see what would happen if the new entrant puts a price between $28 (the minimum currently allowed) and $31 (the hypothetical maximum bid of the currently winning bidder). Suppose the entrant puts in a maximum bid of $29. He will instantly get a message telling him that he had been 1 Many bidders, however, opt instead to bid some increment above the currently displayed high price and revise their bids as they are outbid. In addition, some bidders may submit last-minute bids in the hope that the competition will not have time to respond, a practice known as sniping.
outbid and to please try again. The winning bid will be updated to $1 above the new bid -in this example $30. If, on the other hand, the entrant put in a bid above $31 (the hypothetical winning bidder's max bid), he will become the new winner and the winning bid will be shown at $32 (the increment above the old highest maximum) or the new maximum bid, whichever is smaller. This bidding system is a hybrid between the ascending bid format and the second-price sealed-bid format. Because sniping excluded, the economically relevant endgame of each auction resembles a second-price sealed-bid auction, researchers often treat the last bid observed by any non-winning bidder as his revealed valuation for the item. Zeithammer and Adams (2010) call this practice the "sealed bid abstraction" and investigate whether this is an empirically valid approach for estimating demand from several high bids in each auction. If the abstraction were a reasonable assumption, then the conditional distribution of the top bid given the second-highest bid would be just a right tail of the distribution of valuations, allowing demand estimation without knowing the number of bidders (Song, 2004). Zeithammer and Adams (2010) proposed several tests of the sealed bid abstraction based on the magnitude and timing of the top two proxy bids in each auction, and the tests consistently rejected the abstraction across three diverse data sets from eBay (MP3 players, movies on DVD, and used cars). Specifically, the following empirical regularities are not consistent with interpreting the bids as if they came from a sealed-bid auction: the highest bid comes after the second-highest bid too often (in about two-thirds of the auctions), the difference between the top two bids is smaller when the highest bid comes after the second-highest bid and when both bids arrive later in the auction. Moreover, about 15% of the auctions end with the top two bids exactly one increment apart, suggesting some amount of incremental bidding. All of these findings replicate with surprising regularity across three different data sets spanning three years and diverse product categories. Most importantly for demand estimation, the top two bids do not look like the top two-order statistics of independent and identically distributed draws, and the demand estimates based on Song's (2004) approach are thus biased. To assess the magnitude of the bias, Zeithammer and Adams (2010) propose and estimate a model with heterogeneous bidding styles. The model assumes that although some bidders conform to the sealed-bid abstraction, other bidders bid in a reactive fashion. The results suggest that assuming the sealed-bid abstraction results in a large downward bias in demand estimates.
Two things are important to stress about this empirical rejection of the sealed bid abstraction. First, these tests are not generally implementable for eBay data sets scraped from the eBay webpage, because the tests use the highest proxy bid. Zeithammer and Adams (2010) obtained data directly from eBay on the winning maximum bid. Second, while Zeithammer and Adams (2010) clearly rejected the interpretation of the highest proxy bid as equal to the highest valuation; it is still possible that the second-highest proxy bid is equal or close to the second-highest valuation. Therefore, at least some bids may correspond to the private valuations of the bidders who submitted them. The rejection of the sealed bid abstraction suggests more incremental bidding as in an ascending English auction, where it is optimal to bid up to one's true valuation, and so it may still be reasonable to use a losing bidder's last bid as a rough measure of his valuation. A rejection of that approach would involve showing that some bidders either do not want to or do not get a chance to bid up to their valuation. For example, bidders may experience a transaction cost of bidding or they may switch to other concurrent auctions before reaching their maximum.
Despite the concern with the sealed bid abstractions, this abstraction is useful in auction research. For example, Bapna et al. (2008a) were able to estimate consumer surplus using the difference between bid of the highest bidder (which is normally not publically available and the authors obtain from a proprietary data set) and the bid of the second-highest bidder (the price paid) in a method equivalent to the sealed bid abstraction. Specifically, they assume a private value second-price sealed-bid auction setting. Based on a sample of 4,514 eBay auctions, they find that consumers extract a median surplus of at least $4 per eBay auction. This estimate is unbiased under the sealed bid abstraction, but it is a useful lower bound otherwise. They find that eBay's auctions generated at least $7.05 billion in total consumer surplus in 2003 and could generate up to $7.68 billion if the sealed-bid abstraction does not hold.

Auction Duration
An important feature of online auctions is participation by a geographically dispersed population of bidders over a period of several days. This has made auction duration a particularly salient attribute. While a traditional offline auction may last several minutes or even seconds, most online auctions run over several days. On eBay, sellers can choose between duration of 1, 3, 5, 7, or 10 days.
Most auction platforms, and certainly eBay, charge sellers a listing fee as well as a fee -often a percentage of the sale price -on the sale of the item. They do not charge for the length of the auction as long as bidders select from the standard menu of duration choices. Thus, it can be expected that if auction revenue was either monotonically increasing or monotonically decreasing in auction duration, one would see the duration at one of the two boundaries of allowed durations as well as little heterogeneity in auction duration. Instead, there is great heterogeneity in auction durations on eBay and many auctions are in the interior of the duration choice spectrum. While auction duration is often considered as an explanatory variable in empirical studies of 13 auctions (e.g., Wang et al., 2008a), its consideration may lead to seemingly contradictory conclusions in different studies. For example, Cox (2005) finds that longer auctions result in significantly higher prices, whereas Ariely and Simonson (2003) find that shorter duration auctions result in higher prices.
Theory is clearly needed but the building blocks are already in place. A good place to start in our opinion is by looking at the many theories proposed in the literature for buy-it-now prices (see relevant section). Those theories touch on buyer and seller impatience and risk aversion, competition, and price discrimination. However, since such theories have not been developed to answer the issue of duration, in this section we will highlight the two elements one has to tradeoff in deciding on the optimal duration for an auction. One element is the number of bidders in the auction. The other is competitive intensity, which tends to be greater in shorter duration auctions.
Number of bidders in the auction. It has been proposed that longer auctions attract more bidders (Cox, 2005). In general, if bidders' arrival time at the auction site is a random variable -bidders visit at different times -then longer auctions would be seen by more bidders and more bidders would participate. A higher number of bidders should in turn lead to higher selling prices (e.g., Bulow and Klemperer, 1996). It has also been proposed that a larger number of bidders may result in herd behavior resulting in higher prices (Dholakia et al., 2002). 1 Competitive Intensity. Impatience is an undeniable consideration in auction design (Matthews, 2004;Isaac et al., 2007;Kwasnica and Katok, 2007;Katok and Kwasnica, 2008;Wang et al., 2008a). 2 Impatience has implications for jump bidding -defined as bidding in increments greater than the minimum increment. Jump bidding is a commonly observed phenomenon in many auction settings, ranging from standalone consumer auctions to multiple unit auctions, including FCC and 3G spectrum auctions (Isaac et al., 2007). Kwasnica and Katok (2007) showed that when bidders are impatient -the opportunity cost of time is higher in one experimental manipulation in that study -bidders respond by choosing larger jump bids. A second argument is increased bidding activity as a result of excitement or competitive arousal in shorter auctions, also known as frenzy (Ariely and Simonson, 2003;Häubl and Popkowski Leszczyc, 2003) and auction fever (see review in Park and Wang, 2009). Frenzy refers to a mental state induced by the dynamic interaction among bidders in an ascending bid auction and characterized by a high level of excitement, a strong sense of competition, and an intense desire to win (Häubl and Popkowski Leszczyc, 2003). Competitive arousal is also supported by research into price dynamics. Jank et al. (2008, andsee Dass et al., 2009) find that competition reduces the need to include price velocity (speed of price increases) as an explanatory variable, suggesting that bidder competition may be a major source of the price dynamics, consistent with notions of competitive arousal. Bapna et al. (2008b) similarly find that longer auctions can negatively affect the price dynamics because of a "bidding drought" which typically occurs in the middle of the auction.
Haruvy and Popkowski Leszczyc (2010a) conducted field experiments on auctions of different durations. In one set of experiments on eBay, they ran pairs of auctions for identical items simultaneously with the same ending time, but with a different starting time. In another set of auctions on a local auction platform they ran pairs of auctions for identical items sequentially.
The eBay treatments involved nine different products ranging from baby products to popular books. The eBay durations were one day for one auction in the pair and three days for the other auction in a pair. As a control, auction pairs were run with three days in both auctions.
The auctions on the local site involved a bidding format as on eBay but were locally advertised through posters and newspaper advertisements. Unlike eBay, where bidders arrived in a more or less random arrival process, on the local auction site auctions ran on specific dates and bidders were informed via e-mail that was sent to a limited pool of several thousand registered users. A simple design was used where 164 different pairs of auctions (items were identical within a pair of auctions but different between pairs of auctions) were sold sequentially either in auctions with one-day or ten-day durations. The study was conducted in two batches (distinct time periods) where each of the items was sold in both a one-day auction and a ten-day auction. Within each batch, half of the products were randomly selected to be sold first in a oneday auction, followed by a ten-day auction; the other half of products are first sold through a ten-day auction, followed by a one-day auction. The items included electronics, computer products, musical CDs, health and beauty, tools, toys, collectibles, handicrafts, jewelry, gift certificates, and household items.

Results for eBay Auctions
Summary statistics for both studies are provided below. For each experimental condition, Table 3.1 displays the average number of bids entered, average number of bidders, and average selling price. Note that the eBay data come from sets of two simultaneous auctions with identical ending times but different durations.
On average bidders in the one-day eBay auctions paid on average approximately 11% less than bidders in the three-day auctions and this was significant in a pair-wise t-test, with a p-value of 0.001. This coincided with significantly more bidders and more bids in the longer auctions. In contrast, in the local auction site (where bidder arrival process was more concentrated) one-day auctions result in a 16.6% increase in the average price over their longer counterparts. In contrast to the eBay study, shorter auctions here attracted slightly more bidders and generated more bids, although these differences were not statistically significant. The main difference in individual bidder behavior across durations is in the jump bids which are substantially larger in short durations. The average jump is significantly different, $3.20 vs. $2.70, for durations 1 and 10, respectively (p = 0.008). Moreover, the size of the jump bid is negatively correlated with the response time of the previous bidder's bid. A regression showed that when one accounts for the average jump in an auction, the duration is no longer a significant explanatory variable in predicting final price. That is, the effect of duration on final price is entirely mediated by the average magnitude of jump bids in the auction.
The results by Haruvy and Popkowski Leszczyc (2010a) thus replicated the seemingly contradictory findings in the literature. In some settings, longer duration can result in higher prices and this relationship is significant. In other settings they can result in lower prices and this is also significant. There are several differences between the two settings. In our opinion the main difference is in the fact that on eBay the bidder arrival process is random. Hence, on eBay longer auctions resulted in more bidders and therefore in higher prices. In contrast, in the local auction site bidders arrived for a specific batch via e-mail invitation and so the number of bidders was not sensitive to auction duration. Once that effect was eliminated, the elements of competitive arousal were more dominant and resulted in the anticipated higher prices for shorter auctions. In summary, duration is consequential but the direction of duration's impact on final prices critically depends on both the arrival process of bidders and the opportunity cost of time for the bidders.

Buy-It-Now Price
One of the most perplexing puzzles in online auctions is the buy-it-now price (BNP) which allows a bidder to prematurely end the auction by agreeing to buy at a fixed price. BNP constitutes a puzzle, because it effectively places a ceiling on the maximum price the seller may obtain (see related argument and survey in Park and Wang, 2009). Thus, auctioneers who seek to maximize auction outcomes should not provide a BNP option (Buddish and Takeyaman, 2001).
BNPs are ubiquitous on eBay and as well as on Yahoo and Amazon (when these firms competed with eBay). Approximately half of eBay auctions offer a BNP option and these auctions account for about a third of sales volume (Wang et al., 2008c;Popkowski Leszcyc et al., 2009). In reference to another auction website -one of the largest Internet auction sites in Korea - Park and Bradlow (2005) reported that 90% of laptop auctions offered BNPs.
A BNP may be either permanent or temporary (Gallien and Gupta, 2007). A permanent BNP remains available until the auction ends (similar to Amazon and Yahoo auctions), while a temporary BNP (on eBay) disappears as soon as a binding bid has been placed. An eBay bidding window for an auction with a BNP is shown in Figure 3.1.
Since this is an important puzzle in online auction research, many theories have been proposed. However, we classify such theories into broad sets of explanations, as indicated in Table 3.2 below.

Impatience
One reason proposed for the usage of BNPs is that bidders may execute a BNP to save time or the effort of bidding in an auction. As Internet auctions tend to last for several days, time-sensitive bidders are willing  Kirkegaard and Overgaard (2003) Reference price BNP signals quality and anchors prices higher.

Seller Costs of Posting a Price and Auctioning
Zeithammer (2006) focus on explaining the coexistence of posted price items, auctions, and BNP on eBay. They propose that neither finding the optimal posted or BNP price nor holding an auction is costless to the seller: finding the optimal posted price involves product-category expertise as well as demand and competition research, while auctioning involves a delay needed to gather bidders and risk of getting a low price. These costs differ in the way they accumulate across multiple objects in a seller's inventory: the demand research only needs to be done once for a batch of identical items, while the costs of auctioning accrue per auction. Therefore, sellers with large and piece-wise homogeneous inventories should use the posted price (called "fixed price" by eBay) mechanism or BNP, whereas less sellers with smaller or heterogeneous inventories should use auctions. Since category expertise is more useful for posted pricing, experienced sellers specializing in a product category should use posted prices more than auction. This theory implies that each pricing format is used by a distinct set of sellers with identifiable experience, specialization, and inventory characteristics. Zeithammer and Liu (2006) find strong empirical evidence to support these predictions using a unique data set from the digital-camera category. The data set is unique, because it captures not only seller behavior in the focal product category but also in all other eBay categories. Thus, it is possible to empirically separate a seller-specific cross-category habit from measures specific to the focal category. Both sources of seller heterogeneity turn out to be important: the seller's inventory of digital cameras is correlated with the way the seller sells the cameras, but so is the way that same seller sells computers or books.

Bidder Participation Cost
Some bidders may execute a BNP to reduce different types of transaction costs: e.g., the cost of bidding and "hassle costs" due to frequent log-ons to the auction sites to check the ongoing results of the auction (Wang et al., 2008c;Carare and Rothkopf, 2005). This usage of BNP tends to be more applicable for auctions with longer duration or for time-sensitive products. Wang et al. (2008c) argued that a seller can charge a premium in the buy-it-now price to bidders who would incur a high participation cost going through the auction. Empirically bidder costs are not observed, but Wang et al. pointed out that customer feedback rating, which is observed on eBay, measures the experience a customer has at eBay. They therefore use it as an inverse proxy for the auction participation cost (e.g., List and Lucking-Reiley, 2000): more experience means more familiarity with the auction rules and lower participation cost.
They collected data for four product categories: Apple iPod MP3 player 10 GB (including both old and new items), Lexar memory stick 128 MB (only new items), KitchenAid mixer (only refurbished), and KitchenAid KSM103 professional mixers (all new). The incidence of BNP use by sellers varies across the four categories: 34% for iPod auctions, 21% for Lexar memory sticks, and 78% for the two KitchenAid mixers (Table 3.3). The BNP incidence appears negatively correlated with bidder experience which provides limited support to the bidding cost argument.

Risk Aversion
One stream of research proposes that risk-averse bidders may execute a BNP to avoid certain risks associated with auctions (e.g., Hidvégi et al., 2006). One such risk is related to payment variation and suggests that risk-averse bidders may execute a BNP to avoid the risk of paying too much, or of losing the auction to a bidder with a higher value (Buddish and Takeyaman, 2001). As a result, sellers should provide a BNP option and the BNP should be set somewhat above the expected selling price. Then risk-averse bidders are willing to pay an extra insurance premium (the higher BNP) to ensure they win the auction. This usage of BNPs tends to be most applicable when products have considerable variability in their ending prices or have limited supply. Kirkegaard and Overgaard (2003) suggest another rationale for setting a buy price in eBay auctions. Their model has two risk-neutral bidders, each of whom demands two units with diminishing marginal utility. Two sellers sequentially offer a unit for sale in second-price sealed-bid auctions. Kirkegaard and Overgaard show that the first seller can raise his expected revenue by setting a buy price.

Bidder Competition
A sketch of the proof is as follows. In the case of no BNP, in the second auction each bidder will bid his true valuation (by property of sealed bid second price auctions). In the first stage, in equilibrium a bidder's strategy is dictated by the indifference relation: the bidder should be indifferent between winning and losing, if his toughest competitor is equal to himself. Hence, each bidder will bid his true value for the second unit. The outcome in the absence of BNP is thus efficient in that the first item is won by the highest valuation bidder and the second item is won by the highest valuation bidder if his value for the second unit is greater than the lower valuation bidder's value for the first unit, and by the lower valuation bidder otherwise. The buyout option introduces inefficiency in that the lower valuation bidder may win the first item. In the case of inefficiency, the revenue from the second item (the low-value bidder's second item valuation) is lower than the efficient case (the minimum of the high-value bidder's second item valuation and the low-value bidder's first item valuation). Lower expected surplus to the second seller implies higher expected surplus to the second item's winner and therefore higher willingness to bid in the first auction.

Reference Price
Popkowski Leszcyc et al. (2009) propose that a retailer may use BNPs as external reference prices, influencing bidders' valuations in Internet auctions. They focus on the effect of BNPs on bidders' willingness to pay and study under what conditions a BNP can be effectively used as an external reference price. Their design involves seventy-five pairs of identical products. Each product was sold once with a BNP and once without a BNP. Products were randomly assigned to days of the week. Half were randomly picked to be auctioned first with a BNP; the others were first auctioned without a BNP. At the completion of an auction, each winner was asked to complete a survey.
Consider the following question: "Based on the information provided in the auction, how easy or difficult was it for you to assess the value of the item you won?" The mean score without BNP was 5.22 vs. 3.84 with BNP, where 0 = very easy and 10 = very difficult to assess the value. The result of a paired-sample t-test (t = 3.99, df = 74, p < 0.001) provided strong support for a reference price role of BNP.
The authors further examined the winning bidder's maximum (proxy) bid, which they call willingness to pay (WTP). On average, WTP was $25.47 when a BNP was present and $20.70 without a BNP (average ending prices were $23.89 with a BNP, and $19.35 without BNP, and the average BNP was $30.20). This difference is highly statistically significant, based on a paired-sample t-test (t = 3.84, df = 74, p < 0.001). A sophisticated regression analysis was conducted as well and confirmed these results. A second study by the authors in an eBay environment replicated these findings.

Trust and Feedback Ratings
One of the key differences between online auctions and offline auctions is the critical role of trust. In an offline auction, very little trust is involved or needed. An auctioned item can be seen and inspected by the buyer, the auction process and its integrity are largely observable, and payment and ownership transfer take place nearly simultaneously. In the offline world, a consumer might engage in ex ante investigation prior to bidding. That is, the consumer could go out and sample the suppliers' prices (e.g., Stigler, 1961;Diamond, 1971;MacMinn, 1980) or get such information through intermediaries (Salop and Stiglitz, 1977).
In an online auction, in contrast, there is a great need for trust. The need for trust applies to both price and non-price attributes. On price, the buyer seeks reassurance that the seller's price is competitive. On non-price attributes, the buyer's trust centers on the items' quality and delivery as represented by the seller.
In online exchanges, online intermediaries can provide both price and non-price information for the consumer (Chen et al., 2002) as well as vouch for quality or provide implicit or explicit insurance to customers as an attractive alternative to costly research (Allen and Gale, 1999). With regard to non-price information, online intermediaries spend some resources on monitoring and policing, but they mainly rely on feedback-based community policing (e.g., Bradley, 2001). As such, feedback ratings are buyers' main source of information on seller reliability and the main vehicle for reducing customers' perceptions of risk and increasing trust (e.g., Kollock, 1999).
Feedback mechanisms allow buyers to post positive, neutral, or negative comments following a completed transaction. A positive feedback to the seller is an indication by the buyer that the item has arrived in a timely manner, was undamaged, and was correctly represented by the seller. A negative feedback expresses dissatisfaction with regard to product representation, auction process or delivery.
Feedback ratings on eBay are presented to bidders in the format shown in Figure 3.2. The bidder can observe how many positive, negative, and neutral feedbacks a seller has received, broken down by one month, six months, and twelve months. Below the summary feedback shown in the figure, detailed comments typically appear for recent transactions, but they usually take the form of "thank you" or "great seller" and are rarely informative, except in the case of negative or neutral feedbacks, which are extremely rare.
Beginning in May 2007, eBay introduced the detailed seller ratings, shown on the right side of the figure below. These include four categories: (1) accuracy of item description, (2) communication, (3) shipping time, and (4) shipping and handling charge. A buyer can rate the sellers on a scale of 1-5 stars on each aspect. These detailed ratings are optional but must be done simultaneously with the more traditional ratings. The idea was to provide buyers with more detailed feedback for more informed decisions, to make it easier for buyers to provide information on less than perfect experiences without being too harsh, and to minimize the prospect of retaliation by the seller. The "classic" ratings appear next to the seller name, whereas the detailed ratings require an extra click. Moreover, while the classic ratings are known to the seller for each buyer and transaction, the detailed ratings are anonymous with respect to the buyer and transaction and are only shown as average ratings, so possible seller retaliation or disapproval is difficult. It remains to be seen whether this change increases the usefulness of feedback ratings.
In a seminal work on feedback ratings in eBay, Resnick and Zeckhauser (2002) identified the following categories as most likely to contribute to buyer reported dissatisfaction resulting in neutral or negative feedback. Listed here from most frequent to least frequent: (1) the item is in poor condition, not as advertised, or not original, (2) item not received after payment, (3) seller backed out of transaction, (4) seller not communicative, and (5) slow shipping. In addition to these concerns, the seller may misrepresent himself (Dini and Spagnolo, 2007) or take advantage of the auction process by using it to engage in price discrimination outside the auction Engelbrecht-Wiggans and Katok (2006). On eBay, for example, the seller may contact non-winning bidders with offers, and this may result in unfair discrimination since these bidders' reservation prices have been revealed in the auction.
None of these practices constitutes outright fraud although they are discouraged. In addition to these common practices, fraud is not rare in online auctions. According to the Internet Fraud Watch operated by the National Consumers League, online auction sales are the number one source of Internet fraud. Cases of outright fraud will generally result in the seller being removed from eBay and so this information will generally not be observed nor reported in studies of feedback ratings.
The widespread use of customer feedback and its apparent success is puzzling: buyers have no discernible incentive to furnish any feedback, and have a non-negligible incentive to avoid giving negative feedback since the seller could retaliate. Seller retaliation to negative feedback could be carried out by not replacing the defective or misrepresented product. Prior to May 2008, the seller was also able to leave a negative feedback in return (but not with recent changes to eBay's feedback system).
The academic literature has been trying to confirm or refute the effectiveness of the feedback mechanism with mixed results. For example, Kaufmann and Wood (2006) found that feedbacks ratings had no effect on the prices of collectible coins traded in online auctions. Melnick and Alms (2002), on the other hand, found that both positive and negative feedbacks had an impact on the prices of collectible coins. Bajari and Hortaçsu (2003) found that only positive feedback affected the price of collectible coins. Lucking- Reiley et al. (2007) found that only negative feedback had an effect. Auction studies with Pentium chips (Houser and Wooders, 2000), Palm Pilots and PDAs (Kalyanam and McIntyre, 2001) and used computer monitors and printers (Lee et al., 2000) find significant impact of feedback on price. On the other hand, the price of electric guitars (Eaton, 2002) was not found to be significantly affected by feedback, nor were new computer monitors and printers (Lee et al., 2000), or MP3 players (Resnick and Zeckhauser, 2002). However, implications of the above studies are limited due to problems with endogeneity, since feedback ratings are not exogenous. However, Resnick et al. (2006) conducted a controlled field experiment, with identical product auctions sold through a new and established seller id. They found that selling prices for vintage postcards were 8.1% higher for the established seller. Detailed surveys of the literature are provided in Resnick et al. (2006) and Bruce et al. (2004).

Reciprocation
Positive feedback on eBay is often reciprocated by the other party to the transaction with a corresponding positive feedback and negative feedback is often reciprocated with a negative feedback. This is likely to inflate the incidence of positive feedback. These concerns, among others, were raised by Dellarocas and Wood (2008), Klein et al. (2006), and Resnick and Zeckhauser (2002). Resnick and Zeckhauser (2002) show that 99.8% of positive feedback is reciprocated by the other party (reciprocated by the seller in the case of buyer feedback and vice versa). The data set they used was a sample of single items that were open for public bidding. 36,233 of these items attracted a bid at least as high as both the starting bid and the reserve price and hence were officially sold. All the selected items were listed on February 20, 1999. There were 13,695 distinct sellers who listed items for sale in the data set and 25,103 distinct buyers. This data set was small enough to permit computation of buyer and seller feedback profiles for each transaction prior to the transaction and extraction of feedback about the transaction. Table 3.4 shows the co incidence of positive and non-positive feedbacks by buyers and sellers and allows for an unambiguous rejection of the independence hypothesis. In this table, the seller is positive 99.8% of the time when the buyer is positive compared to 39.3% of the time when not. The buyer is positive 99.7% of the time when the seller is but only 23.9% of the time when not. Resnick and Zeckhauser (2002) argue that this clear correlation is likely due to two factors: (1) when transactions work poorly, both sides get upset, and (2) retaliation.
Additional evidence for retaliation in Resnick and Zeckhauser (2002) is given by the sequential nature of feedback. Since the buyer's responsibility -only payment -is completed first, one should expect the feedback to be given first by the seller. However, in their data buyers provide first feedback only 37.6% of the time and sellers are first 32.8% of the time (the rest are instances where neither party provides feedback). When both parties provide feedback, buyers are first twice as often as sellers. This is very telling of a reciprocity threat by sellers and the authors find even stronger correlation between feedbacks when accounting for the sequence of feedbacks. The authors suggest that the two-way nature of eBay's feedback mechanism is responsible and that leaving a positive feedback is a dominant strategy in such two-way mechanisms. Dellarocas et al. (2004) argue that the expectation of feedback reciprocation increases the participation in reputation systems. Knowing that they will be reciprocated with high probability, users are encouraged to leave a positive feedback. Chwelos and Dhar (2006) confirm the results of Dellarocas et al. (2004) when comparing Amazon and eBay's feedback mechanisms. They attribute the differences in the behavior of feedback providers in the two systems to the nature of the mechanism (one way of Amazon vs. two way of eBay). Resnick and Zeckhauser (2002), Klein et al. (2006), Dellarocas et al. (2004), and Dellarocas and Wood (2008) also highlight a concern for feedback retaliation, i.e., for the possibility that negative feedbacks are deterred by the possibility of the poorly rated party retaliating with another negative, even when not deserved, after receiving a first negative feedback. Dellarocas and Wood (2008) even manage to quantify the likely negative effects of the threat of retaliation. Accounting for "silence" (feedback not provided) they estimate that only 86% of eBay users are actually happy with their partners, even though positive ratings reach 98% of feedbacks.
Beginning in May 2008, sellers can no longer rate buyers negatively. This eliminates the threat of retaliation by sellers for negative buyer feedback. Moreover, the detailed ratings aspect added in May 2007 is anonymous in that the seller cannot see the identity of the bidder providing the feedback and can only see the averages of the detailed ratings. This is another hindrance to any potential seller retaliation. There is no strong evidence that the elimination or reduction of the possibility of retaliation has resulted in more negative feedback ratings or that the detailed star rating system adds a level of differentiation between sellers. When buyers rate sellers positively they almost always give them five stars on each of the four dimensions.

How Effective are Online Feedback Systems?
The feedback system has always existed in some form in eBay and subsequent entrants into the online consumer auction market have adopted variants of this system. Hence, it is virtually impossible to know by observing field data whether feedback improves efficiency. Bolton et al. (2004) address this question by examining laboratory data for three simulated markets they call the strangers market, the partners market, and the feedback market. In the strangers market, a buyer can meet a particular seller only once and no transaction history is given. In the partners market, buyers and sellers repeatedly interact and so transaction history is fully known. In the feedback market, interaction between buyers and sellers is as in the strangers market but they have complete feedback about the transaction history of their partners. The transaction takes the form of an extensive form game as shown in Figure 3.3.  The buyer moves first and decides whether to buy (trust the seller) or not buy (not trust the seller). If the buyer decides to buy, he sends payment to the seller. The seller can then decide whether to ship the item (be trustworthy) or not ship it and pocket the payment. Transaction history when given indicates the number of times a seller has delivered and the number of times he has defaulted. The payoffs in the figure were used in all three markets and each point was worth 1 cent. All three markets were run for 30 rounds and this was known to participants.

Trust and Feedback Ratings 29
The results, summarized in Figure 3.4, indicate that the feedback market was more efficient than a market without feedback but not nearly as efficient as a market where reputation is direct. These differences were significant in pair-wise testing.
The authors highlight the public goods aspect of a feedback system. That aspect means that a trusting buyer under a feedback system provides a great service to future buyers by revealing the nature of the seller, but he does so at a costly risk to himself. In the data, a buyer matched to a seller without established feedback stands to lose money relative to not buying, so the selfish best response is not buy from (not trust) such a seller. The efficiency improvement implies that buyers under feedback systems buy more than buyers in the non-feedback system, but they likely do so as a contribution to a public good rather than motivated by self-interest. The public goods literature is a welldeveloped literature and the link between that literature and feedback systems is an important direction for future research.

Shipping Costs
Online purchases invariably involve at least two numbers which must be added up to arrive at the true purchase price. One number is the price for the item; the other number is the shipping cost. While shipping is in many cases "free" (covered by the seller), it is not uncommon in online auctions for the shipping cost to be a large multiple of the item's price. And there is no requirement that the shipping charge corresponds to the actual mailing cost. In other words, shipping is more like "Shipping and Handling" and can be a source of profit for the seller.
Bidders should theoretically be indifferent to shipping cost, but this may or may not be a reasonable theoretical abstraction. First, bidders are allowed to sort items by price (which does not include the shipping cost). Hence, charging a high shipping cost and a low reserve may move a seller's item up the list and result in greater participation and therefore greater revenue. Second, bidders may engage in "mental accounting" which does not treat the two numbers as equivalent. Third, comparison of different alternatives on multiple attributes has been shown to be quite difficult for many decision makers. The more attributes there are, the less trivial the comparison task is.
Thus, shipping costs in auctions are critical to the final price (where price incorporates shipping costs) and dispersion in previous studies (Clark and Ward, 2008;Häubl and Popkowski Leszczyc, 2003;Morwitz et al., 1998;Hossain and Morgan, 2006). Clark and Ward (2008) studied the effect of shipping costs on eBay auctions for collectible Pokemon cards. They examined 218 completed auctions for the Charizard Pokemon card with shipping costs from $0.55 to $4.20. They concluded that bidders ignored shipping costs. That is, there was no significant impact of shipping costs on winning bids. This is a powerful finding. Another way to think of this finding is that a one-dollar increase in shipping cost will increase revenue by $1. Clearly, this cannot be the case for the entire possible range of shipping costs or else we would expect shipping costs to be very high (as would be optimal for sellers). More likely, for larger shipping costs differences, the increase in revenue is lower than the full amount of shipping. Other empirical studies, described next, confirm this assertion. Häubl and Popkowski Leszczyc (2003) studied the effect of shipping costs in a controlled field experiment on eBay. They systematically varied the shipping costs for identical sets of collectible postage stamps (four different levels of shipping costs). For each of twelve different stamps, four identical replicates were auctioned off with each replicate having different shipping costs. With zero shipping cost, the average total selling price was $5.89, as compared to a mean of $6.71 when shipping cost was positive (paired-samples t = 2.281, df = 11, p < 0.05). The highest level of shipping cost resulted in a mean total selling price of $7.99. On average across the 48 auctions, and all else being equal, increasing the shipping cost component by one dollar resulted in an increase in the total selling price by 66 cents. This effect was moderated by the ease of assessing the value of the auctioned good.
In addition, they conducted a laboratory experiment, where subjects participated in an auction for a fleece sweater or a down jacket. Average bidder valuations were $28.48 (without an additional shipping and handling fee) and $31.90 (with a $15 fee added) for the fleece sweater and $54.88 and $66.25 (with a $15 fee added) for the down jacket. However, results of a post-auction survey indicated that these differences were due to bidders underestimating their total financial commitment when additional shipping and handling charges were added. Hossain and Morgan (2006) showed a somewhat smaller effect but nevertheless significant. They auctioned off 10 popular CD titles and 10 popular Xbox titles. For each title, four copies were sold with two levels of open reserve price (low, high; this is also known as the starting bid, see Section 3.9) and two levels of shipping cost (free, moderate). Specifically, in treatment A, the reserve price was $4 and the shipping cost was zero, whereas in treatment B, the reserve price was $0.01 and the shipping cost was $3.99. In treatment C, the numbers were $6 and $2 for reserve price and shipping cost, respectively, and in treatment D they were switched. The reserve price was a apparently a bit too large for the CD treatments since five out of twenty CDs were not sold in treatment D. In all Xbox treatments, all titles were sold. A summary of their findings is reported in Table 3.5. The largest effect was found in the comparison of treatments A and B for CDs. The revenue increased from an average of $7.54 to an average of $10.14. Excluding unsold CDs (one title unsold in that set), the averages are 8.37 and 10.16, which is still large and significant. This is a difference of $1.79 or about 21% of the price under treatment A. Dividing this revenue difference by the shipping cost difference of $4, the effect is smaller than that reported by Häubl and Popkowski Leszczyc (2003), but note that this design simultaneously lowered the reserve price which has a depressing effect on price (see Section 3.9). In other words, Hossain and Morgan (2006) showed that increasing shipping costs increases prices by a magnitude exceeding the decrease in price caused by lowering the reserve price. This effect goes away for the high reserve price treatments (C and D) for CDs, where the revenues were $9.30 ($12.15 for the titles that sold) in treatment C and $9.01 ($12.87 for the titles that sold) in treatment D (a reversal of the previous effect). For Xbox titles, the difference between treatments is always increasing in shipping costs but is far higher in the high reserve price treatments. Specifically, the difference in prices between treatments A and B is $0.71 (2% of revenue), whereas the difference in price between treatments C and D is $4.11 (11%) and is highly significant. The authors point out that for Xbox games the reserve is less than 27% of the retail price, whereas for CDs it is 53% of the retail price. Thus, reserve price needs to be relatively low for shipping costs to generate a large increase in price in a design where shipping costs changes are offset by changes in reserve prices.

Search Incentives
In a price comparison over several retailers, one can typically find a wide range of prices for pretty much any product. This is generally attributed to search costs (Stigler, 1961;Varian, 1980). Specifically, if consumers need to engage in costly search to find the lowest price for an item, retailers need not fiercely compete on price. The great expectations for online shopping in the 1990s (Bakos, 1997(Bakos, , 1998, 3 was that the Internet would reduce search costs and therefore reduce both average price and price variance. This was not an unreasonable expectation. After all, a quick and easy search on Google or any one of various price comparison sites will quickly yield a detailed price comparison with quality ratings, buyer reviews, and all the information one would need to make an informed decision. Despite the high expectations for the Internet to reduce price dispersion, there is ample empirical evidence that price dispersion and high prices persist on the Internet (Bailey, 1998;Brynjolfsson and Smith, 2000;Pan et al., 2004;Smith et al., 2000).
One possible explanation for price dispersion comes from a surprising yet familiar source -geographical distance. Hortacsu et al. (2009) analyze geographic patterns of trade between individuals using transaction data from eBay and MercadoLibre, two large online auction sites. They find that distance continues to be an important deterrent to trade between geographically separated buyers and sellers, though it is much smaller than reported in studies of non-Internet commerce between business counterparties. They also find a strong "home bias" for trading with counterparties located in the same city or state. This, for the most part, cannot be attributed to shipping costs. Especially in the United States, a flat shipping fee is quoted by most sellers for sales within the continental United States. Nevertheless, the authors account for shipping costs in their regressions and find that this does not eliminate the geographical effect. The authors also statistically reject the effect of time zone effects on the preference for geographical proximity. Further analyses suggest that location-specific goods such as opera tickets, cultural factors, and the possibility of direct contract enforcement in case of breach may be one reason behind the same-city bias. However, this surprising finding of geographical friction poses a strong challenge to the idea that online commerce would be frictionless commerce.
Contrary to expectations, it appears that price dispersion online may even be higher than it is offline (Clay et al., 2003;Degeratu et al., 2000;Lee, 1998;Lynch and Ariely, 2000;Shankar et al., 1998). The failure of easy search to reduce price dispersion is most evident in online auctions (Ahlee and Malmendier, 2005;Ariely and Simonson, 2003). Ariely and Simonson (2003) tracked 500 auctions for CDs, books, and movies on a large online auction site. For each of the auctions, final price was recorded including shipping cost. At the time the auction finished, the researchers spent up to 10 minutes searching for an identical item outside of the auction site offered by any reputable retailer and available for immediate shipment. They recorded those prices as well, including shipping and handling. The results showed that the auction site was the cheapest in 6 out of 500 cases, and more expensive in 494 (98.8%) of the 500 cases searched. Moreover, two online retailers were cheaper than auctions 79.9% of the time, three were cheaper 44.7% of the time, and four were cheaper 30.5% of the time. Thus, bidders could have simply checked any of these sellers without great effort. In terms of price differences, consumers paid on average 15.3% more in auctions compared with the low price retailers. The authors note that such price premiums cannot be attributed to better service or reliability of anonymous auction sellers relative to reputable retailers (the opposite is typically the case). Nevertheless, they surveyed bidders to find if they were willing to pay more to win in the auction. Bidders unequivocally indicated that they would strictly prefer the item for less money at a retailer's site than for more money at an auction. Thus, the results can be fully attributed to lack of search.
What Ariely and Simonson (2003) showed was that bidders failed to search outside the auctions. But do bidders search within auctions? That is, if multiple concurrent auctions exist, do bidders cross-bid (bid in more than one of the concurrent auctions)? Theory (Peters and Severinov, 2006) strictly prescribes that they do. While it is understandable that bidders may neglect to Google-search an item they are bidding on, it takes a greater disregard to fail to see an item on the same auction platform. This is because similar items with similar ending times are listed next to one another on the list of available auctions when the bidder initially queries for the item. Anwar et al. (2006) addressed the issue of cross-bidding. They studied groups of eBay auctions for CPUs. In particular, they choose simultaneous auctions run by the same sellers, selling items with the same product descriptions, the same delivery method, and the same shipping cost. This is possible due to the fact that some sellers on eBay sell a number of almost identical items. Except for different ending times, these auctions are arguably indistinguishable to bidders. Since the ending times are not exactly the same, the authors examine auctions with ending times within a minute, within an hour, and within a day. They find that in the daily sample only 19% of bidders cross-bid. Looking at auctions ending within an hour of one another or within a minute, they find that 31% and 32% of bidders cross-bid. However, some of these bidders have a multi-unit demands. When they exclude these bidders, the proportions of cross-bidders drop to 14%, 20%, and 20% in the daily, hourly, and minute samples, respectively. They also find a great deal of price dispersion in the concurrent auctions, which they attribute to the failure to cross-bid.
As in the above study, Haruvy and Popkowski Leszczyc (2010b) were interested in search between concurrent auctions. However, they sought more experimental control and so they set out to auction off pairs of items that were exactly identical, including starting and ending times. They then started varying auction features (including starting time, but not ending time), one at a time, to see how these variations affect search. In addition to design features, they also provided direct incentives to search in some instances. To do that, they ran pairs of identical auctions, where they offered bidders an incentive to search by promising to waive shipping costs if the final price in the auction ended up lower than any identical auction ending within 30 minutes of the current auction. In total they obtained 281 pairs of auctions for 9 different products.
They note the existence of price dispersion in pairs of auctions for identical goods. The average price difference (here price is the full price paid by the bidder, including shipping cost) over 281 auction pairs is $2.87. This is significant (t = 16.36, df = 280, p < 0.001). In percentage terms, this is 15.25% of the average price observed (average price is $18.82). Limiting attention to auction pairs that had identical auction design features (shipping costs, open reserve price, secret reserve price, and duration), the price difference is $2.69 (t = 9.88, df = 101, p < 0.001). Hence, price dispersion is highly significant for both nearly identical and completely identical auctions in a pair.
The next set of results pertains to the effect of having a search incentive on variables related to price dispersion. In the present design, the search incentive is directly tied to the shipping cost since this incentive constitutes a refund on one's shipping cost. Therefore, manipulating the shipping cost changes the incentive to search and this is the effect we seek to quantify. A reasonable conjecture is that price dispersion is inversely related to the magnitude of the search incentive. While the low search incentive condition is identical in shipping cost (in all but the diapers category) to the no search baseline condition in terms of shipping cost, the high search incentive condition involves a higher shipping cost and this is expected to have an adverse effect on demand. Nevertheless, the conjecture is that moving from the no search incentive to a low search incentive will result in increased switching within the auction pairs, resulting in an increase in the number of bidders and the number of bids per auction, while reducing price differences in both absolute and relative terms. We summarize the findings below. The averages and standard errors are reported in the top panel. The t-tests between search conditions are reported in the bottom panel (Table 3.6).
Thus, in terms of price differences (here price is the full price paid by the bidder including shipping cost) between two auctions in a pair, there is a decrease in price dispersion when providing bidders with a low search incentive relative to no search incentive. This difference is significant at the 3% level (based on a one-sided t-test) for dollar difference and at 2.5% for relative (percentage) difference. Hence, it appears that search incentives serve to reduce price dispersion. Equally interesting is the relative lack of cross-bidding in their data, similar to that identified by Anwar et al. Only 19.2% bidders ever switched between auctions in a pair and this number is very similar to that identified for cross-bidding by Anwar et al. This number is not significantly different over search conditions. Of all switches from one auction to a different auction, 78.8% of bids were to the lowest price. That means that 21.2% of switches were to a higher priced option.

Competition Between Auctions
In the literature on offline auctions, an auction is typically treated as having a single auctioneer. That is, models generally have a single seller facing multiple buyers or as a single buyer facing multiple sellers. As is apparent by now, this is not really a reasonable abstraction with online auctions. Audiences -academic and practitioners alike -expect a full account for the choice set faced by a bidder in an auction. This is a far more stringent demand relative to what a researcher would face in most offline retail settings. For example, it is standard practice when modeling demand for a grocery item to only consider competing brands from a narrowly defined category. In auctions, categories are not as narrowly or well defined and items are not perfect substitutes. Haruvy et al. (2008) review the state-of-the-art of competition between auctions. They consider three different types of competition: competition between auctions, competition between formats, and competition between auctioneers vying for auction traffic. We break down the present discussion along these dimensions.

Competition between Concurrent Auctions
The modeling of between-auction competition is not trivial and may require some additional work. Chan et al. (2007) study notebook computers and characterize market competition through two dimensions. They first identify a set of similar items for each auction and next measure competition by the number of auctions for similar items (which they refer to as breath) and the number auction of the same brand name (referred to as depth). They find that increased competition from the number of simultaneous auctions for similar items reduces bidders' willingness to pay. Jank and Shmueli (2007), studying the effect of concurrent auctions on laptop auction prices, construct a similarity measure based on the distance of laptop features. They included a spatial component that reflects the distance between products in feature space.
Their distance measure combines all quantitative and qualitative product attributes (similar products lie in close proximity in feature space). They show that the complete model with information about concurrent competing auctions outperforms simpler models. The methods in both of these studies are rigorous and apply really well to items with clearly defined product features, but they do not necessarily generalize to other product categories (e.g., books, CDs). In other categories (books, audio books, baby products, etc.), Haruvy and Popkowski Leszczyc (2010b) computed the number of competing auctions for identical product auctions, using search terms and giving equal weight to all search results that passed a filter. This is not as technical as the methods just described, but it is a commonly used approach.
More importantly, Haruvy and Popkowski Leszczyc (2010b) accounted for competition with a careful pair-wise design. By running pairs of auctions simultaneously, their experimental design controls for the outside competition and other external factors in three ways: (1) the pair-wise design ensures that whatever else is happening should affect both auctions in the pair. (2) they use a design over several monthsthis should result in averaging out competition over a longer period; and (3) they use some categories with lots of outside competition and some with nearly none.

Forward-Looking Bidders
One way auctions for substitute products to compete on eBay is through forward-looking strategies of the bidders. In sequential auctions, Zeithammer (2006) shows that when rational forward-looking bidders participate in a sequence of auctions for substitute products, they should decrease (shade) their bids when the future auctions are revealed to be more plentiful and when they occur sooner. Specifically, as near-future offerings become visible on eBay, bidders should base their bids on the available information. In the two eBay product categories of MP3 players and DVDs, a test of the model predictions rejects a model without forward-looking bidders in favor of forward-looking bidders. Moreover, in both categories information about auctions for substitute goods in the near future leads to price reductions of between 3% and 7%.
The forward-looking behavior of the bidders suggested and documented in Zeithammer (2006) raises a number of theoretical questions, for example, "How should a bidder bid against other strategic forwardlooking bidders?" or "How should the seller sell in a sequential auction marketplace with forward-looking bidders?" The first question is answered by Milgrom and Weber (2000) for the case of a sequential auction for exactly identical perfect substitutes: all bidders who participate in each auction shade their bids just enough to arbitrage away any potential price-decline. When the objects being auctioned are not identical but nevertheless substitutes because of buyer capacity constraints (e.g., two pairs of tickets to different but concurrent shows), Zeithammer (2010) shows that equilibrium bidding can be non-intuitive. For example, even bidders who much prefer the object auctioned second will still bid on the object auctioned first because the auction involves a loser's curse -losing the first auction at a low bid indicates that the competition remaining to bid on the second object is quite strong. The bidding strategies in sequential auctions remain an active area of both theoretical and empirical research.
The impact of forward-looking bidding on seller profits is almost always negative because of bid-shading. Thus, it may seem that the seller may be able to eliminate bid-shading by selling infrequently. Zeithammer (2007b) shows that a long-lived seller facing overlapping generations of consumers will not be able to eliminate bidshading entirely. Interestingly, however, he finds that bid-shading is self-regulating in that it turns itself off whenever the existence of the market is threatened by low seller profits. Zeithammer calls this effect the market's "self-preservation instinct" and suggests that auctioneers and regulators need not worry about forward-looking bid shading. On the contrary, such bid-shading helps markets clear at the correct prices whenever sellers merely offer units for sale and the market determines prices through auctions.
One way bid-shading can be eliminated is through commitment. Zeithammer (2007a) shows that when announcing a future auction for a second unit of the same good reduces today's bids, the seller may want to credibly limit the number of units he sells. On the other hand, the seller could use the outcome of the first auction to inform the selling of the second unit. These two selling regimes are mutually exclusive, and Zeithammer (2007a) labels them commitment and adaptation, respectively. Adaptation involves learning about remaining demand from early prices. Commitment requires a seller to forgo such learning and make all selling decisions in the beginning of the game. The analysis implies that neither regime dominates the other. Commitment can reduce bid-shading by bidders, but adaptation resolves some demand uncertainty inherent in auction markets and can be more efficient. Thus, an adaptive seller can capture enough of the efficiency gains to prefer adaptation to commitment. Zeithammer shows that the seller's choice of regime should depend on the gains from trade: when the expected gains from trade are low, commitment dominates adaptation, and vice versa. The finding that adaptation can dominate pre-commitment seems to contradict Schelling's principle of commitment: in a dynamic game, the player who can pre-commit to arbitrary future actions should be better off than the player who is restricted to play subgame perfect strategies. In other words, the seller should never prefer adaptation. Zeithammer (2009) resolves this inconsistency by showing that the commitment not to sell another unit is too coarse: a seller who can commit not to hold the second auction unless the first-price exceeds a publicly announced threshold is always better off than an adaptive seller. Interestingly, the commitment to a threshold may unravel informative bidding strategies in the first auction in that unique invertible bidding equilibrium exists only for the Dutch auction format, and only when the second auction is sufficiently discounted by the bidders. Therefore, when the bidders are sufficiently strategic, then the seller may not be able to learn much about remaining demand from early auctions.

Competition Between Sellers
Empirical analysis conducted by Xie et al. (2007) indicate that while sellers tend not to adapt to competitor's strategies, they can be better off by differentiating their strategies. Results of their simulation analysis show optimal reserve price strategies under different competitive market conditions. Recent literature has emerged which looks at competition in procurement auctions, where buyers and sellers have non-price preferences over each other. In such competitive settings, the problem can be reduced to a matching problem. Losing bidders migrate to other auctions and the converged outcome can be mapped to a stable outcome where no buyer-seller coalition can block the resulting match (see, e.g., Haruvy and Unver, 2007;Li et al., 2007).

Competition Between Formats, Mechanisms, and Institutions
As auctions become a mainstream mode of exchange, sellers in forward auctions and buyers in reverse auctions demand and receive more choices of formats. For example, a seller on eBay can choose among many auction features. Some of the more researched features are the reserve price and secret reserve price (Katkar and Reiley, 2006), the buy-it-now option (Chan et al., 2006;Park and Bradlow, 2005;Popkowski Leszcyc et al., 2009), hard and soft ending (Roth and Ockenfels, 2002), and the posted price (Wang, 1993). Buyers in procurement auctions can choose to combine auctions with negotiations (Engelbrecht-Wiggans and Katok, 2006), can choose between price and various scoring rules for winner determination (Che, 1993), or can choose new formats that limit price visibility (and presumably limit opportunism), such as rank-based auctions or auctions where only the lowest price is displayed.

Award Rules
Unlike consumer auctions, procurement auctions often utilize "buyerdetermined" award rules, meaning that while the auction provides a format for price competition, the buyer ultimately selects the final winner based on a set of preferences (Jap, 2002;Rangan, 1998). 4 For example, Engelbrecht-Wiggans et al. (2007) compared buyer-determined and price-determined auction formats experimentally and found that for a small number of bidders, price-based auction formats may provide greater buyer surplus and vice versa. Haruvy and Katok (2007) compared dynamic and sealed bid auctions with different information structures. They found that sealed bid auctions provided greater buyer surplus, and that more transparency to bidders resulted in less buyer surplus. The use of buyer-determined award rules can be detrimental to the buyer-supplier relationship (Jap, 2003;Jap and Haruvy, 2007). Jap (2007) finds that full price visibility English (reverse) auctions raise suppliers' beliefs that the buyer is using such auctions to opportunistically gain price concessions. Jap (2007) shows that partial price visible formats (e.g., particularly the rank format over the low price format) are better at preserving the buyer-supplier relationship (e.g., minimizing opportunism suspicions and protecting overall satisfaction and future expectations) than full price visibility formats.

Competition Between Auction Hosts
In auction markets, competition exists not only on two sides of a market but also among auction operators that provide similar services.
One of the factors that determines the result of competition is network externalities. Chen et al. (2005) showed, using adaptive agent-based simulations, that equilibrium exists and that coexistence equilibrium is more likely to emerge than monopoly equilibrium in online auction marketplaces. When online auction marketplaces have different design features, different bidder behavior may emerge. For example, sniping was more common on eBay than on Amazon, largely due to the fixed end time on eBay relative to the soft close rule of Amazon Roth and Ockenfels (2002). Sinha and Greenleaf (2000) study the "aggressiveness" of auction bidders, a behavior related to sniping.
Buy-in penalties (Greenleaf and Sinha, 1996) are another practice that auction houses use to manage their relations with sellers. These penalties are an amount that the seller must pay the auction house if the auctioned property does not sell. Greenleaf and Sinha model the tradeoff between buy-in penalties and the seller's commissions that auction houses also charge. They find that auctions that use them dominate auctions that use only sellers' commissions. The buy-in penalties motivate sellers to set lower reserve prices, which increases expected total auction revenue. This result can be used to explain why eBay and other Internet auctioneers charge sellers a "Reserve Fee" (to use eBay terminology) that is refunded to the seller only if the item sells. The reserve fee is a buy-in penalty.
Another important issue is how much auction houses should spend to attract and retain buyers and sellers. This issue has been tackled by Sinha and Greenleaf (2001), and Park and Bradlow (2005). Yao and Mela (2008) find that targeting commission reductions to high value sellers increases auction house revenues.

Auction Networks
Jank and Yahav (2010) investigate bidder-seller networks and find that bidders typically compete for the same seller. They use the concept of network analysis to quantify the amount of loyalty a seller receives from the set of all potential bidders online.
Consider Figure 3.5 which shows a snapshot of the total auction network investigated in Jank and Yahav (2010). Sellers are marked by white nodes, bidders are marked by dots. A (black) cone between a seller and bidder denotes transactions. The width of the cone is proportional to the number of transactions and hence measures the strength of a link. This figure shows a subset of only the top 10% of high volume sellers. Sellers often interact with several hundred bidders (895, on average). Some sellers are exclusive in the sense that they are the only ones that transact with a set of bidders (look at the margins of the network) while other sellers share are common set of bidders (center of the network). Serving bidders exclusively vs. sharing them with other sellers has huge implications on the outcome of the auction.

Bundling and Complementarities
Bundling is a strategy of selling multiple products as a package for a single price. It is commonly observed in the marketplace. For example, bundling of vacation components such as flight and hotel or flight and car rental would be familiar to most people, as would bundling of software and hardware, cameras and accessories, toothpaste and toothbrush, etc. For a firm with flexible production, bundling is a very attractive mechanism of extraction of additional surplus from customers with heterogeneous preferences.
As an example for why bundling is profitable with flexible volume consider a segment of consumers that really like toothbrushes made by the firm and is willing to pay $5.00 for the toothbrush but is only willing to pay $1.00 for their toothpaste. Another segment values both the toothbrushes only at $2.00 and the toothpaste at $4.50. With bundling, the firm can price the package at the sum of valuations for one of the segments (the segment with the lower sum of valuations). The bundle price which they can charge to both segments (such that both segments purchase the bundle) is $6.00, which is substantially higher than the $1.00 for the toothpaste and $2.00 for the toothbrush if they wanted to cover the entire market. Any higher prices for the individual products results in the loss of sales from one of the segments. Now this is a very simplistic example, but it captures the essence of the idea of bundling with flexible production.
The key feature of the above scenario was that the firm's revenue depends not only on price but also on quantity (Adams and Yellen, 1976;Stremersch et al., 2007). In auctions, in contrast, the supply of each component is fixed at one unit. The seller's only interest is in extracting the highest price for each of its components. When the number of bidders is sufficiently large and there is little or no complementarity, this happens with separate component auctions -not in bundled auctions (Palfrey, 1983;Chakraborty, 1999;Subramanian and Venkatesh, 2009;Krishna and Rosenthal, 1996).
Complementarities between two components occur when the utility of a consumer from obtaining a component increases if he can also obtain the other component. The classical example is right shoe and left shoe, but one can also think of two concert tickets next to each other, or a romantic dinner for two. The bundling of two complementary goods tends to lead to bundle valuations that are greater than the sum of valuations of the components (Guiltinan, 1987;Stremersch et al., 2007;Venkatesh and Kamakura, 2003).
One might be able to obtain the full bundle value by winning two separate auctions for the individual components. However, this brings us to the "exposure problem." Exposure risk happens when the individual faces the possibility of winning the auction for one of the components but losing the other (Bykowsky et al., 2000;Rothkopf et al., 1998;Chakraborty, 2004). This results in less aggressive bidding for the separate components than in the bundled auction. Consequently, one may expect lower revenue for the separate component auctions.
To test the effect of bundling on revenues in the presence of complementarities, Popkowski Leszczyc and Häubl (2010) conducted a series of field experiments. In Study 1, they sold 168 sets of collectible stamps broken into two components. Two identical replicates of each of 112 components were sold. In one replicate, the 112 components were sold separately (112 auctions), in the other they were sold as bundles of two components each (56 auctions). The bundles were chosen such that the components were substitutes (collectors' joint valuations, assessed via surveys were below the sum of valuations for the individual components). The auctions were run on eBay for a period of three weeks. The authors found that in this case, the separate auctions were more profitable. Average revenue was $14.68 from the separate auctions vs. $10.99 for the bundled auctions and this was highly significant. Thus, with substitute components (the opposite of complements), a bundling strategy is not a profitable proposition.
In Study 2, they repeated the procedure for two replicates of 96 components of stamp sets -sold separately and bundled. This time they chose bundles that measured high on complementarities based on a survey. The auctions were run on eBay for a period of two weeks. The average revenue from the bundled auctions was $24.87 compared to $16.52 for the separate auctions. This was highly significant. Hence, when complementarities are present, bundled auctions produce higher revenues.
Whereas Studies 1 and 2 involved stamp sets sold on eBay, Study 3 involved a smaller Internet auction site and more diverse categories of products. Half the bundles consisted of services and the other half were tangible products. The bundles also differed on the degree of complementarity. An important manipulation was whether the bundled auction was run simultaneously with the separate auctions or not. Hence, the following treatments were examined: (1) bundle and components (known as mixed bundling), (2) bundle only (known as pure bundling), and (3) components only.
They found that complementary items yielded average revenue of $65.79, compared to $43.92 from separate auctions. For substitute items, the opposite was the case with $50.11 for bundles vs. $62.27 for separate auctions. This pattern is consistent with the results of Studies 1 and 2.
Regarding selling strategies, revenues from pure bundling ($63.01) were higher than under mixed bundling ($54.19), but revenues from the separate auctions did not differ based on whether a bundle was present or not.
In summary, auction bundles are more profitable than separate auctions when even moderate complementarities are present but less profitable for substitute products. The authors also found some indication that for services the effect may be more pronounced than for tangible products. Additionally, they ran analyses indicating that much of the effect was due to the exposure risk discussed earlier.
Popkowski Leszcyc et al. (2008) used second-price sealed-bid auctions to determine bidder's valuations for bundled items and separate bundle components. They find that bidders make inferences about the value of uncertain items based on the value of certain items. More specifically they find that when bundling two unrelated items, a low-value certain item with a high-value uncertain item, may lead to extreme sub-additivity (where average bundle valuations are lower than that of one of the components sold separately). They also find super-additivity for non-complementary items when bundling a highvalue certain item with a low-value uncertain item. Therefore, bundle composition may have an important impact on bundle profitability in auctions.
In summary, while bundling is a profitable strategy outside of auctions, in auctions it is not expected to yield increased revenue unless complementarities are present and exposure risk is salient.

Closing Rules and Sniping
In early research on online auctions, sniping was a critical topic of research because closing rules were the key feature differentiating auction giants eBay, Amazon, and Yahoo, of which only eBay survived as an auction house in the United States. Of these auction houses, eBay had fixed ending times, Amazon had a soft ending time, and Yahoo provided a choice between the two.
When an auction has a fixed ending time, an individual bidder may wait until the last possible moment to place a bid in the auction. If the bid goes through, competing bidders do not have any time to respond and the bidder may win the auction without a bidding war. This is a very attractive idea to many bidders and it is therefore not uncommon to see an auction with multiple bids in the last minute. In Figure 3.6, the auction had an ending time of 08:15:01. 17 bids were placed over a period of 7 days by 11 bidders. However three of these bids were placed in the last minute of the auction, and the winning bid was placed in the last five seconds. This is a typical pattern in auctions with more than a few bids (Roth and Ockenfels, 2002).
While the reasoning behind sniping is largely intuitive, it was considered to be a puzzle until a seminal work by Roth and Ockenfels (2002) showed it in equilibrium terms. A sketch of the proof is as follows: suppose there are two bidders and they each have a choice between engaging in a bidding war or waiting for the last second to out in their true valuation as a bid. Given some probability of either last-minute bid not going through, the bidders face some probability of losing when they might have won, but each bidder also has some (possibly small) probability of winning the auction at a price near the reservation price of the seller. This happens only when the other bidder's bid does not go through and it may happen rarely. However, the surplus in that case is large and this large surplus is generally sufficient, even when multiplied by a small probability to make it desirable to wait. However, if other bidders bring the price up earlier, sniping becomes quickly unattractive. Ariely et al. (2005) conducted laboratory experiments, where they manipulated the ending rule to be either fixed (i.e., a la eBay) or variable (like Amazon). Consistent with Roth and Ockenfels (2002) they observe a greater proportion of late bidding in auctions with a fixed ending time. In addition, they suggest that snipe bidding may be an optimum strategy against incremental bidders. In addition, sniping may be an effective strategy to not to reveal a bidder's private information in common value auctions (e.g., Bajari and Hortaçsu, 2003).
Sniping may also be an effective weapon to fight against shill (false) bidders. Furthermore, bidders who have a reputation about being knowledgeable about the true value of the product may choose to snipe in order not to give other bidders a signal about the quality of the product.
Nonstrategic reasons for sniping may also exist such as lack of knowledge about the proxy bidding system, a personal desire to avoid getting caught in bidding frenzy, procrastination, and impatience. If self-control is an issue the bidder may choose to snipe in order not to get caught in bidding frenzy himself. Gönül and Popkowski Leszczyc (2010) find that although the incidence of snipe bidding is low (about 3-5%), the winner of an auction is more likely to be a snipe bidder, everything else held constant. Additionally, they show that high prices faced by a bidder, including high reserve prices, are more likely to result in sniping behavior on that bidder's part. This sniping behavior, they find, is effective in keeping prices from rising further. Consistent with the findings by Ockenfels and Roth (2002), they find that experience increases the chance of sniping by a bidder. Interestingly, they find that shorter auctions have higher sniping activity and this is consistent with competitive arousal. Combined, these findings imply that if sellers or auction designers want to discourage snipe bidding they are better off having longer auctions without reserve prices.
Gray and Reiley (2007) set out to test whether sniping can be said to be generally profitable for bidders. Using a field experiment, they selected pairs of nearly identical auctions (in seller, closing time, etc.) on eBay and placed sniping bids in one auction but not the other. This resulted in 2.54% lower prices for the sniped auctions but not significantly so. They therefore rejected that sniping was profitable for bidders. Since this result is in such contrast to presently accepted notions about sniping, they compared their data and categories with those in Roth and Ockenfels (2002) and found similar sniping patterns, thus rejecting that their result was due to some differences in the data. There are probably two lessons from that exercise. One lesson is that any effect of sniping is not merely a simple a multiplier on each snipe bid -it is likely an equilibrium outcome, as is claimed by Roth and Ockenfels. Hence, for one to observe the effect of sniping it is not sufficient to go and place snipes in one auction but not in the other. A second lesson is that the effect of sniping is perhaps not as large as once believed. While expert bidders do snipe and most bids do occur late in the auction, the advantage to a single bidder from unilaterally sniping is probably extremely small if at all (and this is consistent the equilibrium explanation of Roth and Ockenfels). Ely and Hossain (2009) conducted a field experiment with a similar design, where they placed sniping bids or early bids in auctions for 20 popular DVD titles. Unlike Gray and Reiley (2007), they did not attempt to do so simultaneously, did not have a balanced design, nor controlled for seller identity and end time. However, the products in their design were new DVDs and could be argued to be relatively undifferentiated. They found that their winning probability with sniping improved from 47.6% to 52.6%. However, this translated to a statistically significant but still unimpressive payoff difference of 17 cents (or about 1% of the bid amount). They found that while sniping was effective in reducing bidding response (their opponents were shown to be largely nonstrategic), early bidding was also effective as a competitive deterrent. This further demonstrates that the effect of sniping is not likely to be large.
Sniping is important for online auction researchers because it is a well-known phenomenon and will generally be inquired about by audiences and readers. Hence, it is always a good idea to prepare for such inquiries by collecting the incidence of sniping in one's data and being able to report both the incidence and effect of sniping. However, at the same time, one should not be overly occupied with the inclusion of sniping in every model. A model without sniping built in is likely missing an element, but this is not likely to be the biggest assumption made for an auction model. The studies show that this omission is not likely enormous in dollar terms.

Open and Secret Reserve Prices
A reserve price is the minimum price (see related survey of minimum price in Park and Wang, 2009) a seller agrees in advance to receive as a winning bid for his item. In selecting the level of the reserve price, there is a tradeoff between the selling price conditional on a sale, which increases in the reserve price, and the probability that the item will sell, which decreases in the reserve price (e.g., Greenleaf, 2004). In other words, a reserve price protects a seller from selling an item at too low a price, and may extract additional surplus from the highest bidder (e.g., Levin and Smith, 1996;McAfee and McMillan, 1987;Milgrom and Weber, 2000;Riley and Samuelson, 1981). 5 On the other hand, setting a reserve price too high may result in foregoing a profitable sale.
A seller may choose between using an open reserve price and a secret reserve price and this is an important strategic choice (see related discussion in Park and Wang, 2009). Under an open reserve price no bids are allowed below the reserve price. The reserve price is also called the minimum bid or starting bid. Open reserve price has been shown to be critical to price determination in auctions (Bajari and Hortaçsu, 2003;Engelbrecht-Wiggans, 1987;Levin and Smith, 1996;Riley and Samuelson, 1981;Reiley, 2006). In addition, a number of papers have suggested that open reserve prices may have a significant reference price effect on final price (Ariely and Simonson, 2003;Häubl and Popkowski Leszczyc, 2003;Kamins et al., 2004;Suter and Hardesty, 2005;Shunda, 2007). Häubl and Popkowski Leszczyc (2003) conducted laboratory experiments where they obtained bidders' valuations. Their results suggested that higher reference prices had a positive effect on bidders' valuations.
In a standalone auction, the theory is unambiguous that a higher open reserve price will result in higher conditional final price (conditional on the item being sold). However, in online auctions, the theory of standalone auctions (which constitutes most of traditional auction theory) is often challenged. Specifically, in the case of competing auctions there is an indirect negative effect of reserve price on selling price due to fewer participating bidders in the auction (see Reiley, 2006;Bajari and Hortaçsu, 2003;Engelbrecht-Wiggans and Nonnenmacher, 1999;Bulow and Klemperer, 1996). Hence, auctions with a very low starting bid may obtain higher prices due to the larger number of bidders they attract.
Some important insights regarding reserve price are offered by Park and Bradlow (2005) who study dynamic auction data for notebook computers. They model the joint outcome of who bids, when they bid, and what they bid. They find that reserve price is positively related to bidder valuations. However, Bradlow and Park (2007), with the same data, find that higher reserve prices negatively affect bid time increments (resulting in slower arrival of bids).
In the case of a secret reserve price, bidders know that a reserve price exists but they do not know what it is. The seller specifies a nonbinding starting bid and a binding secret reserve price. Sellers may use a low starting bid amount to induce bidder entry, together with a secret reserve to ensure that the item will not sell at a price that they are not willing to accept. The item will not sell unless the secret reserve has been met.
Whereas open reserve price, in theory, affects auction process by eliminating potential entrants early on, it need not affect choice between simultaneous auctions once it is exceeded. In contrast, secret reserve looms throughout the auction as a threat that the bidder may not get the item even if he wins. Hence, the bidder needs to form a prior belief of what that secret reserve might be. Katkar and Reiley (2006) find a significant negative effect of a secret reserve on ending price. 6 Therefore, the presence of a secret reserve price is expected to decrease price.
As with open reserve prices, results of extant studies of the effect of secret reserve on selling prices are mixed. On the one hand, the presence of a secret reserve price may cause some bidders to shun these auctions, since there is a chance that the highest bid will not meet the reserve (e.g., Vincent, 1995;Li and Tan, 2000;Katkar and Reiley, 2006). On the other hand, secret reserve prices induce bidder entry relative to open reserve prices. Moreover, a secret reserve price may be thought of as the addition of another competing bidder (with valuation at the secret reserve), leading to higher prices (e.g., Elyakime et al., 1994;Lucking-Reiley et al., 2007;Bajari and Hortaçsu, 2003).

Auction Dynamics
Online consumer auctions are largely dynamic. Sealed bid versions are not common in online consumer auction settings. Thus, much of the action in auction takes place dynamically in the course of the auction. Unfortunately, economic theory has relatively little to say on the matter. In the theoretical economics literature, it is common to assume equivalences between the dynamic in English auction, where bidders dynamically submit bids that exceed the previously high bid, and the second-price sealed-bid auction. This equivalence is due to the fact that in both formats the bidder has a weakly dominant strategy of giving his or her true valuation for the item as the maximum willingness to pay.
There are two problems with using this equivalence in analyzing online auction data. First, this equivalence tells us nothing about the bid-by-bid pattern. It only allows us to use the final bid by each bidder to assess this bidder's valuation. Second, as Zeithammer and Adams (2010) showed, this theoretical abstraction can be rejected empirically even when looking at final bids. The marketing literature is far less bound by this theoretical restriction, and more flexible tools have been devised to analyze bid-by-bid data.
A few patterns regarding dynamics have been explored in the literature and we briefly review them here.

Early and Late Bidding
Most bids in an auction take place early on in the auction and toward the end of the auction. The middle region generally contains very few and infrequent bids. Roth and Ockenfels (2002) found strong evidence for late bidding which they attributed to sniping. Sniping is the practice of placing last second bids in the hope that a competing bidder will not have time to respond. Many late bids are placed minutes or hours from the end of the auction so the sniping interpretation may be an oversimplification. Shmueli et al. (2007) found that auctions could be characterized as containing three distinct parts -an early part with some bidding, a middle part with little bidding, and a late part with frequent bidding. They found that the early part of the auction consists of the first day and half of a seven-day auction. Bapna et al. (2004) found a similar pattern of early bidding and characterized it as lasting 1.4 days out of 7 days. Wang et al. (2008b) showed that the extent of early bidding depends on the value of the item. For high value items like an Xbox machine, they found that 57% of auctions had early bidding. In contrast, for low-valued items like a Harry Potter book only 29% of auctions had early bidding.

Jump Bidding
Mainstream theory prescribes that a bid placed in a dynamic auction should be one increment above the previous high bid. In the absence of impatience, cost of time, and strategic signaling considerations, such an action dominates placing higher bids. A bid that exceeds the minimum increment above the previous bid by a non-negligible amount is defined as a jump bid (Easly and Tenorino, 2004;Isaac et al., 2007;Daniel and Hirshleifer, 1998). Wang et al. (2008b) based on rigorous analysis define a jump bid as a bid that is at least 30% higher than the previous bid. They find that 25% of low-valued auctions have at least one jump bid, whereas 9% of high-valued auctions have jump bidding.
In order to study the entire auction-path, Wang et al. (2008b) devised a method they called Functional Data Analysis (FDA). This method focuses on sets of curves, shapes, objects, or more generally, a set of functional observations. Knowledge of the entire auction-path leads to new and useful concepts such as an auction's velocity or acceleration which allows the researcher to study competition in a dynamic setting. Hyde et al. (2006) used FDA to create new data-visualization tools, known as Auction Rug Plots, to study simultaneous auction price processes. Wang et al. (2008b) show that incorporation of an auction's price dynamics can lead to considerable improvements in the accuracy of predictions of interim and final price. Bapna et al. (2008b) provide more evidence that dynamics exist and matter. Park and Bradlow (2005) study bidding behavior over the entire sequence of bids by building a comprehensive descriptive model, which models several key components of the bidding process (e.g., whether an auction prompts any bids; if so, who bids, when they bid, and how much they bid). Jap and Naik (2008) use Kalman filtering estimation techniques to provide a method for forecasting interim and final prices and selecting among dynamic bidding models based on point-by-point data in reverse auctions. Their approach also aids the buyer's price discovery process. Specifically, given the estimated price distribution from the forecasting model "BidAnalyzer," the buyer can determine whether a supplier has bid too aggressively (and faces a winner's curse) or has held back (leaving "money on the table").
Dynamics in simultaneous or overlapping auctions, where the price dynamic in one auction interacts with the price dynamic in another auction, is considerably more complex and creates new challenges.
There is also a need for characterization of bidding strategies in concurrent auctions (see Jank and Zhang, 2007). A major challenge of modeling concurrent (or simultaneous) auctions is the modeling of (1) a temporal component, consisting of unevenly spaced time-series of closing times of auctions, and (2) a spatial component that reflects the distance between two products in feature space. Jank and Shmueli (2007) proposed a new solution by applying a spatial-temporal model. This model solves the problem of unevenly spaced time-series and includes an overall distance measure for combining all quantitative and qualitative product attributes (similar products lie in close proximity in feature space). The resulting model is estimated within a semiparametric framework which allows for a flexible estimation of the many unknown functional relationships. Jank and Shmueli (2007) found that temporal price and the effect of similar items both influence final auction price.
To model a bidder's willingness to pay in ascending first-price auctions, Chan et al. (2007) consider two-dimensional market competition using notebook PC data. They use breadth and depth measures to characterize market competition in online auctions. Chan et al. (2006) also incorporate these measures in examining sellers' decisions about whether and where to set buy-it-now prices. Bradlow and Park (2007) consider a sequence of bids in online auctions with an analogy of record-breaking events. Similar to Chan et al. (2007), they find significant negative effects for bid increments and variances in both market competition measures. This informs both breadth and depth measures and helps bidders better determine bid amounts, reducing the uncertainty in bid increments.
Learning is another dynamic component and can occur either in the course of an auction or in participants' auction use over time (e.g., Wilcox, 2000;Roth and Ockenfels, 2002). In an analysis of 785 online wholesale automotive first-price auctions over a 29-month period, Overby and Jap (2009) find that the initial appeal of the online auction for buyers is the reduction of transaction costs (i.e., travel). But over time, buyers leveraged the reduced search costs associated with these mechanisms to source rare vehicles. Similarly, sellers adapt their use of auction mechanisms, increasingly relying over time on online auctions as a means for selling their low condition grade vehicles and physical auctions as the primary channel for their better condition grade vehicles.

Charity Auctions
Charities are increasingly using online auctions for their fundraising efforts. Several auction websites have been created specifically for this purpose (e.g., shopgoodwill.com and cMarket.com). In addition, eBay has a special section dedicated to charity auctions (eBay giving). While non-profit organizations use charity auctions for fundraising purposes, for profit organizations use them as part of their corporate social responsibility strategy to establish themselves as good corporate citizens.
Though charity auctions have increased in popularity little is known about their effectiveness or about the motives of bidders participating in them. Auction research has generally assumed that bidders are selfinterested, but how do bidders bid in charity auctions when they care about the charity's revenue? Theoretical models predict superior revenues because money goes to a good cause (Engers and McManus, 2007;Goeree et al., 2005;Salmon and Isaac, 2006). Haruvy and Popkowski Leszczyc (2009) and Popkowski Leszcyc and Rothkopf (2010) study the extent to which bidders with charitable intent are willing to pay a premium in charity auctions and what motivates these bidders. Haruvy and Popkowski Leszczyc (2009) study cause-related auctions where a percentage of the auction selling price is donated to charity. They ran 672 auctions on a local Internet auction website for products like collectible stamps, computer products, cosmetics, electronics, gift certificates, and tools. Two identical product auctions were always sold simultaneously but different percentages of the selling price were donated to charity (there were two conditions (1) 0 vs. 100% donated, and (2) different partial donations). The average charity premium across all products was 18.2%.
Results of a mixture model identified three different bidder segments: a selfish segment, a warm glow segment with bidders who derive pleasure from giving, and a segment with other-regarding bidders who give for selfless reasons. The warm glow segment tended to select auctions where the charity premium was low while the other-regarding segment was willing to pay a higher premium in auctions where a greater percentage is donated to charity. They also concluded that the charitable premium was higher for frivolous products, consistent with the finding by Strahilevitz and Myers (1998).
Popkowski Leszcyc and Rothkopf (2010) conducted three controlled field experiments to study the type and the extent of bidders' charitable motives. They used a pair-wise design where identical products are sold in simultaneous auctions, where a charity auction is always paired with a non-charity auction.
Results across three experiments show that bidders are willing to pay a significant premium in charity auctions (the average premium in the three studies ranges from 11% to 63%). Furthermore, results indicated that higher ending prices in charity auctions are due to bidders' charitable motives and not due to increased participation, as on average fewer bidders participated in the charity auctions. They also find that charitable bidders are motivated by trying to increase proceeds to charity, acting like volunteer shill-like bidders.
In their third experiment they compare auctions where 0%, 25%, or 100% of revenue is donated to charity (average selling prices were $5.48, $7.72, and $8.94, respectively). Interestingly, when 25% is donated to charity, net revenue (after deducting the 25% donated) is higher than revenue in non-charity auctions. This suggests that companies may be socially responsible and increase profitability at the same time. Therefore, charity auctions may be an important alternative to direct monetary donations as part of a social corporate responsibility strategy.

Conclusions
A well-known joke tells of a man searching intently for his lost wallet by pacing up and down the street and looking carefully on the ground. A person wishing to help asks him exactly where he lost the wallet. The searching man replies that he lost his wallet on another street but that he is looking on this particular street because of the better lighting. Traditional auction theory (by which we are referring to auction theory that ignores critical features relevant to online consumer auctions)the well-lit area where the theory is well developed -is simply not where the answers to questions pertaining to online consumer auctions are.
As discussed earlier, the four standard auction formats (Section 2.1) and variations, which are relatively well understood in traditional auction theory, do not seem to fit well the commonly practiced format of proxy bidding (Section 2.3) with a fixed closing time (Section 3.8) that is used in eBay auctions and most other online auctions. This is discussed in Section 2.3 and explored in great detail in Zeithammer and Adams (2010) but is generally noted in empirical works on online auctions. One should also refer to our discussion of sniping (Section 3.8) and auction dynamics (Section 3.10) to see how standard online formats make existing auction theory less relevant than one might hope for. The discussion of reserve price (Section 3.9) is especially illuminating in that it explores a format choice that was thought to be well understood in the established theory. Variations on reserve price formats such as different degrees of transparency (public vs. private) and reference price considerations are not unique to online auctions but are more salient in online consumer auctions since these are among the key choices faced by online sellers. The discussion of buy-it-now-price (Section 3.2) is another example of a common practice in online consumer auctions that is unsupported in traditional auction theory.
Traditional auction theory builds heavily on bidder rationality, in many cases risk neutrality, bidder symmetry, revenue and strategic equivalences, and other key properties that are clearly violated in online bidding behavior. These assumptions are known to be violated in traditional auction settings as well. But, as highlighted in this review as well as in Park and Wang (2009), online bidding behavior is notorious for issues such as bidding frenzy (Section 3.1), impatience (Sections 3.1 and 3.2), and other behavioral considerations that are more likely to be present in online auctions and exploited by online sellers than elsewhere (Section 3.1).
Finally, much of traditional auction theory has been concerned with standalone auctions. In online auctions, a bidder must choose between numerous alternatives -both simultaneous and sequential. Thus, the design of the auction should be examined not only from the perspective of what maximizes revenue within the auction, but also from the perspective of what maximizes the chance of the auction being selected from among all competing alternatives (see auction competition in Section 3.6). All eleven topics of Section 3 actively address the issue of auction attractiveness. Particularly, unique to online auctions in that regard is the issue of trust and feedback ratings (Section 3.3). However, online search considerations (Section 3.5) are critical as are issues of complementarities (Section 3.7).
It is important to stress that auction theory is a well-developed field with a great deal of theoretical building blocks. While these building blocks may sometimes be unhelpful and occasionally hinder the search for answers, a researcher wishing to pursue auction data or auction puzzles will find it necessary to be familiar with the basic theory and terminology. Our short background did not even scratch the surface of that basic theory because this overview is intended to cover recent works in marketing. Therefore, we only reviewed the most basic terminology necessary to understand the studies that we discussed. However, the references within this overview, especially in the introduction and background, should be sufficient in getting sufficient familiarity with the field.
The eleven topics we covered in Section 3 are key topics which present great puzzles to auction researchers and practitioners. Researchers in marketing are ideally suited to address these topics as many of them have parallels that have been researched in the marketing literature. In our opinion, based on the research reviewed here, questions of optimal design choice in online auctions can often be addressed empirically and have as much to do with bidder preferences, bidder search, mental processing of information, and issues of trust and reputation as they do with the traditional focus on equilibrium predictions. Both experimental and statistical methods in marketing are well suited to such investigations. All eleven topics covered in Section 3 thus provide important directions for research in marketing.
Finally, a marketing researcher who is interested in non-auction topics may nevertheless find auction data readily and publically available to test marketing models regarding shopping behavior, preferences, rationality, search, strategic response, trust, information mediation, dynamic shopping behavior, etc. For such researchers, the present overview is intended as a primer on the type of data that is available, the methodology used to analyze it, and its connection to non-auction topics (we were particularly careful to make these connections).