Paid Placement Strategies for Internet Search Engines

Transcription

1 Paid Plaement Strategies for Internet Searh Engines Hemant K. Bhargava Smeal College of Business Penn State University 342 Beam Building University Park, PA Juan Feng Smeal College of Business Penn State University 118 Beam Building University Park, PA ABSTRACT Internet searh engines and omparison shopping have reently begun implementing a paid plaement strategy, where some ontent providers are given prominent positioning in return for a plaement fee. This bias generates plaement revenues but reates a disutility to users, thus reduing userbased revenues. We formulate the searh engine design problem as a tradeoff between these two types of revenues. We demonstrate that the optimal plaement strategy depends on the relative benefits (to providers and disutilities (to users of paid plaement. We ompute the optimal plaement fee, haraterize the optimal bias level, and analyze sensitivity of the plaement strategy to various fators. In the optimal paid plaement strategy, the plaement revenues are set below the monopoly level due to its negative impat on advertising revenues. An inrease in the searh engine s quality of servie allows it to improve profits from paid plaement, moving it loser to the ideal. However, an inrease in the value-per-user motivates the gatekeeper to inrease market share by reduing further its reliane on paid plaement and fration of paying providers. Categories and Subjet Desriptors H.3 [Information Systems]: Information Storage and Retrieval General Terms Eonomis Keywords Searh engines, information gatekeepers, paid plaement, bias, promotion 1. INTRODUCTION The Internet and World Wide Web are home to vast repositories of information from text to multimedia, from amateur opinions to expert thought, from voluntary ontributions to ommerial interests on every oneivable topi. Lawrene & Giles [12] estimated the publily indexable Web at 800 million pages, 6 terabytes of text data, on 2.8 million servers, as of February Internet searh engines, whih serve as a gateway to this information repository, have established a ruial role in today s important soiety. Most Copyright is held by the author/owner(s. WWW2002, May 7 11, 2002, Honolulu, Hawaii, USA. ACM /02/0005. studies of Internet usage find that searh engines play a vital role in information retrieval over the Internet. A reent USA Today (De. 11, artile states that 100 million queries are made on U.S. searh engines eah weekday, and a study of Web usage 2 by Media Metrix found that the top 3 searh engines were eah visited by 61%, 56% and 40% of traked Internet users during the past month. The widespread use of searh engines has failitated tehnology transfer, so that searh engine tehnologies are now liensed to business Web sites, used in digital library systems, et. For the purpose of this paper, the term searh engine enompasses various appliations of these indexing-retrieval tehnologies, inluding traditional Web searh engines (e.g., Google, metasearh engines (Metarawlers, nihe searh engines (e.g., DEADLINER (Gruger etal (2000 [10], information portals (Yahoo!, and omparison shopping engines (mysimon. Most searh engines began as university projets that foused more on development and algorithms, and less on revenue generation. Even after transitioning into ommerial entities, searh engines tended to operate as a free resoure to ontent providers and users alike. However, the reent drop in supply of heap venture apital and sweat equity has fored ommerial searh engines to investigate mehanisms for generating revenue from ontent providers. These mehanisms whih we generially label as paid plaement inlude a fee for inlusion in the database, an inreased relevane sore in response to a query, or featured listings on the results pages. A paid plaement strategy usually requires a minor modifiation of the ranking algorithm or to the display of results, either of whih an be made at very low ost. Paid plaement is widespread in searh engines (e.g., Google, information portals (e.g., Yahoo!, and metasearh engines (Metarawler. Nearly all major searh engines and portals employ paid plaement. 3 Table 1 presents data on the extent of paid plaement for metasearh engines. And, as Figure 1 indiates, the major omparison shopping engines also employ paid plaement. The fous of this paper is on a searh engine s strategy regarding revenues from ontent providers in its database, and how this objetive onflits with its other revenue soures whih are a funtion of its user base, suh as advertising and liensing revenues. We develop a mathematial model to analyze the dilemma that searh engine faes in raising At Searh Engine Wath webmasters/paid.html 117

2 revenues: it wants to harge ontent providers for priority plaement, but this redues the searh engine s redibility, hene its market share and potential user-based revenues. Speifially, we determine the optimal paid plaement poliy, i.e., the optimal plaement fee and the resulting perentage of sites that hoose paid plaement. Our longer term interest is to determine the optimal bias-level that would give a searh engine the best balane between revenues from ontent providers and revenues based on its user base. The revenue problem is a ritial one for searh engines, sine it impats both urrent performane and future development and improvements. In spite of many years of researh on information retrieval, searh engines are still far from perfet in terms of the usual metris of relevane and reall. Hene, there remains onsiderable researh and ommerial interest in refining the indexing and ranking algorithms, and user interfaes, employed by searh engines. Reent researh examines a variety of topis, inluding Web page ranking algorithms evaluation and omparison of alternative ranking algorithms (Singhal & Kaszkiel [14], ontextual and topi-based searh (e.g., Bharat & Henzinger [2], design and evaluation of metasearh engines (e.g., Dreilinger & Howe [6], metasearh using full-text analysis of Web pages (e.g., Lawrene & Giles [11], and visualization of results (Hearst [9]. Sine further researh and development is expensive, ommerial searh engines need to find new revenue soures in order to balane these osts. Paid plaement offers an intriguing possibility: plaement revenues in one period an support researh and development aimed at improving indexing and retrieval algorithms, database index, or user interfae. Hene the negative impat (on users of paid plaement ould be reversed by using plaement revenues to improve searh engine quality. The rest of this paper is organized as follows. In 2, we develop our model of the searh engine s revenue problem, onsidering network effets, the effet of paid plaement, and third-party revenues. We haraterize the optimal paid plaement strategy in 3. In 4 we disuss the sensitivity of the paid plaement strategy to various ontrollable parameters suh as the extent of bias and searh engine quality, and other fators suh as pereived disutility and the advertising rate. We onlude with a summary of our results and possible appliation of our work to other forms of Internet-based information intermediaries. 2. MODEL OF SEARCH ENGINES REVENUE PROBLEM Consider a searh engine whih offers indexing and retrieval servies over a virtual library of aggregated ontent from multiple ontent providers, and presents an ordered list of results in response to a user s query term. In developing an eonomi model of searh engines, we onsider three types of entities: users of searh engines, ontent providers, and third-parties suh as advertisers and liensing firms. The searh engine serves a market of users who are heterogeneous in their preferenes. Let Θ represent user types in a desending order of users valuation for the searhing servie, and let q represent quality of the searh engine as pereived by users, whih may be a omposite measure of its database of ontent providers, user interfae, and indexing and retrieval algorithms. We write U(θ, q to denote the value to a type-θ user for a given quality q. For onveniene we assume that θ is uniformly distributed in the interval [1, 2]. We onsider q to be exogenously speified in the period of interest. The searh engine benefits ontent providers by direting users to their sites. Content providers are also heterogeneous in their profit expetations. Let Φ represent a desending order of provider types, where (for onveniene φ is uniformly distributed in [1, 2]. 2.1 Network Externalities We oneptualize the searh engine as offering network benefits to both users and ontent providers i.e., the overall value of the searh engine to users inreases in the total number of ontent providers, and the value to ontent providers inreases in the number of users. Considering first the users valuation funtion, this means that U(θ, q is an inreasing funtion in q sine q represents the index of ontent providers. For onveniene we assume that this funtion is U(θ, q = q. Similarly, the value to ontent providers θ V (Φ, M is an inreasing funtion of the searh engine s market overage or user base M. We write the provider valuation funtion as V (φ = M. φ The searh engine s generates revenues on the basis of its user base, inluding revenues from third-party firms suh as advertisers and fees for liensing their information retrieval tehnologies. Advertisers are interested in exposure to users of the searh engine, hene advertising revenues are a funtion of the searh engine s user base. Firms that liense the searh engine tehnology pay a per-lik-through fee, hene these revenues are also a funtion of the user base. For simpliity in exposition, we denote all user-based revenues as advertising revenues. Let a represent the advertising value per user, so that the searh engines revenues equal π 1 = a M 2.2 Effet of Paid Plaement Internet searh engines exeute a user s searh query on a database index, and typially return a set of results ranked aording to their relevane sore. Given these results, the user selets speifi ontent providers in the list for further transations. It is well known that the ranking of a result term is strongly orrelated with the probability that the user will follow up on the result term (MLuhan ( [13]. Commerial ontent providers are interested in likthroughs and onversion rates i.e., the likelihood that a searh engine user will enter into a ommerial transation with the ontent provider. For this reason, ontent providers have an inentive towards paid plaement to pay the searh engine in order to be inluded, ranked highly, or prominently featured in the searh result. 4 In pratie, this may mean a higher relevane sore, a featured listing, or perhaps even a guaranteed retrieval for ertain searh terms. Figure 1 displays a sreen shot from a omparison shopping engine that inludes paid plaement (featured listing, normal plaement, and advertising. What is the impat of paid plaement on a searh engine s pereived quality? Here we assume that searh engine an not hide the fat that they perform paid plaement, beause this an not exist in equilibrium in the long run (bidders an 4 The USA Today, ited earlier, quotes the CEO of GoTo.om as saying that ommere-related requests make up nearly 50% of searh engine queries, and that over 32,000 businesses pay to be listed on GoTo. 118

3 Figure 1: Paid plaement, regular listings, and advertising in a omparison shopping engine. The top listing and graphial ion inrease the likelihood that the paid plaement listing will be followed up. pereive finally, or may ause some serious legal issues. 5 Artiles in the business press and data from ommerial researh firms suggest that paid plaement strategies have a negative impat on a searh engine s pereived quality and redibility. Goodman [8] argues that searh engines must at as referees fair arbiters of relevane or they will lose market share. Sine loss of market share auses a fall in advertising revenue, searh engines must trade-off potential revenues from paid plaement with those from advertising. To model the effet of paid plaement, suppose that the searh engine offers priority plaement to ontent providers who pay a plaement fee γ. Let x represent the perentage of free listings, hene 1 x is the fration of providers who hoose paid plaement. The searh engine an bias its relevane soring algorithm and displays in many ways. Let β be the extent of bias hosen by the searh engine. We represent the positive effet of the bias β on ontent providers with the funtion, and the negative effet on users (after normalization, without loss of generality as β itself. Assume λ > 0, and 2 λ 0. Hene β represents the users β β 2 pereived disutility of paid plaement. In the urrent model, β is treated as exogenous. However, our future work aims to endogenize β and determine the optimal bias level. Due to the negative impat on users, we rewrite the utility funtion as U(θ, q, β = q(1 β(1 x. Providers who θ 5 For example,commerial Alert filed its omplaint with the FTC on Monday, laiming that AltaVista, AOL Time Warner, Diret Hit, iwon, LookSmart, Mirosoft and Terra Lyos are violating US law by inserting paid listings within their searh engine results without lear and onspiuous dislosure that the ads are ads. hoose paid plaement get an additional benefit, so that provider φ s valuation inreases to V (φ(1+. The searh engine now gets additional revenues π 2 = γ (1 x 2.3 Searh Engine s Profit Funtion To ompute the searh engine s profits, we first determine the fration of users that will visit the searh engine and the fration of ontent providers that will hoose paid plaement. Let be the threshold value desired by users before they use the searh engine. This may represent an opportunity ost or effort in using the searh engine, or the value provided by a ompeting searh engine. Hene the users who use the searh engine s servie are {θ : U(θ, q, β }. Sine θ is uniformly distributed in [1, 2], the searh engine s market overage is M = q(1 β(1 x 1 (1 To make the problem meaningful, we require that at least the highest valuation user (θ = 1 will use the searh engine when there is no paid plaement, i.e., that q. The searh engine benefits ontent providers by direting users to their sites. Content providers are heterogeneous in their profit expetations, whih is a funtion of the searh engine s market overage M. Providers have a hoie between regular plaement (whih provides a value V (φ at no ost and paid plaement (whih provides value V (φ(1 + at ost γ. Rational providers will hoose paid plaement if and only if V (φ(1 + γ V (φ i.e., φ M. γ Hene the fration of providers who hoose paid plaement 119

4 is (1 x = M γ 1 (2 The searh engine obtains revenues from two soures, third party firms and paid plaement. The first type of revenue π 1 = a M is a funtion of the searh engine s market overage, M, and profit rate brought by eah user, a. If M is interpreted as the number of queries to the searh engine, a may be onsidered as the rate per impression. Hene ( q(1 β(1 x π 1 = a The searh engine s plaement revenue π 2 is γ(1 x. Substituting for M and rearranging terms, we get ( ( q(1 β(1 x q(1 β(1 x π 2 = (2 x The searh engine s total profits are π = π 1 + π 2, and it aims to hoose the optimal fration of paid plaement 1 x (alternately, the optimal degree of independene, x in order to maximize π. 2.4 Literature Review In related work, Bhargava & Choudhary [3] and Corbett & Karmarkar [4] study the ase where an intermediary has the option to harge subsription fees for ustomers and listing fees for suppliers. However, Bhargava & Choudhary [3] onsider only a one-sided network benefit, and their model does not inorporate advertising revenue. Corbett & Karmarkar [4] model two-sided network benefits, but assume homogeneous ontent providers and do not inorporate advertising revenue. Baye & Morgan [1] applied a game theoreti model to study a similar question. None of these papers onsider the possibility that the gatekeeper may bias its outputs due to payments from ontent providers. Dewan et al. [5] study the problem faed by a ontent web sites to balane ontent and advertising by an infinite horizon ontrol program. Gabszewis et al. [7] analyze the ase of two TV hannels ompeting in both the audiene market and the advertising market. But in both these models, advertising is the only revenue soure, hene no trade-off between different resoures is onsidered. 3. OPTIMAL PLACEMENT STRATEGY Solving first-order onditions for the searh engine s profit funtion, we see that the optimal degree of independene x is given by (2 x 2 = ( ( β + 1 ( + aβ q It an be verified that when β > 0, the profit funtion is onave and that the optimal x given by Eq. 3 satisfies seond-order onditions for optimality. 6 Sine we require that x [0, 1], Eq. 3 gives the optimal solution when 1 (β + (1 q 4. (+aβ Lemma 1. For any funtion this inequality yields an interval [β, β] suh that for all β within this interval, Eq. 3 determines the optimal x. 6 The ase where β < 0 is not of interest here, but it is easy to show that x = 0, i.e., the searh engine an obtain full paid plaement. (3 To further develop the results, we first state the following property. Lemma 2. Let ε represent the elastiity of λ with respet to β, hene ε = λ / β. Then for every given β and, λ β there exists a ˆε(β suh that if ε < ˆε(β, then [β, β] > 0, and if ε > ˆε(β, then [β, β] < 0. Speifially, this value ˆε(β equals (+aβ 1 q a(2 q. Proof. From the first order ondition we have (2 x 2 = (β + (1 q. We know that x is dereasing with the right hand side value. Take derivative of the right hand side with respetive to β, we have the sign of the first order ondition is the same as the sign of (2 λ aβ (1 q β. Define the elastiity of λ with respet to β, ε, as λ λ q λ This is the perentage of hanges in λ indued by a small 1 q perentage hange in β. When ε a(2 q, the right hand side is dereasing in β, and it should be between [1, 4], there exists an interval [β, β] of β to satisfy this ondition. Sine when β = 0, the right hand side value is infinity, we know that 0 is to the left of the interval [β, β]. So when ε is small enough and when β is between this range, x is inreasing in β. On the ontrary, if ε 1 q a(2 q, x is dereasing in β. In the same way we know that 0 is to the right of the interval [β, β], whih is the simple ase when there is no disutility toward paid plaement strategy. To understand this result, onsider ε, the responsiveness of listing ompany s benefit to the users disutilities from paid plaement strategy. When the responsiveness is relatively low, the searh engine gets limited inrease in revenue due to the limited inrease in benefit of the listing ompanies by hurting users utilities, so the searh engine should be areful about his paid plaement strategy. From the seond part of Lemma 2, the ase where responsiveness is high (i.e., ε > ˆε(β has no interior optimal solution, and x = 0, i.e., the searh engine an take full advantage of paid plaement. This is beause for the searh engine, the benefit for the listing ompanies is far greater than the disutilities the users suffer, so he an make every listing ompany pay and inrease his revenue at the same time. Hene the searh engine s optimal plaement poliy is speified as below. Lemma 3. When ε < ˆε(β, then for all β in [β, β], the optimal plaement fee is γ = q(1 β(1 x, and the 2 x optimal fration of paid plaement is given by (2 x 2 = (β + (1 q. When the relative disutility to users (+aβ is below β, the optimal fration of paid plaement is 1 x = 1, whereas when the disutility is very high (β > β, the searh engine should not offer paid plaement. This an be easily understood by onsidering the relationship between and β. If ε is large, i.e., the ontent providers benefit muh more than the users suffer, the searh engine makes as many providers pay as possible. Otherwise, the searh engine must trade-off plaement revenue with advertising and restrit the fration of paid plaements. β. 120

5 4. ANALYSIS In this setion we interpret the optimal plaement strategy and examine the sensitivity of the plaement strategy to exogenous fators and fators that the information searh engine an ontrol. We examine the ase where ε < ε(β, ˆ omitting the less interesting ase of high ε whih always results in the boundary solution x = 0. Consider the effet of users disutility (β for paid plaement. The level of bias is ontrollable by searh engine. Hene it s useful to examine the sensitivity of plaement strategy and fees to β. If users were indifferent to paid plaement (β = 0, the searh engine would maximize its plaement revenues without regard for the effet on users. A disutility β fores the searh engine to tradeoff between Proposition 1 elabo- plaement and advertising revenues. rates on this. Proposition 1. There exists a threshold β [0, β] suh that when β is below β, the searh engine an improve profits by inreasing its bias, and when β > β, an inrease in the bias auses the searh engine s profits to derease. Proof. From Lemma 3, when the responsiveness of λ with respet to β is small (ε 1 q a(2 q then β β and x = 0, hene the searh engine an employ its ideal plaement strategy. When β > β, then from Eq. 3 it follows that x > 0. Hene the fration of paid plaements 1 β x falls. Further, we see that when β is lose to 0,x = 0, and π β = aq + 1 [ ] λ (q(1 β λq 2 β By definition of, λ x β β when β 0, whih makes > 0 when β 0, hene the fration of paid plaements 1 x falls. For example, onsider = k β m (where m < 1: we see that λ when β 0, hene the first β order ondition is positive. That means, when β is small enough, the searh engine an inrease the bias a little bit to inrease its profit. An impliation of this result is that there exists an optimal bias β for some funtional forms. The next result desribes how the searh engine s optimal fration of paid plaement hanges with the extent of bias. Proposition 2. When β β, then an inrease in β leaves x unhanged at zero. When β > β, an inrease in β auses a derease in the optimal fration of paid plaements. This result an be easily seen from Eq. 3 and Lemma 3. The hange of the searh engine s revenue will be determined by proposition 1. One of the ontrollable fators of the information searh engine is its quality of servie, whih is determined by the size of the database, the algorithms, and the user interfae. Hene the searh engine an improve quality via investments in these areas. Intuitively, the searh engine ould give up some plaement revenues, improve q, attrat more ustomers and get more advertisement revenues. What is the tradeoff between the searh engine s quality and the plaement revenue? Proposition 3. An inrease in the searh engine s quality q allows it to inrease the fration of paid plaement (level of independene x goes down, inreasing its plaement revenues and total profits. The searh engine s market overage inreases as well, hene an inrease in q inreases surplus for all players. Proof. In Eq. 3, note that an inrease in q inreases the RHS, thus inreasing the fration (1 x. We see that π 2 q = dπ2 dq + dπ2 dx dx dq whih is positive sine dπ 2 > 0 and the remaining two terms dq are negative. To analyze the hange in market overage M as q inreases, note that M q = 1 2 [ ( 2(1 + β 2(1 + β ] ( + a(2 x q whih is positive sine < 1. Hene the searh (+a(2 x engine s market overage and revenues from the third party π 1 = a M inrease, hene its total profit inreases. Sine both frations M and 1 x inrease with q, the total surplus for both users and ontent providers also inreases. In general, to inrease plaement revenues, the searh engine must inrease its fration of paid plaement, but this inreases users disutility and redues demand and advertising revenues. An inrease in q, however, ompensates for the inreased disutility from inreased paid plaement. Hene the searh engine is able to inrease its plaement revenues and yet inrease total profits. Finally, we onsider the impat of per user profit. How do hanges in a affet the searh engine s paid plaement strategy? Proposition 4. An inrease in the per user profit a allows the searh engine to inrease its degree of independene, so that the fration of paid plaements 1 x dereases while the plaement fee γ inreases. As a result, the searh engine inrease its market overage M and total profits π. Proof. From Eq. 3, an inrease in a dereases the RHS, thus inreasing x. Sine 1 x dereases, we an infer from Eq. 2 that γ inreases. That M inreases is obvious from Eq. 1. The searh engine s revenues brought by users and total profits inrease. To understand this result, onsider the searh engine s tradeoff between its two revenue soures. As a inreases, the potential for advertising revenue inreases, hene a partial sarifie of revenues brought by users imposes a greater ost on the getekeeper. Therefore, it redues its level of paid plaement in order to provide greater utility to users, and aptures a greater perentage of potential advertising revenues. 5. CONCLUSION This artile onsiders a paid plaement strategy for searh engines. On the one hand, paid plaement appears to be a finanial neessity, embraed by most major Web searh engines. On the other, paid plaement an hurt the searh engine s market share and its potential for revenues brought 121

6 by users. We have developed a mathematial model for optimal design of a paid plaement strategy, examined this tradeoff and analyzed sensitivity of the plaement strategy to users pereived disutility, the servie quality of the gatekeeper, and the advertising rate. Our preliminary results are as follows. We show that the negative impat of paid plaement on users auses the searh engine to set paid plaements at a below-ideal level. However, when disutility for paid plaement is quite low (though not zero, the searh engine an maintain its ideal plaement revenues. We find that an inrease in the searh engine s quality of servie allows it to improve its utilization of paid plaement, moving it loser to the ideal; this also inreases surplus for all players. However, an inrease in the advertising rate motivates the searh engine to inrease market share by reduing further its reliane on paid plaement and fration of paying providers. As onsumers get a better understanding of the fators underlying paid plaement, the searh engine would likely need to spend heavily on marketing ampaigns in order to minimize users pereived disutility for paid plaement. While this researh is set in the ontext of Internet searh engines, our model and results apply more generally to many other ontexts that share similar harateristis as searh engines. This broader ategory is often alled information gatekeepers, that intermediate between a set of users (or buyers, or onsumers and a set of produts (or ontent providers, or vendors. Baye & Morgan [1] argue that modern markets for information tend to be dominated by information gatekeepers that speialize in ollating, aggregating, and searhing massive amounts of information available on the Web and an often harge onsumers, advertisers, and information providers, for their ability to aquire and transmit information. Wise & Morrison [15] emphasize the inreasing role of information gatekeepers in today s eonomy, noting that in business-to-business markets, value has shifted from the produt itself to information about the produt. Speifi ategories of information gatekeepers to whih our work applies inlude reommender systems (e.g., at Amazon.om, omparison shopping servies (e.g., mysimon.om, e-marketplaes and exhanges (e.g., FreeMarkets, and more traditional information gatekeepers suh as investment advisors and television networks. Like searh engines, many information gatekeepers generate user-based revenues, but also seek to obtain revenues from their provider-base by offering some form of preferential plaement. For example, some Internet booksellers are influened by advertising fees in determining their bestseller lists. Similarly, ertain Internet exhanges provide preferential servie (suh as real time notifiation or favorable reommendation to buyers to some lients in return for higher fees. We are pursuing extensions of this work, inluding a formal derivation of the optimal bias, generalization of demand assumptions, and elimination of free plaement by the gatekeeper. Our models an be extended to examine onditions under whih the information gatekeeper will begin to harge users, and speifially the ase where the gatekeeper differentiates between users by offering two versions: a fee-based premium servie with no bias in the query results, and a free basi version with paid plaement bias. The fee-based premium version will bring additional user revenues to the searh engine, however it may redue plaement revenues beause paid plaement beomes less attrative to ontent providers. In addition, the searh engine s market overage and plaement fee may hange as well, and the models an be used to determine if it is optimal for the gatekeeper to offer differentiated servie. Similar models an be developed to examine the impat of differentiation based on advertising. Some searh engines have already began to offer fee-based premium searh servies that ontain no advertising. If this is the trend, it may eventually hange people s view of Internet searh engines as a free resoure for fair information. 6. APPENDIX Meta Searh Paid Links Total Links % Paid Dogpile qbsearh MetaCrawler Mamma Searh.om ProFusion Ixquik Vivisimo Table 1: Paid plaement in metasearh engines. what perentage of the links shown on the first page of results from a meta searh servie were paid listings. The table shows what perentage of the links shown on the first page of results from a meta searh servie were paid listings. The searh query was "anada," done using eah meta searh servie s default settings. sereport/01/05-metasearh.html 7. REFERENCES [1] M. R. Baye and J. Morgan. Information gatekeepers on the internet and the ompetitiveness of homogeneous produt markets. Amerian Eonomi Review, 91(3: , June [2] K. Bharat and M. Henzinger. 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