The two leading online consumer-to-consumer platforms use very different revenue models: eBay.com in the United States uses a brokerage model in which sellers pay eBay on a transaction basis, whereas Taobao.com in China uses an advertising model in which sellers can use the basic platform service for free and pay Taobao for advertising services to increase their exposure. This paper studies how the chosen revenue model affects the revenue of a platform, buyers' payoffs, sellers' payoffs, and social welfare. We find that when little space can be dedicated to advertising under the advertising model, the brokerage model generates more revenue for the platform than the advertising model. When a significant proportion of space is dedicated to advertising under the advertising model, matching probability on a platform plays a critical role in determining which revenue model can generate more revenue: If the matching probability is high, the brokerage model generates more revenue; otherwise, the advertising model generates more revenue. Buyers are always better off under the advertising model because of larger participation by the sellers in the platform's free service. Sellers are better off under the advertising model in most scenarios. The only exception is when the matching probability is low and the platform dedicates considerable space to advertising. Under these conditions, the sellers with payoffs similar to the marginal advertiser who is indifferent about advertising can be worse off under the advertising model. Finally, the advertising model generates more social welfare than the brokerage model
This paper studies the effect of online product reviews on different players in a channel structure. We consider a retailer selling two substitutable products produced by different manufacturers, and the products differ in both their qualities and fits to consumers' needs. Online product reviews provide additional information for consumers to mitigate the uncertainty about the quality of a product and about its fit to consumers' needs. We show that the effect of reviews on the upstream competition between the manufacturers is critical in understanding which firms gain and which firms lose. The upstream competition is affected in fundamentally different ways by quality information and fit information, and each information type has different implications for the retailer and manufacturers. Quality information homogenizes consumers' perceived utility differences between the two products and increases the upstream competition, which benefits the retailer but hurts the manufacturers. Fit information heterogenizes consumers' estimated fits to the products and softens the upstream competition, which hurts the retailer but benefits the manufacturers. Furthermore, reviews may also alter the nature of upstream competition from one in which consumers' own assessment on the quality dimension plays a dominant role in consumers' comparative evaluation of products to one in which fit dimension plays a dominant role. If manufacturers do not respond strategically to reviews and keep the same wholesale prices regardless of reviews (i.e., the upstream competition is assumed to be unaffected by reviews), then, we show that reviews never hurt the retailer and the manufacturer with favorable reviews, and never benefit the manufacturer with unfavorable reviews, a finding that demonstrates why reviews' effect on upstream competition is critical for firms in online marketplaces.
Online publishers and advertisers have recently shown increasing interest in using targeted advertising online. Such targeting allows them to present users with advertisements that are a better match, based on their past browsing and search behavior and other available information (e.g., hobbies registered on a web site). This technique, known as behavioral targeting, has been hailed as the new “Holy Grail” in online advertising because of its potential effectiveness. In this paper, we study the economic implications when an online publisher engages in behavioral targeting. The publisher auctions off an advertising slot and is paid on a cost-per-click basis. Using a horizontal differentiation model to capture the fit between a user and an advertisement being displayed, we identify the factors that affect the publisher’s revenue, the advertisers’ payoffs, and social welfare. We show that revenue for the online publisher in some circumstances can double when behavioral targeting is used. However, increased revenue for the publisher is not guaranteed: in some cases, the prices of advertising and hence the publisher’s revenue can be lower, depending on the degree of competition and the advertisers’ valuations. We identify two effects associated with behavioral targeting: a competitive effect and a propensity effect. The relative strength of the two effects determines whether the publisher’s revenue is positively or negatively affected. We also demonstrate that, although social welfare is increased and small advertisers are better off under behavioral targeting, the dominant advertiser might be worse off and reluctant to switch from traditional advertising.
This paper analyzes how the presence of organic listing as a competing information source affects advertisers' sponsored bidding and the equilibrium outcomes in search advertising. We consider a game-theoretic model in which two firms bid for sponsored advertising slots provided by a monopolistic search engine and then compete for consumers in price in the product market. Firms are asymmetrically differentiated in market preference and are given different exposure in organic listing aligned with their market appeal. We identify two aspects of a firm's sponsored bidding incentive, namely, the promotive and the preventive incentives. The presence of organic listing alters firms' sponsored bidding incentives such that the stronger firm has primarily preventive incentive, whereas the weaker has mainly promotive incentive. We show that the preventive incentive decreases and the promotive incentive increases as the difference in firms' market appeal decreases, and as a result, even the weaker firm may outbid the stronger competitor under such a co-listing setting. We further examine how the presence of organic listing affects the equilibrium outcomes by comparing it with a benchmark case in which there is only a sponsored list. We show that the differentiated exposure in the organic list gives the weaker advertiser chances to win a better sponsored position, which improves the overall information structure the search engine provides. As a result, the equilibrium social welfare, sales diversity, and consumer surplus increase. Although the presence of the free exposure from the organic list may reduce advertisers' sponsored bidding incentive per se, the overall effect benefits the search engine's growth in the long run.
Online communities provide a social sphere for people to share information and knowledge. While information sharing is becoming a ubiquitous online phenomenon, how to ensure information quality or induce quality content remains a challenge because of the anonymity of commentators. This paper introduces moderation into reputation systems. We show that moderation directly affects strategic commentators' incentive to generate useful information, and moderation is generally desirable to improve information quality. We find that when being moderated with different probabilities based on their reputations, commentators might display a pattern of reputation oscillation, in which they generate useful content to build up high reputation and then exploit their reputation. As a result, the expected performance from high-reputation commentators can be inferior to that from low-reputation commentators (reverse reputation). We then investigate the optimal moderation resource allocation and conclude that the seemingly abnormal reverse reputation could arise as an optimal result. Our study underscores the importance of moderation and highlights that the frequency of moderation should be properly chosen for better performance of online communities.
Keyword advertising, including sponsored links and contextual advertising, powers many of today's online information services such as search engines and Internet-based emails. This paper examines the design of keyword auctions, a novel mechanism that keyword advertising providers such as Google and Yahoo! use to allocate advertising slots. In our keyword auction model, advertisers bid their willingness-to-pay per click on their advertisements, and the advertising provider can weight advertisers' bids differently and require different minimum bids based on advertisers' click-generating potential. We study the impact and design of such weighting schemes and minimum-bid policies. We find that weighting scheme determines how advertisers with different click-generating potential match in equilibrium. Minimum bids exclude low-valuation advertisers and at the same time may distort the equilibrium matching. The efficient design of keyword auctions requires weighting advertisers' bids by their expected click-through-rates, and requires the same minimum weighted bids. The revenue-maximizing weighting scheme may or may not favor advertisers with low click-generating potential. The revenue-maximizing minimum-bid policy differs from those prescribed in the standard auction design literature. Keyword auctions that employ the revenue-maximizing weighting scheme and differentiated minimum bid policy can generate higher revenue than standard fixed-payment auctions. We draw managerial implications for pay-per-click and other pay-for-performance auctions and discuss potential applications to other areas.
In this paper, we set up a game-theoretic model to examine oligopolistic price competition, considering two features of online search: the existence of a common search ordering and shoppers who have nonpositive search cost. We find that in equilibrium firms set their prices probabilistically rather than deterministically, and different firms follow different price distributions. The equilibrium pricing pattern exhibits an interesting local-competition feature in which direct price competition occurs only between firms adjacent to each other. Further, we incorporate consumers' search strategies into the model so that both search order and stopping rules are determined rationally by consumers. We show that similar patterns may continue to hold in the fully rational framework when consumers have higher inspection costs for inferior positions.