This research note reports on an empirical investigation of the effect of the Internet on racial hate crimes in the United States from the period 2001Ð2008. We find evidence that, on average, broadband availability increases racial hate crimes. We also document that the Internet's impact on these hate crimes is not uniform in that the positive effect is stronger in areas with higher levels of racism, which we identify as those with more segregation and a higher proportion of racially charged search terms, but not significant in areas with lower levels of racism. We analyze in depth whether Internet access will enhance hate group operations but find no support for the idea that this mechanism is driving the result. In contrast, we find that online access is increasing the incidence of racial hate crimes executed by lone wolf perpetrators. Several other mechanisms that could be driving the results are described. Overall, our results shed light on one of the many offline societal challenges from increased online access.
In this paper, we analyze patterns of transaction between individuals using data drawn from Kiva.org, a global online crowdfunding platform that facilitates prosocial, peer-to-peer lending. Our analysis, which employs an aggregate dataset of country-to-country lending volumes based on more than three million individual lending transactions that took place between 2005 and 2010, considers the dual roles of geographic distance and cultural differences on lenders’ decisions about which borrowers to support. While cultural differences have seen extensive study in the Information Systems literature as sources of friction in extended interactions, here, we argue and demonstrate their role in individuals’ selection of a transaction partner. We present evid ence that lenders do prefer culturally similar and geographically proximate borrowers. An analysis of the marginal effects indicates that an increase of one standard deviation in the cultural differences between lender and borrower countries is associated with 30 fewer lending actions, while an increase of one standard deviation in physical distance is associated with 0.23 fewer lending actions. We also identify a substitution effect between cultural differences and physical distance, such that a 50 percent increase in physical distance is associated with an approximate 30 percent decline in the effect of cultural differences. Considering approaches to overcoming the observed cultural effect, we offer some empirical evidence of the potential of IT-based trust mechanisms, focusing on Kiva’s reputation rating system for microfinance intermediaries. We discuss the implications of our findings for prosocial lending, online crowdfunding, and electronic markets more broadly.
Online platforms offer access to a larger social group than is generally available through offline contacts, making the Internet an emerging venue for seeking casual sex partners. The ease of seeking sex partners through classified ad sites may promote risky behaviors that increase the transmission of STDs. In this paper, using a natural experiment setup, we investigate whether the entry of a major online personals ad site, Craigslist, increases the prevalence of HIV over a 10 year period from 1999 to 2008 across 33 states in the United States. After controlling for extraneous factors, our results suggest that the entry of Craigslist is related to a 15.9 percent increase in HIV cases. Our analysis suggests that the site entry produces an average of 6,130 to 6,455 cases of HIV infection in the United States each year, mapping out to between $62 million and $65.3 million in annual treatment costs. In addition, the analyses reveal that nonmarket-related casual sex is the primary driver of the increase in HIV cases, in contrast to paid transactions solicited on the site (e.g., escort services and prostitution), which has a negative relationship with HIV trends. These findings are essential to understanding the social routes through which HIV transmission takes place and the extent to which site entry can influence HIV trends. Implications for healthcare practitioners and policy makers are discussed..
Crowd-funded markets have recently emerged as a novel source of capital for entrepreneurs. As the economic potential of these markets is now being realized, they are beginning to go mainstream, a trend reflected by the explicit attention crowdfunding has received in the American Jobs Act as a potential avenue for economic growth, as well as the recent focus that regulators such as the U.S. Securities and Exchange Commission have placed upon it. Although the formulation of regulation and policy surrounding crowd-funded markets is becoming increasingly important, the behavior of crowdfunders, an important aspect that must be considered in this formulation effort, is not yet well understood. A key factor that can influence the behavior of crowd funders is information on prior contribution behavior, including the amount and timing of others' contributions, which is published for general consumption. With that in mind, in this study, we empirically examine social influence in a crowd-funded marketplace for online journalism projects, employing a unique data set that incorporates contribution events and Web traffic statistics for approximately 100 story pitches. This data set allows us to examine both the antecedents and consequences of the contribution process. First, noting that digital journalism is a form of public good, we evaluate the applicability of two competing classes of economic models that explain private contribution toward public goods in the presence of social information: substitution models and reinforcement models. We also propose a new measure that captures both the amount and the timing of others' contribution behavior: contribution frequency (dollars per unit time). We find evidence in support of a substitution model, which suggests a partial crowding-out effect, where contributors may experience a decrease in their marginal utility from making a contribution as it becomes less important to the recipient. Further, we find that the duration of funding and, more importantly, the degree of exposure that a pitch receives over the course of the funding process, are positively associated with readership upon the story's publication. This appears to validate the widely held belief that a key benefit of the crowdfunding model is the potential it offers for awareness and attention-building around causes and ventures. This last aspect is a major contribution of the study, as it demonstrates a clear linkage between marketing effort and the success of crowd-funded projects.
We explore how Internet browsing behavior varies between mobile phones and personal computers. Smaller screen sizes on mobile phones increase the cost to the user of browsing for information. In addition, a wider range of offline locations for mobile Internet usage suggests that local activities are particularly important. Using data on user behavior at a (Twitter-like) microblogging service, we exploit exogenous variation in the ranking mechanism of posts to identify the ranking effects. We show that (1) ranking effects are higher on mobile phones suggesting higher search costs: links that appear at the top of the screen are especially likely to be clicked on mobile phones and (2) the benefit of browsing for geographically close matches is higher on mobile phones: stores located in close proximity to a user's home are much more likely to be clicked on mobile phones. Thus, the mobile Internet is somewhat less "Internet-like": search costs are higher and distance matters more. We speculate on how these changes may affect the future direction of Internet commerce.
Price dispersion is an important indicator of market efficiency. Internet-based electronic markets have the potential to reduce transaction and search costs, thereby creating more efficient, "frictionless" markets, as predicted by theories in information economics. However, earlier work has reported significant levels of price dispersion on the Internet, which is in contrast to theoretical predictions. A key feature of the existing stream of work has been its use of posted prices to estimate price dispersion. In theory, this can lead to an overestimation of price dispersion because a sale may not have occurred at the posted price. In this research, we use a unique data set of actual transaction prices collected from both the electronic and offline markets of buyers in a business-to-business market to evaluate the extent of price dispersion. We find that price dispersion in the electronic market is as low as 0.22%, which is substantially less than that reported in the existing literature. This near-zero price dispersion suggests that in some electronic markets the "law of one price" can prevail when we consider transaction prices, instead of posted prices. We further develop a theoretical framework that identifies several new drivers of price dispersion using transaction data. In particular, we focus on four product-level and market-level attributes-product cost, order cycle time, own price elasticity, and transaction quantity, and we estimate their impact on price dispersion. We also examine the electronic market's moderating role in the relationship between these drivers and price dispersion. Finally, we estimate the efficiency gains that accrue from transactions in the relatively friction-free market and find that the electronic market can enhance consumer surplus by as much as $97.92 million per year.
Sponsored search is the mechanism whereby advertisers pay a fee to Internet search engines to be displayed alongside organic (nonsponsored) web search results. Based on prior literature, we draw an analogy between these markets and financial markets. We use the analogy as well as the key differences to present a theoretical framework consisting of a set of research questions about the pricing of keywords and design choices available to firms in sponsored search markets. These questions define an agenda for future research in sponsored search markets. They also have practical implications for advertisers and online marketplaces such as search engines and social media sites that support advertising.
In the past few years, we have witnessed the increasing ubiquity of user-generated content on seller reputation and product condition in Internet-based used-good markets. Recent theoretical models of trading and sorting in used-good markets provide testable predictions to use to examine the presence of adverse selection and trade patterns in such dynamic markets. A key aspect of such empirical analyses is to distinguish between product-level uncertainty and seller-level uncertainty, an aspect the extant literature has largely ignored. Based on a unique, 5-month panel data set of user-generated content on used good quality and seller reputation feedback collected from Amazon, this paper examines trade patterns in online used-good markets across four product categories (PDAs, digital cameras, audio players, and laptops). Drawing on two different empirical tests and using content analysis to mine the textual feedback of seller reputations, the paper provides evidence that adverse selection continues to exist in online markets. First, it is shown that after controlling for price and other product, and for seller-related factors, higher quality goods take a longer time to sell compared to lower quality goods. Second, this result also holds when the relationship between sellers' reputation scores and time to sell is examined. Third, it is shown that price declines are larger for more unreliable products, and that products with higher levels of intrinsic unreliability exhibit a more negative relationship between price decline and volume of used good trade. Together, our findings suggest that despite the presence of signaling mechanisms such as reputation feedback and product condition disclosures, the information asymmetry problem between buyers and sellers persists in online markets due to both product-based and seller-based information uncertainty. No consistent evidence of substitution or complementarity effects between product-based and seller-level uncertainty are found. Implications for research and practice are discussed.
Consumer-generated product reviews have proliferated online, driven by the notion that consumers' decision to purchase or not purchase a product is based on the positive or negative information about that product they obtain from fellow consumers. Using research on information processing as a foundation, we suggest that in the context of an online community, reviewer disclosure of identity-descriptive information is used by consumers to supplement or replace product information when making purchase decisions and evaluating the helpfulness of online reviews. Using a unique data set based on both chronologically compiled ratings as well as reviewer characteristics for a given set of products and geographical location-based purchasing behavior from Amazon, we provide evidence that community norms are an antecedent to reviewer disclosure of identity-descriptive information. Online community members rate reviews containing identity-descriptive information more positively, and the prevalence of reviewer disclosure of identity information is associated with increases in subsequent online product sales. In addition, we show that shared geographical location increases the relationship between disclosure and product sales, thus highlighting the important role of geography in electronic commerce. Taken together, our results suggest that identity-relevant information about reviewers shapes community members' judgment of products and reviews. Implications for research on the relationship between online word-of-mouth (WOM) and sales, peer recognition and reputation systems, and conformity to online community norms are discussed.
In many industries, Internet referral services, hosted either by independent third-party infomediaries or by manufacturers, serve as digitally enabled lead generators in electronic markets, directing consumer traffic to downstream retailers in a distribution network. This reshapes the extended enterprise from the traditional network of upstream manufacturers and downstream retailers to include midstream third-party and manufacturer-owned referral services in the supply chain. We model competition between retailers in a supply chain with such digitally enabled institutions and consider their impact on the optimal contracts among the manufacturer, referral intermediary, and the retailers. Offline, retailers face a higher customer discovery cost. In return, they can engage in price discrimination based on consumer valuations. Online, they save on the discovery costs but lose the ability to identify consumer valuations. This critical trade-off drives firms' equilibrium strategies. We derive the optimal contracts for different entities in the supply chain and highlight how these contracts change with the entry of independent and manufacturer-owned referral services. The establishment of a referral service is a strategic decision by the manufacturer. It leads to diversion of supply chain profit from a third-party infomediary to the manufacturer. Further, it enables the manufacturer to respond to an infomediary, by giving itself greater flexibility in setting the unit wholesale fee to the profit-maximizing level. Both third-party and manufacturer-sponsored referral services play a critical role in enabling retailers to discriminate across consumers' different valuations. Retailers use online referral services to screen out low-valuation consumers and sell only to high-valuation consumers in the online channel. Our model thus endogenously derives a correlation between consumer valuation and online purchase behavior. Finally, we show that under some circumstances, it is too costly for the manufacturer to eliminate the referral infomediary.
Information systems and the Internet have facilitated the creation of used-product markets that feature a dramatically wider selection, lower search costs, and lower prices than their brick-and-mortar counterparts do. The increased viability of these used-product markets has caused concern among content creators and distributors, notably the Association of American Publishers and Author's Guild, who believe that used-product markets will significantly cannibalize new product sales.This proposition, while theoretically possible, is based on speculation as opposed to empirical evidence. In this paper, we empirically analyze the degree to which used products cannibalize new-product sales for books'one of the most prominent used-product categories sold online. To do this, we use a unique data set collected from Amazon.com's new and used book marketplaces to measure the degree to which used products cannibalize new-product sales. We then use these estimates to measure the resulting first-order changes in publisher welfare and consumer surplus.Our analysis suggests that used books are poor substitutes for new books for most of Amazon's customers.The cross-price elasticity of new-book demand with respect to used-book prices is only 0.088. As a result, only16% of used-book sales at Amazon cannibalize new-book purchases. The remaining 84% of used-book sales apparently would not have occurred at Amazon's new-book prices. Further, our estimates suggest that this increase in book readership from Amazon's used-book marketplace increases consumer surplus by approximately $67.21 million annually. This increase in consumer surplus, together with an estimated $45.05 million loss in publisher welfare and a $65.76 million increase in Amazon's profits, leads to an increase in total welfare to society of approximately $87.92 million annually from the introduction of used-book markets at Amazon.com.
Given that information technology (IT) security has emerged as an important issue in the last few years, the subject of security information sharing among firms, as a tool to minimize security breaches, has gained the interest of practitioners and academics. To promote the disclosure and sharing of cyber security information among firms, the U.S. federal government has encouraged the establishment of many industry-based Information Sharing and Analysis Centers (ISACs) under Presidential Decision Directive (PDD) 63. Sharing security vulnerabilities and technological solutions related to methods for preventing, detecting, and correcting security breaches is the fundamental goal of the ISACs. However, there are a number of interesting economic issues that will affect the achievement of this goal. Using game theory, we develop an analytical framework to investigate the competitive implications of sharing security information and investments in security technologies. We find that security technology investments and security information sharing act as "strategic complements" in equilibrium. Our results suggest that information sharing is more valuable when product substitutability is higher, implying that such sharing alliances yield greater benefits in more competitive industries. We also highlight that the benefits from such information-sharing alliances increase with the size of the firm. We compare the levels of information sharing and technology investments obtained when firms behave independently (Bertrand-Nash) to those selected by an ISAC, which maximizes social welfare or joint industry profits. Our results help us predict the consequences of establishing organizations such as ISACs, Computer Emergency Response Team (CERT), or InfraGard by the federal government.
We present a model to investigate the competitive implications of electronic secondary markets that promote concurrent selling of new and used goods on a supply chain. In secondary markets where suppliers cannot directly utilize used goods for practicing intertemporal price discrimination and where transaction costs of resales are negligible, the threat of cannibalization of new goods by used goods becomes significant. We examine conditions under which it is optimal for suppliers to operate in such markets, explaining why these markets may not always be detrimental for them. Intuitively, secondary markets provide an active outlet for some high-valuation consumers to sell their used goods. The potential for such resales leads to an increase in consumers' valuation for a new good, leading them to buy an additional new good. Given sufficient heterogeneity in consumers' affinity across multiple suppliers" products, the "market expansion effect" accruing from consumers' cross-product purchase affinity can mitigate the losses incurred by suppliers from the direct "cannibalization effect." We also highlight the strategic role that the used goods commission set by the retailer plays in determining profits for suppliers. We conclude the paper by empirically testing some implications of our model using a unique data set from the online book industry, which has a flourishing secondary market.