Pai-Ling Yin

Building firm trust online

The recent proliferation of e-commerce has led to a great deal of analysis probing the who, what, where, when, and why’s of new opportunities for conducting business online. In this publication alone, articles have ranged from theoretical predictions of the economic forces that will mold the Internet market (see [1]) to invasion-of-privacy issues reflected in empirical analysis of online consumer preferences (see [5]). Cookies, clickstream data trails, and the ease of conducting online surveys have permitted unprecedented tracking of what consumers search for, click on, and ultimately buy. Aggregating that data results in an entrepreneur’s dream for extracting significant characterizations of the Internet consumer population. The consumer-side research has already repeatedly stated that existing risks in e-commerce pose substantial barriers to consumer participation online. Never before has our understanding of the consumer been both so intimate and so extensive. However, the firm side of e-commerce has comparatively been forgotten. Amazon.com, which operates primarily on a business-to-consumer level, is now the cliché shorthand for describing what the firm side of the online market is all about. Yet in many ways, Amazon.com is an extremely misleading representative of e-business: it is the exception, rather than the rule, for online commerce. The only characteristic it shares with the majority of firms Firm Trust Detlef Schoder and Pai-Ling Yin

Information Dispersion and Auction Prices

Do bidders behave as auction theory predicts they should? How do bidders (and thus, prices) react to different types of information? This paper derives implications of auction theory with respect to the dispersion of private information signals in an auction. I conduct a survey of non-bidders to construct a measure of information dispersion that is independent of bidding data. This permits joint tests of Bayesian-Nash equilibrium bidder behavior and information structure (common vs. private value) in a sample of eBay auctions for computers. The measure also allows me to separately estimate the price effects of seller reputation and product information. eBay prices appear consistent with Bayesian-Nash common value bidding behavior. Uncertainty about the value of goods due to information dispersed over auction participants plays a larger role than uncertainty about the trustworthiness of the sellers, but both are significant drivers of price. Thus, seller reputation complements, rather than substitutes for, information provided in the auction descriptions by lending credibility to that information, creating an incentive for sellers to reduce uncertainty in their auctions.

Economic Value Creation in Mobile Applications

New mobile development platforms are a 21st-century growth pole. By successfully recombining existing information technologies with new innovations, they have spurred a positive feedback loop of consumer adoption of mobile devices and firm entry into a wide variety of applications, or “apps.†Describing the industry calls for superlatives: after just a few years, it has the largest installed base of programmable devices in the history of computing and the largest group of app developers, mostly entrepreneurs, ever to enter a technology industry. Despite this size, the industry is still at an early stage, with rapid growth and a wide variety of economic experiments trying to resolve the uncertainty about how this new industry will create economic value. Because there are hundreds, and may someday be thousands, of app markets, the industry needs economic institutions to support market experimentation. However, as we document in this paper, the sheer volume and market diversity of app product entry has created problems for marketing and commercialization, most importantly the challenges of matching consumers to products. At this early stage in the industry life cycle, the existing market institutions have been overwhelmed. This early pattern is like many earlier information and communications technology industries in that the early stage shows a pattern of technical success but commercialization struggles. However, several important new issues arise, including a (possibly transitory) bias against entrepreneurial commercialization, and the importance of end-user demand in determining market evolution. We conclude by considering how this situation has impacted the industry’s task of discovering economic value and choosing among different app and platform features to make its ultimate contribution to economic growth. We also consider the likely market and institutional responses to the current bottleneck.

Empirical Tests of Information Aggregation

This paper proposes tests to empirically examine whether auction prices aggregate information away from the limit. These tests are applied to a unique dataset containing winning bids and measures of the value and dispersion of information signals in eBay auctions for computers. Results suggest that prices partially aggregate information, but do not converge to the common value. Even away from the limit, more bidders and stronger information have a significant effect on the convergence of prices and information aggregation. Even partial information aggregation may represent a potential efficiency gain over one-to-one trade of used goods with uncertain common values.

Adoption of New Information and Communications Technologies in the Workplace Today

The invention of new applications based on information and communications technologies (ICTs) has had two economic effects up to now. These applications have transformed production, creating value for applications-inventing companies and their customers and increasing economic growth through quality improvements. The same applications have shifted the relative demand for different kinds of labor, raising the demand for already highly compensated managers and professionals relative to other workers. This paper considers the likely impact of new ICT technologies coming into application in the workplace today in light of the economic and technical forces behind ICT application up to now.

Asymmetric Network Effects

When platforms compete for consumers, two types of consumer heterogeneity will matter: consumers value the presence of other consumers on a platform differently, and consumers contribute to the value of the platform differently. The optimal discriminatory pricing policy for platforms will depend on whether those two dimensions of consumer heterogeneity are positively or negatively correlated, which is an empirical question. In a companion paper (Cantillon & Yin, 2008), we study membership decisions of trading firms for two competing exchanges: LIFFE and DTB. Our analysis shows that different traders care about liquidity differently. In this paper, we estimate the heterogeneous contribution to liquidity by different types. We combine the estimates from both papers of heterogeneous preferences and contributions to liquidity. We find that valuations of liquidity tend to be correlated with contributions to liquidity in this setting.

A Survey-Based Procedure for Measuring Uncertainty or Heterogeneous Preferences in Markets, with Application to a New Test for Common Values

The effects of heterogeneous preferences or uncertainty about item values on the variance of dependent variables (e.g., auction prices; retail price dispersion; or investment choices in stocks, R&D, or education) are usually relegated to the error term, which a) confounds these effects with other drivers of the error term and b) could lead to heteroskedasticity at best or omitted variable bias at worst. This paper shows how surveys can generate a measure of the amount of information or heterogeneity of preferences in a market by measuring the distribution of private information signals in eBay online auctions for computers. Regressions utlizing this measure produce significantly different results than those that rely on hedonic measures to control for heterogeneity alone. Furthermore, this measure provides a new way to test for the presence of a common values component in auctions.

Network Isolates: Entrepreneurial Bootstrapping and the Social Disconnection of New Organizations in the Mobile App Ecosystem

Extensive prior literature has studied how young organizations are impacted by and often benefit from embeddedness in key industry networks. Indeed, some research advises that entrepreneurs “don’t go it alone” (Baum, Calabrese, and Silverman, 2000). This literature has also highlighted a dynamic whereby young organizations with higher quality are most likely to sort into these networks. Yet this perspective often fails to consider the drivers and prevalence of high-quality young organizations not becoming embedded, and instead remaining network isolates. Drawing on resource dependence and exchange theory’s emphasis on network ties arising from mutual and balanced interdependence, we explicate how organizational design decisions, current performance, and competitive pressures influence whether a young organization remains a network isolate. We test and find support for our arguments using an unusually rich, complete, and large “big data” dataset that captures all competitors in the mobile app ecosystem on the Apple iPhone ecosystem over a period of 5 years, showing that a substantial fraction of high-performing mobile app developers remain outside of the venture finance network.

Paying Incumbents and Customers to Enter an Industry: Buying Downloads

Success breeds success in many mass market industries, as well known products gain further consumer acceptance because of their visibility. However, new products must struggle to gain consumer’s scarce attention and initiate that virtuous cycle. The newest mass market industry, mobile apps, has these features. Success among apps is highly concentrated, in part because the “top app lists” recommend apps based on past success as measured by downloads. Consequently, in order to introduce themselves to users, new app developers attempt to gain a position on the top app lists by “buying downloads,” i.e., paying a user to download the app onto her device. We build a model to rationalize this behavior, taking into account the impact of buying downloads on top list ranking and optimal investment in buying downloads. We leverage a private dataset from one platform for buying downloads to identify the return on this investment, as a test for the assumption of the model.

Competition Between Exchanges: Lessons from the Battle of the Bund

100 invested will improve the ranking by 2.2%. We provide some informal tests of the two empirical predictions of the model: (1) there are two humps in the diffusion pattern of the app, and (2) early rankings are less persistent than later rankings. We estimate an empirical analog of the model to show the relative importance of buying downloads and rich heterogeneity in the market. We simulate counterfactuals to evaluate the efficiency of top-ranking lists.