Knowledge that Transforms

Sinan Aral (“ Commentary—Identifying Social Influence: A Comment on Opinion Leadership and Social Contagion in New Product Diffusion ”) is a faculty member in the Information, Operations and Management Sciences Department of the New York University Stern School of Business and affiliated faculty at the Massachusetts Institute of Technology (MIT). He is a Phi Beta Kappa graduate of Northwestern University and holds master's degrees from the London School of Economics and Harvard University as well as a Ph.D. from MIT. He studies how behavioral contagions spread through social networks—from products to productivity to public health—by analyzing how the distribution and movement of information inside firms impacts information worker productivity; how information diffusion in massive online social networks influences demand patterns, consumer e-commerce behaviors, and word-of-mouth marketing; and how investments in IT capital and complementary intangible assets combine to create productivity and business value benefits for firms. His research has won numerous awards. Neeraj Arora (“ Efficient Choice Designs for a Consider-Then-Choose Model ”) is the John P. Morgridge Chair in Business Administration at the University of Wisconsin–Madison, where he also serves as the Executive Director of the A.C. Nielsen Center for Marketing Research. He has an undergraduate degree in engineering from Delhi University, and an MBA and Ph.D. from The Ohio State University. He serves on the editorial boards of Journal of Marketing Research and Marketing Science. His papers have appeared in the Journal of Marketing Research, Marketing Science, Journal of Consumer Research, Journal of Marketing, International Journal of Research in Marketing, and Marketing Letters. Nuno Camacho (“ Predictably Non-Bayesian: Quantifying Salience Effects in Physician Learning About Drug Quality ”) is a doctoral student in marketing at the Erasmus School of Economics, Erasmus University Rotterdam (The Netherlands). His research interests include behavioral modeling (i.e., building econometric models to study individual and joint consumer decision processes) and behavioral economics applied to marketing. In terms of substantive focus, he is working on topics in the life sciences industry and is interested in new product adoption, cross-cultural differences, and social influences in decision making. Nicholas A. Christakis (“ Commentary—Contagion in Prescribing Behavior Among Networks of Doctors ”) is an internist and social scientist who conducts research on social factors that affect health, health care, and longevity. He is a professor of medical sociology in the Department of Health Care Policy at Harvard Medical School, a professor of medicine in the Department of Medicine at Harvard Medical School, and a professor of sociology in the Department of Sociology in the Harvard Faculty of Arts and Sciences. In 2009, he was named one of the 100 most influential people in the world by Time magazine. Bas Donkers (“ Predictably Non-Bayesian: Quantifying Salience Effects in Physician Learning About Drug Quality ”) is an associate professor of marketing at the Erasmus School of Economics, Erasmus University Rotterdam (The Netherlands). His research interests are in behavioral modeling with a specific focus on individual decision making. Applications include, among others, charitable giving, search behavior, and patient preferences. His work has been published in Marketing Science, the Journal of Marketing Research, and the International Journal of Research in Marketing. James H. Fowler (“ Commentary—Contagion in Prescribing Behavior Among Networks of Doctors ”) is a social scientist whose work lies at the intersection of the natural and social sciences. His primary areas of research are social networks, behavioral economics, evolutionary game theory, political participation, cooperation, and genopolitics (the study of the genetic basis of political behavior). He is a professor in the School of Medicine and the Division of Social Sciences at the University of California, San Diego. For 2010–2011, he has been named a fellow of the John Simon Guggenheim Foundation. David Godes (“ Commentary—Invited Comment on ‘Opinion Leadership and Social Contagion in New Product Diffusion’ ”) is an associate professor in the Marketing Department at the Robert H. Smith School of Business, University of Maryland. Prior to joining the University of Maryland, he taught at Harvard Business School. He received a B.S. in economics at the University of Pennsylvania and an S.M. and Ph.D. in management science from the Massachusetts Institute of Technology. His research interests include word-of-mouth communication, social networks, media competition, and sales management. Raghuram Iyengar (“ Opinion Leadership and Social Contagion in New Product Diffusion ”; “Rejoinder—Further Reflections on Studying Social Influence in New Product Diffusion ”; “Tricked by Truncation: Spurious Duration Dependence and Social Contagion in Hazard Models ”) is an assistant professor of marketing at The Wharton School of the University of Pennsylvania. He earned his Ph.D. from Columbia University and his B.Tech. from IIT Kanpur, India. His research focuses on social networks, new product diffusion, and pricing. Anja Lambrecht (“ Stuck in the Adoption Funnel: The Effect of Interruptions in the Adoption Process on Usage ”) is an assistant professor of marketing at the London Business School. She received her Ph.D. from Goethe-University, Frankfurt, Germany. Her research interests lie in firms' nonlinear pricing strategies, how consumers choose and use under nonlinear pricing plans, and how consumers adopt new service technologies. Qing Liu (“ Efficient Choice Designs for a Consider-Then-Choose Model ”) is an assistant professor of marketing at the University of Wisconsin–Madison. She received her B.S. degree from the University of Science and Technology of China, and her M.S. and Ph.D. in statistics from The Ohio State University. Her research focuses on the application and development of statistical theories and methodology to help solve problems in marketing and marketing research; areas of interest include conjoint analysis, consumer choice, experimental design, and Bayesian methods. Her papers have appeared in Marketing Science, Quantitative Marketing and Economics, and Statistica Sinica. Vishal Narayan (“ How Peer Influence Affects Attribute Preferences: A Bayesian Updating Mechanism ”) is an assistant professor of marketing at the Johnson School at Cornell University. He holds a Ph.D. in marketing from the Stern School of Business, New York University, and an MBA degree from the Indian Institute of Management, Lucknow, India. His research interests include understanding how social interactions affect market outcomes. He applies Bayesian econometric methods to study consumer and firm behavior. Vithala R. Rao (“ How Peer Influence Affects Attribute Preferences: A Bayesian Updating Mechanism ”) is the Deane Malott Professor of Management and Professor of Marketing and Quantitative Methods, Johnson Graduate School of Management, Cornell University. He holds a Ph.D. in applied economics/marketing from The Wharton School of the University of Pennsylvania. He has published more than 100 papers on topics including conjoint analysis and multidimensional scaling, pricing, bundle design, brand equity, market structure, corporate acquisition, and linking branding strategies to financial performance; his current work includes peer influence, competitive bundling, dynamic attribute trade-offs, and trade promotions. He received several awards, including the 2008 Charles Coolidge Parlin Marketing Research Award, presented by the American Marketing Association and the American Marketing Association Foundation, recognizing his “outstanding leadership and sustained impact on advancing the evolving profession of marketing research over an extended period of time.” Oliver J. Rutz (“ The Evolution of Internal Market Structure ”) is an assistant professor of marketing at the Yale School of Management, New Haven, where he researches and lectures on online marketing with an emphasis on paid search management. He received his Ph.D. in marketing from UCLA Anderson in 2007. He won the 2007 EMAC best dissertation paper award and honorable mention in the 2007 Alden G. Clayton Doctoral Dissertation Proposal Competition. He is a member of the Handelsblatt-Management-Forum, a bimonthly international academic panel in Germany's leading business and financial newspaper. Carolyne Saunders (“ How Peer Influence Affects Attribute Preferences: A Bayesian Updating Mechanism ”) is a Ph.D. student in marketing at the Johnson Graduate School of Management at Cornell University. She holds an M.Sc. in economics and business research from the University of Groningen, The Netherlands, and a B.A. (Hons) in economics from the University of Cambridge, United Kingdom. Her research interests include quantitative analysis of consumer behavior and theoretical and empirical modeling of new product development. Katja Seim (“ Stuck in the Adoption Funnel: The Effect of Interruptions in the Adoption Process on Usage ”) is an assistant professor of business and public policy at The Wharton School of the University of Pennsylvania. She received her Ph.D. in economics from Yale University. Her research interests lie in the empirical analysis of competitive behavior, including firms' product introduction and entry decisions, nonlinear pricing, and the adoption of new technologies. Dmitry Shapiro (“ Profitability of the Name-Your-Own-Price Channel in the Case of Risk-Averse Buyers ”) is an assist

We study a model of film distribution and consumption. The studio can release two goods, a theatrical version and a video version, and has to decide on its versioning and sequencing strategy. In contrast with the previous literature, we allow for the possibility that some consumers may watch both versions. This simple extension leads to novel results. It now becomes optimal to introduce versioning if the goods are not too substitute for one another, even when production costs are zero (pure information goods). We also demonstrate that the simultaneous release of the versions (“day-and-date” strategy) can be optimal when the studio is integrated with the exhibition and distribution channels. In contrast, a sequential release (“video window” strategy) is typically the outcome when the studio negotiates with independent distributors and exhibitors.

This paper evaluates the joint impact of exclusive channels and revenue sharing on suppliers and retailers in a hybrid duopoly common retailer and exclusive channel model. The model bridges the gap in the literature on hybrid multichannel supply chains with bilateral complementary products and services with or without revenue sharing. The analysis indicates that, without revenue sharing, the suppliers are reluctant to form exclusive deals with the retailers; thus, no equilibrium results. With revenue sharing from the retailers to the suppliers, it can be an equilibrium strategy for the suppliers and retailers to form exclusive deals. Bargaining solutions are provided to determine the revenue sharing rates. Our additional results suggest forming exclusive deals becomes less desirable for the suppliers if revenue sharing is also in place under nonexclusivity. In our extended discussion, we also study the impact of channel asymmetry, an alternative model with fencing, composite package competition, and enhanced price-dependent revenue sharing.

The World Wide Web contains a vast corpus of consumer-generated content that holds invaluable insights for improving the product and service offerings of firms. Yet the typical method for extracting diagnostic information from online content—text mining—has limitations. As a starting point, we propose analyzing a sample of comments before initiating text mining. Using a combination of real data and simulations, we demonstrate that a sampling procedure that selects respondents whose comments contain a large amount of information is superior to the two most popular sampling methods—simple random sampling and stratified random sampling—-in gaining insights from the data. In addition, we derive a method that determines the probability of observing diagnostic information repeated a specific number of times in the population, which will enable managers to base sample size decisions on the trade-off between obtaining additional diagnostic information and the added expense of a larger sample. We provide an illustration of one of the methods using a real data set from a website containing qualitative comments about staying at a hotel and demonstrate how sampling qualitative comments can be a useful first step in text mining.

We develop and test an incentive-compatible Conjoint Poker (CP) game. The preference data collected in the context of this game are comparable to incentive-compatible choice-based conjoint (CBC) analysis data. We develop a statistical efficiency measure and an algorithm to construct efficient CP designs. We compare incentive-compatible CP to incentive-compatible CBC in a series of three experiments (one online study and two eye-tracking studies). Our results suggest that CP induces respondents to consider more of the profile-related information presented to them compared with CBC.

Experimental and survey-based research suggests that consumers often rely on their intuition and cognitive shortcuts to make decisions. Intuition and cognitive shortcuts can lead to suboptimal decisions and, especially in high-stakes decisions, to legitimate welfare concerns. In this paper, we propose an extension of a Bayesian learning model that allows us to quantify the impact of salience—the fact that some pieces of information are easier to retrieve from memory than others—on physician learning. We show, using data on actual prescriptions for real patients, that physicians' belief formation is strongly influenced by salience effects. Feedback from switching patients—the ones the physician decided to switch to a clinically equivalent treatment—receives considerably more weight than feedback from other patients. In the category we study, salience effects slowed down physicians' speed of learning and the adoption of a new treatment, which raises welfare concerns. For managers, our findings suggest that firms that are able to eliminate, or at least reduce, salience effects to a greater extent than their competitors can speed up the adoption of new treatments. We explore the implications of these results and suggest alternative applications of our model that are relevant for policy makers and managers.

Sponsored search advertising is ascendant—Forrester Research reports expenditures rose 28% in 2007 to $8.1 billion and will continue to rise at a 26% compound annual growth rate [VanBoskirk, S. 2007. U.S. interactive marketing forecast, 2007 to 2012. Forrester Research (October 10)], approaching half the level of television advertising and making sponsored search one of the major advertising trends to affect the marketing landscape. Yet little empirical research exists to explore how the interaction of various agents (searchers, advertisers, and the search engine) in keyword markets affects consumer welfare and firm profits. The dynamic structural model we propose serves as a foundation to explore these outcomes. We fit this model to a proprietary data set provided by an anonymous search engine. These data include consumer search and clicking behavior, advertiser bidding behavior, and search engine information such as keyword pricing and website design. With respect to advertisers, we find evidence of dynamic bidding behavior. Advertiser value for clicks on their links averages about 26 cents. Given the typical $22 retail price of the software products advertised on the considered search engine, this implies a conversion rate (sales per click) of about 1.2%, well within common estimates of 1%–2% [Narcisse, E. 2007. Magid: Casual free to pay conversion rate too low. GameDaily.com (September 20)]. With respect to consumers, we find that frequent clickers place a greater emphasis on the position of the sponsored advertising link. We further find that about 10% of consumers do 90% of the clicks. We then conduct several policy simulations to illustrate the effects of changes in search engine policy. First, we find the search engine obtains revenue gains of 1% by sharing individual-level information with advertisers and enabling them to vary their bids by consumer segment. This also improves advertiser revenue by 6% and consumer welfare by 1.6%. Second, we find that a switch from a first- to second-price auction results in truth telling (advertiser bids rise to advertiser valuations). However, the second-price auction has little impact on search engine profits. Third, consumer search tools lead to a platform revenue increase of 2.9% and an increase of consumer welfare by 3.8%. However, these tools, by reducing advertising exposures, lower advertiser profits by 2.1%.

Under the sociological theory of homophily, people who are similar to one another are more likely to interact with one another. Marketers often have access to data on interactions among customers from which, with homophily as a guiding principle, inferences could be made about the underlying similarities. However, larger networks face a quadratic explosion in the number of potential interactions that need to be modeled. This scalability problem renders probability models of social interactions computationally infeasible for all but the smallest networks. In this paper, we develop a probabilistic framework for modeling customer interactions that is both grounded in the theory of homophily and is flexible enough to account for random variation in who interacts with whom. In particular, we present a novel Bayesian nonparametric approach, using Dirichlet processes, to moderate the scalability problems that marketing researchers encounter when working with networked data. We find that this framework is a powerful way to draw insights into latent similarities of customers, and we discuss how marketers can apply these insights to segmentation and targeting activities.

We use data from a large-scale field experiment to explore what influences the effectiveness of online advertising. We find that matching an ad to website content and increasing an ad's obtrusiveness independently increase purchase intent. However, in combination, these two strategies are ineffective. Ads that match both website content and are obtrusive do worse at increasing purchase intent than ads that do only one or the other. This failure appears to be related to privacy concerns: the negative effect of combining targeting with obtrusiveness is strongest for people who refuse to give their income and for categories where privacy matters most. Our results suggest a possible explanation for the growing bifurcation in Internet advertising between highly targeted plain text ads and more visually striking but less targeted ads.