Knowledge that Transforms

Paulo Albuquerque (“ Evaluating Promotional Activities in an Online Two-Sided Market of User-Generated Content ”) is an assistant professor of marketing at the Simon Graduate School of Business, University of Rochester. He holds a Ph.D. in management from the UCLA Anderson School of Management. He is currently interested in competition and consumer behavior in online markets, new product diffusion across markets, and spatial competition models. He was named a 2011 MSI Young Scholar, and his articles have appeared in Marketing Science, the Journal of Marketing Research, and Management Science. Udi Chatow (“ Evaluating Promotional Activities in an Online Two-Sided Market of User-Generated Content ”) is a program and research manager at Hewlett-Packard (HP) Labs and a lead on MagCloud.com incubation, which he cofounded. He earned bachelor's and master's degrees in physics and medical physics from Tel Aviv University and an EMBA from Kellogg/Tel Aviv University in their international program. Since joining HP Labs in July 2005, he has led and supported several Web-to-print services and incubations; he previously spent 17 years at HP-Indigo, where he held various research and development positions such as research scientist, project manager, section manager, and director. He has over 30 patents awarded and is active in the information systems and technology organization and in nonimpact printing conferences. Kay-Yut Chen (“ Evaluating Promotional Activities in an Online Two-Sided Market of User-Generated Content ”) is a principal scientist at Hewlett-Packard (HP) Labs. He started behavioral economics research at HP Labs, a first in a corporation, after he received his Ph.D. from Caltech in 1994. He has pioneered the application of behavior economics to business issues in areas such as supply chain contracting and human-based forecasting, and his work has been featured in many popular publications such as Scientific American, Newsweek, the Wall Street Journal, and the Financial Times. He is the author of the book The Secrets of the Moneylab: How Behavioral Economics Can Improve Your Business, published by Portfolio in October 2010. Theodoros Evgeniou (“ Content Contributor Management and Network Effects in a UGC Environment ”) is an associate professor of decision sciences and technology management at INSEAD, Fontainebleau. His current research interests include preference measurement methods and market research, social networks, machine learning, and data analytics for marketing. He has published more than 30 top academic journal and conference papers. Moshe Fresko (“ Mine Your Own Business: Market-Structure Surveillance Through Text Mining ”) is a consulting expert on the topics of text mining, data mining, natural language programming, and machine learning. He holds a B.A. and an M.A. in computer engineering from Boğaziçi University, Istanbul, Turkey, and he received his Ph.D. in computer science from Bar-Ilan University, Israel. Between 2001 and 2010, he worked as a researcher and lecturer at Bar Ilan's Computer Science department, studying text mining, data mining, natural language programming, and machine learning, as well as teaching several programming-related courses; between 2007 and 2008, he worked as a visiting researcher and lecturer at the School of Business Administration at the Hebrew University of Jerusalem. He was active in the founding and progress of two text-mining related start-up companies. Ronen Feldman (“ Mine Your Own Business: Market-Structure Surveillance Through Text Mining ”) currently serves as the Head of the Internet Studies Department at the School of Business Administration of the Hebrew University of Jerusalem. He received his Ph.D. in computer science from Cornell University and his B.Sc. in math, physics, and computer science from the Hebrew University of Jerusalem. In 1997, he founded ClearForest, a Boston-based business intelligence company later acquired by Reuters. He coined the term “text mining” in 1995 and wrote the textbook The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data (Cambridge University Press, 2007); he has given over 30 tutorials on text mining and information extraction and has written numerous scholarly papers on these topics. Anindya Ghose (“ Designing Ranking Systems for Hotels on Travel Search Engines by Mining User-Generated and Crowdsourced Content ”) is an associate professor in the Department of Information, Operations, and Management Sciences at the Stern School of Business of New York University. He received his Ph.D. from Carnegie Mellon University. His expertise is in analyzing how the massive amount of data generated by technological advances such as the Internet and mobile phones can influence marketing and advertising decisions, and his recent research interests include social media, mobile Internet, crowdfunding, Internet marketing, and digital advertising. He has received multiple best paper awards at premier conferences and journals, is a 2011 MSI Young Scholar, and is also a recipient of a National Science Foundation CAREER Award. David Godes (“ Sequential and Temporal Dynamics of Online Opinion ”) is an associate professor in the Marketing Department at the Robert H. Smith School of Business, University of Maryland. He received a B.S. in economics from 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. His work has appeared in Marketing Science, Management Science, Quantitative Marketing and Economics, and the Harvard Business Review. Jacob Goldenberg (“ Mine Your Own Business: Market-Structure Surveillance Through Text Mining ”) is a professor of marketing at the School of Business Administration at the Hebrew University of Jerusalem and a visiting professor at the Columbia Business School. His research focuses on creativity, new product development, diffusion of innovation, complexity in market dynamics social networks effects, and social media. He has published papers in the Journal of Marketing, the Journal of Marketing Research, Management Science, Marketing Science, Nature Physics, and Science; in addition, he is an author of two books by the Cambridge University Press and one by the Chicago Press. His scientific work has been covered by the New York Times, the Wall Street Journal, the Boston Globe, the BBC News Harold Tribune, the Economist, and Wired Magazine. Rajdeep Grewal (“ User-Generated Open Source Products: Founder's Social Capital and Time to Product Release ”) is the Irving & Irene Bard Professor of Marketing at the Smeal College of Business at the Pennsylvania State University and is also the Associate Research Director of the Institute for the Study of Business Markets at the Smeal College of Business. He received his Ph.D. from the University of Cincinnati in 1998. His research focuses on empirical modeling of strategic marketing issues and has appeared in the top field journals. He has received several awards for his research, including a doctoral dissertation award from the Procter & Gamble Market Innovation Research Fund, an honorable mention award at the prestigious MSI/Journal of Marketing competition on “Linking Marketing to Financial Performance and Firm Value,” the 2003 Young Contributor Award from the Society of Consumer Psychology for his article in the Journal of Consumer Psychology, and the AMA Marketing Strategy SIG Early Career Award in 2007. Panagiotis G. Ipeirotis (“ Designing Ranking Systems for Hotels on Travel Search Engines by Mining User-Generated and Crowdsourced Content ”) is an associate professor in the Department of Information, Operations, and Management Sciences at the Stern School of Business of New York University. He received his Ph.D. degree in computer science from Columbia University in 2004, with distinction. His recent research interests focus on crowdsourcing and on mining user-generated content on the Internet. He has received three best paper awards (International Conference on Data Engineering 2005, ACM Special Interest Group on Management of Data 2006, and International World Wide Web Conference 2011), two best paper runner-up awards (Joint Conference on Digital Libraries 2002 and ACM Knowledge Discovery and Data Mining Conference 2008), and is also a recipient of a CAREER Award from the National Science Foundation. Zainab Jamal (“ Evaluating Promotional Activities in an Online Two-Sided Market of User-Generated Content ”) is a research scientist at Hewlett-Packard Labs. She holds a Ph.D. in marketing science from the University of California, Los Angeles. Her area of focus is in developing econometric and statistical models to understand and predict customer response behavior; this area feeds into the broader research stream of enabling businesses to optimize their marketing operations through analytical technologies in the backdrop of major paradigm shifts in the landscape such as personalized marketing. She brings deep industry experience to her research expertise, having worked in different roles in brand management and product development after receiving her master's in economics (Delhi School of Economics) and an MBA (Indian Institute of Management, Ahmedabad). Gerald C. Kane (“ Network Characteristics and the Value of Collaborative User-Generated Content ”) is an assistant professor of information systems at Boston College's Carroll School of Management. He received his Ph.D. from the Goizueta Business School of Emory University and his MBA in computer information systems from Georgia State University. His research interests include

Paulo Albuquerque (“ Measuring the Impact of Negative Demand Shocks on Car Dealer Networks ”; “ Rejoinder to Commentaries on Albuquerque and Bronnenberg ”) is an assistant professor of marketing at the Simon Graduate School of Business, University of Rochester. He holds a Ph.D. in management from the UCLA Anderson School of Management. He is currently interested in competition and consumer behavior in online markets, new product diffusion across markets, and spatial competition models. His articles have appeared in Marketing Science, the Journal of Marketing Research, and Management Science. Bart J. Bronnenberg (“ Measuring the Impact of Negative Demand Shocks on Car Dealer Networks ”; “ Rejoinder to Commentaries on Albuquerque and Bronnenberg ”) is a professor of marketing and CentER research fellow at Tilburg University. He holds Ph.D. and M.Sc. degrees in management from INSEAD, Fontainebleau, France and an M.Sc. in industrial engineering from Twente University, The Netherlands. He is currently interested in marketing strategy and multimarket competition in consumer goods and medical industries; he is also continuing to work on empirical analyses of new product growth and consumer choice behavior. His articles have appeared in the leading field journals, and he was named the recipient of the 2003 Paul Green Award, the 2003 IJRM Best Paper Award, the 2004 John D. C. Little Best Paper Award, and the 2008 Paul Green Award. Gangshu (George) Cai (“ Exclusive Channels and Revenue Sharing in a Complementary Goods Market ”) is an associate professor in the Department of Management at Kansas State University. He received his Ph.D. in operations research from North Carolina State University in 2005, and he received his M.S. in business statistics and economics in 1999 and B.S. in physics in 1996 from Peking University. His research is concentrated on multichannel supply chain management, with a particular focus on the interface between operations management and marketing, finance, and e-commerce. Javier Cebollada (“ Quantifying Transaction Costs in Online/Off-line Grocery Channel Choice ”) is an associate professor of marketing at the Public University of Navarra, Spain. He obtained a Ph.D. in management and a master's degree in economics, both from Pompeu Fabra University (Barcelona), Spain. He has been studying how manufacturers and retailers adapt their strategies to the multichannel online–off-line structure and how consumers behave in the multichannel environment. His research has been published in journals such as Marketing Science, the Journal of Interactive Marketing, and the International Journal of Research in Marketing. Rachel R. Chen (“ Customer Bill of Rights Under No-Fault Service Failure: Confinement and Compensation ”) is an associate professor at the Graduate School of Management, University of California, Davis. She received her Ph.D. from the Johnson Graduate School of Management, Cornell University. Her research addresses economic issues in managing supply chains and distribution channels, including procurement and the marketing–operations interface; she also analyzes decision making under uncertainty in service operations. Her previous research has appeared in Management Science, Marketing Science, Manufacturing & Service Operations Management, Production and Operations Management, IIE Transactions, and other research outlets. Pradeep K. Chintagunta (“ Quantifying Transaction Costs in Online/Off-line Grocery Channel Choice ”) is the Joseph T. and Bernice S. Lewis Distinguished Service Professor of Marketing at the Booth School of Business, University of Chicago. He earned a Ph.D. in marketing from Northwestern University in 1990. He is interested in empirically studying strategic interactions among firms in vertical and horizontal relationships, measuring the effectiveness of marketing activities in pharmaceutical markets, investigating aspects of technology product markets, and analyzing household purchase behavior. Junhong Chu (“ Quantifying Transaction Costs in Online/Off-line Grocery Channel Choice ”) is an assistant professor of marketing at the National University of Singapore (NUS) Business School. She earned a Ph.D. in marketing and an MBA from the University of Chicago Booth School of Business in 2006. Her research interests include structural modeling (both classic and Bayesian approaches) of consumer and firm behavior, distribution channels, e-commerce, and retailing. Her research has appeared in Marketing Science, the Journal of Marketing Research, the Journal of Marketing, and the Journal of Interactive Marketing; she was the 2011 MSI Young Scholar. Yue Dai (“ Exclusive Channels and Revenue Sharing in a Complementary Goods Market ”) is an associate professor at Fudan University, China. She received her Ph.D. in industrial engineering from North Carolina State University. Her scholarly work has appeared in Production and Operations Management and Naval Research Logistics. Martijn G. de Jong (“ Measuring Consumer Preferences Using Conjoint Poker ”) is the J. Tinbergen Associate Professor of Marketing, Erasmus University. He applies statistical and psychometric methods to improve marketing decision making; often his research is cross-cultural in nature, relying on large-scale data sets. He received several major research grants, including an NWO (Netherlands Organization for Scientific Research) innovation grant. His awards include the J. C. Ruigrok award (awarded once every four years to the most productive young scholar in the Economic Sciences in the Netherlands) and the Christiaan Huygens award (presented by HRH princess Máxima of the Netherlands; awarded once every five years to a young economist in the Netherlands). Johann Füller (“ Measuring Consumer Preferences Using Conjoint Poker ”) is the CEO of Hyve AG, a leading innovation and community agency in Germany, and a researcher at the Innsbruck University School of Management. His research explores innovation and cocreation communities from multiple perspectives. He advises and speaks to major corporations worldwide in the areas of innovation communities, social media, crowdsourcing, and cocreation. He has published in journals such as the Journal of Product Innovation Management, California Management Review, MIS Quarterly, the Journal of Business Research, and others. Eitan Gerstner (“ Customer Bill of Rights Under No-Fault Service Failure: Confinement and Compensation ”) is a professor of management at the Faculty of Industrial Engineering and Management, The Technion–Israel Institute of Technology. His research areas include marketing strategies and social responsibility. He contributes regularly to this journal; recent titles include “Should Captive Sardines Be Compensated? Serving Customers in a Confined Zone” and “For a Few Cents More: Why Supersize Unhealthy Food?” He served Marketing Science as an editorial board member and an area editor. Dominique M. Hanssens (“ Response Models, Data Sources, and Dynamics ”) is the Bud Knapp Professor of Marketing at the UCLA Anderson School of Management. His research focuses on strategic marketing problems—in particular, the assessment of long-term marketing impact on business performance. He received his Ph.D. from Purdue University, and from 2005 to 2007, he served as executive director of the Marketing Science Institute in Cambridge, MA. He is a fellow of the INFORMS Society for Marketing Science. Dan Horsky (“ Disentangling Preferences and Learning in Brand Choice Models ”) is the Benjamin L. Forman Professor of Marketing at the William E. Simon Graduate School of Business, University of Rochester. He has published on a wide variety of marketing topics and has twice won the John D. C. Little Best Paper Award. His outside interests include swimming and art collecting. Sanjay Jain (“ Marketing of Vice Goods: A Strategic Analysis of the Package Size Decision ”; “ Rejoinder: Package Size Issues and Vice Goods ”) is a professor and JCPenney Chair of Marketing and Retailing Studies at the Mays Business School, Texas A&M University. His research interests are in the areas of competitive strategy, behavioral economics, and experimental game theory. He has been a finalist for the Paul Green Award, the John D. C. Little Award, the INFORMS Society of Marketing Science Long Term Impact Award, and he has received the INFORMS Society of Marketing Science Practice Prize Award. He is an associate editor for Management Science and serves on the editorial boards of the Journal of Marketing Research and Marketing Science. Sanjog Misra (“ Disentangling Preferences and Learning in Brand Choice Models ”) is an associate professor of marketing and applied statistics at the William E. Simon School of Business, University of Rochester. His current research interests include the development and application of structural econometric methods to marketing problems. His research has been published in journals such as Marketing Science, Quantitative Marketing and Economics, the International Journal of Research in Marketing, and the Journal of Law and Economics, among others. Rik Pieters (“ Ad Gist: Ad Communication in a Single Eye Fixation ”) is a professor of marketing at Tilburg University. He holds a Ph.D. in social psychology from Leiden University. He researches consumer behavior to improve the effectiveness of marketing and public policy decisions; his work focuses on the determinants and implications of visual attention. He is in search of interesting main effects and surmountable hills. Devavrat Purohit (“ A Strategic Perspective on Durable Goods ”) is the Bob J. White Professor of Business Administra

We propose a method to include seasonality in any diffusion model that has a closed-form solution. The resulting diffusion model captures seasonality in a way that naturally matches the original diffusion model's pattern. The method assumes that additional sales at seasonal peaks are drawn from previous or future periods. This implies that the seasonal pattern does not influence the underlying diffusion pattern. The model is compared with alternative approaches through simulations and empirical examples. As alternatives, we consider the standard Generalized Bass Model (GBM) and the basic Bass Model, which ignores seasonality. One of the main findings is that modeling seasonality in a GBM generates good predictions but gives biased estimates. In particular, the market potential parameter is underestimated. Ignoring seasonality in cases where data of the entire diffusion period are available gives unbiased parameter estimates in most relevant scenarios. However, ignoring seasonality leads to biased parameter estimates and predictions when only part of the diffusion period is available. We demonstrate that our model gives correct estimates and predictions even if the full diffusion process is not yet available.

Physicians may learn about prescription drug effectiveness directly from the firm via detailing or from patient experience. Patient-mediated learning is aided by the use of free drug samples. The effective use of samples is hampered by a lack of understanding of its exact return on investment implications. We seek to fill this gap by incorporating the physician's sample allocation behavior in the firm's decision making. We uncover the following implications for firms as well as policy makers. First, we find that the optimal sampling level for a drug category is a nonmonotonic function of patient payment ability and the price of the drug. Second, an increase in the cost of samples can lead to an increase in sampling and a decrease in detailing when the physician's propensity to provide sample subsidies is high. Third, when future market growth is expected to be high (early stage product life cycle and/or chronic drugs) and sampling efficiency is low, the use of sampling is profitable for the firm but leads to lower market coverage than when sampling is disallowed.

This article examines cross-price promotional effects in a dynamic context. Among other things, we investigate whether previously established findings hold when consumer and competitive dynamics are taken into account. Five main influential effects (asymmetric price effect, neighborhood price effect, asymmetric share effect, neighborhood share effect, and private label versus national brand asymmetry) appear jointly in the second layer of a pooled HB-VEC-VARX model, together with brand- and category-specific variables. This study tests the relative importance of these key factors across three scenarios: with no market dynamics, when only consumer dynamics are considered, and when competitive reactions are also taken into account. The results confirm all five influential effects, even if they are jointly estimated, and consumer and competitive dynamics are taken into account. National brand/private label asymmetry has the strongest influence on the cross-price promotional effects and becomes significantly stronger when consumer and competitive dynamics are taken into account. Dynamic consumer responses and competitive reactions both affect cross-brand price elasticities, and contrary to expectations, competitive reactions accumulate rather than diminish cross-price elasticities. Preemptive switching does occur; i.e., a brand's promotion in period t hurts a competitor's sales in subsequent periods. Our findings are based on an extensive data set. To attain generalizable results, we analyze 33 categories in five stores—that is, 165 store/category combinations.

We use laboratory experiments to examine the relative performance of the English auction (EA) and the first-price sealed-bid auction (FPA) when procuring a commodity. The mean and variance of prices are lower in the FPA than in the EA. Bids and prices in the EA agree with game-theoretic predictions, but they do not agree in the FPA. To resolve these deviations found in the FPA, we introduce a mixture model with three bidding rules: constant absolute markup, constant percentage markup, and strategic best response. A dynamic specification in which bidders can switch strategies as they gain experience is estimated as a hidden Markov model. Initially, about three quarters of the subjects are strategic bidders, but over time, the number of strategic bidders falls to below 65%. There is a corresponding growth in those who use the constant absolute markup rule.

In recent years academic research has focused on understanding and modeling the survey response process. This paper examines an understudied systematic response tendency in surveys: the extent to which observed responses are subject to state dependence, i.e., response carryover from one item to another independent of specific item content. We develop a statistical model that simultaneously accounts for state dependence, item content, and scale usage heterogeneity. The paper explores how state dependence varies by response category, item characteristics, item sequence, respondent characteristics, and whether it becomes stronger as the survey progresses. Two empirical applications provide evidence of substantial and significant state dependence. We find that the degree of state dependence depends on item characteristics and item sequence, and it varies across individuals and countries. The article demonstrates that ignoring state dependence may affect reliability and predictive validity, and it provides recommendations for survey researchers.

Patients increasingly request their physicians to prescribe specific brands of pharmaceutical drugs. A popular belief is that requests are triggered by direct-to-consumer advertising (DTCA). We examine the relationship between DTCA, patient requests, and prescriptions for statins. We find that although the effect of requests on prescriptions is significantly positive, the mean effect of DTCA on patient requests is negative, yet very small. More interestingly, both effects show substantial heterogeneity across physicians, which we uncover using a hierarchical Bayes estimation procedure. We find that specialists receive more requests than primary care physicians but translate them less into prescriptions. In addition, we find that the sociodemographic profile of the area a physician practices in moderates the effects of DTCA on requests and of requests on prescriptions. For instance, physicians from areas with a higher proportion of minorities (i.e., blacks and Hispanics) receive more requests that are less triggered by DTCA and are accomodated less frequently than physicians from areas with a lower proportion of minorities. Our results challenge managers to revisit the role of DTCA in stimulating patient requests. At the same time, they may trigger public policy concerns regarding physicians' accommodation of patient requests and the inequalities they may induce.

Contextual advertising entails the display of relevant ads based on the content that consumers view, exploiting the potential that consumers' content preferences are indicative of their product preferences. This paper studies the strategic aspects of such advertising, considering an intermediary who has access to a content base, sells advertising space to advertisers who compete in the product market, and provides the targeting technology. The results show that contextual targeting impacts advertiser profit in two ways: First, advertising through relevant content topics helps advertisers reach consumers with a strong preference for their product. Second, heterogeneity in consumers' content preferences can be leveraged to reduce product market competition, especially when competition is intense. The intermediary has incentives to strategically design its targeting technology, sometimes at the cost of the advertisers. When product market competition is moderate, the intermediary offers accurate targeting such that the consumers see the most relevant ads. When competition is high, the intermediary lowers the targeting accuracy such that the consumers see less relevant ads. Doing so intensifies competition and encourages advertisers to bid for multiple content topics in order to prevent their competitors from reaching consumers. In some cases, this may lead to an asymmetric equilibrium where one advertiser bids high even for the content topic that is more relevant to its competitor.