2012 INFORMS Marketing Science Conference June 7

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TA06 MARKETING SCIENCE CONFERENCE – 2011 3 - An Empirical Analysis of New Product Launch Michael Cohen, Assistant Professor in Residence, University of Connecticut, 1376 Storrs Road Unit 4021, Storrs, CT, 06269-4021, United States of America, michael.cohen@uconn.edu, Rui Huang Validating the decision to launch a new product and its accompanying introduction strategy is a keystone component of the marketing planning process. Launch includes product development as well as the roll-out and marketing campaign. A common product introduction strategy is to develop products from the attributes of existing products. For example, Reese’s brand Whipps candy bar fuses the soft nougat of Three Musketeers with the peanut butter flavor of Reese’s Cups. This low cost product development strategy is typical in many experience-product markets, most prominently in packaged food products. This research introduces an empirical method to analyze market data for testing new product introduction strategies in an established marketplace. Our paper takes the approach of estimating a limited consumer awareness discrete choice demand model that specifies television advertising, display, coupons, and feature advertisements in a way that augments a product’s salience in the marketplace. We use Nielsen household purchase histories and gross rating point data for popular chocolate candy bars to estimate the model. The data are compiled from the New York, Chicago, and Los Angeles television markets and span 2006-2008. We use demand estimates to investigate the relative effectiveness of in-store promotion versus television advertising and demonstrate how to predict the performance of a new product before committing to launch. Thanks to Reese’s introduction of the Whipps candy bar during our data period, we are able to validate our model’s predictions. Results from these analyses provide guidance for both product development and promotion planning. ■ TA06 Legends Ballroom VII Competition I: Measuring the Impact of Competition in Retail Markets I Cluster: Special Sessions Invited Session Chair: A. Yesim Orhun, University of Chicago, Booth School of Business, 5807 Woodlawn Avenue, Chicago, IL, 60637, United States of America, yesim.orhun@chicagobooth.edu 1 - The Impact of Competition on Endogenous Product Provision: The Case of Motion Picture Exhibition Market A. Yesim Orhun, University of Chicago, Booth School of Business, 5807 Woodlawn Avenue, Chicago, IL, 60637, United States of America, yesim.orhun@chicagobooth.edu, Pradeep Chintagunta, Sriram Venkataraman In industries where products have short life cycles, and where products offered are a primary driver of demand to the firm, it is important to study the impact of a change in market structure on product choices. A canonical example of such an industry is the motion picture exhibition market. Using weekly U.S and Canada movie revenues at the theater level for the September 2003 - March 2005 period, we provide evidence on how a new theater entry affects an incumbent firms product choices and its revenues. As expected, we find significant negative impact of entry on incumbents revenues. Interestingly, the decrease in revenues is larger for incumbents that face entry of a theater of the same chain compared to incumbents that face entry by a rival chain. We show that as a result of entry of a co-owned firm, not only does the number of titles decrease more so than when the entrant is a rival, but also that the average tickets sold per title is lower. Importantly, we find that the decline in the ticket sales per title of the co-owned incumbent is fully explained by the decrease in the inherent box office potential of movies it screens. While we present movie choices of the incumbent as an explanation for the difference in business stealing and cannibalization, we note that our findings can shed empirical light on the larger issue of the impact of competition on product quality provision. We discuss the drivers of the observed quality response to competition. 2 - Empirical Investigation of Retail Expansion and Cannibalization in a Dynamic Environment S. Sriram, University of Michigan, Ross School of Business, Ann Arbor, MI, 48109, United States of America, ssrira@umich.edu, Joseph Pancras, V. Kumar Managers seeking to modify their portfolio of retail locations either by adding new stores or closing existing ones need to know the net impact of a store’s opening/closure on the overall chain performance. The answer to this question lies in inferring the extent to which each store generates incremental sales as opposed to competing with other stores belonging to the chain for the same set of customers. However, when the chain is experiencing a growth or a decline in sales, not accounting for these dynamics in brand preference is likely to yield misleading estimates regarding the magnitude of incremental sales versus cannibalization. We develop a demand model that accounts for dynamics in brand preferences and spatial competition between geographically proximate retail outlets. We calibrate the model parameters on both attitudinal and behavioral data for a fast food chain in a large U.S. city. The data reveal that the chain experienced increases in (a) overall chain sales, (b) number of stores, and (c) average sales per store during the period of our analysis. The results suggest that, on average, 69.7% of a store’s sales constitute incremental purchases with the rest derived from cannibalized sales from nearby stores belonging to the chain. However, there is significant heterogeneity across stores 4 with the incremental sales percentage ranging from a low of 59% to a high of 85%. As regards the individual stores that get cannibalized, we find significant decay in cannibalization with distance. For example, the average cannibalization rate at a distance of one mile or less is approximately 4%. This figure drops to less than 1% at a distance of 7-10 miles. We discuss how managers can use the model presented in this paper to make two key decisions: (a) isolating locations that can be closed by identifying stores that yield the lowest marginal benefit to the chain and (b) choosing the best location for a new store from a set of viable alternatives. 3 - How Wal-Mart’s Entry Affects Incumbent Retailers Huihui Wang, Duke University, Fuqua School of Business, 100 Fuqua Drive, Durham, NC, United States of America, huihui.wang@duke.edu, Carl Mela, Andrés Musalem Walmart is America’s largest company (Fortune 2010). In light its magnitude, the competition induced by Walmart’s expansion constitutes a sizable economic phenomenon. Surprisingly, the majority of the academic literature, in contrast to the media reports, concludes that price responses of incumbent retailers are mixed or minimal. However, this need not imply that there is no price reaction. On one hand, Walmart’s low price strategy may lead incumbent stores to lower their prices to compete with the new entrant. On the other hand, Wal-Mart may attract the more price sensitive customers from the incumbent firms’ customer mix. This would leave the incumbents with customers that are less price sensitive allowing them to raise their prices. Given that price response is a complex interplay of horizontal and vertical differentiation and costs, our objective is to disentangle these factors to provide insights into the nature of competitive response to Walmart’s entry. We start by formulating a model of consumer store choice which extends Kim et al. (2002). Given this model we consider how Walmart’s entry affects consumer behavior and prices in the market. Combining the results from different policy experiments we decompose how these different drivers (marginal costs, horizontal and vertical differentiation) affect incumbent retailers’ reactions to Walmart’s entry. 4 - Retail Market Expansion and Substitution Effects: Evidence from a Field Experiment Frederico Rossi, University of North Carolina, Kenan-Flagler Business School, Chapel Hill, NC, United States of America, federico_rossi@unc.edu, Eric Anderson, Ralf Elsner, Duncan Simester Price discounts and price cues typically increase demand but is this due to: substitution between products, substitution between retailers, or market expansion? We conduct a large-scale field experiment with a European direct mail firm to answer these questions. We experimentally vary price discounts and price cues (“Sale” claims) for twenty books and CDs sold at one retailer. A unique aspect of the data is that we measure sales not just at the retailer that conducted the test, but also at two competing retailers who offer the same twenty items at the control price. This yields direct measures of retail store substitution and reveals findings that differ from outcomes elsewhere in the literature. We find evidence of retail store substitution and this is largest (i) when a discount is combined with a price cue (ii) for established items that have been previously sold by the retailer (iii) among store switching customers. Moreover, we find evidence of a shopping basket effect that leads to positive spillovers and increased demand of non-promoted items. We find evidence of market expansion for discounted items, but there is little evidence of overall market expansion. Our results offer empirical support for theories of retail price competition, including loss leader pricing and loyal/switcher models of promotion. ■ TA07 Founders I ASA Special Session on the Marketing- Statistics Interface - I Cluster: Special Sessions Invited Session Chair: Peter J. Lenk, University of Michigan, 701 Tappan Street, Ann Arbor, MI, United States of America, plenk@umich.edu 1 - Dynamic Market Segmentation Models and Methods Timothy J. Gilbride, University of Notre Dame, 399 Mendoza College of Business, Notre Dame, IN, United States of America, tgilbrid@nd.edu, Peter J. Lenk Commercial market segmentation studies can be expensive, time consuming projects that are infrequently updated. Because of the time between subsequent studies, managers may miss important changes in the market. This research suggests that smaller, less expensive studies can be used to track changes in market segments. The proposed Bayesian methodology adapts a general state-space model where the space equations relate the cross-sectional data to population-level parameters at a given point in time, and the state model captures the time dynamics in the population-level parameters. Improved algorithms for two generalized dynamic models are detailed. Sequential analysis is appropriate when the goal is to estimate model parameters as new data arrives without re-estimating past results. For the sequential analysis, we propose a novel particle filter samplings scheme that is more efficient and better suited for the structure of the data than methods proposed in the literature. When several data sets are available and the goal is to quantify trends and simultaneously estimate parameters for all time periods, a retrospective or full-information approach is preferred. We find that substantially smaller samples can be used in the tracking studies because information is shared across studies. The paper identifies the important
ole that prior distributions play in the Bayesian analysis and suggests the predictive log likelihood be used for model selection when the managerial goal is to smooth parameter estimates and make short term predictions. The methods are illustrated with a unique 20 year data set on U.S. consumers’ attitudes towards marketing. 2 - Viral Marketing: Understanding the Diffusion of User Generated Content Within and Across Networks Yuchi Zhang, University of Maryland, 3330J Van Munching Hall, College Park, MD, 20742, United States of America, yzhang2@rhsmith.umd.edu, Wendy W. Moe This research proposes a methodology for modeling the diffusion of user-generated content (UGC). The diffusion of UGC can be characterized as a process that is highly dependent on word-of-mouth referrals, more so than other contexts. Specifically, we examine the daily views for a sample of videos posted on YouTube. We assume that the audience for each video is drawn from multiple diffusion networks in the population. The UGC spreads within each network according to network-specific Weibull processes and across networks according to a stochastic process that allows each network to begin its diffusion process at different points in time. We examine covariate effects and correlations between networks in this process. Our results indicate that UGC tends to diffuse simultaneously across different subsets of the population. We identify three drivers of these diffusion processes: content, video poster, and the poster’s initial network and show the impact of each on the overall diffusion process. We find that when videos diffuse rapidly at the onset, the overall diffusion of the content is less likely to include secondary diffusion networks. Furthermore, if these secondary diffusion networks exist, they tend to view the video much later in time. We also find significant effects (that vary across genres) of the size of the poster’s subscriber base on secondary diffusion networks. Our analysis provides insight into how managers should tailor both their content and diffusion strategy to more effectively employ viral marketing tactics. 3 - Bayesian Model Selection and Simulation Bias of the Harmonic Mean Estimator of Integrated Likelihoods Peter J. Lenk, University of Michigan, 701 Tappan Street, Ann Arbor, MI, United States of America, plenk@umich.edu Bayesian model selection depends on the integrated likelihood of the data given the model. Newton and Raftery’s harmonic mean estimator (HME) is simple to implement by computing the likelihood of the data at MCMC draws from the posterior distribution. Alternative methods in the literature require additional simulations or more extensive computations. In theory HME is consistent but can have an infnite variance. In practice, the computed HME is simulation biased. This talk identifies the source of the bias and recommends several algorithms for adjusting the HME to remove it. The bias can be substantial and can negatively affect HME’s ability to select the correct model in Bayesian model selection. The bias often causes the computed HME to overestimate the integrated likelihood, and the amount of bias tends to be larger for more complex models. When the computed HME errs, it tends to select models that are too complex. Simulation studies of linear and logistic regression models demonstrate that the adjusted HME effectively removes the simulation bias, is more accurate, and indicates more reliably the best model. ■ TA08 Founders II Innovation I: Open Innovation Contributed Session Chair: Sanjay Sisodiya, Assistant Professor of Marketing, University of Idaho, 875 Campus Drive / P.O. Box 443161, Moscow, ID, 83844, United States of America, sisodiya@uidaho.edu 1 - Open Innovation Practices and Market Outcomes: The Moderating Role of Product Capabilities Deepa Chandrasekaran, Assistant Professor of Marketing, Lehigh University, 621 Taylor St., Bethlehem, PA, 18015, United States of America, dec207@lehigh.edu, Gaia Rubera, Andrea Ordanini While the use of open innovation (OI) has gained importance in the last decade, empirical evidence about its effects is largely anecdotal. Particularly how OI interacts with a firm’s new product (NPD) capabilities is not clear. While an absorptive capacity perspective leads us to believe that only firms with good NPD capabilities can benefit from OI; the Non-Invented-Here Syndrome literature suggests that OI is more useful for firms with poor NPD capabilities. The authors contend that the effect of OI in fact depends on the interaction of NPD capabilities with the nature of input acquired via OI. They identify three types of OI practices: ideas-centric; technology-centric and product-centric. Their empirical analysis combines primary data from a survey of 239 Italian firms with secondary data on innovation and financial outcomes from proprietary databases. They demonstrate that the effect of OI on innovation rate is indeed contingent upon the level of a firm’s NPD capabilities and differs according to the type of OI. Theoretical and managerial implications are provided. 2 - Network and Knowledge Asset Alignment in Open Innovation Tanya Tang, University of Illinois at Urbana-Champaign, 350 Wohlers Hall, 1206 South Sixth Street, Champaign, IL, 61820, United States of America, yatang2@illinois.edu, Eric Fang, William Qualls Open innovation paradigm has received a lot of attention in managerial practices during the last several years. Companies like IBM, Cisco Systems, DuPont have all MARKETING SCIENCE CONFERENCE – 2011 TA09 5 been embracing open innovation through which they successfully bring knowledge outside the boundary of the firm into internal knowledge systems and integrate both external and internal knowledge to create high impact innovations. Even though there is growing interest in this new innovation paradigm, empirical academic research on this area is still very scant (Chesbrough 2006).We intend to integrate two most prominent perspectives in marketing strategy, social network theory and knowledge-based view, by exploring the alignments of a firm’s internal knowledge asset and external network asset in order to improve open innovation performance. We disaggregate knowledge asset and network asset into depth and breadth (Luca and Atuahene-Gima 2007). And argue the depth and breadth of knowledge and network assets form the “building blocks” of open innovation, which may depend on the alignment or “fit” of these assets. We refer to this as network-knowledge alignment strategy. Specifically, we explore network-knowledge alignment strategy in the context of Open Source Software (OSS) development, which has emerged as an important open innovation phenomenon (von Hippel 2005). By collecting the project embeddedness network data as well as developer’s knowledge across different knowledge domains from SourceForge.Net, the largest OSS development website, we investigate how OSS development teams can align its network and knowledge asset depth and breadth in order to achieve high project performance in terms of project internal efficiency and project external effectiveness. 3 - Innovative Capability: Investigating Open Innovation, Marketing Capability, and Firm Performance Sanjay Sisodiya, Assistant Professor of Marketing, University of Idaho, 875 Campus Drive / P.O. Box 443161, Moscow, ID, 83844, United States of America, sisodiya@uidaho.edu, Jean Johnson, Yany Grégoire The recently emerging trend of open innovation is gaining considerable interest. Here firms seek out external inputs for innovation and identify external paths for internally developed innovations (Chesbrough 2003, 2006). While there is evidence that firms following open innovation a successful (e.g., Huston and Sakkab, 2006), firms must also maintain a closed innovation perspective in order to develop unique innovations. Firms that are capable of performing both open and closed innovation may benefit by combining these two capabilities into a higher order innovative capability that could lead to even greater levels of firm performance. Firms with high innovative capability should outperform other firms that excel at either but not both open or closed innovation. While an innovative capability is intriguing, maintaining an innovative capability might not be enough to provide a firm with a competitive advantage. Thus it is important to also consider other combinative firm capabilities that could lead to heightened levels of firm success. Critical to success with an innovative capability, is the firms marketing capability (e.g., Vorhies and Morgan 2005). Those firms that maintain both an innovative capability, as manifested through open and closed innovation, combined with a marketing capability should achieve superior levels of firm performance as compared to those firms with a weak marketing capability. These hypotheses are tested on a sample of over 210 publicly traded technology firms. Results support the notion that while following open innovation firms may achieve a competitive advantage, it is the combination of maintaining an innovative capability and a marketing capability that contributes to greater levels of performance. ■ TA09 Founders III Promotions I Contributed Session Chair: Dinesh Gauri, Syracuse University, 721 University Ave, Syracuse, NY, 13214, United States of America, dkgauri@syr.edu 1 - Timing of Retailer Price-promotions Huseyin Karaca, Northwestern University, 2001 Sheridan Road, Evanston, IL, United States of America, h-karaca@kellogg.northwestern.edu, Lakshman Krishnamurthi, Vincent Nijs, Anne T. Coughlan Sales promotions have been the subject of numerous studies in the literature. A quick overview of this literature, however, reveals surprisingly little research on the subject of timing of promotions. Our paper addresses retailers’ promotion timing problem by studying the strategic behavior of a retailer in scheduling its price promotions for frequently purchased packaged goods around peak demand periods, facing consumer segments that differ in their propensity to purchase in and out of peak demand periods. We present and analyze an analytical framework from the perspective of a profit-maximizing retailer facing demand for two brands within a product category, i.e. a national brand and a private label, from two different segments of utilitymaximizing consumers over a two-period time frame. Our paper makes both theoretical and substantive contributions to the literature. The consideration of dynamically varying segment participation in the market is novel from a theoretical perspective. As opposed to the static segments considered in the previous literature, the dynamic segmenting approach in this research considers inter-temporal changes in the structure of aggregate demand. On the substantive front, the research brings to light a particular segmented consumption behavior in the empirical data and uses this managerial observation to explain and predict retail promotion activity across markets and products. Our research has immediate managerial implications. Considering the time pressure due to scheduling of promotions of hundreds of categories in any given week, the retailers might resort to rule-of-thumb promotional decisions rather than optimal ones. Our analytic framework, however, generates sensible rules retailers can use when scheduling their price-promotions.
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Session Index Thursday, 8:30am - 10
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2012 INFORMS Marketing Science Conference June 7

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