Knowledge that Transforms

Marketing scholars have reflected upon the marketing discipline's internal evolution before. However, no prior study has assessed the globalization of authorship in our discipline, let alone assessed its consequences for the field. This paper addresses the following two questions: (1) Is there evidence of increasing globalization of authorship in the marketing discipline? (2) If so, does it help or hinder the field? Our work shows empirically how the globalization of our discipline evolved, how U.S. dominance is fading, and which countries experienced a rise in productivity of their affiliate and native scholars. Globalization hinders the field, because it has a negative effect on the impact of several major journals (most importantly, the Journal of Marketing and the Journal of Marketing Research). Globalization helps the field, because it has a positive effect on the diversity of our discipline. Important implications of our research are: (1) Journals and sponsoring organizations should strive for more international meetings. (2) Editors, reviewers, and authors should pay more attention to the global relevance of the research they publish, review, and submit. (3) Individual researchers should aim to be part of the global community of marketing scientists through, for instance, international research visits.

We discuss the development and implementation of CHAN4CAST, a sales forecasting model, by pack size, category, channel, region, customer account and a Web-based decision support system (DSS) for consumer packaged goods. In addition to capturing the effects of such variables as past sales, trend, own and competitor prices and promotional variables, and seasonality, the model accounts for the effects of temperature, significant holidays, new product introductions, trading day corrections, and adjustments to the wholesale level. In general, the model forecasts sales volume satisfactorily for a leading consumer packaged goods company. The DSS enables top- and mid-level executives in sales, marketing, strategic planning, and finance to develop accurate forecasts of sales volume, plan prices, and promotional activities over a long time horizon; to track sales response to marketing actions over time; and to simulate forecast scenarios based on possible marketing decisions and other variables. CHAN4CAST is being rolled out for more users and more divisions in the company. The key take-aways are that successful development and implementation of a rigorous marketing science model require a strong internal champion, a careful balance between modeling sophistication and practical relevance, good diagnostic features, regular validations, and greater attention to the development of a fast and responsive DSS.

The ability to keep up with changing technology is critical for a company's long-term survival. However, companies have to balance the risk of rushing into new areas and potentially cannibalizing their existing business against the risk of missing the emerging market. This paper investigates when incumbents enter into new market niches created by technological innovation. We argue that market conditions and company-specific characteristics do not suffice to explain incumbents' entry timing, but that entry is a contagious process. Our results demonstrate that incumbents are more likely to respond to innovations in their industry when their counterparts do so. In particular, we show that incumbents are affected by the entry of firms that are similar in size and resources. When a highly similar company enters the new market, it raises the probability that the company enters itself beyond levels based solely on the attractiveness of the market.

Sellers often explicitly suggest to buyers that they compare one option to other (reference) options. Building on the notion that loss aversion is more pronounced when comparisons are explicit rather than implicit, we propose that the mere fact that consumers are explicitly told to make particular comparisons induces more risk-averse, cautious choice and bidding behavior. This proposition was supported in a field experiment involving real online auctions, in which comparisons among listings either were done spontaneously by bidders or were encouraged using an explicit instruction to compare the focal auction with adjacent listings. Results showed that explicit reference points (1) diminished the influence of adjacent auction prices on the focal auction’s price; (2) led participants to submit fewer, lower, and later bids; (3) increased the incidence of sniping; (4) decreased bidding frenzy; and (5) decreased the tendency to bid on multiple items simultaneously. The impact of explicit comparisons on risk-averse behavior was further tested in a very different context using a laboratory choice experiment. In that study, explicit instructions to compare option sets increased the tendency to choose the compromise, low-risk, and all-average alternatives. We discuss the theoretical and practical implications of this research.

I provide a general formulation of the channel pass-through problem as a comparative static of the retail price equilibrium, and I analyze the impact of category management and retail competition on pass-through, focusing on brand and retailer differences, and the nature of the cost change being passed through—whether it is brand specific, retailer specific, both, or neither. With category management, a retailer's response to a brand-specific cost change is not limited to that brand; in general, a retailer will also change the prices of other brands. The cross-brand effect can be positive or negative, and, depending on its sign, it either enhances or attenuates pass-through. I explain the cross-brand effect as an interaction between two forces: a demand-substitution force that pushes for a negative cross-brand effect, and a strategic-complementarity force that pushes for a positive cross-brand effect. Retail competition adds another layer of strategic complementarity, causing other retailers to respond even for retailer-specific cost changes and increasing pass-through of categorywide cost changes. But its effect for brand-specific cost changes is ambiguous. I apply the theory to two commonly used demand functions—linear demand and nested logit—and show that they have significantly different pass-through properties. The paper concludes with a discussion of how the theory relates to the empirical literature, including the companion piece by Besanko et al. (Besanko, D., J-P. Dubé, S. Gupta. 2005. Own-brand and cross-brand retail pass-through. Marketing Sci. 24(1) 123–137.)

This paper investigates the competitive market for mass-customized products. Competition leads to surprising conclusions: Manufacturers customize only one of a product's two attributes, and each manufacturer chooses the same attribute. Customization of both attributes cannot persist in an equilibrium where firms first choose customization and then choose price, because effort to capture market with customization makes a rival desperate, putting downward pressure on prices. Equilibrium involves partial or no customization. In partial customization, rival firms do not differentiate their mass-customization programs: If firms customize different attributes, many more consumers are indifferent between the two firms. The elasticity of demand is increased and the resulting price war makes differentiated customization unprofitable. If firms customize the same attribute of a two-attribute product, they should concentrate on the attribute with the smaller heterogeneity in consumers' preferences. We incorporate consumers’ effort in portraying their preferences as a cost of interaction and provide public policy findings on the well-being of these consumers: When this cost is low, consumers are better off with customization than with standard goods, but firms choose too little customization. The loss in consumer surplus is sometimes captured by the firms, but for low interaction costs, firms' profit-driven behavior is economically inefficient.

It has been shown in the behavioral decision making, marketing research, and psychometric literature that the structure underlying preferences can change during the administration of repeated measurements (e.g., conjoint analysis) and data collection because of effects from learning, fatigue, boredom, and so on. In this research note, we propose a new class of hierarchical dynamic Bayesian models for capturing such dynamic effects in conjoint applications, which extend the standard hierarchical Bayesian random effects and existing dynamic Bayesian models by allowing for individual-level heterogeneity around an aggregate dynamic trend. Using simulated conjoint data, we explore the performance of these new dynamic models, incorporating individual-level heterogeneity across a number of possible types of dynamic effects, and demonstrate the derived benefits versus static models. In addition, we introduce the idea of an unbiased dynamic estimate, and demonstrate that using a counterbalanced design is important from an estimation perspective when parameter dynamics are present.

Much of the empirical research on competitive reactions describes how or why rivals react to a firm's past actions, but stops short of examining whether managers attempt to predict such reactions, which we call strategic competitive reasoning. In three exploratory studies, we find evidence of managers' thinking about competitors' past and future behavior, but little incidence of strategic competitive reasoning. Competitive intelligence experts and other experienced managers' assessment of the results suggests that the relatively low incidence of strategic competitor reasoning is due to perceptions of low returns from anticipating competitor reactions more than to the high cost of doing so. Both the difficulty of obtaining competitive information and the uncertainty associated with predicting competitor behavior contribute to these perceptions. The paper suggests both a need for research on competitive behavior and an opportunity to influence and improve managerial judgment and decision making.

Over the years, researchers have found that promotion makes consumers switch brands and purchase earlier or more. However, it is unclear how promotion affects consumption, especially for product categories that are perceived to be versatile and substitutable. In this paper, we propose a dynamic structural model with endogenous consumption under promotion uncertainty to analyze the promotion effect on consumption. This model recognizes consumers as rational decision makers who form promotion expectations and plan their purchase and consumption decisions in light of promotion schedule. Applying the proposed model to packaged tuna and yogurt, we find that endogenous consumption responds to promotion as a result of forward-looking and stockpiling behavior. This is the first empirical paper that recognizes consumption as an endogenous decision variable and proposes a structural model to offer behavioral explanations on whether, how, and why promotion encourages consumption for product categories with flexible consumption.

Many marketing problems require accurately predicting the outcome of a process or the future state of a system. In this paper, we investigate the ability of the support vector machine to predict outcomes in emerging environments in marketing, such as automated modeling, mass-produced models, intelligent software agents, and data mining. The support vector machine (SVM) is a semiparametric technique with origins in the machine-learning literature of computer science. Its approach to prediction differs markedly from that of standard parametric models. We explore these differences and benchmark the SVM's prediction hit-rates against those from the multinomial logit model. Because there are few applications of the SVM in marketing, we develop a framework to position it against current modeling techniques and to assess its weaknesses as well as its strengths.