About

Professor James L. Ginter is a Professor Emeritus in the Department of Marketing & Logistics at the Max M. Fisher College of Business. Prior to his retirement in June 2003, he held several distinguished positions including Dean’s Distinguished Professor, Professor of Marketing, Co-Director of the Supply Chain Management Research Group, Academic Director of MBA Programs, and Chair of the Department of Marketing. His research focuses on strategic management and the development and application of quantitative models for management decisions. He has presented his research across the United States, Europe, and Australia, and has been published in reputable journals such as the Journal of Marketing Research, Marketing Sciences, and Management Science. Professor Ginter has also served as a consultant to various organizations on research and management problems and is active in numerous professional organizations. Throughout his career, he has been recognized with three college awards for outstanding graduate teaching.

Research topics

• Business
• Marketing
• Psychology
• Econometrics
• Advertising

Selected publications

• A Hierarchical Bayes Model of Primary and Secondary Demand

  Marketing Science · 1998-02-01 · 213 citations

  articleSenior author

  Product design, pricing policies, and promotional activities influence the primary and secondary demand for goods and services. Brand managers need to develop an understanding of the relationships between marketing mix decisions and consumer decisions of whether to purchase in the product category, which brand to buy, and how much to consume. Knowledge about factors most effective in influencing primary and secondary demand of a product allows firms to grow by enhancing their market share as well their market size. The purpose of this paper is to develop an individual level model that allows an investigation of both the primary and secondary aspects of consumer demand. Unlike models of only primary demand or only secondary demand, this more comprehensive model offers the opportunity to identify changes in product features that will result in the greatest increase in demand. It also offers the opportunity to differentially target consumer segments depending upon whether consumers are most likely to enter the market, increase their consumption level, or switch brands. In the proposed hierarchical Bayes model, an integrative framework that jointly models the discrete choice and continuous quantity components of consumer decision is employed instead of treating the two as independent. The model includes parameters that capture individual specific reservation value, attribute preference, and expenditure sensitivity. The model development is based upon the microeconomic theory of utility maximization. Heterogeneity in model parameters across the sample is captured by using a random effects specification guided by the underlying microeconomic model. This requires that some of the effects are strictly positive. This is accommodated through the use of a gamma distribution of heterogeneity for some of the parameters. A normal distribution of heterogeneity is used for the remaining parameters. Gibbs sampling is used to estimate the model. The key methodological contribution of this paper is that we show how to specify a hierarchical Bayes continuous random effects model that integrates consumer choice and quantity decisions such that individual-level parameters can be estimated. Individual level estimates are desirable because insights into primary demand involve nonlinear functions of model parameters. For example, consumers not in the market are those whose utilities for the choice alternatives fall below some reservation value. The proposed methodology yields individual specific estimates of reservation values and expenditure sensitivity, which allow assessment of the origins of demand other than the switching behavior of consumers. The methodology can also be used to help identify changes in product features most likely to bring new customers into a market. Our work differs from previous research in this area as we lay the framework needed to obtain individual-level parameter estimates in a continuous random effects model that integrates choice and quantity. The methodology is demonstrated with survey data collected about consumer preferences and consumption for a food item. For the data available, a large response heterogeneity was observed across all model parameters. In spite of limited data available at the individual level, a majority of the individual level estimates were found to be significant. Predictive tests demonstrated the superiority of the proposed model over existing latent class and aggregate models. Particularly, significant gains in predictive accuracy were observed for the “no-buy” behavior of the respondents. These gains demonstrate that by structurally linking the choice and quantity models results in a more accurate characterization of the market than existing finite mixture approaches that model choice and quantity independently. We show that our joint model makes more efficient use of the available data and results in better parameter estimates than those that assume independence. Finally, the individual level demand analysis is illustrated through a simple example involving a $1.00 price cut. We demonstrate practical usefulness of the model for targeting by developing the demographic, attitudinal, and behavioral profiles of consumer groups most likely to increase consumption, enter the market, or switch brands because of a price cut decision.

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• On the Heterogeneity of Demand

  Journal of Marketing Research · 1998-08-01 · 258 citations

  articleSenior author

  Demand heterogeneity traditionally has been defined as segments of consumers that are homogeneous with regard to the benefits they seek or in their response to marketing programs (e.g., product offering, price discounts). Although it often is acknowledged that truly homogeneous segments of consumers do not exist, the approximation is assumed to be sufficiently accurate to provide a reasonable basis for the development of marketing strategy. In this article, the authors provide evidence that the homogeneous segment assumption might not be reasonable. By using a normal component mixture model that nests other, more commonly used models of heterogeneity, the authors find that the within-component heterogeneity remains substantial, even when multiple components are present. Predictive tests substantiate their finding of large within-component heterogeneity.

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• On the Heterogeneity of Demand

  Journal of Marketing Research · 1998-08-01 · 119 citations

  articleSenior author
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• Using Extremes to Design Products and Segment Markets

  Journal of Marketing Research · 1995-11-01 · 278 citations

  articleSenior author

  Current marketing methodologies used to study consumers are inadequate for identifying and understanding respondents whose preferences for a product offering are most extreme. These “extreme respondents” have important implications for product design and market segmentation decisions. The authors develop a hierarchical Bayes random-effects model and apply it to a conjoint study of credit card attributes. Their proposed model facilitates an in-depth study of respondent heterogeneity, especially of extreme respondents. The authors demonstrate the importance of characterizing extremes in identifying product attributes and predicting the success of potential products.

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• Incorporating Prior Knowledge into the Analysis of Conjoint Studies

  Journal of Marketing Research · 1995-05-01 · 240 citations

  articleSenior author

  The authors use conjoint analysis to provide interval-level estimates of part-worths allowing tradeoffs among attribute levels to be examined. Researchers often possess prior information about the part-worths, such as the order and range restrictions of product attribute levels. It is known, for example, that consumers would rather pay less for a specific product given that all other product attribute levels are unchanged. The authors present a Bayesian approach to incorporate prior ordinal information about these part-worths into the analysis of conjoint studies. Their method results in parameter estimates with greater face validity and predictive performance than estimates that do not utilize prior information or those that use traditional methods such as LINMAP. Unlike existing methods, the authors’ methods apply to both rating and choice-based conjoint studies.

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• The effects of in-store displays and feature advertising on consideration sets

  International Journal of Research in Marketing · 1995-05-01 · 161 citations

  articleSenior author
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• Incorporating Prior Knowledge into the Analysis of Conjoint Studies

  Journal of Marketing Research · 1995-05-01 · 117 citations

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• Using Extremes to Design Products and Segment Markets

  Journal of Marketing Research · 1995-11-01 · 106 citations

  articleSenior author
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• A Probabilistic Dealing Strategy

  Decision Sciences · 1991-01-01 · 1 citations

  articleSenior author

  A probabilistic dealing strategy is proposed which allows all premium brands in an established market to earn nonnegative profits without cooperation. Following the strategy, brands take turns attracting deal‐responsive customers. Relative to a reactive competitive strategy, the proposed strategy improves the positions of all premium brands. With use of the strategy, average deal sizes are positively related to a brand's market share, the proportion of quality conscious customers, the proportion of informed customers, the span of regular prices in a market, and the range of customers' acceptable prices.

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• Segmentation de marché, différenciation de produit et stratégie marketing

  Recherche et Applications en Marketing (French Edition) · 1988-03-01 · 2 citations

  articleSenior author

  Malgré l'usage répandu des termes «segmentation de marché» et «différenciation de produit», il y a eu et il continue d'y avoir un malentendu considérable sur leur signification et leur emploi. Les auteurs tentent de dissiper la confusion en s'appuyant sur la théorie économique contemporaine et classique, et sur les cartes de préférence des produits.

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Frequent coauthors

• Greg M. Allenby

  The Ohio State University

  14 shared

• Neeraj Arora

  6 shared

• Carl Obermiller

  Seattle University

  2 shared

• Martha C. Cooper

  The Ohio State University

  2 shared

• Cynthia Fraser

  University of Virginia

  2 shared

• Frederick D. Sturdivant

  2 shared

• Thomas J. Page

  University Hospital Lewisham

  2 shared

• Peter R. Dickson

  2 shared

Awards & honors

• outstanding graduate teaching award (three-time)