MARKETING; ENTREPRENEURSHIP & INNOVATION; INTERNATIONAL BUSINESS & MARKETS
Sandy and Morton Goldman Professor of Entrepreneurial Studies
Professor of Marketing
Former Dean
Dipak C. Jain is the Sandy and Morton Goldman Professor in Entrepreneurial Studies and a professor of marketing at the Kellogg School of Management, where he has been a member of the faculty since 1987. From 2001-2009, Jain served as Dean of the Kellogg School, bringing more than 20 years of experience in management and education to his position at the school’s helm. Prior to his appointment as Dean, he served as the Associate Dean of Academic Affairs for five years and worked closely with former Kellogg School Dean Donald P. Jacobs to set the agenda for the school’s curriculum, faculty and research activities.
Dean Jain’s areas of research include the marketing of high-tech products; market segmentation and competitive market structure analysis; cross-cultural issues in global product diffusion; new product diffusion; and forecasting models. He has had more than 50 articles published in leading academic journals.
Dean Jain teaches courses on marketing research, new products and services, and statistical models in marketing. In 2003, he was appointed as a foreign affairs adviser for the Prime Minister of Thailand. He has served as a consultant to Microsoft, Novartis, American Express, Sony, Nissan, Motorola, Eli Lilly, Phillips and Hyatt International. He also serves as a member of the board of directors of Hartmarx Corporation, Deere & Company, Northern Trust Corporation and Reliance Industries (India). He is also a former director at United Airlines and Peoples Energy.
Dean Jain’s teaching honors include the Sidney Levy Award for Excellence in Teaching in 1995; the John D.C. Little Best Paper Award in 1991; Kraft research professorships in 1989-90 and 1990-91; the Beatrice research professorship in 1987-88; the Outstanding Educator Award from the State of Assam in India in 1982; the Gold Medal for the Best Post-Graduate of the Year from Gauhati University in India in 1978; the Gold Medal for the Best Graduate of the Year from Darrang College in Assam in India in 1976; the Gold Medal from Jaycees International in 1976; the Youth Merit Award from Rotary International in 1976; and the Jawaharlal Nehru Merit Award, Government of India in 1976.
Dean Jain has served as the departmental editor for the journal Management Science, the area editor for Marketing Science and associate editor for the Journal of Business and Economic Statistics. He is also a former member of the editorial board of the Journal of Marketing Research.
His long career in education began as a student in Tezpur (Assam), India. He went on to earn his bachelor’s degree in mathematics and statistics in 1976 and his master’s degree in mathematical statistics in 1978 from Gauhati University in India. He taught at Gauhati for the next five years before leaving for Dallas to pursue his PhD in marketing at the University of Texas. In addition to his positions at the Kellogg School, Dean Jain has been a visiting professor of marketing since 1989 at the Sasin Graduate Institute of Business Administration at Chulalongkorn University in Bangkok.
Data Analysis
Entrepreneurship
High-Tech Marketing
Marketing Research
Marketing Strategy/Planning/Policy
New Product Development
New Product Forecasting
Small Business Management
Technology
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Adler and Harzing (this issue) point out a host of methodological and isomorphic shortcomings that trouble a measurement tool familiar to most educators: academic rankings. In their view, this ruler fails both in terms of correctly assessing individual performance and institutional quality, yet often still exerts an exaggerated influence among key stakeholders, such as administrators, faculty, students, alumni and recruiters. The authors, like the songwriter David Byrne in his lyrics, call to mind Disraeli’s quip: “There are three kinds of lies — lies, damned lies and statistics.” In careless hands, numbers can hide as much as they reveal. Of course exceptionally careful hands, if unchecked, can manipulate the figures with equal dexterity.
The increasing number of consumer goods and services offered in recent years suggests that product-line extensions have become a favored strategy of product managers. A larger assortment, it is often argued, keeps customers loyal and allows firms to charge higher prices. There is disagreement, however, about the extent to which a longer product line translates into higher profits. We develop an econometric model derived from a game-theoretic perspective that explicitly considers firms' use of product-line length as a competitive tool. On the demand side, we analytically establish the link between consumer choice and the length of the product line. Based on our derivations, we include a measure of line length in the utility function to investigate consumer preference for variety using a brand-level discrete-choice model. The supply side is characterized by price and line length competition between oligopolistic firms. For the empirical analysis we use market-level data for the yogurt category. We find that there are decreasing returns to product-line length. Based on a series of“what-if” experiments, we derive recommendations for effective product line decisions in a competitive environment.
Logit models have been widely used in marketing to predict brand choice and to make inference about the impact of marketing mix variables on these choices. Most researchers have followed the pioneering example of Guadagni and Little, building choice models and drawing inference conditional on the assumption that the logit model is the correct specification for household purchase behaviour. To the extent that logit models fail to adequately describe household purchase behaviour, statistical inferences from them may be flawed. More importantly, marketing decisions based on these models may be incorrect. This research applies White's robust inference method to logit brand choice models. The method does not impose the restrictive assumption that the assumed logit model specification be true. A sandwich estimator of the covariance corrected for possible mis-specification is the basis for inference about logit model parameters. An important feature of this method is that it yields correct standard errors for the marketing mix parameter estimates even if the assumed logit model specification is not correct. Empirical examples include using household panel data sets from three different product categories to estimate logit models of brand choice. The standard errors obtained using traditional methods are compared with those obtained by White's robust method. The findings illustrate that incorrectly assuming the logit model to be true typically yields standard errors which are biased downward by 10-40 per cent. Conditions under which the bias is particularly severe are explored. Under these conditions, the robust approach is recommended.
Discrete choice models of demand have typically been estimated assuming that prices are exogenous. Since unobservable (to the researcher) product attributes, such as coupon availability, may impact consumer utility as well as price setting by firms, we treat prices as endogenous. Specifically, prices are assumed to be the equilibrium outcomes of Nash competition among manufacturers and retailers. To empirically validate the assumptions, we estimate logit demand systems jointly with equilibrium pricing equations for two product categories using retail scanner data and cost data on factor prices. In each category, we find statistical evidence of price endogeneity. We also find that the estimates of the price response parameter and the brand-specific constants are generally biased downward when the endogeneity of prices is ignored. Our framework provides explicit estimates of the value created by a brand, i.e., the difference between consumers' willingness to pay for a brand and its cost of production. We develop theoretical propositions about the relationship between value creations and competitive advantage for logit demand systems and use our empirical results to illustrate how firms use alternative value creation strategies to accomplish competitive advantage.
A comprehensive framework that incorporates infrastructure and communication influences is developed analyzing the diffusion processes of five consumers in Thailand. The model parameters are estimated using the non-linear least square procedure. On the basic of the empirical findings, managerial implications are derived and discussed to provide insights into: 1) the role of mass and interpersonal communications in determining the pattern of diffusion of innovations; 2) the role of the infrastructure factors in determining the rate of diffusion of innovations; and 3) the relationship between diffusion parameters and country-specific characteristics. These insights may serve as guidelines for private firms in formulating effective marketing strategies for introducing new products into the Thai market. They may also help the state in formulating effective infrastructure development strategy for stimulating economic growth and prosperity.
An analytical framework is presented that specifies optimal search strategies when consumers use cutoff decision rules when information is formatted by brand or attribute and when the task is either screening alternatives or choosing the first acceptable alternative. The results show that formatting effects determine optimal processing strategies for screening but not for satisficing choice tasks. A laboratory experiment was conducted to test the validity of the analytical results. Most results were validated. However, under certain conditions, consumers use brand processing in choice tasks even when the analytical model predicts attribute processing. Results from a follow-up study suggest that this deviation occurs because brand processors have different subjective search costs than attribute processors.
The multinomial logit model in the context of the Luce choice axiom implies that the logarithm of the ratio of choice probabilities (the log odds ratio) of two objects depends only on the attributes of the two objects and not on the attributes of other objects in the choice set. Noting this weakness of the Luce choice axiom and the conventional logit model, [Batsell and Polking, 1985] prove the existence of a unique set of additive numbers that equate to the log odds ratio and that depend on the objects in the choice set. They observed that the Luce model spawned multiattribute extensions and applications and call for such extensions and applications of their generalization of the Luce model. We provide such an extension here and show analytically that it is possible to stay within the multinomial logit family while avoiding the IIA restriction.
The most commonly used functional forms in measuring market response functions are the linear and log-linear (double-log) specifications. Although the two models are mutually non-nested, they are both nested within the class of Box-Cox regression models. This enables one to test the statistical validity of these two models using nested tests, the power characteristics of which are better established relative to non-nested hypotheses tests, at least in large samples. In this paper, an application of the Lagrange multiplier (LM) test to determine the validity of linear, log-linear, and attraction-type formulations of market share models is illustrated using marketing data. The test is easy to compute and involves running only one extra linear regression. A Monte Carlo simulation is performed to study the properties of the test for samples of varying size and different levels of error variance. The simulation results indicate that the LM test should not be used with samples of less than 100 observations. We also compare the performance of the LM test to that of the PE test developed by MacKinnon, White, and Davidson for non-nested models. The results show that the PE test has a lower probability of a type 1 error for all sample sizes and different error levels. The power of the LM test, however, is greater when the error variance of the true model is high, given a fixed sample size.
Knowledge about difficulties encountered while using a product can provide marketing managers with useful insights about customer needs and how to meet them. Current exploratory methods such as focus groups and depth interviews may not easily uncover such information, because they require customers to remember detailed information about cognitive processes. This paper introduces a new exploratory research method, called subproblem decomposition, that gives a clearer view of customer cognitive processes. With this method, customers are asked to perform product-related tasks, and to think out loud while doing so. Their spoken thoughts are broken down into subproblems (naturally occurring goals and their attendant methods) and individually labeled. By counting subproblems, inferences can be made about product-use difficulties. In this paper, customer data collected via subproblem decomposition are compared with data collected by focus groups and depth interviews that were specifically designed to elicit product-use difficulties. The results highlight the relative advantages of all methods examined, and highlight the potential complementarity of these methods.
Following the publication of the Bass model in 1969 in Management Science, the earliest attempt to modify this model to include decision variables was an often-cited 1975 paper by Robinson and Lakhani that was also published in Management Science. In the ensuing years numerous modifications and extensions of the Bass model have been proposed to study the effects of decision variables on he diffusion process for new products. We review here the various published papers that include decision variables in diffusion models. In evaluating these publications, we provide benchmarks of desirable properties of diffusion models with marketing-mix variables. In further discussion and exploration of desirable properties for such models we present and evaluate proportional-hazard models, originally proposed by Cox (1972) for applications to survival data, that include decision variables. We provide a comparison of these models with the generalized Bass model developed by Bass, Krishnan, and Jain (1994). We conclude with a discussion of the limitations of the generalized Bass models and suggest possible areas for future research.
This paper provides an application of differential games to determine marketing expenditure strategies for channel members in a two-member channel. Assuming that each channel member maximizes individual profits, we determine the closed-loop equilibrium strategies for marketing effort over time for both members by explicitly recognizing that each member's decisions are affected by the other's actions. To account for this interdependence in the decisions of the manufacturer and the retailer, channel sales are directly related to the marketing efforts of channel members. Our results show that the optimal effort levels of the channel members are constant over time. We also compare these results with the case where the channel members determine their optimal effort strategies by maximizing total channel profits rather than individual profits. In this case, though total channel profits will be higher, we show that channel members expend greater effort on marketing expenditures.
This is a capstone course in marketing, applying and extending into the future many previously introduced marketing concepts. The course addresses questions critical to the future of marketing from a number of different perspectives. Leading thinkers in marketing will provide their insights as well as guide discussions to address some of the most important marketing questions facing leaders in the future, including what marketing's role will be, how marketing concepts and tools need to be adapted to conform to the future marketplace, and what marketing principles and frameworks will remain valuable into the future. In addition to their class sessions, students will work individually and in teams.
Consumer Insight and Marketing Stratey addresses three key areas: the future of marketing, sales-force management and marketing services to “nanosecond customers.” The course focuses on customer-centricity, creating innovative frameworks, developing strategic perspectives toward the company’s sales force, and implementing effective marketing programs in service sectors.
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