Knowledge that Transforms

This paper surveys economic choice theory, stressing developments that permit use of data from psychometric and conjoint experiments to produce market demand forecasts. Alternatives to the widely used multinomial logit model are summarized, and a new method for estimating multinomial probits is described. An integration of choice models with attitudinal scaling and perceptual mapping, within a latent variable system, is described. Estimation of such systems under either “random effects” or “fixed effects” descriptions of heterogeneity across individuals is discussed. Issues in the use of choice models to describe responses from conjoint experiments are presented. New regression diagnostic tests for the consistency of multinomial logit representations are discussed.

Schmittlein and Mahajan (Schmittlein, D. C., V. Mahajan. 1982. Maximum likelihood estimation for an innovation diffusion model of new product acceptance. Marketing Sci. 1 (Winter) 57–78.) made an important improvement in the estimation of the Bass (Bass, F. M. 1969. A new product growth model for consumer durables. Management Sci. 15 (January) 215–227.) diffusion model by appropriately aggregating the continuous time model over the time intervals represented by the data. However, by restricting consideration to only sampling errors and ignoring all other errors (such as the effects of excluded marketing variables), their Maximum Likelihood Estimation (MLE) seriously underestimates the standard errors of the estimated parameters. This note uses an additive error term to model sampling and other errors in the Schmittlein and Mahajan formulation. The proposed Nonlinear Least Squares (NLS) approach produces valid standard error estimates. The fit and the predictive validity are roughly comparable for the two approaches. Although the empirical applications reported in this paper are in the context of the Bass diffusion model, the NLS approach is also applicable to other diffusion models for which cumulative adoption can be expressed as an explicit function of time.

A new model of consumer behavior is developed using a hybrid of cognitive psychology and microeconomics. The development of the model starts with the mental coding of combinations of gains and losses using the prospect theory value function. Then the evaluation of purchases is modeled using the new concept of “transaction utility.” The household budgeting process is also incorporated to complete the characterization of mental accounting. Several implications to marketing, particularly in the area of pricing, are developed.

A theory of salesforce compensation plans is presented where the sales of a product depend not only on the salesperson's effort but also on the uncertainty in the selling environment. The firm chooses a compensation plan to maximize its profit taking into account the salesperson's likely effort levels under alternative compensation plans and his or her alternative job opportunities. The salesperson (agent) chooses an effort level considering both the disutility from effort and the expected utility from earnings under the compensation plan. The Agency Theory framework provides an explanation for the differences across firms in the types of compensation plans used such as straight salary, straight commissions, or a combination of salary and commissions. It is shown that the optimal compensation plan is a convex (concave) increasing function of sales if the risk tolerance of the salesperson increases “rapidly” (stays constant) with income. We identify several structural parameters that affect the compensation plan and show that the implication of changes in some of these parameters is consistent with those mentioned in the sales management literature. For example, we show that the proportion of salary to total compensation would increase with an increase in one or more of the following parameters: (i) uncertainty, (ii) marginal cost of production, and (iii) attractiveness of alternative job opportunities for the salesperson. We conclude with a discussion of the implications of the theory for managing salesforce compensation plans.

The objective of this paper is to analyze the consumer's decision in electing to use cents-off coupons distributed by manufacturers of consumer products. Arguing that the decision to use coupons is based on the tradeoff between costs of using coupons and the savings obtained, it is shown that coupons can serve as a price discrimination device to provide a lower price to a particular segment of consumers. Based on a price theoretic model, it is shown that the users of coupons are more price elastic than nonusers of coupons and that the opportunity cost of time and other household resource variables are determinant factors in consumers' decisions. Implications derived from the model are tested using diary panel data.

Due to the risk inherent in dependence on foreign oil, there is a social benefit in aiding the introduction of alternative energy sources into the market place. The Federal government has initiated a number of programs, including price subsidies, to help accelerate the market diffusion of new, alternative energy systems. We develop a model to investigate analytically the effects of a price subsidy over time on the rate of market diffusion. The model considers word-of-mouth effects and learning curve cost declines. Under a set of conditions that a new technology should be expected to meet before commercialization, the optimal subsidy level is shown to be nonincreasing in time. The related market price is shown to be closely related to the diffusion effect. If there is no such effect, the price to the customer is constant. If there is positive diffusion effect, price increases in time, while if market saturation causes demand to decline over time price decreases in time.

A maximum likelihood approach is proposed for estimating an innovation diffusion model of new product acceptance originally considered by Bass (Bass, F. M. 1969. A new product growth model for consumer durables. Management Sci. 15 (January) 215–227.). The suggested approach allows: (1) computation of approximate standard errors for the diffusion model parameters, and (2) determination of the required sample size for forecasting the adoption level to any desired degree of accuracy. Using histograms from eight different product innovations, the maximum likelihood estimates are shown to outperform estimates from a model calibrated using ordinary least squares, in terms of both goodness of fit measures and one-step ahead forecasts. However, these advantages are not obtained without cost. The coefficients of innovation and imitation are easily interpreted in terms of the expected adoption pattern, but individual adoption times must be assumed to represent independent draws from this distribution. In addition, instead of using standard linear regression, another (simple) program must be employed to estimate the model. Thus, tradeoffs between the maximum likelihood and least squares approaches are also discussed.