Marketing Science

Anderson, Eugene W.; Sullivan, Mary W.

doi: 10.1287/mksc.12.2.125pmid: N/A

This research investigates the antecedents and consequences of customer satisfaction. We develop a model to link explicitly the antecedents and consequences of satisfaction in a utility-oriented framework. We estimate and test the model against alternative hypotheses from the satisfaction literature. In the process, a unique database is analyzed: a nationally representative survey of 22,300 customers of a variety of major products and services in Sweden in 1989–1990. Several well-known experimental findings of satisfaction research are tested in a field setting of national scope. For example, we find that satisfaction is best specified as a function of perceived quality and “disconfirmation”—the extent to which perceived quality fails to match prepurchase expectations. Surprisingly, expectations do not directly affect satisfaction, as is often suggested in the satisfaction literature. In addition, we find quality which falls short of expectations has a greater impact on satisfaction and repurchase intentions than quality which exceeds expectations. Moreover, we find that disconfirmation is more likely to occur when quality is easy to evaluate. Finally, in terms of systematic variation across firms, we find the elasticity of repurchase intentions with respect to satisfaction to be lower for firms that provide high satisfaction. This implies a long-run reputation effect insulating firms which consistently provide high satisfaction.

Boulding, William; Staelin, Richard

doi: 10.1287/mksc.12.2.144pmid: N/A

In this paper we develop a model relating market share to average costs. We start with a theoretical model of the factors that affect the firm's average cost curve, partitioning these factors into (a) measurable firm and competitive environment characteristics, and (b) unobserved factors that are either fixed, random, or follow a first-order autoregressive process. We then link this theoretical model to an empirical model in which we specify three average cost equations for the organizational areas of purchasing, production, and marketing. Main effects for initial (lagged) market share position, as well as their interactions with factors characterizing the firm's competitive environment, represent the variables of key theoretical interest in our equations. We estimate these equations using PIMS data, and control for fixed, contemporaneous, and autoregressive unobservable factors. Our results suggest that market share can often lead to market power in the form of lower average costs. However, the firm's operating environment greatly moderates the effect of market share on average cost. In particular, we find that market share position only leads to lower average costs when the organizational unit operates in a competitive environment that gives it both motivation and ability to realize power from its market share position.

Schmittlein, David C.; Cooper, Lee G.; Morrison, Donald G.

doi: 10.1287/mksc.12.2.167pmid: N/A

Among the more prominent truisms in marketing are 80/20 type laws, e.g., 20 percent of the customers account for 80 percent of the purchases. These kinds of statistics indicate a certain degree of concentration in customer purchases; i.e., the extent to which a large portion of the product's total purchases are made by a small fraction of all customers. Such concentration levels, suggesting that markets can be segmented in various ways, are often reported in basic marketing texts.We show that a meaningful interpretation of these concentration statistics is not nearly as easy or immediate as it is to compute them. The key factors influencing the degree of apparent concentration in purchases are reviewed, and we present a modeling approach for estimating the true level of relevant concentration among customers.

Chintagunta, Pradeep K.

doi: 10.1287/mksc.12.2.184pmid: N/A

We develop a comprehensive utility maximizing framework to study the impact of marketing variables on the category purchase, brand choice and purchase quantity decisions of households for frequently purchased packaged goods. The model allows for dependence among the three decisions while ensuring that these decisions provide, in combination, the greatest possible utility to the household. By accounting for variations in reservation prices and intrinsic brand preferences across households, the modeling framework explicitly captures the effects of unobserved heterogeneity on all three purchase decisions.The principal empirical finding from analyzing the A. C. Nielsen data for the yogurt product category is that the substantive implications for the effects of marketing variables are sensitive to whether these effects are determined conditional or unconditional on a product category purchase. Our results show that reservation prices and intrinsic brand preferences vary across households, and not accounting for these variations in the estimation could lead to biased estimates for the coefficients of the marketing variables. A comparison of our results to those obtained from a nested logit model of purchase incidence and brand choice reveals that our proposed model performs better using both a formal statistical test as well as the criterion of predictive validity in a holdout sample of panelists. Further, the purchase quantity model compares favorably with two alternative models of quantity choice in the validation sample.

Jacobson, Robert; Aaker, David A.

doi: 10.1287/mksc.12.2.209pmid: N/A

Farris, Parry and Ailawadi (1992; hereafter denoted FPA) demonstrate that bias can arise in a regression involving a composite dependent variable where a subset of components of the dependent variable are used as explanatory factors. They correctly observe that the Jacobson and Aaker (1985; hereafter denoted JA) model has explanatory factors that are also components of the ROI dependent variable and, as such, is subject to “composite variable bias.” FPA note that another way of viewing composite variable bias is that the coefficients in the model reflect not their impact on the dependent variable but rather their impact on the dependent variable less the elements of the components included as explanatory factors. As such, additional effects (analogous to indirect effects) may be present to the extent strategic factors influence the included components. FPA conclude that such bias explains the low estimate of the market share effect reported in JA. However, FPA's attempt to replicate our analysis and assess composite variable bias is flawed by a mistake in their analysis, i.e., their disaggregate models do not follow from the JA aggregate specification. The purpose of this note is to correctly assess the extent to which the JA estimate of the market share effect is affected by composite variable bias and to suggest approaches for modeling a composite dependent variable in the presence of unobservable factors. In particular, we(i) show that the disaggregate specifications of FPA do not follow from JA,(ii) look at specifications not subject to composite variable bias to investigate the magnitude of the composite variable bias in JA, and(iii) provide a disaggregate modeling framework that controls for unobservable effects.