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The Miles and Snow strategic type framework is re‐examined with respect to interrelationships with several theoretically relevant batteries of variables, including SBU strategic capabilities, environmental uncertainty, and performance. A newly developed constrained, multi‐objective, classification methodology is modified to empirically derive an alternative quantitative typology using survey data obtained from 709 firms in three countries (China, Japan, United States). We compare the Miles and Snow typology to the classification empirically derived utilizing this combinatorial optimization clustering procedure. With respect to both variable battery associations and objective statistical criteria, we show that the empirically derived solution clearly dominates the traditional P‐A‐D‐R typology of Miles and Snow. Implications and directions for future research are provided. Copyright © 2004 John Wiley & Sons, Ltd.

The PIMS (Profit Impact of Marketing Strategies) data entail sparse time-series observations for a large number of strategic business units (SBUs), In order to estimate disaggregate marketing mix elasticities of demand, a natural solution is to pool different SBUs. The traditional, a priori approach is to pool together those SBUs which one believes in advance to be very similar with respect to their marketing mix elasticities. We propose an alternative maximum likelihood, latent-pooling method for simultaneously pooling, estimating, and testing linear regression models . This method enables the determination of a “fuzzy” pooling scheme, while directly estimating a set of marketing mix elasticities and intertemporal covariances for each pool of SBUs. Our analyses reveal different magnitudes and patterns of marketing mix elasticities for the derived pools. Pool membership is influenced by demand characteristics, business scope, and order of market entry.econometric models, regression and other statistical techniques, marketing mix, competitive strategy

Two endemic problems face researchers in the social sciences (e.g., Marketing, Economics, Psychology, and Finance): unobserved heterogeneity and measurement error in data. Structural equation modeling is a powerful tool for dealing with these difficulties using a simultaneous equation framework with unobserved constructs and manifest indicators which are error-prone. When estimating structural equation models, however, researchers frequently treat the data as if they were collected from a single population (Muthén [Muthén, Bengt O. 1989. Latent variable modeling in heterogeneous populations. 557–585.]). This assumption of homogeneity is often unrealistic. For example, in multidimensional expectancy value models, consumers from different market segments can have different belief structures (Bagozzi [Bagozzi, Richard P. 1982. A field investigation of causal relations among cognitions, affect, intentions, and behavior. 562–584.]). Research in satisfaction suggests that consumer decision processes vary across segments (Day [Day, Ralph L. 1977. Extending the concept of consumer satisfaction. W. D. Perreault, ed. , Vol. 4. Association for Consumer Research, Atlanta, 149–154.]). This paper shows that aggregate analysis which ignores heterogeneity in structural equation models produces misleading results and that traditional fit statistics are not useful for detecting unobserved heterogeneity in the data. Furthermore, sequential analyses that first form groups using cluster analysis and then apply multigroup structural equation modeling are not satisfactory. We develop a general finite mixture structural equation model that simultaneously treats heterogeneity and forms market segments in the context of a specified model structure where all the observed variables are measured with error. The model is considerably more general than cluster analysis, multigroup confirmatory factor analysis, and multigroup structural equation modeling. In particular, the model subsumes several specialized models including finite mixture simultaneous equation models, finite mixture confirmatory factor analysis, and finite mixture second-order factor analysis. The finite mixture structural equation model should be of interest to academics in a wide range of disciplines (e.g., Consumer Behavior, Marketing, Economics, Finance, Psychology, and Sociology) where unobserved heterogeneity and measurement error are problematic. In addition, the model should be of interest to market researchers and product managers for two reasons. First, the model allows the manager to perform response-based segmentation using a consumer decision process model, while explicitly allowing for both measurement and structural error. Second, the model allows managers to detect unobserved moderating factors which account for heterogeneity. Once managers have identified the moderating factors, they can link segment membership to observable individual-level characteristics (e.g., socioeconomic and demographic variables) and improve marketing policy. We applied the finite mixt...

The drive to satisfy customers in narrowly defined market segments has led firms to offer wider arrays of products and services. Delivering products and services with the appropriate mix of features for these highly fragmented market segments requires understanding the value that customers place on these features. Conjoint analysis endeavors to unravel the value or partworths, that customers place on the product or service's attributes from experimental subjects' evaluation of profiles based on hypothetical products or services. When the goal is to estimate the heterogeneity in the customers' partworths, traditional estimation methods, such as least squares, require each subject to respond to more profiles than product attributes, resulting in lengthy questionnaires for complex, multiattributed products or services. Long questionnaires pose practical and theoretical problems. Response rates tend to decrease with increasing questionnaire length, and more importantly, academic evidence indicates that long questionnaires may induce response biases. The problems associated with long questionnaires call for experimental designs and estimation methods that recover the heterogeneity in the partworths with shorter questionnaires. Unlike more popular estimation methods, Hierarchical Bayes (HB) random effects models do not require that individual-level design matrices be of full rank, which leads to the possibility of using fewer profiles per subject than currently used. Can this theoretical possibility be practically implemented? This paper tests this conjecture with empirical studies and mathematical analysis. The random effects model in the paper describes the heterogeneity in subject-level partworths or regression coefficients with a linear model that can include subject-level covariates. In addition, the error variances are specific to the subjects, thus allowing for the differential use of the measurement scale by different subjects. In the empirical study, subjects' responses to a full profile design are randomly deleted to test the performance of HB methods with declining sample sizes. These simple experiments indicate that HB methods can recover heterogeneity and estimate individual-level partworths, even when individual-level least squares estimators do not exist due to insufficient degrees of freedom. Motivated by these empirical studies, the paper analytically investigates the trade-off between the number of profiles per subject and the number of subjects on the statistical accuracy of the estimators that describe the partworth heterogeneity. The paper considers two experimental designs: each subject receives the same set of profiles, and subjects receive different blocks of a fractional factorial design. In the first case, the optimal design, subject to a budget constraint, uses more subjects and fewer profiles per subject when the ratio of unexplained, partworth heterogeneity to unexplained response variance is large. In the second case, one can maintain a given level of estimation accuracy as the number of p...