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Wayne S. DeSarbo
Researcher at Pennsylvania State University
Publications - 357
Citations - 16782
Wayne S. DeSarbo is an academic researcher from Pennsylvania State University. The author has contributed to research in topics: Multidimensional scaling & Market segmentation. The author has an hindex of 62, co-authored 357 publications receiving 15962 citations. Previous affiliations of Wayne S. DeSarbo include College of Business Administration & University of Michigan.
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Response Determinants in Satisfaction Judgments
TL;DR: In this article, the effects of five determinants of satisfaction are tested as well as individual differences in satisfaction formation in stock market trading scenarios in a full factorial design, showing that all main effects and four ordinal two-way interactions are significant.
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An Empirical Pooling Approach for Estimating Marketing Mix Elasticities with PIMS Data
TL;DR: This article proposed an alternative maximum likelihood, latent-pooling method for simultaneously pooling, estimating, and testing linear regression models empirically, which 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.
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Revisiting the miles and snow strategic framework : uncovering interrelationships between strategic types, capabilities, environmental uncertainty, and firm performance
TL;DR: It is shown that the empirically derived solution clearly dominates the traditional P-A-D-R typology of Miles and Snow, and implications and directions for future research are provided.
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A Maximum Likelihood Methodology for Clusterwise Linear Regression
Wayne S. DeSarbo,William L. Cron +1 more
TL;DR: This paper presented a conditional mixture, maximum likelihood methodology for clusterwise linear regression, which simultaneously estimates separate regression functions and membership in K clusters or groups, and performed a Monte Carlo analysis via a fractional factorial design.
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Hierarchical Bayes Conjoint Analysis: Recovery of Partworth Heterogeneity from Reduced Experimental Designs
TL;DR: In this paper, the authors investigate the tradeoff between the number of profiles per subject and number of subjects on the statistical accuracy of the estimators that describe the partworth heterogeneity.