Finite Mixture Partial Least Squares Analysis: Methodology and Numerical Examples
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In wide range of applications for empirical data analysis, the assumption that data is collected from a single homogeneous population is often unrealistic. In particular, the identification of different groups of consumers and their appropriate consideration in partial least squares (PLS) path modeling constitutes a critical issue in marketing. In this work, we introduce a finite mixture PLS software implementation which separates data on the basis of the estimates’ heterogeneity in the inner path model. Numerical examples using experimental as well as empirical data allow the verification of the methodology’s effectiveness and usefulness. The approach permits a reliable identification of distinctive customer segments along with characteristic estimates for relationships between latent variables. Researchers and practitioners can employ this method as a model evaluation technique and thereby assure that results on the aggregate data level are not affected by unobserved heterogeneity in the inner path model estimates. Otherwise, the analysis provides further indications on how to treat that problem by forming groups of data in order to perform a multi-group path analysis.
KeywordsPartial Little Square Unobserved Heterogeneity Finite Mixture Partial Little Square Analysis Brand Preference
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