As mixture regression models increasingly receive attention from both theory and practice, the question of selecting the correct number of segments gains urgency. A misspecification can lead to an under- or oversegmentation, thus resulting in flawed management decisions on customer targeting or...

Since its first introduction in the Schmalenbach Business Review, Hahn et al.'s (2002) finite mixture partial least squares (FIMIX-PLS) approach to response-based segmentation in variance-based structural equation modeling has received much attention from the marketing and management...

Mixture regression models are an important method for uncovering unobserved heterogeneity. A fundamental challenge in their application relates to the identification of the appropriate number of segments to retain from the data. Prior research has provided several simulation studies that compare...

Part I Methodology -- Chapter 1 Empirical Validation of the 10-Times Rule for SEM -- Chapter 2 Missing Values in RGCCA: Algorithms and Comparisons -- Chapter 3 Comparing Local vs Global Clustering with FIMIX-PLS. Application to Marketing -- Chapter 4 Partial Least Squares Structural Equation...

Partial least squares structural equation modeling (PLS-SEM) has become a popular method for estimating path models with latent variables and their relationships. A common goal of PLS-SEM analyses is to identify key success factors and sources of competitive advantage for important target...