When applying structural equation modeling methods, such as partial least squares (PLS) path modeling, in empirical studies, the assumption that the data have been collected from a single homogeneous population is often unrealistic. Unobserved heterogeneity in the PLS estimates on the aggregate data level may result in misleading interpretations. Finite mixture partial least squares (FIMIX-PLS) and PLS genetic algorithm segmentation (PLS-GAS) allow the classification of data in variance-based structural equation modeling. This research presents an initial application and comparison of these two methods in a computational experiment in respect of a path model which includes multiple endogenous latent variables. The results of this analysis reveal particular advantages and disadvantages of the approaches. This study further substantiates the effectiveness of FIMIX-PLS and PLS-GAS and provides researchers and practitioners with additional information they need to proficiently evaluate their PLS path modeling results by applying a systematic means of analysis. If significant heterogeneity were to be uncovered by the procedures, the analysis may result in group-specific path modeling outcomes, thus allowing further differentiated and more precise conclusions to be formed.
CITATION STYLE
Ringle, C. M., Sarstedt, M., & Schlittgen, R. (2009). Finite Mixture and Genetic Algorithm Segmentation in Partial Least Squares Path Modeling: Identification of Multiple Segments in Complex Path Models (pp. 167–176). https://doi.org/10.1007/978-3-642-01044-6_15
Mendeley helps you to discover research relevant for your work.