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The identification of different homogeneous groups of observations and theirappropriate analysis in PLS-SEM has become a critical issue in many appli-cation fields. Usually, both SEM and PLS-SEM assume the homogeneity of allunits on which the model is estimated, and approaches of segmentation presentin literature, consist in estimating separate models for each segments ofstatistical units, which have been obtained either by assigning the units tosegments a priori defined. However, these approaches are not fully accept- ablebecause no causal structure among the variables is postulated. In other words,a modeling approach should be used, where the obtained clusters are homogeneouswith respect to the structural causal relationships. In this paper, a newmethodology for simultaneous non-hierarchical clus- tering and PLS-SEM isproposed. This methodology is motivated by the fact that the sequentialapproach of applying first SEM or PLS-SEM and second the clustering algorithmsuch as K-means on the latent scores of the SEM/PLS-SEM may fail to find thecorrect clustering structure existing in the data. A simulation study and anapplication on real data are included to evaluate the performance of theproposed methodology.

Structural Equation Modeling and simultaneous clustering through the Partial Least Squares algorithm