PLS-PM presents some inconsistencies in terms of coherence with the direction of the relationships specified in the path diagram (i.e. the path directions). The PLS-PM iterative algorithm analyses interdependence among blocks and misses to distinguish explicitly between dependent and explanatory blocks in the structural model. This inconsistency of PLS-PM is illustrated by using the simple two-blocks model. For the case of more than two blocks of variables, it is necessary a close look at the different criteria optimized by PLS-PM in order to show this issue. In general, the role of latent variables in the structural model depends on the way the outer weights are calculated. A recently proposed method, called Non-Symmetrical Component-based Path Modeling, which is based on the optimization of a redundancy-related criterion in a multi-block framework, respects the direction of the relationships specified in the structural model. In order to assess the quality of the model, we provide a new goodness-of-fit index based on redundancy criterion and prediction capability. Furthermore, we provide a procedure to address the problem of multicollinearity within blocks of variables.
DAVINO, C., ESPOSITO VINZI, V. and DOLCE, P. (2016). Assessment and Validation in Quantile Composite-Based Path Modeling. In: The Multiple Facets of Partial Least Squares and Related Methods. 1st ed. Springer, pp. 169-180.