Finite Mixture Partial Least Squares (FIMIX-PLS) in service research

Ensuring the robustness of analytical results is a fundamental responsibility in empirical research, particularly when working with complex models and heterogeneous populations. In the context of Partial Least Squares Structural Equation Modeling, one often-overlooked threat to validity is unobserved heterogeneity – latent subgroups in the data that follow distinct structural patterns. Despite clear methodological guidance, robustness checks for this issue remain rare in applied service research. This article addresses that gap by reviewing the relevance and practical implementation of Finite Mixture Partial Least Squares (FIMIX-PLS) as a theoretically grounded yet underutilized technique for detecting unobserved heterogeneity after verifying the limited use of this method in recent publications, despite its recognized importance. We then provide a critical overview of FIMIX-PLS in comparison with other segmentation techniques, emphasizing its practical advantages and limitations. To support researchers, we offer a step-by-step, didactic example using SmartPLS 4.