Latent class analysis in PLS-SEM: A review and recommendations for future applications

With the increasing prominence of partial least squares structural equation modeling (PLS-SEM) in business research, the use of latent class analyses for identifying and treating unobserved heterogeneity has also gained momentum. Researchers have introduced various latent class approaches in a PLS-SEM context, of which finite mixture PLS (FIMIX-PLS) plays a central role due to its ability to identify heterogeneity and indicate a suitable number of segments to extract from the data. However, applying FIMIX-PLS requires researchers to make several choices that, if incorrect, could lead to wrong results and false conclusions. Addressing this concern, we present the results of a systematic review of FIMIX-PLS applications published in major business research journals. Our review provides an overview of the interdependencies between researchers' choices and identifies potential problem areas. Based on our results, we offer concrete guidance on how to prevent common pitfalls when using FIMIX-PLS, and identify future research areas.

Automated summary

With the increasing use of partial least squares structural equation modeling (PLS-SEM) in business research, latent class analyses for identifying and addressing unobserved heterogeneity have also gained attention. Finite mixture PLS (FIMIX-PLS) plays a central role in this area, but researchers need to make careful choices to avoid incorrect results.