Uncovering unobserved heterogeneity is a requirement to obtain valid results when using structural equation modeling (SEM). Conventional segmentation methods usually fail in an SEM context because they account for the indicator data, but not for the latent variables and their relationships in...

Applications of the partial least squares (PLS) path modeling approach — which have gained increasing dissemination in business research — usually build on the assumption that the data stem from a single population. However, in empirical applications, this assumption of homogeneity is...

When applying the partial least squares structural equation modeling (PLSSEM) method, the assumption that the data stem from a single homogeneous population is often unrealistic. For the full set of data, unobserved heterogeneity in the PLS path model estimates may result in misleading...

Applications of the partial least squares (PLS) path modeling approach—which have gained increasing dissemination in business research—usually build on the assumption that the data stem from a single population. However, in empirical applications, this assumption of homogeneity is...