Assessing the overall fit of composite models estimated by partial least squares path modeling

Purpose This study aims to examine the role of an overall model fit assessment in context partial least squares path modeling (PLS-PM). In doing so, it will explain when is important assess and provides ways assessing composite models. Moreover, resolve major concerns about that have been raised literature on PLS-PM. Design/methodology/approach paper explains how PLS Furthermore, discusses PLS-PM concise guidelines Findings as for models common factor To models, researchers can use a statistical test several indices known through structural equation (SEM) with latent variables. Research limitations/implications Researchers who aim understand mechanism underlying population draw inferences should take concept seriously. Practical implications facilitate this presents two-step procedure adopted from SEM Originality/value clarifies necessity not question which estimator be used (PLS-PM, maximum likelihood, etc). but purpose modeling. Whereas, paramount explanatory modeling, imperative predictive