Partial least square structural equation modelling (PLS-SEM) technique / Salwa Muda ... [et al.]

What is PLS-SEM? Herman Wold originally developed partial least square-structural equation modeling (PLS-SEM) in the 1970s with the aim of maximizing the explained variance of the dependent variables. PLS-SEM is similar to using multiple regression analysis in predicting causal relationships between exogenous and endogenous variables and has become a standard approach for analyzing complex interrelationships between observed and latent variables. The statistical objective of PLS-SEM is to maximize the explained variance of the dependent variables, known as endogenous variables. As a second-generation technique of multivariate data analysis, the applications have grown in the past decade in the social sciences discipline and other fields such as agricultural science, engineering, environmental science, and medicine.