Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R Quotes

“Compared to CB-SEM, PLS-SEM emphasizes prediction, while simultaneously relaxing the demands regarding the data and specification of relationships. PLS-SEM aims at maximizing the endogenous latent variables’ explained variance by estimating partial model relationships in an iterative sequence of ordinary least squares regressions.”
― Joseph F. Hair Jr., Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R: A Workbook

“The research is based on secondary data, which may lack a comprehensive substantiation on the grounds of measurement theory”
― Joseph F. Hair Jr., Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R: A Workbook

“However, if the primary research objective is prediction and explanation of target constructs (Rigdon, 2012), PLS-SEM should be given preference”
― Joseph F. Hair Jr., Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R: A Workbook

“CB-SEM is particularly suitable for testing a theory in the confinement of a concise theoretical model.”
― Joseph F. Hair Jr., Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R: A Workbook

“1.5 Guidelines for Choosing Between PLS-SEM and CB-SEM”
― Joseph F. Hair Jr., Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R: A Workbook

“which is therefore the preferred method when the research objective is theory development and explanation of variance”
― Joseph F. Hair Jr., Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R: A Workbook