Open AccessFive Common Mistakes for Using Partial Least Squares Path Modeling (PLS-PM) in Management Research
Author(s) -
Asyraf Afthanorhan,
Zainudin Awang,
Nazim Aimran
Publication year - 2020
Publication title -
contemporary management research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.192
H-Index - 2
ISSN - 1813-5498
DOI - 10.7903/cmr.20247
Subject(s) - structural equation modeling , partial least squares regression , path analysis (statistics) , confirmatory factor analysis , covariance , exploratory factor analysis , path coefficient , statistics , exploratory research , sample size determination , computer science , mathematics , econometrics , psychology , sociology , anthropology
The value of Partial Least Squares Path Modeling (PLS-PM) in management research has now been acknowledged, although the PLS-PM was developed for a reason. First, the PLS-PM was developed as an alternative to Covariance based Structural Equation Modeling (CBSEM) when exploratory research is conducted. As far as this method concerned, many researchers are misused or overuse the application of PLS-PM without understanding the basic knowledge in structural equation modeling. Thus, the purpose of this paper is to discuss the five common mistakes (data distributions, sample size limitations, unsatisfactory fitness index, misunderstanding between confirmatory and exploratory research, and poor factor loadings) for using PLS-PM over CB-SEM in management research. We concluded that the researchers should respect these methods and justify their use when conducting the research projects because some of the projects might be better for CB-SEM or PLS-PM.