Skip to Main Content
Article navigation
Purpose

The purpose of this paper is to discuss the pros and cons of partial least squares approach to structural equation modeling (PLS-SEM). The topics include bias, consistency, maximization of R2, reliability and model validation.

Design/methodology/approach

The approach in this study is descriptive, and the method consists of logical arguments and analysis that are supported by results in references.

Findings

Several optimal properties of the PLS-SEM methodology are clarified. A proposal for transforming PLS-SEM mode A to mode B is highlighted, and the transformed mode possesses the desired properties of both modes A and B. Issues with the application of regression analysis using composite scores are also discussed. The strength of PLS-SEM is also compared against that of covariance-based SEM.

Research limitations/implications

Additional studies on PLS-SEM are needed when the population structure contains cross-loadings and/or correlated errors.

Practical implications

PLS-SEM may have inflated type I errors and R2 values even with normally distributed data.

Originality/value

The content of this paper is new, and there does not exist such an in-depth discussion of the pros and cons of PLS-SEM methodology in the literature.

Licensed re-use rights only
You do not currently have access to this content.
Don't already have an account? Register

Purchased this content as a guest? Enter your email address to restore access.

Please enter valid email address.
Email address must be 94 characters or fewer.
Pay-Per-View Access
$41.00
Rental

or Create an Account

Close Modal
Close Modal