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Over the past 20 years, partial least squares (PLS) has become a popular method in marketing research. At the same time, several methodological studies have demonstrated problems with the technique but have had little impact on its use in marketing research practice. This study aims to present some of these criticisms in a reader-friendly way for non-methodologists.

Key critiques of PLS are summarized and demonstrated using existing data sets in easily replicated ways. Recommendations are made for assessing whether PLS is a useful method for a given research problem.

PLS is fundamentally just a way of constructing scale scores for regression. PLS provides no clear benefits for marketing researchers and has disadvantages that are features of the original design and cannot be solved within the PLS framework itself. Unweighted sums of item scores provide a more robust way of creating scale scores.

The findings strongly suggest that researchers abandon the use of PLS in typical marketing studies.

This paper provides concrete examples and techniques to practicing marketing and social science researchers regarding how to incorporate composites into their work, and how to make decisions regarding such.

This work presents a novel perspective on PLS critiques by showing how researchers can use their own data to assess whether PLS (or another composite method) can provide any advantage over simple sum scores. A composite equivalence index is introduced for this purpose.

Structural equation modeling (SEM) is a well-established and frequently applied method in various disciplines. New methods in the context of SEM are being introduced in an ongoing manner. Since formal proof of statistical properties is difficult or impossible, new methods are frequently justified using Monte Carlo simulations. For SEM with covariance-based estimators, several tools are available to perform Monte Carlo simulations. Moreover, several guidelines on how to conduct a Monte Carlo simulation for SEM with these tools have been introduced. In contrast, software to estimate structural equation models with variance-based estimators such as partial least squares path modeling (PLS-PM) is limited.

As a remedy, the R package cSEM which allows researchers to estimate structural equation models and to perform Monte Carlo simulations for SEM with variance-based estimators has been introduced. This manuscript provides guidelines on how to conduct a Monte Carlo simulation for SEM with variance-based estimators using the R packages cSEM and cSEM.DGP.

The author introduces and recommends a six-step procedure to be followed in conducting each Monte Carlo simulation.

For each of the steps, common design patterns are given. Moreover, these guidelines are illustrated by an example Monte Carlo simulation with ready-to-use R code showing that PLS-PM needs the constructs to be embedded in a nomological net to yield valuable results.