Abstract
Partial least squares structural equation modeling (PLS-SEM) is a highly popular multivariate data analysis method. The SmartPLS 3 software program helped many marketing researchers analyze the complex relationships between latent variables (i.e., mediation, moderation, etc.), which they measured by means of sets of observed variables. This program’s intuitive graphical user interface and various features, such as new metrics (e.g., HTMT, model fit indexes), advanced techniques (multigroup analysis, PLSpredict), and complementary techniques (e.g., confirmatory tetrad analysis, importance-performance map analysis), which impacted many business disciplines. SmartPLS 4 represents a significant leap forward in development with its completely revamped graphical user interface, faster processing speed for data estimation, and new model assessment features (i.e., cross-validated predictive ability test, endogeneity assessment, and a necessary condition analysis). This paper reviews SmartPLS 4 and discusses its various features, thereby providing researchers with concrete guidance that fits their analytical research goals.
Notes
SmartPLS 3 was released in 2015, contributing to the wide-scale adoption of PLS-SEM (Sarstedt and Cheah 2019).
Even though the software’s correct name is SmartPLS, we also included the keyword “Smart PLS,” as this label is used in a remarkable number of published articles.
A binary variable is a categorical variable with only two possible values (e.g., yes/no), which a dummy-coded (0/1) variable commonly represents (Sarstedt and Mooi 2019). As Becker et al. (2023) outline, the results that SmartPLS 4 provides in respect of such moderator effects can subsequently be interpreted to obtain meaningful insights. Specifically, SmartPLS 4 allows binary variables to not be standardized; consequently, researchers are not required to make manual corrections to interpret the interaction effects involving binary variables (Becker et al. 2023).
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