The main force of PLS-SEM analysis is SmartPLS. SmartPLS software is widely used in management, marketing, organizational behavior, information systems and other fields. Its principle is to use partial least square (PLS) for statistical analysis.

In SmartPLS, advanced analysis. This software can perform modeling in a convenient environment in less than a minute. In addition, with the ability to manage projects, you can manage them well. You can also customize the built-in model using existing colors, borders, and fonts. This software makes your project better.

SmartPLS is a milestone in latent variable modeling. It combines good methods such as PLS-POS, IPMA, and complex bootstrap routines, as well as an easy-to-use and intuitive graphical user interface.

The following is a list of all our (growing) available calculation methods. Related innovative algorithms will also be provided in SmartPLS in a short period of time. We promise.

• Partial Least Squares (PLS) Path Modeling
• Ordinary Least Squares (OLS) Regression Based on Sum Score
• Consistency PLS (PLSc)
• Weighted PLS (WPLS), Weighted OLS (WOLS), and Weighted Consistency PLS (WPLSc)
• Boot and use advanced boot options
• Blindfold
• Importance Performance Map Analysis (IPMA)
• PLS Multigroup Analysis (MGA): Analyzing the differences and significance of group specific PLS path model estimates
• High-order model
• Mediation: Estimation of Indirect Effects and Significance Testing Based on Bootstrap
• Moderate: Estimation of Interaction Effects and Its Significance Test Based on Bootstrap
• Nonlinear Relationships: Estimation of Secondary Effects and Significance Testing Based on Bootstrap
• Confirmatory Quaternal Analysis (CTA): A statistical technique that allows for empirical testing of measurement model settings
• Finite Mix (FIMIX) segmentation: a potential class method that allows for the identification and processing of unobserved heterogeneity in path models
• Predictive Segmentation (POS): A Method for Identifying Data Groups
• PLS prediction: a technique for determining the prediction quality of PLS path models
• Model selection for prediction

Easily delve deeper into your data!

• A powerful modeling environment allows you to create path models in minutes.
• The project manager can help you track all analyses and documents.
• Customize your model with colors, borders, and fonts to emphasize your ideas separately!
• The built-in interpretation of the algorithm and meaningful default values allow you to easily enter the PLS-SEM world.
• A well organized report provides comprehensive insights into the results.
• Permanently save the results as an HTML report or Excel file.
• Create data groups to easily run multi group analysis.
• Create interactive terms and run moderator analysis without any issues.