Presentation of the new SmartPLS 4 software at the PLS2022 conference скачать в хорошем качестве

Presentation of the new SmartPLS 4 software at the PLS2022 conference

At the PLS2022 conference hosted by the UBB Babes-Bolyai University in Cluj-Napoca (www.pls2022.org), Jan-Michael Becker and Christian M. Ringle (SmartPLS co-founders and co-developers) present their new software for partial least squares structural equation modeling (PLS-SEM) and more. In the beginning, the first steps in SmartPLS 4 will be shown: Data import (scales, min/max, missing values, new SPSS, Excel data import files types) and model building (ALT-Key for drawing arrows, alignment of constructs, change connections, move labels, change shapes for constructs, double-bend of connections). After that, the model will be calculated and various new features for displaying results in the model will be presented. Then the results report is opened and its main new features are explained (saving reports, saving LV scores to data file). After these basics, a higher-or model is created, estimated, and evaluated via bootstrapping. The new options (e.g. fixed seed), the exceptional bootstrapping speed in SmartPLS 4 (10,000 bootstrap samples in a few seconds), and the histograms in the report are shown. The new result comparison function (with synchronized navigation) then allows a comparison of the previous results for the corporate reputation model example with those of the higher-order model alternative. In the last part the project "Advanced analysis" is imported into SmartPLS 4. First, the new comment field is explained, and then it is demonstrated how a moderator is inserted into the model (two-way interaction). New is also the graphical simple slope representation for the moderation in the result report via customizable charts. This is followed by the implementation of a moderated moderation in SmartPLS 4 (three-way interactions and more). Finally, a short note on new SmartPLS 4 features such as linear regression models, path analysis (PROCESS), necessity condition analysis (NCA), and endogeneity checking with Gaussian copulas is given.