Article: Joseph F., Jr, Hair, & Risher, Jeff & Sarstedt, Marko & Ringle, Christian. (2018). When to use and how to report the results of PLS-SEM. European Business Review. 31. 10.1108/EBR-11-2018-0203.
Link: https://www.emeraldinsight.com/doi/abs/10.1108/EBR-11-2018-0203

SEM-analysis, Structural Equation Modelling, is described as a second-generation technique within multivariate methods (Hair et al. 2017). First-generation methods comprise of exploratory methods like cluster analysis, exploratory factor analysis and multidimensional scaling, and confirmatory methods like analysis of variance, logistic regression, multiple regression, and confirmatory factor analysis. Second generation techniques go further in statistical strength and flexibility with the use of Partial Least Squares Structural Equation Modelling (PLS-SEM) as an exploratory tool and Covariance-Based Structural Equation Modelling (CB-SEM) as a confirmatory tool.

Hair (2017) describes some of the benefits of using SEM-analysis approach as

• the ability to use composite variables (often called variates),
• the ability to use unobservable (latent) variables – through indicator variables
• the ability to account for measurement error in observed variables
• simultaneously examine relationships among measured variables and latent variables.

SEM-analysis is used as a powerful predictive statistical approach – the analysis of the sample giving predictability to the population.

This article shows the most recent update on how to report the results when using PLS-SEM. I use SMART-PLS 3 as my preferred program for doing PLS-SEM analysis, and it gives me a lot of the data which Hair et.al proscribes in this article. Older articles do not neccesarily live up to the most recent standards of reporting – therefore this article is a good help to reporting well.