Application of SMART- PLS SEM : Empirical Study of Lean Six Sigma, IR 4.0 towards Balance Scorecard
Structural Equation Modeling or SEM is a second generation statistical analysis techniques for analyzing the inter-relationships among multiple variables in a model. SEM is an extension of the general linear model (GLM) that enables a researcher to test a set of regression equations simultaneously. SEM software can examine complex relationships and models, such as confirmatory factor analysis and second order latent variables which improves the weaknesses of ordinary least square (OLS) method. There are two ways to proceed with the structural equation modelling (SEM technique); Covariance-based SEM and PLS-SEM. The selection of the method is based on the normality of data and the type of research. PLS SEM method is used for non-normal data and exploratory research. This book provides researchers with the application of PLS SEM through empirical data focusing on quality management. Empirical data will provide better understanding of SEM application.