This book offers a practical and accessible guide to Partial Least Squares Structural Equation Modeling (PLS-SEM) in business research, while addressing its limitations by integrating complementary methods such as artificial neural networks (ANN), fuzzy-set qualitative comparative analysis (fsQCA), and multi-criteria decision-making (MCDM). It supports early-career researchers, postgraduate students, and practitioners in navigating complex models, predictive analytics, and latent construct measurement. By focusing on emerging business issues like digital transformation, metaverse, and sustainability, this book delivers clear, applied insights. Readers gain not only foundational knowledge of PLS-SEM but also strategies for enhancing research rigor, prediction, and decision-making using hybrid approaches. This is a timely and essential resource for scholars aiming to advance their methodological toolkit for impactful and actionable business research.

Presents research on Partial Least Squares Structural Equation Modeling and Complementary Methods in business research Identifies the possible future developments and applications of the Partial Least Squares Structural Equation Modeling Presents theory and applications

Inhalt

Partial Least Squares Structural Equation Modeling.- PLSbSEM Path Model Estimation.- Overview of ANN Analysis.- Business Research Applications in SEM ANN Analysis.- Opportunity for ANN Analysis.- Artificial Neural Network and Theories.- Hybrid SEM and ANN Approach.- Fuzzy set Qualitative Comparative Analysis FsQCA.- Outline of ANFIS Analysis.- Multi Criteria Decision Making.- Machine Learning in Business Research.- Application of Multi Criteria Decision Making Methods.