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Table of Contents
Intro
Foreword
Preface
Abbreviations
Contents
Editors and Contributors
About the Editors
Contributors
Part I Basic Concepts and Extensions
1 Introduction to the Partial Least Squares Path Modeling: Basic Concepts and Recent Methodological Enhancements
1.1 Introduction
1.2 Overview of Three Primary SEM Methods
1.3 Recent Developments in the PLS-PM/PLS-SEM Method
1.4 Essential Emerging PLS-SEM Tools for Social Sciences Scholars
1.4.1 Mediation
1.4.2 Moderation
1.4.3 Moderated Mediation
1.4.4 Non-linear SEM Solutions
1.4.5 Out-of-Sample Prediction
1.5 Observations and Conclusions
References
2 Quantile Composite-Based Path Modeling with R: A Hands-on Guide
2.1 Introduction
2.2 Quantile Composite-Based Path Modeling in a Nutshell
2.3 Data Description
2.4 Running QC-PM with R
2.4.1 Loading and Pre-processing of the Data
2.4.2 Model Specification, Estimation, and Results
2.4.3 Model Assessment and Validation
2.4.4 Post-processing: Graphs and Result Exporting
2.5 Concluding Remarks
References
3 Use of Partial Least Squares Path Modeling Within and Across Business Disciplines
3.1 Introduction
3.2 Frequency of PLS-PM Use in Financial Times Journals
3.3 Rationale for PLS-PM Use in Financial Times Journals
3.3.1 Problematic Rationale: Small Sample Size
3.3.2 Problematic Rationale: Data Normality
3.3.3 Questionable Rationale: Model Complexity
3.3.4 Appropriate Rationale: Model Assessment
3.4 The Future of PLS-PM Use in Business Disciplines
3.5 Conclusions
References
4 Statistical and Psychometric Properties of Three Weighting Schemes of the PLS-SEM Methodology
4.1 Introduction
4.2 Two Distinctive Features of PLS-SEM and the Environmental Variable
4.3 PLS-SEM Modes A and B
4.4 PLS-SEM Mode normal upper B Subscript normal upper ABA
4.5 Scale Invariance and Scale-Inverse Equivariance
4.5.1 Analytical Results
4.5.2 Numerical Results
4.5.3 Sample Results
4.6 Sensitivity of Weights to Misspecified Models
4.6.1 PLS-SEM Mode A
4.6.2 PLS-SEM Mode B
4.6.3 PLS-SEM Mode normal upper B Subscript normal upper ABA
4.7 Two Real Data Examples
4.8 Conclusion and Discussion
References
5 Software Packages for Partial Least Squares Structural Equation Modeling: An Updated Review
5.1 Introduction
5.2 Software for PLS-SEM
5.2.1 ADANCO
5.2.2 SmartPLS
5.2.3 WarpPLS
5.2.4 XLSTAT-PLSPM
5.2.5 plssem
5.2.6 cSEM
5.2.7 SEMinR
5.2.8 Summary of Software Features
5.3 Conclusion
References
Part II Methodological Issues
6 Revisiting and Extending PLS for Ordinal Measurement and Prediction
6.1 Introduction
6.2 Ordinal (Consistent) Partial Least Squares Path Modeling
6.2.1 Calculating Polychoric/Polyserial Correlations.
Foreword
Preface
Abbreviations
Contents
Editors and Contributors
About the Editors
Contributors
Part I Basic Concepts and Extensions
1 Introduction to the Partial Least Squares Path Modeling: Basic Concepts and Recent Methodological Enhancements
1.1 Introduction
1.2 Overview of Three Primary SEM Methods
1.3 Recent Developments in the PLS-PM/PLS-SEM Method
1.4 Essential Emerging PLS-SEM Tools for Social Sciences Scholars
1.4.1 Mediation
1.4.2 Moderation
1.4.3 Moderated Mediation
1.4.4 Non-linear SEM Solutions
1.4.5 Out-of-Sample Prediction
1.5 Observations and Conclusions
References
2 Quantile Composite-Based Path Modeling with R: A Hands-on Guide
2.1 Introduction
2.2 Quantile Composite-Based Path Modeling in a Nutshell
2.3 Data Description
2.4 Running QC-PM with R
2.4.1 Loading and Pre-processing of the Data
2.4.2 Model Specification, Estimation, and Results
2.4.3 Model Assessment and Validation
2.4.4 Post-processing: Graphs and Result Exporting
2.5 Concluding Remarks
References
3 Use of Partial Least Squares Path Modeling Within and Across Business Disciplines
3.1 Introduction
3.2 Frequency of PLS-PM Use in Financial Times Journals
3.3 Rationale for PLS-PM Use in Financial Times Journals
3.3.1 Problematic Rationale: Small Sample Size
3.3.2 Problematic Rationale: Data Normality
3.3.3 Questionable Rationale: Model Complexity
3.3.4 Appropriate Rationale: Model Assessment
3.4 The Future of PLS-PM Use in Business Disciplines
3.5 Conclusions
References
4 Statistical and Psychometric Properties of Three Weighting Schemes of the PLS-SEM Methodology
4.1 Introduction
4.2 Two Distinctive Features of PLS-SEM and the Environmental Variable
4.3 PLS-SEM Modes A and B
4.4 PLS-SEM Mode normal upper B Subscript normal upper ABA
4.5 Scale Invariance and Scale-Inverse Equivariance
4.5.1 Analytical Results
4.5.2 Numerical Results
4.5.3 Sample Results
4.6 Sensitivity of Weights to Misspecified Models
4.6.1 PLS-SEM Mode A
4.6.2 PLS-SEM Mode B
4.6.3 PLS-SEM Mode normal upper B Subscript normal upper ABA
4.7 Two Real Data Examples
4.8 Conclusion and Discussion
References
5 Software Packages for Partial Least Squares Structural Equation Modeling: An Updated Review
5.1 Introduction
5.2 Software for PLS-SEM
5.2.1 ADANCO
5.2.2 SmartPLS
5.2.3 WarpPLS
5.2.4 XLSTAT-PLSPM
5.2.5 plssem
5.2.6 cSEM
5.2.7 SEMinR
5.2.8 Summary of Software Features
5.3 Conclusion
References
Part II Methodological Issues
6 Revisiting and Extending PLS for Ordinal Measurement and Prediction
6.1 Introduction
6.2 Ordinal (Consistent) Partial Least Squares Path Modeling
6.2.1 Calculating Polychoric/Polyserial Correlations.