Joseph F. Hair, Jr. is Cleverdon Chair of Business, and director of the PhD degree in business administration, Mitchell College of Business, University of South Alabama. He previously held the Copeland Endowed Chair of Entrepreneurship and was Director of the Entrepreneurship Institute, Ourso College of Business Administration, Louisiana State University. Joe was recognized by Clarivate Analytics from 2018 through 2024 for being in the top 1% globally of all business and economics professors based on his citations and scholarly accomplishments, which exceed 550,000 over his career. He has authored more than 145 editions of his books, including Multivariate Data Analysis (8th edition, 2019; cited 240,000+ times), PLS-SEM Primer (4th edition; 2026); Advanced PLS (2nd edition, 2024); MKTG (14th edition, 2024); Essentials of Business Research Methods (5th edition, 2024); Essentials of Marketing Analytics (2nd edition, 2025); and Essentials of Marketing Research (6th edition, 2026). He also has published numerous articles in scholarly journals and was recognized as the Academy of Marketing Science Marketing Educator of the Year. As a popular guest speaker, Professor Hair often presents seminars on research techniques, multivariate data analysis, and marketing issues for organizations in Europe, Australia, China, India, and South America. He has a forthcoming book on Sales Analytics (Sage 2026).

Chapter 1: An Introduction to Structural Equation Modeling
What Is Structural Equation Modeling?
Considerations in Using Structural Equation Modeling
Structural Equation Modeling With Partial Least Squares Path Modeling
PLS-SEM, CB-SEM, and Regressions Based on Sum Scores
Organization of Remaining Chapters
Chapter 2: Specifying the Path Model and Examining Data
Stage 1: Specifying the Structural Model
Stage 2: Specifying the Measurement Models
Stage 3: Data Collection and Examination
Case Study Illustration: Specifying the PLS-SEM Model
Path Model Creation Using the SmartPLS Software
Chapter 3: Path Model Estimation
Stage 4: Model Estimation and the PLS-SEM Algorithm
Case Study Illustration: PLS Path Model Estimation (Stage 4)
Chapter 4: Assessing PLS-SEM Results Part I: Evaluation of Reflective Measurement Models
Overview of Stage 5: Evaluation of Measurement Models
Stage 5a: Assessing Results of Reflective Measurement Models
Case Study Illustration-Reflective Measurement Models
Running the PLS-SEM Algorithm
Reflective Measurement Model Evaluation
Chapter 5: Assessing PLS-SEM Results Part II: Evaluation of the Formative Measurement Models
Stage 5b: Assessing Results of Formative Measurement Models
Bootstrapping Procedure
Bootstrap Confidence Intervals
Case Study Illustration-Evaluation of Formative Measurement Models
Chapter 6: Assessing PLS-SEM Results Part III: Evaluation of the Structural Model
Stage 6: Assessing PLS-SEM Structural Model Results
Case Study Illustration-How Are PLS-SEM Structural Model Results Reported?
Chapter 7: Mediator and Moderator Analysis
Mediation
Moderation
Chapter 8: Outlook on Advanced Methods
Importance-Performance Map Analysis
Hierarchical Component Models
Confirmatory Tetrad Analysis
Dealing With Observed and Unobserved Heterogeneity
Consistent Partial Least Squares