A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM)

The third edition of A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) guides readers through learning and mastering the techniques of this approach in clear language. Authors Joseph H. Hair, Jr., G. Tomas M. Hult, Christian Ringle, and Marko Sarstedt use their years of conducting and teaching research to communicate the fundamentals of PLS-SEM in straightforward language to explain the details of this method, with limited emphasis on equations and symbols. A running case study on corporate reputation follows the different steps in this technique so readers can better understand the research applications. Learning objectives, review and critical thinking questions, and key terms help readers cement their knowledge. This edition has been thoroughly updated, featuring the latest version of the popular software package SmartPLS 3. New topics have been added throughout the text, including a thoroughly revised and extended chapter on mediation, recent research on the foundations of PLS-SEM, detailed descriptions of research summarizing the advantages as well as limitations of PLS-SEM, and extended coverage of advanced concepts and methods, such as out-of-sample versus in-sample prediction metrics, higher-order constructs, multigroup analysis, necessary condition analysis, and endogeneity.

Table of Contents:
Preface About the Authors Chapter 1. An Introduction to Structural Equation Modeling Chapter Preview What Is Structural Equation Modeling? Considerations in Using Structural Equation Modeling Principles of Structural Equation Modeling PLS-SEM, CB-SEM, and Regressions Based on Sum Scores Considerations When Applying PLS-SEM Guidelines for Choosing Between PLS-SEM and CB-SEM Organization of Remaining Chapters Summary Review Questions Critical Thinking Questions Key Terms Suggested Readings Chapter 2. Specifying the Path Model and Examining Data Chapter Preview 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 Summary Review Questions Critical Thinking Questions Key Terms Suggested Readings Chapter 3. Path Model Estimation Chapter Preview Stage 4: Model Estimation and the PLS-SEM Algorithm Case Study Illustration—PLS Path Model Estimation (Stage 4) Summary Review Questions Critical Thinking Questions Key Terms Suggested Readings Chapter 4. Assessing PLS-SEM Results—Part I: Evaluation of the Reflective Measurement Models Chapter Preview Overview of Stage 5: Evaluation of Measurement Models Stage 5a: Assessing Results of Reflective Measurement Models Case Study Illustration—Evaluation of the Reflective Measurement Models (Stage 5a) Summary Review Questions Critical Thinking Questions Key Terms Suggested Readings Chapter 5. Assessing PLS-SEM Results—Part II: Evaluation of the Formative Measurement Models Chapter Preview Stage 5b: Assessing Results of Formative Measurement Models Case Study Illustration—Evaluation of the Formative Measurement Models (Stage 5b) Summary Review Questions Critical Thinking Questions Key Terms Suggested Readings Chapter 6. Assessing PLS-SEM Results—Part III: Evaluation of the Structural Model Chapter Preview Stage 6: Structural Model Results Evaluation Case Study Illustration—Evaluation of the Structural Model (Stage 6) Summary Review Questions Critical Thinking Questions Key Terms Suggested Readings Chapter 7. Mediator and Moderator Analysis Chapter Preview Mediation Moderation Case Study Illustration—Moderation Summary Review Questions Critical Thinking Questions Key Terms Suggested Readings Chapter 8. Outlook on Advanced Methods Chapter Preview Importance-Performance Map Analysis Necessary Condition Analysis Higher-Order Constructs Confirmatory Tetrad Analysis Examining Endogeneity Treating Observed and Unobserved Heterogeneity Measurement Model Invariance Consistent PLS-SEM Summary Review Questions Critical Thinking Questions Key Terms Suggested Readings Glossary References Index

About the Author :
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). G. Tomas M. Hult, PhD, has been Professor in the Broad College of Business at Michigan State University (USA) since 2001 and a visiting professor at the Företagsekonomiska Institutionen at Uppsala University (Sweden) since 2013. Professor Hult is a member of the Expert Networks of the World Economic Forum and United Nations/UNCTAD’s World Investment Forum and is also part of the Expert team at the American Customer Satisfaction Index (ACSI). Dr. Hult was recognized in 2016 as the Academy of Marketing Science/CUTCO-Vector Distinguished Marketing Educator; he is an elected fellow of the Academy of International Business, and is one of the world’s most cited marketing academics. At Michigan State University, Dr. Hult was recognized with the Beal Outstanding Faculty Award in 2019 (MSU’s highest award “for outstanding total service to the University”). He has also been recognized with the John Dunning AIB Service Award for outstanding service to AIB as the longest-serving executive director in AIB’s history (2004-2019) (the most prestigious service award given by the Academy of International Business). Professor Hult regularly teaches doctoral seminars on multivariate statistics, structural equation modeling, and hierarchical linear modeling. He is a dual citizen of Sweden and the United States. More information about Professor Hult can be found at https://broad.msu.edu/profile/hult/. Christian M. Ringle is a Chaired Professor and the Director of the Institute of Management and Decision Sciences at the Hamburg University of Technology (Germany), an Adjunct Professor at the James Cook University (Australia), a Visiting Guest Researcher at the University of California, Berkeley (USA), and an RHB-UKM Endowment Fund Distinguished Chairholder at the National University of Malaysia (UKM). His research focuses on management, marketing, technology and innovation management, method development, business analytics, machine learning, artificial intelligence, and the application of business research methods to decision making. His contributions have been published in journals such as Decision Sciences, European Journal of Marketing, International Journal of Research in Marketing, Information Systems Research, Journal of Business Research, Journal of Service Research, Journal of the Academy of Marketing Science, Long Range Planning, MIS Quarterly, and Organizational Research Methods. Since 2018, Christian has been included in the Clarivate Analytics′ list of Highly Cited Researchers (HCR). He is a co-developer and co-founder of the statistical software SmartPLS (https://www-smartpls.com). More info: https://www.tuhh.de/mds/team/prof-dr-c-m-ringle. Marko Sarstedt is a chaired professor of marketing at the Ludwig-Maximilians-University Munich (Germany) and an adjunct research professor at Babe?-Bolyai-University Cluj-Napoca (Romania). His main research interest is the advancement of research methods and psychological measurement to further the understanding of consumer behavior. His research has been published in Nature Human Behaviour, Journal of Marketing Research, Journal of the Academy of Marketing Science, Multivariate Behavioral Research, Organizational Research Methods, MIS Quarterly, and Psychometrika, among others. His research ranks Marko among the most frequently cited in the social sciences with more than 300,000 citations according to Google Scholar. Marko has won numerous best paper and citation awards and has been repeatedly named member of Clarivate Analytics’ Highly Cited Researchers List. In March 2022, he was awarded an honorary doctorate from Babe?-Bolyai-University Cluj-Napoca for his research achievements and contributions to international exchange.