The PLS-SEM Book
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A Primer on Partial Least Squares Structural Equation Modeling. 2nd Ed. Thousand Oaks: Sage.
All case studies in this Second Edition use the SmartPLS 3 software.
With applications using SmartPLS—the primary software used in partial least squares structural equation modeling (PLS-SEM)—this practical guide provides concise instructions on how to use this evolving statistical technique to conduct research and obtain solutions. Featuring the latest research, new examples, and expanded discussions throughout, the Second Edition is designed to be easily understood by those with limited statistical and mathematical training who want to pursue research opportunities in new ways.
New to this edition:
- A new overview of the latest research on composite-based modeling (e.g., distinction between composite and causal indicators) enhances reader understanding of the conceptual foundation for PLS-SEM.
- New coverage includes recent discussion of PLS-SEM as a composite-based method to SEM, the distinction between PLS-SEM and CB-SEM and the model constellations which favor the use of PLS-SEM, the concept of model fit in a PLS-SEM context, and a new criterion for discriminant validity assessment: the heterotrait-monotrait (HTMT) ratio of correlations.
- A new in-depth discussion explores the various types of mediation, alternative mediation analysis procedures, mediated moderation, and moderated mediation.
- Coverage of several methods for constructing bootstrap confidence intervals includes percentile, studentized, bias-corrected, and accelerated, as well as two double bootstrap methods.
- An updated chapter on advanced topics briefly introduces, among others, the importance-performance map analysis, confirmatory tetrad analysis, multigroup analysis, and measurement invariance assessment in PLS-SEM.
- An extended description of moderation covers different approaches for creating the interaction term (e.g., the orthogonalizing approach), three-way interactions, measurement model evaluation, and the interpretation of results by using slope plots.
- Simple instructions give readers the “how-tos” of using the SmartPLS software to obtain solutions, and prepare manuscripts using PLS-SEM for academic journal submissions.
- Rules of Thumb in every chapter provide guidelines on best practices in the application and interpretation of PLS-SEM.
- A focus on accessibility is reflected through limited equations, formulas, and Greek symbols to facilitate clear understanding.
- Chapters organized around learning outcomes simplify the process of learning the concepts and statistical terms.
- Concepts consistently defined in plain language further facilitate comprehension of methods.
Brief Table of Contents
- About the Authors
- Chapter 1: An Introduction to Structural Equation Modeling
- Chapter 2: Specifying the Path Model and Examining Data
- Chapter 3: Path Model Estimation
- Chapter 4: Assessing PLS-SEM Results Part I: Evaluation of Reflective Measurement Models
- Chapter 5: Assessing PLS-SEM Results Part II: Evaluation of the Formative Measurement Models
- Chapter 6: Assessing PLS-SEM Results Part III: Evaluation of the Structural Model
- Chapter 7: Mediator and Moderator Analysis
- Chapter 8: Outlook on Advanced Methods
- Author Index
- Subject Index
Joseph F. Hair, Jr. is Founder and Senior Scholar of the Doctoral Degree in Business Administration, Coles College, Kennesaw State University. He previously held the Copeland Endowed Chair of Entrepreneurship and was Director, Entrepreneurship Institute, Ourso College of Business Administration, Louisiana State University. He has authored more than 50 books, including Multivariate Data Analysis (7th edition, 2010) (cited 140,000+ times), MKTG (9th edition, 2015), Essentials of Business Research Methods (2016), and Essentials of Marketing Research (3rd edition, 2013). He also has published numerous articles in scholarly journals and was recognized as the Academy of Marketing Science Marketing Educator of the Year. 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.
G. Tomas M. Hult is Professor and Byington Endowed Chair in International Business and Director of the International Business Center in the Eli Broad College of Business at Michigan State University. He has been Executive Director of the Academy of International Business and President of the AIB Foundation since 2004, was Editor-in-Chief of the Journal of the Academy of Marketing Science from 2009 to 2015, and has been on the U.S. Department of Commerce’s District Export Council since 2012. Professor Hult is one of some 80 elected Fellows of the Academy of International Business. He is one of the world’s leading authorities in global strategy, with a particular focus on topics dealing with the intersection of global strategy and supply chain management. In various ranking studies, Hult is listed as one of the top-cited authors in business and economics (e.g., Thomson Reuters). He regularly teaches doctoral seminars on multivariate statistics, structural equation modeling, and hierarchical linear modeling worldwide. Dr. Hult is a dual citizen of Sweden and the United States. More information about Tomas Hult can be found at http://www .tomashult.com.
Christian M. Ringle is a Chaired Professor of Management at the Hamburg University of Technology (Germany) and Conjoint Professor at the Faculty of Business and Law at the University of Newcastle (Australia). He holds a master’s degree in business administration from the University of Kansas and received his doctor of philosophy from the University of Hamburg (Germany). His widely published research addresses the management of organizations, strategic and human resource management, marketing, and quantitative methods for business and market research. He is cofounder and the Managing Director of SmartPLS (http://www .smartpls.com), a software tool with a graphical user interface for the application of the partial least squares structural equation modeling (PLS-SEM) method. Besides supporting consultancies and international corporations, he regularly teaches doctoral seminars on multivariate statistics, the PLS-SEM method, and the use of SmartPLS worldwide. More information about Christian Ringle can be found at http://www.tuhh.de/hrmo/team/prof-dr-c-m-ringle.html.
Marko Sarstedt is Chaired Professor of Marketing at the Otto-von-Guericke-University Magdeburg (Germany) and Conjoint Professor to the Faculty of Business and Law at the University of Newcastle (Australia). He previously was an Assistant Professor of Quantitative Methods in Marketing and Management at the Ludwig-Maximilians-University Munich (Germany). His main research is in the application and advancement of structural equation modeling methods to further the understanding of consumer behavior and to improve marketing decision making. His research has been published in journals such as Journal of Marketing Research, Journal of the Academy of Marketing Science, Organizational Research Methods, MIS Quarterly, International Journal of Research in Marketing, Long Range Planning, Journal of World Business, and Journal of Business Research. According to the Handelsblatt ranking, Marko Sarstedt is among the top three young academic marketing researchers in Germany, Austria, and Switzerland. He regularly teaches doctoral seminars on multivariate statistics, structural equation modeling, and measurement worldwide.
Claes Fornell, Chairman, CFI Group Worldwide: “Partial least squares’ modeling is a very robust and practical technique to tackle many of today’s multi-equation econometric models. In many situations, researchers are interested in both prediction and causality. Since PLS aims to account for the trace (sum of the diagonal in the covariance matrix), it is well suited for prediction. This is in contrast to covariance structure models, where the objective is to account for all the observed variable covariances, which is not particularly relevant for prediction. For the American Customer Satisfaction Index, we have used our own version of PLS since the very beginning. This book, by a great author team, puts PLS more practically into the hands of researchers by creating a logical and understandable way of applying PLS-based predictions based on structural relationships. The result is that we will likely see more use of PLS in research, and significant advances to complex data problems.”
Yves Doz, Solvay Chaired Professor of Technological Innovation, INSEAD: “Partial least squares’ modeling is an important statistical technique in management research but one that is most often used by very statistically oriented academicians. The PLS book written by a great team of authors who are all very familiar with using PLS makes the technique more practically understandable. Given the type of data used in management research, this book will facilitate the confident use of PLS by a much larger number of researchers to test holistic multi-equation models.”
David Ketchen, Lowder Eminent Scholar, Auburn University: “This PLS book is concise and application-oriented while being scientifically rigorous. With the use of PLS becoming more widespread and important as a tool in the field of management, this PLS book, by a superb author team, gives more scholars the needed practical knowledge to conduct rigorous research on partial least squares modeling.”
Roger Calantone, Eli Broad Chaired University Professor of Business, Michigan State University: “Partial least squares’ modeling is a great solution technique for a variety of small and large multivariate data problems. This book provides a deeply informed, yet practical, guide to understanding and using PLS for both novice and advanced researchers. This approach to understanding PLS carries one from a preliminary overview of the technique and its application, through the many subtle, but powerful nuances of the method. After 27 years of teaching variations of SEM, I am happy to discover a book that provides a gateway for the novice and a roadmap for the expert to confidently and appropriately model and estimate with PLS in a broad range of research contexts.”
Ketchen Jr, D. J. 2013. "A Primer on Partial Least Squares Structural Equation Modeling." Long Range Planning 46 (1–2): 184-185.
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