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 with limited emphasis on equations and symbols, instead, explaining the details in straightforward language. 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 Smart PLS 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, distinctions between PLS-SEM and CB-SEM, use with secondary data, model fit and comparison, information on control variables, sample size calculations, and more.
1113992238
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 with limited emphasis on equations and symbols, instead, explaining the details in straightforward language. 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 Smart PLS 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, distinctions between PLS-SEM and CB-SEM, use with secondary data, model fit and comparison, information on control variables, sample size calculations, and more.
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A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM)

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

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

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

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Overview

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 with limited emphasis on equations and symbols, instead, explaining the details in straightforward language. 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 Smart PLS 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, distinctions between PLS-SEM and CB-SEM, use with secondary data, model fit and comparison, information on control variables, sample size calculations, and more.

Product Details

ISBN-13: 9781544396408
Publisher: SAGE Publications
Publication date: 08/06/2021
Edition description: Third Edition
Pages: 384
Product dimensions: 6.00(w) x 9.00(h) x (d)

About the Author

Joseph F. Hair, Jr. is Cleverdon Chair of Business, and Director of the Ph D Degree in Business Administration, Mitchell College of Business, University of South Alabama. He previously held the Copeland Endowed Chair of Entrepreneurship and was Director, Entrepreneurship Institute, Ourso College of Business Administration, Louisiana State University. Joe was recognized by Clarivate Analytics in 2018, 2019 and 2020 for being in the top 1% globally of all Business and Economics professors based on his citations and scholarly accomplishments, which exceed 238,000 over his career. He has authored more than 75 books, including Multivariate Data Analysis (8th edition, 2019) (cited 140,000+ times), MKTG (13th edition, 2020), Essentials of Business Research Methods (2020), and Essentials of Marketing Research (4th edition, 2020). 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. He has a new book on Marketing Analytics, forthcoming in 2021 (Mc Graw-Hill).

G. Tomas M. Hult is Professor and Byington Endowed Chair at Michigan State University (USA), and holds a visiting Chaired Professorship at Leeds University Business School (United Kingdom) and a visiting professorship at Uppsala University (Sweden). 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 he ranks in the top-10 scholars in marketing per the prestigious “world ranking of scientists.” 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“), and 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 worldwide. He is a dual citizen of Sweden and the United States. More information about Professor 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 an Adjunct Professor at the University of Waikato (New Zealand). His research addresses management of organizations, human resource management, methods development for business analytics and their application to business research. His contributions in these fields have been published in journals such as International Journal of Research in Marketing, Information Systems Research, Journal of the Academy of Marketing Science, MIS Quarterly, Organizational Research Methods, and The International Journal of Human Resource Management. Since 2018, he has been named member of Clarivate Analytics’ Highly Cited Researchers List. In 2014, Professor Ringle co-founded Smart PLS (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 business analytics and multivariate statistics, the PLS-SEM method, and the use of Smart PLS worldwide. More information about Professor Christian M. Ringle can be found at https://www.tuhh.de/hrmo/team/prof-dr-c-m-ringle.html.

Marko Sarstedt is a chaired Professor of Marketing at the Otto-von-Guericke-University Magdeburg (Germany) and an Adjunct Professor at Babeş-Bolyai University, Romania. His main research interest is the advancement of research methods to enhance the understanding of consumer behavior. His research has been published in Nature Human Behavior, 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 among the most frequently cited in the social sciences with more than 70,000 citations according to Google Scholar. Professor Sarstedt has won numerous best paper and citation awards, including five Emerald Citations of Excellence awards and two AMS William R. Darden Awards. According to the 2020 F.A.Z. ranking, he is the second most influential researcher in Germany, Austria, and Switzerland. Professor Sarstedt has been named member of Clarivate Analytics’ Highly Cited Researchers List, which includes the “world’s most impactful scientific researchers.”

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
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