This open-access guide offers a practical, step-by-step introduction to partial least squares structural equation modeling (PLS-SEM) using R and the SEMinR package. PLS-SEM is widely used for analyzing complex relationships between observed and latent variables, valued for its flexibility, predictive capabilities, and ability to handle complex models.

Now in its second edition, the book incorporates the latest research, enhanced model evaluation routines, and updated guidance on methodological choices and result interpretation. Each chapter combines a concise overview of key concepts, metrics, and rules of thumb with a detailed case study to support hands-on learning.

Designed for researchers and practitioners alike, this guide provides the essential tools and insights needed to apply PLS-SEM effectively using SEMinR.

This book is open access, which means that you have free and unlimited access Latest edition with concise guidelines on how to apply and interpret PLS-SEM results Includes an llustrative step-by-step application of PLS-SEM within the R software environment Draws on the highly user-friendly SEMinR package, co-developed by two of the co-authors

Autorentext

Joseph F. Hair, Jr. is Cleverdon Chair of Business and Director of the PhD Program at the Mitchell College of Business, University of South Alabama (USA). He previously held the Copeland Endowed Chair at Louisiana State University. Recognized by Clarivate Analytics through 2024 as a top 1% global scholar in Business and Economics, he has over 600,000 citations. He has co-authored 140+ book editions and frequently lectures worldwide on research methods and marketing analytics.

G. Tomas M. Hult, PhD, is a globally recognized expert in international marketing, strategy, and supply chains. He is a Fellow of the Academy of International Business and serves on the leadership team of the American Customer Satisfaction Index (USA). He advises the UN and World Economic Forum and contributes to outlets like Time and Harvard Business Review. He received the AIB's highest service award and the Academy of Marketing Science's Distinguished Marketing Educator Award.

Christian M. Ringle is a Chaired Professor and Director of the Institute of Management and Decision Sciences at Hamburg University of Technology (Germany) and an Adjunct Professor at James Cook University (Australia). 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. Since 2018, he has been included in Clarivate Analytics' list of Highly Cited Researchers (HCR). Christian is a co-developer and co-founder of the statistical software SmartPLS.

Marko Sarstedt is Chaired Professor of Marketing at Ludwig-Maximilians-University Munich (Germany) and Adjunct Research Professor at Babe-Bolyai University Cluj-Napoca (Romania). His research advances methods for understanding consumer behavior and appears in journals like Nature Human Behavior and Journal of Marketing Research. He is a Clarivate Highly Cited Researcher and received an honorary doctorate from Babe-Bolyai University in 2022 for his scholarly contributions.

Nicholas P. Danks is Associate Professor of Business Analytics at Trinity College Dublin (Ireland). His research focuses on structural equation modeling, predictive methods, and programming. He is co-author and lead maintainer of SEMinR, an open-source R package for PLS path modeling. His work appears in journals such as Management Science and Decision Sciences.

Susanne J. Adler is a postdoctoral researcher at the Institute for Marketing, Ludwig-Maximilians-University Munich (Germany). Her research explores meta-science, reproducibility, open science, and consumer decision-making. She has published in journals including International Journal of Research in Marketing and Journal of Product Innovation Management.

Inhalt

An Introduction to Structural Equation Modeling.- Introduction to R and RStudio.- Introduction to SEMinR.- Evaluation of Reflective Measurement Models.- Evaluation of Formative Measurement Models.- Evaluation of the Structural Model.- Moderation Analysis.- Mediation Analysis.- Supplementary Information.