Partial Least Squares Structural Equation Modeling (Pls-Sem) Using R: A Workbook

ISBN-13: 9783030805180 / Angielski / Twarda / 2021 / 201 str.

Joseph F. Hair Jr; Tomas M. Hult; Christian M. Ringle

Spis treści

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.- Mediation Analysis.- Moderation Analysis.

O autorze

Joseph F. Hair, Jr. is Cleverdon Chair of Business and Ph.D. Program Director at the University of South Alabama (USA). His research interests are multivariate data analysis, particularly structural equation modelling (SEM), and B-to-B marketing. Joe has published numerous highly cited peer-reviewed journal articles in highly ranked journals. He has co-authored several market-leading books on multivariate statistics and marketing, and a new book on marketing analytics.

G. Tomas M. Hult is a Professor at Michigan State University (USA). He is part of Expert Networks of the World Economic Forum and UNCTAD's World Investment Forum, and Researcher at the American Customer Satisfaction Index (ACSI). Tomas is a Fellow of the Academy of International Business and 2016 Academy of Marketing Science Distinguished Marketing Educator.

Christian Ringle is a Professor of Management at Hamburg University of Technology (Germany) and an Adjunct Professor at the University of Waikato (New Zealand). His research addresses human resource management, organization, marketing, strategic management, and quantitative methods for business and market research. Since 2018, he has been included in Clarivate Analytics' Highly-cited Researchers list.

Marko Sarstedt is a Professor of Marketing at Otto-von-Guericke-University Magdeburg (Germany) and an Adjunct Research Professor at Babeș-Bolyai-University (Romania). His research interest is focused on the advancement of research methods to further the understanding of consumer behavior. His research has been published in top-tier journals such as Nature Human Behaviour, Journal of Marketing Research, Journal of the Academy of Marketing Science, and MIS Quarterly.

Nicholas P. Danks is an Assistant Professor of Business Analytics at Trinity College, Dublin (Ireland). His research focuses on structural equation modeling, partial least squares path modeling, predictive methodology, and programming. Nicholas is a co-author and the primary maintainer of SEMinR, an open-source package for the R Statistical Environment for the estimation and evaluation of PLS path models. He publishes in journals such as Decision Science, and Journal of Business Research.

Soumya Ray is an Associate Professor at National Tsing Hua University (Taiwan). He is the creator and co-author of the 'SEMinR' package along with Nicholas P. Danks. His research investigates the user behavior of information technology and related methodological issues. His work is published in such venues as Information Systems Research, Journal of Management Information Systems.

Blurb

Partial least squares structural equation modeling (PLS-SEM) has become a standard approach for analyzing complex inter-relationships between observed and latent variables. Researchers appreciate the many advantages of PLS-SEM such as the possibility to estimate very complex models and the method’s flexibility in terms of data requirements and measurement specification.

This practical open access guide provides a step-by-step treatment of the major choices in analyzing PLS path models using R, a free software environment for statistical computing, which runs on Windows, macOS, and UNIX computer platforms. Adopting the R software’s SEMinR package, which brings a friendly syntax to creating and estimating structural equation models, each chapter offers a concise overview of relevant topics and metrics, followed by an in-depth description of a case study. Simple instructions give readers the “how-tos” of using SEMinR to obtain solutions and document their results. Rules of thumb in every chapter provide guidance on best practices in the application and interpretation of PLS-SEM.