Volume 35, Issue 4 pp. 501-529
EMPIRICAL RESEARCH QUANTITATIVE

PLS-SEM: A method demonstration in the R statistical environment

Amanda E. Legate

Corresponding Author

Amanda E. Legate

Department of Human Resource Development, University of Texas at Tyler, Tyler, Texas, USA

Correspondence

Amanda E. Legate, Department of Human Resource Development, University of Texas at Tyler, Tyler, TX, USA.

Email: [email protected]

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Christian M. Ringle

Christian M. Ringle

Institute of Management and Decision Sciences, Hamburg University of Technology, Hamburg, Germany

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Joseph F. Hair Jr.

Joseph F. Hair Jr.

Department of Marketing & Quantitative Methods, University of South Alabama, Mobile, Alabama, USA

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First published: 28 November 2023
Citations: 15

Abstract

In line with calls to stimulate methodological diversity and support evidence-based human resource development (HRD) through quantitative competencies, we present a methods demonstration leveraging open-source tools and lesser-known quantitative research methods to support the HRD research community and applied HRD in the workplace. In this paper, we provide an informative introduction to partial least squares structural equation modeling (PLS-SEM). We discuss PLS-SEM application trends in the field of HRD, present key characteristics of the method, and demonstrate up-to-date metrics and evaluation guidelines using an illustrative model. Our PLS-SEM demonstration and explanations can serve as a valuable resource for practitioners concerned with substantiating results for organizational stakeholders and support researchers in methodological decision-making while avoiding common pitfalls associated with less familiar methods. Our step-by-step demonstration is conducted in open-source software and accompanied by explicitly coded operations so that readers can easily replicate the illustrative analyses presented.

DATA AVAILABILITY STATEMENT

All underlying data, software code, and supplementary materials supporting this manuscript are available in the Open Science Framework (OSF) repository: PLS-SEM CPM in R https://osf.io/jhcx4/.

The full text of this article hosted at iucr.org is unavailable due to technical difficulties.