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Rethinking some of the rethinking of partial least squares

Rethinking some of the rethinking of partial least squares Partial least squares structural equation modeling (PLS-SEM) is an important statistical technique in the toolbox of methods that researchers in marketing and other social sciences disciplines frequently use in their empirical analyses. The purpose of this paper is to shed light on several misconceptions that have emerged as a result of the proposed “new guidelines” for PLS-SEM. The authors discuss various aspects related to current debates on when or when not to use PLS-SEM, and which model evaluation metrics to apply. In addition, this paper summarizes several important methodological extensions of PLS-SEM researchers can use to improve the quality of their analyses, results and findings.Design/methodology/approachThe paper merges literature from various disciplines, including marketing, strategic management, information systems, accounting and statistics, to present a state-of-the-art review of PLS-SEM. Based on these findings, the paper offers a point of orientation on how to consider and apply these latest developments when executing or assessing PLS-SEM-based research.FindingsThis paper offers guidance regarding situations that favor the use of PLS-SEM and discusses the need to consider certain model evaluation metrics. It also summarizes how to deal with endogeneity in PLS-SEM, and critically comments on the recent proposal to adjust PLS-SEM estimates to mimic common factor models that are the foundation of covariance-based SEM. Finally, this paper opposes characterizing common concepts and practices of PLS-SEM as “out-of-date” without providing well-substantiated alternatives and solutions.Research limitations/implicationsThe paper paves the way for future discussions and suggests a way forward to reach consensus regarding situations that favor PLS-SEM use and its application.Practical implicationsThis paper offers guidance on how to consider the latest methodological developments when executing or assessing PLS-SEM-based research.Originality/valueThis paper complements recently proposed “new guidelines” with the aim of offering a counter perspective on some strong claims made in the latest literature on PLS-SEM. It also clarifies some misconceptions regarding the application of PLS-SEM. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png European Journal of Marketing Emerald Publishing

Rethinking some of the rethinking of partial least squares

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Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
0309-0566
DOI
10.1108/ejm-10-2018-0665
Publisher site
See Article on Publisher Site

Abstract

Partial least squares structural equation modeling (PLS-SEM) is an important statistical technique in the toolbox of methods that researchers in marketing and other social sciences disciplines frequently use in their empirical analyses. The purpose of this paper is to shed light on several misconceptions that have emerged as a result of the proposed “new guidelines” for PLS-SEM. The authors discuss various aspects related to current debates on when or when not to use PLS-SEM, and which model evaluation metrics to apply. In addition, this paper summarizes several important methodological extensions of PLS-SEM researchers can use to improve the quality of their analyses, results and findings.Design/methodology/approachThe paper merges literature from various disciplines, including marketing, strategic management, information systems, accounting and statistics, to present a state-of-the-art review of PLS-SEM. Based on these findings, the paper offers a point of orientation on how to consider and apply these latest developments when executing or assessing PLS-SEM-based research.FindingsThis paper offers guidance regarding situations that favor the use of PLS-SEM and discusses the need to consider certain model evaluation metrics. It also summarizes how to deal with endogeneity in PLS-SEM, and critically comments on the recent proposal to adjust PLS-SEM estimates to mimic common factor models that are the foundation of covariance-based SEM. Finally, this paper opposes characterizing common concepts and practices of PLS-SEM as “out-of-date” without providing well-substantiated alternatives and solutions.Research limitations/implicationsThe paper paves the way for future discussions and suggests a way forward to reach consensus regarding situations that favor PLS-SEM use and its application.Practical implicationsThis paper offers guidance on how to consider the latest methodological developments when executing or assessing PLS-SEM-based research.Originality/valueThis paper complements recently proposed “new guidelines” with the aim of offering a counter perspective on some strong claims made in the latest literature on PLS-SEM. It also clarifies some misconceptions regarding the application of PLS-SEM.

Journal

European Journal of MarketingEmerald Publishing

Published: Apr 30, 2019

Keywords: Guidelines; Partial least squares; PLS-SEM; Structural equation modeling

References