Guenther, P, Guenther, M, Ringle, CM et al. (2 more authors) (2023) Improving PLS-SEM use for business marketing research. Industrial Marketing Management, 111. pp. 127-142. ISSN 0019-8501
Abstract
A review of studies published in Industrial Marketing Management over the past two decades and more shows that these studies not only used partial least squares structural equation modeling (PLS-SEM) widely to estimate and empirically substantiate theoretically established models with constructs, but did so increasingly. In line with their study goals, researchers provided reasons for using PLS-SEM (e.g., model complexity, limited sample size, and prediction). These reasons are frequently not fully convincing, requiring further clarification. Additionally, our review reveals that researchers' assessment and reporting of their measurement and structural models are insufficient. Certain tests and thresholds that they use are also inappropriate. Finally, researchers seldom apply more advanced PLS-SEM analytic techniques, although these can support the results' robustness and may create new insights. This paper addresses the issues by reviewing business marketing studies to clarify PLS-SEM's appropriate use. Furthermore, the paper provides researchers and practitioners in the business marketing field with a best practice orientation and describes new opportunities for using PLS-SEM. To this end, the paper offers guidelines and checklists to support future PLS-SEM applications.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
Keywords: | Partial least squares, Structural equation modeling, PLS-SEM, Review, Results assessment, Evaluation, Guidelines |
Dates: |
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Institution: | The University of Leeds |
Depositing User: | Symplectic Publications |
Date Deposited: | 20 Jun 2023 09:58 |
Last Modified: | 20 Jun 2023 09:58 |
Published Version: | http://dx.doi.org/10.1016/j.indmarman.2023.03.010 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.indmarman.2023.03.010 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:200118 |
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