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A primer on partial least squares structural equation modeling (PLS-SEM)

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TLDR
The Second Edition of this practical guide to partial least squares structural equation modeling is designed to be easily understood by those with limited statistical and mathematical training who want to pursue research opportunities in new ways.
Abstract
With applications using SmartPLS (www.smartpls.com)—the primary software used in partial least squares structural equation modeling (PLS-SEM)—this practical guide provides concise instructions on how to use this evolving statistical technique to conduct research and obtain solutions. Featuring the latest research, new examples, and expanded discussions throughout, the Second Edition is designed to be easily understood by those with limited statistical and mathematical training who want to pursue research opportunities in new ways.

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A new criterion for assessing discriminant validity in variance-based structural equation modeling

TL;DR: In this paper, the heterotrait-monotrait ratio of correlations is used to assess discriminant validity in variance-based structural equation modeling. But it does not reliably detect the lack of validity in common research situations.
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When to use and how to report the results of PLS-SEM

TL;DR: A comprehensive overview of the considerations and metrics required for partial least squares structural equation modeling (PLS-SEM) analysis and result reporting can be found in this paper, where the authors provide an overview of previously and recently proposed metrics as well as rules of thumb for evaluating the research results based on the application of PLSSEM.
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Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research

TL;DR: Partial least squares (PLS) is an evolving approach to structural equation modeling (SEM), highlighting its advantages and limitations and providing an overview of recent research on the method across various fields as discussed by the authors.
Posted Content

Editorial - Partial Least Squares Structural Equation Modeling: Rigorous Applications, Better Results and Higher Acceptance

TL;DR: This second special issue provides a forum for topical issues that demonstrate the usefulness of PLS-SEM by piloting applications of this method in the field of strategic management with strong implications for strategic research and practice.
References
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An assessment of the use of partial least squares structural equation modeling in marketing research

TL;DR: An extensive search in the 30 top ranked marketing journals allowed us to identify 204 PLS-SEM applications published in a 30-year period (1981 to 2010), and a critical analysis of these articles addresses the following key methodological issues: reasons for using PLS, data and model characteristics, outer and inner model evaluations, and reporting.
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A critical look at the use of PLS-SEM in MIS Quarterly

TL;DR: Gefen et al. as mentioned in this paper presented a comprehensive, organized, and contemporary summary of the minimum reporting requirements for structural equation modeling (PLS-SEM) applications.
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The Use of Partial Least Squares Structural Equation Modeling in Strategic Management Research: A Review of Past Practices and Recommendations for Future Applications

TL;DR: It is found that researchers still often do not fully make use of the method's capabilities, sometimes even misapplying it, which is important to disseminate rigorous research and publication practices in the strategic management discipline.
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Using Partial Least Squares in Operations Management Research: A Practical Guideline and Summary of Past Research

TL;DR: This study provides a practical guideline for evaluating and using PLS and uses examples from the operations management literature to demonstrate how the specific points in this guideline can be applied.
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Partial Least Squares Structural Equation Modeling (PLS-SEM): A Useful Tool for Family Business Researchers

TL;DR: A systematic procedure for PLS-SEM results evaluation is presented and applied to an annotated example, and the analysis of mediating effects, which researchers are increasingly testing in their models.
Related Papers (5)