C
Christian M. Ringle
Researcher at Hamburg University of Technology
Publications - 228
Citations - 102589
Christian M. Ringle is an academic researcher from Hamburg University of Technology. The author has contributed to research in topics: Structural equation modeling & Partial least squares regression. The author has an hindex of 74, co-authored 207 publications receiving 68196 citations. Previous affiliations of Christian M. Ringle include Freiberg University of Mining and Technology & University of Newcastle.
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Book
A primer on partial least squares structural equation modeling (PLS-SEM)
TL;DR: 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.
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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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PLS-SEM: Indeed a Silver Bullet
TL;DR: The authors conclude that PLS-SEM path modeling, if appropriately applied, is indeed a "silver bullet" for estimating causal models in many theoretical models and empirical data situations.
Posted Content
The Use of Partial Least Squares Path Modeling in International Marketing
TL;DR: An evaluation of double-blind reviewed journals through important academic publishing databases revealed that more than 30 academic articles in the domain of international marketing (in a broad sense) used PLS path modeling as means of statistical analysis.
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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.