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The partial least squares approach for structural equation modeling.
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The article was published on 1998-01-01 and is currently open access. It has received 10147 citations till now. The article focuses on the topics: Non-linear least squares & Generalized least squares.read more
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Journal ArticleDOI
User acceptance of information technology: toward a unified view
TL;DR: The Unified Theory of Acceptance and Use of Technology (UTAUT) as mentioned in this paper is a unified model that integrates elements across the eight models, and empirically validate the unified model.
Journal ArticleDOI
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.
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.
Journal ArticleDOI
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.
Journal ArticleDOI
Structural equation modeling and regression: guidelines for research practice
TL;DR: The article presents a running example which analyzes the same dataset via three very different statistical techniques and compares two classes of SEM: covariance-based SEM and partial-least-squaresbased SEM, and discusses linear regression models and guidelines as to when SEM techniques and when regression techniques should be used.
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