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Journal ArticleDOI

Mediation analysis in partial least squares path modeling: Helping researchers discuss more sophisticated models

Christian Nitzl, +2 more
- 20 Oct 2016 - 
- Vol. 116, Iss: 9, pp 1849-1864
TLDR
This study illustrates the state-of-the-art use of mediation analysis in the context of PLS-structural equation modeling (SEM) by challenging the conventional approach to mediation analysis and providing more accurate alternatives.
Abstract
Indirect or mediated effects constitute a type of relationship between constructs that often occurs in partial least squares (PLS) path modeling. Over the past few years, the methods for testing mediation have become more sophisticated. However, many researchers continue to use outdated methods to test mediating effects in PLS, which can lead to erroneous results. One reason for the use of outdated methods or even the lack of their use altogether is that no systematic tutorials on PLS exist that draw on the newest statistical findings. The paper aims to discuss these issues.,This study illustrates the state-of-the-art use of mediation analysis in the context of PLS-structural equation modeling (SEM).,This study facilitates the adoption of modern procedures in PLS-SEM by challenging the conventional approach to mediation analysis and providing more accurate alternatives. In addition, the authors propose a decision tree and classification of mediation effects.,The recommended approach offers a wide range of testing options (e.g. multiple mediators) that go beyond simple mediation analysis alternatives, helping researchers discuss their studies in a more accurate way.

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Citations
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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.
Book ChapterDOI

Partial Least Squares Structural Equation Modeling

TL;DR: Partial least squares structural equation modeling (PLS-SEM) has become a popular method for estimating path models with latent variables and their relationships as discussed by the authors, and a common goal of PLSSEM analyses is to identify key success factors and sources of competitive advantage for important target constructs such as customer satisfaction, customer loyalty, behavioral intentions, and user behavior.
Journal ArticleDOI

Assessing measurement model quality in PLS-SEM using confirmatory composite analysis

TL;DR: In this article, confirmatory composite analysis (CCA) is applied to confirm measurement models when using partial least squares structural equation modeling (PLS-SEM) to confirm both reflective and formative measurement models of established measures that are being updated or adapted to a different context.
Journal ArticleDOI

An assessment of the use of partial least squares structural equation modeling (PLS-SEM) in hospitality research

TL;DR: This work systematically examines how PLS-SEM has been applied in major hospitality research journals with the aim of providing important guidance and, if necessary, opportunities for realignment in future applications.
Journal ArticleDOI

How to Specify, Estimate, and Validate Higher-Order Constructs in PLS-SEM:

TL;DR: Higher-order constructions as discussed by the authors facilitate modeling a construct on a more abstract higher-level dimension and its more concrete lower-order subdimensions, have become an increasingly visible trend.
References
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Book

Statistical Power Analysis for the Behavioral Sciences

TL;DR: The concepts of power analysis are discussed in this paper, where Chi-square Tests for Goodness of Fit and Contingency Tables, t-Test for Means, and Sign Test are used.
Journal ArticleDOI

The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations.

TL;DR: This article seeks to make theorists and researchers aware of the importance of not using the terms moderator and mediator interchangeably by carefully elaborating the many ways in which moderators and mediators differ, and delineates the conceptual and strategic implications of making use of such distinctions with regard to a wide range of phenomena.
Journal ArticleDOI

Multivariate Data Analysis

TL;DR: In this paper, a six-step framework for organizing and discussing multivariate data analysis techniques with flowcharts for each is presented, focusing on the use of each technique, rather than its mathematical derivation.
Journal ArticleDOI

Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models

TL;DR: An overview of simple and multiple mediation is provided and three approaches that can be used to investigate indirect processes, as well as methods for contrasting two or more mediators within a single model are explored.
Journal ArticleDOI

Multivariate data analysis

TL;DR: This chapter discusses Structural Equation Modeling: An Introduction, and SEM: Confirmatory Factor Analysis, and Testing A Structural Model, which shows how the model can be modified for different data types.
Related Papers (5)
Trending Questions (2)
What cause non significant direct or indirect effect in pls sem?

Non-significant direct or indirect effects in PLS-SEM can be caused by outdated mediation analysis methods, leading to erroneous results due to lack of modern statistical techniques.

Can mediators improve the predictive power of a research model using PLS-SEM?

Yes, mediators can improve the predictive power of a research model using PLS-SEM by capturing indirect effects between constructs.