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

By Christian Nitzl, José L. Roldán, Gabriel Cepeda. Industrial management & data systems, 2016

2,078 citations recorded by the source corpus.

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 However, many researchers continue to use outdated methods to test mediating effects in PLS, which can lead to erroneous results. One reason for the use of outdated methods or even the lack of their use altogether is that no systematic tutorials on PLS exist that draw on the newest statistical findings.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.

Subfields: Information Systems

View Paper: https://www.semanticscholar.org/paper/3b0684ef82ffb6fdc77beaa0c229d386bffa5def

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Citations in the corpus

An assessment of the use of partial least squares structural equation modeling (PLS-SEM) in hospitality research (2017, methodology): “’s (2010) procedure and compare direct and indirect effects using bootstrapping, rather than using Baron and Kenny’s (1986) approach in conjunction with the Sobel test (Nitzl et al., 2016).”
Workplace bullying, psychological distress, resilience, mindfulness, and emotional exhaustion (2020, background): “The VAF is the ratio of the indirect-to-total effect and determines the extent to which a mediator explains the variance in the criterion variable (Nitzl et al., 2016).”