ESTIMATING MODERATING EFFECTS IN PLS-SEM AND PLSc-SEM: INTERACTION TERM GENERATION*DATA TREATMENT

By Jan-Michael Becker, Christian M. Ringle, Marko Sarstedt. Journal of Applied Structural Equation Modeling, 2018

301 citations recorded by the source corpus.

When estimating moderating effects in partial least squares structural equation modeling (PLSSEM), researchers can choose from a variety of approaches to model the influence of a moderator on a relationship between two constructs by generating different interaction terms. While prior research has evaluated the efficacy of these approaches in the context of PLS-SEM, the impact of different data treatment options on their performance in the context of standard PLS-SEM and consistent PLS-SEM (PLSc-SEM) is as yet unexplored. Our simulation study addresses these limitations and explores if the choice of approach and data treatment option has a pronounced impact on the methods’ parameter recovery. An empirical application substantiates these findings. Based on our results, we offer recommendations for researchers wishing to estimate moderating effects by means of PLS-SEM and PLSc-SEM.

Subfields: Artificial Intelligence, Information Systems and Management

View Paper: https://www.semanticscholar.org/paper/f7462288b12108f445f9bbb4ea989e534a6dc895

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Citations in the corpus

Beyond a tandem analysis of SEM and PROCESS: Use of PLS-SEM for mediation analyses! (2020, background): “…product indicator approach, which involves multiplying each indicator of the independent construct with each indicator of the moderator variable (Chin et al., 2003), researchers have proposed more complex approaches for generating the interaction term’s measurement model (Becker et al., 2018).”
When to use and how to report the results of PLS-SEM (2019, methodology): “If suitable explanatory variables are available, a moderator (Henseler and Fassott, 2010; Becker et al., 2018) or multigroup analysis (Chin and Dibbern, 2010; Matthews, 2017), in combination with a measurement invariance assessment (Henseler et al., 2016b), offers further particularized findings,…”
Structural model robustness checks in PLS-SEM (2020, background): “Sarstedt et al. 545 Schuberth et al., 2018), more complex modeling approaches (Becker et al., 2018; Henseler and Chin, 2010; Nitzl et al., 2016), and novel model evaluation metrics (Henseler et al., 2015; Franke and Sarstedt, 2019; Shmueli et al., 2016).”
Sampling weight adjustments in partial least squares structural equation modeling: guidelines and illustrations (2020, methodology): “should explore the efficacy of the WPLS algorithm when estimating moderating effects in PLS-SEM, such as assessing using the interaction approach (Becker et al., 2018), multigroup analysis (Sarstedt et al.”