the Analysis of Interaction Effects Between Latent Variables Using PLS
In social and business sciences, the importance of the analysis of interaction effects between
manifest as well as latent variables steadily increases. Researchers using partial least squares (PLS)
to analyze interaction effects between latent variables need an overview of the available approaches
as well as their suitability. This article presents 4 PLS-based approaches: a product indicator
approach (Chin, Marcolin, & Newsted, 2003), a 2-stage approach (Chin et al., 2003; Henseler &
Fassott, in press), a hybrid approach (Wold, 1982), and an orthogonalizing approach (Little, Bovaird,
& Widaman, 2006), and contrasts them using data related to a technology acceptance model. By
means of a more extensive Monte Carlo experiment, the different approaches are compared in
terms of their point estimate accuracy, their statistical power, and their prediction accuracy. Based
on the results of the experiment, the use of the orthogonalizing approach is recommendable under
most circumstances. Only if the orthogonalizing approach does not find a significant interaction
effect, the 2-stage approach should be additionally used for significance test, because it has a higher
statistical power. For prediction accuracy, the orthogonalizing and the product indicator approach
provide a significantly and substantially more accurate prediction than the other two approaches.
Among these two, the orthogonalizing approach should be used in case of small sample size and
few indicators per construct. If the sample size or the number of indicators per construct is medium
to large, the product indicator approach should be used.
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