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Addressing Endogeneity with interaction terms (moderators, PLS-MGA)

Posted: Wed Aug 14, 2019 3:09 pm
by ljaeger
Hi all,

in their recent paper, Hult et al. (2018) mention that they did not address how to deal with endogeneity issues in PLS path models that include interaction terms (e.g., moderator variables). I was wondering if there was any recent development in this regard?

Particularly, I am interested in how I could also address endogeneity in PLS-MGA with the different approaches mentioned by the authors (instrumental variable, Gaussian copula, etc.). In other words, how endogenity could be also addressed for the grouping variable or the respective two models.

I'd be happy to hear any comments regarding its feasibility or references that could help.

Thank you.
Lennart


References

Hult, G. Tomas M., Joseph F. Hair Jr, Dorian Proksch, Marko Sarstedt, Andreas Pinkwart, and Christian M. Ringle. "Addressing endogeneity in international marketing applications of partial least squares structural equation modeling." Journal of International Marketing 26, no. 3 (2018): 1-21.

Re: Addressing Endogeneity with interaction terms (moderators, PLS-MGA)

Posted: Mon May 01, 2023 10:43 am
by jmbecker
For instrumental variable approaches there are guidelines for normal regression models on how to deal with interaction terms and nonlinear relationships. I would think that most of this also applies to endogeneity concerns in the structural model of PLS-SEM.

A very good article is Papies, D., Ebbes, P., & Van Heerde, H. J. (2017). Addressing endogeneity in marketing models. Advanced methods for modeling markets, 581-627. http://dx.doi.org/10.1007/978-3-319-53469-5_18

For Gaussian copula models, I have not seen any paper addressing interaction terms or nonlinear relationships.
However, if you want to apply the Gaussian copula approach, you may also want to have a look at recent guidelines for regression models on when and when not they are usable:
Becker, J. M., Proksch, D., & Ringle, C. M. (2022). Revisiting Gaussian copulas to handle endogenous regressors. Journal of the Academy of Marketing Science, 50(1), 46-66. https://doi.org/10.1007/s11747-021-00805-y