I share with Evermann and Rönkkö (2023) the belief that classic composite-based partial least squares path modeling (PLS-PM) presents shortcomings when used to conduct structural equation modeling (SEM) analyses. The shortcomings can be traced back to one fundamental problem, which is that latent variables (LVs) are approximated in PLS-PM as exact linear combinations of their corresponding indicators. In SEM, each LV is in fact a factor; i.e., a linear combination of the indicators and a measurement residual. My approach to addressing the shortcomings of PLS-PM is rather unique among researchers concerned with quantitative methods. I have employed an action research approach, helping investigators employ SEM in their empirical studies. This has led to my development of a widely used software tool for SEM analyses. I illustrate my action research orientation by discussing three recent methodological developments with which I have been closely involved.
Kock, N. (2023). Contributing to the Success of PLS in SEM: An Action Research Perspective. Communications of the Association for Information Systems, 52, 730-734. https://doi.org/10.17705/1CAIS.05233
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