This article addresses the criticisms of the partial least squares (PLS) approach to structural equation modeling by Rönkkö and Evermann (2013). The authors argue that the alleged shortcomings of PLS are not due to problems with the technique itself but rather to three issues with Rönkkö and Evermann’s study: (a) adherence to the common factor model, (b) limited simulation designs, and (c) over-stretched generalizations of their findings. They contend that PLS offers advantages for exploratory research and is a viable estimator for composite factor models, which can be particularly useful if the common factor model does not hold. The article contributes to reestablishing a constructive discussion about PLS and its properties, emphasizing its suitability as an important statistical tool for management and organizational research, as well as other social science disciplines.This article addresses the criticisms of the partial least squares (PLS) approach to structural equation modeling by Rönkkö and Evermann (2013). The authors argue that the alleged shortcomings of PLS are not due to problems with the technique itself but rather to three issues with Rönkkö and Evermann’s study: (a) adherence to the common factor model, (b) limited simulation designs, and (c) over-stretched generalizations of their findings. They contend that PLS offers advantages for exploratory research and is a viable estimator for composite factor models, which can be particularly useful if the common factor model does not hold. The article contributes to reestablishing a constructive discussion about PLS and its properties, emphasizing its suitability as an important statistical tool for management and organizational research, as well as other social science disciplines.