The article addresses Rönkkö and Evermann's criticisms of the partial least squares (PLS) approach to structural equation modeling. The authors argue that the alleged shortcomings of PLS are not due to problems with the technique, but instead to three problems with Rönkkö and Evermann's study: (a) the adherence to the common factor model, (b) a very limited simulation design, and (c) overstretched generalizations of their findings. While Rönkkö and Evermann claim to be dispelling myths about PLS, they have in reality created new myths that the authors, in turn, debunk. By examining their claims, the article contributes to reestablishing a constructive discussion of the PLS method and its properties. The authors show that PLS does offer advantages for exploratory research and that it is a viable estimator for composite factor models. This can pose an interesting alternative if the common factor model does not hold. Therefore, the authors conclude that PLS should continue to be used as an important statistical tool for management and organizational research, as well as other social science disciplines. The authors critique Rönkkö and Evermann's claims that PLS is not an SEM method, that PLS construct scores are less reliable than sum scores, that PLS cannot be used to validate measurement models, that PLS cannot be used for null hypothesis significance testing (NHST), that PLS has minimal requirements on sample size, and that PLS is inappropriate for exploratory or early-stage research. The authors argue that these claims are based on flawed assumptions and limited simulation studies. They demonstrate that PLS is indeed an SEM method designed for estimating composite factor models and that the common factor model is nested within the composite factor model. The authors also show that Rönkkö and Evermann's conclusions about the characteristics of PLS are not justified and are partly an artifact of the setup of their simulation study. The authors conclude that PLS is clearly an SEM method that is designed to estimate composite factor models and that it can be used for exploratory research and early-stage theory development. The authors also show that PLS can be used for NHST and that it has minimal requirements on sample size. The authors argue that PLS is appropriate for exploratory research and that it has the necessary requisites for exploratory modeling.The article addresses Rönkkö and Evermann's criticisms of the partial least squares (PLS) approach to structural equation modeling. The authors argue that the alleged shortcomings of PLS are not due to problems with the technique, but instead to three problems with Rönkkö and Evermann's study: (a) the adherence to the common factor model, (b) a very limited simulation design, and (c) overstretched generalizations of their findings. While Rönkkö and Evermann claim to be dispelling myths about PLS, they have in reality created new myths that the authors, in turn, debunk. By examining their claims, the article contributes to reestablishing a constructive discussion of the PLS method and its properties. The authors show that PLS does offer advantages for exploratory research and that it is a viable estimator for composite factor models. This can pose an interesting alternative if the common factor model does not hold. Therefore, the authors conclude that PLS should continue to be used as an important statistical tool for management and organizational research, as well as other social science disciplines. The authors critique Rönkkö and Evermann's claims that PLS is not an SEM method, that PLS construct scores are less reliable than sum scores, that PLS cannot be used to validate measurement models, that PLS cannot be used for null hypothesis significance testing (NHST), that PLS has minimal requirements on sample size, and that PLS is inappropriate for exploratory or early-stage research. The authors argue that these claims are based on flawed assumptions and limited simulation studies. They demonstrate that PLS is indeed an SEM method designed for estimating composite factor models and that the common factor model is nested within the composite factor model. The authors also show that Rönkkö and Evermann's conclusions about the characteristics of PLS are not justified and are partly an artifact of the setup of their simulation study. The authors conclude that PLS is clearly an SEM method that is designed to estimate composite factor models and that it can be used for exploratory research and early-stage theory development. The authors also show that PLS can be used for NHST and that it has minimal requirements on sample size. The authors argue that PLS is appropriate for exploratory research and that it has the necessary requisites for exploratory modeling.