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The specification of a measurement model as reflective or formative is the object of a lively debate. Part of the existing literature focuses on measurement model misspecification. This means that a true model is assumed and the impact on the path coefficients of using a wrong model is investigated. The majority of these studies is restricted to Structural Equation Modeling (SEM). Regarding PLS-Path Modeling (PLS-PM), a few authors have carried out simulation studies to investigate the robustness of the estimates, but their focus is the comparison with SEM. The present paper discusses the misspecification problem in the PLSPM context from a novel perspective. First, a real application on Alumni Satisfaction will be used to verify whether different assumptions for the measurements models influence the results. Second, the results of a Monte-Carlo simulation study, in the reflective case, will help to bring some clarity on a complex problem that has not been sufficiently studied yet.

The specification of a measurement model as reflective or formative is the object of a lively debate. Part of the existing literature focuses on measurement model misspecification. This means that a true model is assumed and the impact on the path coefficients of using a wrong model is investigated. The majority of these studies is restricted to Structural Equation Modeling (SEM). Regarding PLS-Path Modeling (PLS-PM), a few authors have carried out simulation studies to investigate the robustness of the estimates, but their focus is the comparison with SEM. The present paper discusses the misspecification problem in the PLSPM context from a novel perspective. First, a real application on Alumni Satisfaction will be used to verify whether different assumptions for the measurements models influence the results. Second, the results of a Monte-Carlo simulation study, in the reflective case, will help to bring some clarity on a complex problem that has not been sufficiently studied yet.

The specification of a measurement model as reflective or formative is the object of a lively debate. Part of the existing literature focuses on measurement model misspecification. This means that a true model is assumed and the impact on the path coefficients of using a wrong model is investigated. The majority of these studies is restricted to Structural Equation Modeling (SEM). Regarding PLS-Path Modeling (PLS-PM), a few authors have carried out simulation studies to investigate the robustness of the estimates, but their focus is the comparison with SEM. The present paper discusses the misspecification problem in the PLSPM context from a novel perspective. First, a real application on Alumni Satisfaction will be used to verify whether different assumptions for the measurements models influence the results. Second, the results of a Monte-Carlo simulation study, in the reflective case, will help to bring some clarity on a complex problem that has not been sufficiently studied yet.

The specification of a measurement model as reflective or formative is the object of a lively debate. Part of the existing literature focuses on measurement model misspecification. This means that a true model is assumed and the impact on the path coefficients of using a wrong model is investigated. The majority of these studies is restricted to Structural Equation Modeling (SEM). Regarding PLS-Path Modeling (PLS-PM), a few authors have carried out simulation studies to investigate the robustness of the estimates, but their focus is the comparison with SEM. The present paper discusses the misspecification problem in the PLSPM context from a novel perspective. First, a real application on Alumni Satisfaction will be used to verify whether different assumptions for the measurements models influence the results. Second, the results of a Monte-Carlo simulation study, in the reflective case, will help to bring some clarity on a complex problem that has not been sufficiently studied yet.

The specification of a measurement model as reflective or formative is the object of a lively debate. Part of the existing literature focuses on measurement model misspecification. This means that a true model is assumed and the impact on the path coefficients of using a wrong model is investigated. The majority of these studies is restricted to Structural Equation Modeling (SEM). Regarding PLS-Path Modeling (PLS-PM), a few authors have carried out simulation studies to investigate the robustness of the estimates, but their focus is the comparison with SEM. The present paper discusses the misspecification problem in the PLSPM context from a novel perspective. First, a real application on Alumni Satisfaction will be used to verify whether different assumptions for the measurements models influence the results. Second, the results of a Monte-Carlo simulation study, in the reflective case, will help to bring some clarity on a complex problem that has not been sufficiently studied yet.

The specification of a measurement model as reflective or formative is the object of a lively debate. Part of the existing literature focuses on measurement model misspecification. This means that a true model is assumed and the impact on the path coefficients of using a wrong model is investigated. The majority of these studies is restricted to Structural Equation Modeling (SEM). Regarding PLS-Path Modeling (PLS-PM), a few authors have carried out simulation studies to investigate the robustness of the estimates, but their focus is the comparison with SEM. The present paper discusses the misspecification problem in the PLSPM context from a novel perspective. First, a real application on Alumni Satisfaction will be used to verify whether different assumptions for the measurements models influence the results. Second, the results of a Monte-Carlo simulation study, in the reflective case, will help to bring some clarity on a complex problem that has not been sufficiently studied yet.