Common method bias: A full collinearity assessmentmethod for PLS-SEM

Abstract

In the context of structural equation modeling employing the partial least squares (PLS-SEM) method, common method bias is a phenomenon caused by common variation induced by the measurement method used and not by the network of causes and effects in the model being studied. Two datasets were created through a Monte Carlo simulation to illustrate our discussion of this phenomenon: one contaminated by common method bias and the other not contaminated. A practical approach is presented for the identification of common method bias based on variance inflation factors generated via a full collinearity test. Our discussion builds on an illustrative model in the field of e-collaboration, with outputs generated by the softwareWarpPLS.We demonstrate that the full collinearity test is successful in the identification of common method bias with a model that nevertheless passes standard convergent and discriminant validity assessment criteria based on a confirmation factor analysis.