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Common Method Bias: A Full Collinearity Assessment Method for PLS-SEM

In: Partial Least Squares Path Modeling

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  • Ned Kock

    (Texas A&M International University, Division of International Business and Technology Studies)

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 software WarpPLS. 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.

Suggested Citation

  • Ned Kock, 2017. "Common Method Bias: A Full Collinearity Assessment Method for PLS-SEM," Springer Books, in: Hengky Latan & Richard Noonan (ed.), Partial Least Squares Path Modeling, chapter 0, pages 245-257, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-64069-3_11
    DOI: 10.1007/978-3-319-64069-3_11
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