Abstract
The author discusses common method bias in the context of structural equation modeling employing the partial least squares method (PLS-SEM). Two datasets were created through a Monte Carlo simulation to illustrate the discussion: 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. The author's discussion builds on an illustrative model in the field of e-collaboration, with outputs generated by the software WarpPLS. They 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.
Keywords
CollinearityComputer scienceIdentification (biology)Context (archaeology)Monte Carlo methodStatisticsPartial least squares regressionData miningEconometricsArtificial intelligenceMathematicsMachine learning
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Publication Info
- Year
- 2015
- Type
- article
- Volume
- 11
- Issue
- 4
- Pages
- 1-10
- Citations
- 6625
- Access
- Closed
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Cite This
Ned Kock
(2015).
Common Method Bias in PLS-SEM.
International Journal of e-Collaboration
, 11
(4)
, 1-10.
https://doi.org/10.4018/ijec.2015100101
Identifiers
- DOI
- 10.4018/ijec.2015100101