Common Method Bias in PLS-SEM: A Full Collinearity Assessment Approach

Common Method Bias in PLS-SEM: A Full Collinearity Assessment Approach

Ned Kock (Department of International Business and Technology Studies, Texas A&M International University, Laredo, TX, USA)
Copyright: © 2015 |Pages: 10
DOI: 10.4018/ijec.2015100101
OnDemand PDF Download:
$30.00
List Price: $37.50

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.
Article Preview

What Is Common Method Bias?

Common method bias, in the context of PLS-SEM, is a phenomenon that is caused by the measurement method used in an SEM study, and not by the network of causes and effects in the model being studied. For example, the instructions at the top of a questionnaire may influence the answers provided by different respondents in the same general direction, causing the indicators to share a certain amount of common variation. Another possible cause of common method bias is the implicit social desirability associated with answering questions in a questionnaire in a particularly way, again causing the indicators to share a certain amount of common variation.

A mathematical understanding of common method bias can clarify some aspects of its nature. The adoption of an illustrative model can help reduce the level of abstraction of a mathematical exposition. Therefore, our discussion is based on the illustrative model depicted in Figure 1, which is inspired by an actual empirical study in the field of e-collaboration (Kock, 2005; 2008; Kock & Lynn, 2012). The illustrative model incorporates three latent variables, each measured through six indicators. It assumes that the unit of analysis is the firm.

Complete Article List

Search this Journal:
Reset
Open Access Articles: Forthcoming
Volume 13: 4 Issues (2017): 2 Released, 2 Forthcoming
Volume 12: 4 Issues (2016)
Volume 11: 4 Issues (2015)
Volume 10: 4 Issues (2014)
Volume 9: 4 Issues (2013)
Volume 8: 4 Issues (2012)
Volume 7: 4 Issues (2011)
Volume 6: 4 Issues (2010)
Volume 5: 4 Issues (2009)
Volume 4: 4 Issues (2008)
Volume 3: 4 Issues (2007)
Volume 2: 4 Issues (2006)
Volume 1: 4 Issues (2005)
View Complete Journal Contents Listing