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

In: Partial Least Squares Path Modeling

Author

Listed:
  • 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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    Cited by:

    1. Tiberius Phillip Mlowosa & Gwahula Raphael & Dionis Ndolage, 2025. "Financial retirement planning of government employees in Tanzania: Moderating effect of financial decision behavior," International Journal of Research in Business and Social Science (2147-4478), Center for the Strategic Studies in Business and Finance, vol. 14(6), pages 121-137, August.
    2. Muhammad Iskandar Hamzah, 2025. "Market, Entrepreneurial Orientations, Crisis Adaptiveness, and Performance of SMEs: A Moderated Mediation Analysis," SAGE Open, , vol. 15(4), pages 21582440251, October.
    3. Sarfo, Christian & Sarpong, David & Owusu, Joseph & Igwe, Paul, 2026. "Performance measurement systems, organisational learning, and the sustainability–finance tension," Technological Forecasting and Social Change, Elsevier, vol. 223(C).
    4. James Ndone & Mary Kiura, 2025. "Mitigating Emotional Exhaustion Among College Students During COVID-19: The Role of Crisis Communication, Social Support, and Coping," SAGE Open, , vol. 15(2), pages 21582440251, May.
    5. S. Divya & Prabu Christopher B, 2025. "The Role of Supervisor Support and Change Information in Determining Behavioral Attitudes Toward Change in an Academic Setting: Empirical Validation of Intra-Organizational Dynamics Using IPMA Analysis," SAGE Open, , vol. 15(3), pages 21582440251, August.
    6. Jianhong Huang & Raja Nerina Raja Yusof & Azmawani Abd Rahman & Rozanah Ab Rahman, 2026. "The Antecedents and Outcomes of Dynamic Capabilities in Digital Transformation: A Study of Chinese Manufacturing Companies," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 17(1), pages 642-669, February.
    7. Saumyaranjan Sahoo & Arvind Upadhyay, 2025. "Improving triple bottom line (TBL) performance: analyzing impacts of industry 4.0, lean six sigma and circular supply chain management," Annals of Operations Research, Springer, vol. 355(1), pages 951-982, December.
    8. Lim, Weng Marc & Jasim, K. Mohamed & Malathi, A., 2025. "Service robots in healthcare: Toward a healthcare service robot acceptance model (sRAM)," Technology in Society, Elsevier, vol. 82(C).

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