Common Method Bias in PLS-SEM: A Full Collinearity Assessment Approach
Open accessAbstract
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.
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The LV method showed inner VIF values less than 3.3, indicating that the data is not infected by common method bias.
“Following the LV method, we subjected all indicators to a single construct and rotated the process across all constructs, observing that the inner VIF values were less than 3.3, indicating that the data is not infected by CMB”
From Antecedents to Green Personal Care Product Purchase Intention Among Gen Z Consumers from India (2026) · cites this paper as Kock, 2015
Collinearity is not a major concern in the research model.
“Based on Kock [146], VIF value lower than 3.3 demonstrates that collinearity is not a major concern in the research model.”
From Consumers purchase intention in live-streaming e-commerce: A consumption value perspective and the role of streamer popularity (2024) · cites this paper as [146]
Assessing CMV with VIF returned values under 3.3, and correlations between constructs below 0.9, suggesting findings were not impacted by CMV.
“Moreover, assessing the Common Method Variance (CMV) with Variance Inflation Factor (VIF) returned values under 3.3 (Kock 2015;Kock and Lynn 2012), and correlations between constructs below 0.9 (Rasoolimanesh et al. 2021).This outcome suggests that the findings were not impacted by CMV.”
From Influencers and the choice of a travel destination: A customer journey and information processing perspective (2025) · cites this paper as Kock 2015
The results of the full collinearity test showed that the variance inflation factors were between 1.163 and 1.625, below the maximum threshold of 3.3, which suggested that common method bias was not a severe issue for this study.
“The results of the full collinearity test showed that the variance inflation factors were between 1.163 and 1.625 (Table 2), below the maximum threshold of 3.3, which suggested that common method bias was not a severe issue for this study (Kock, 2015).”
From Risk, Trust, and the Roles of Human Versus Virtual Influencers (2024) · cites this paper as Kock, 2015
To ensure robustness in the assessment for Study 1, two types of statistical assessments for common method bias were incorporated: Harman's single-factor test and the full collinearity test.
“To ensure robustness in the assessment for Study 1, two types of statistical assessments for common method bias were incorporated: Harman's single-factor test (MacKenzie & Podsakoff, 2012) and the full collinearity test (Kock, 2015).”
From Risk, Trust, and the Roles of Human Versus Virtual Influencers (2024) · cites this paper as Kock, 2015
The model is not multicollinear and does not show common method bias.
“According to the results in Table 5, the VIFs of the variables are within acceptable ranges.”
From INFLUENCE OF SOCIAL MEDIA ADDICTION ON ACADEMIC ACHIEVEMENT IN DISTANCE LEARNING: INTERVENING ROLE OF ACADEMIC PROCRASTINATION (2023) · cites this paper as Kock, 2015
Full collinearity assessment showed all VIF values below 3.3 (IC=1.82; PSR=1.47; TR=2.31; PI=1.36), confirming absence of pathological collinearity that could indicate common method bias.
“all VIF values obtained were below the threshold of 3.3 (IC = 1.82;PSR = 1.47;TR = 2.31; PI = 1.36), confirming the absence of pathological collinearity that could indicate the presence of common method bias”
From Understanding consumer purchase intention in social media influencer marketing: the mediating role of parasocial relationship and trust in the Moroccan context (2026) · cites this paper as Kock, 2015
Procedural and statistical measures were implemented to mitigate common method bias, including randomized item ordering, guaranteed anonymity, Harman's single-factor test (31.2% of variance explained by the first factor), and a full collinearity assessment in which all construct VIF values were below 3.3.
“Although both procedural and statistical measures were implemented to mitigate this concern, including randomized item ordering, guaranteed anonymity, Harman's single-factor test (31.2% of variance explained by the first factor), and a full collinearity assessment (Kock, 2015) in which all construct VIF values were below 3.3”
From Understanding consumer purchase intention in social media influencer marketing: the mediating role of parasocial relationship and trust in the Moroccan context (2026) · cites this paper as Kock, 2015
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- Authors
- Kock, N.
- Affiliations
- Texas A&M International University (US)
- Corresponding author
- Kock, Ned
- ORCID
- Kock, Ned
- Venue
- International Journal of e-Collaboration
- Publisher
- IGI Global Scientific Publishing
- Published
- 2015-10-01
- Volume
- 11
- Issue
- 4
- Pages
- 1-10
- ISSN
- 1548-3673 (ISSN-L)
- 1548-3681
- OpenAlex
- W1204404493
- Cited by
- 10,215
- References
- 21
- Citations by year
- 2026: 3334 · 2025: 2114 · 2024: 1493 · 2023: 1086 · 2022: 848 · 2021: 643 · 2020: 391 · 2019: 193 · 2018: 75 · 2017: 26 · 2016: 11 · 2015: 1
- FWCI
- 47.03
- Type
- journal article
- Language
- ng
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- International Journal of e-Collaboration (publishedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Statistical Methods and Models
- Social Sciences › Decision Sciences › Management Science and Operations Research › Business Strategies and Management Research
- Keywords
- common method bias, partial least squares structural equation modeling, variance inflation factor, e-collaboration, convergent validity, discriminant validity, Monte Carlo simulation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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