2017
DOI: 10.1145/3051473.3051479
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Examining the Impact and Detection of the "Urban Legend" of Common Method Bias

Abstract: Common Method Bias (CMB) represents one of the most frequently cited concerns among Information System (IS) and social science researchers. Despite the broad number of commentaries lamenting the importance of CMB, most empirical studies have relied upon Monte Carlo simulations, assuming that all of the sources of bias are homogenous in their impact. Comparatively analyzing field-based data, we address the following questions: (1) What is the impact of different sources of CMB on measurement and structural mode… Show more

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Cited by 187 publications
(136 citation statements)
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“…Therefore, CMB could imply a threat in social science research given that bias may affect findings, due to systematic errors [74]. Consequently, it has been attempted to prevent CMB during the research design phase by applying the procedural remedies proposed by Podsakoff, MacKenzie and Podsakoff [75].…”
Section: Common Methods Biasmentioning
confidence: 99%
“…Therefore, CMB could imply a threat in social science research given that bias may affect findings, due to systematic errors [74]. Consequently, it has been attempted to prevent CMB during the research design phase by applying the procedural remedies proposed by Podsakoff, MacKenzie and Podsakoff [75].…”
Section: Common Methods Biasmentioning
confidence: 99%
“…We also adopted an unmeasured latent construct method (ULCM) to examine the potential influence of CMV, and it indicated no change in any of the correlative path coefficients or significance levels, and the chi-square difference test was significant (∆χ 2 (12) = 407.86, p < 0.001). In sum, the influence of common method variance bias was carefully examined via three tests and the results affirmed a very slim probability of common method variance influence [76,77].…”
Section: Initial Analysesmentioning
confidence: 90%
“…To avoid common method bias (CMB), we do not include complex items, we use a different choice of scale anchors for the LEBQ and MSQ items, we eliminate reverse-coded items, and we avoid prime effects (Podsakoff et al, 2003; Schwarz et al, 2017). In addition, we verified a posteriori that there was no evidence of CMB by using Harman’s single-factor test or the measured latent marker variable (MLMV) approach (Chin et al, 2013; Schwarz et al, 2017).…”
Section: Methodsmentioning
confidence: 99%