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Editorial: How to Prevent, Detect and Control Common Method Variance in Electronic Commerce Research
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Editorial: How to Prevent, Detect and Control Common Method Variance in Electronic Commerce Research
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Journal Article
Editorial: How to Prevent, Detect and Control Common Method Variance in Electronic Commerce Research
2020
Overview
When, in an electronic commerce study, all scale items have been measured by means of a single questionnaire survey and at the same time, there is the prospect that the tested relationships among the constructs might be distorted by the effect of common method variance (CMV)-a serious and problematic issue that has the potential to jeopardize the validity of the research findings. The bias generated by CMV, known as common method bias, appears when the estimated relationship between one construct and another might be inflated; put differently, CMV produces a systematic covariation above the true relationship between the scale items. As a consequence, the altered values of the observed correlations and of other relevant indicators might lead to: either incorrect estimates of the reliability and convergent validity constructs in the study, or erroneous parameter estimates related to the magnitude and the significance of the relationships among constructs.
Publisher
MDPI AG,Journal of Theoretical and Applied Electronic Commerce Research
