From The Editors: Common Method Variance In International Business Research

We conclude that lots of JIBS submissions in the past, when the data came from research (and/or “quantified” interviews, for example), have overlooked the problem of CMV. Where in fact the authors have attended to CMV, the normal response has gone to report apparently reassuring results from Harman’s single-factor test. The JIBS editors believe this process has been inadequate. For the existing editorial team, it is currently standard practice to come back a manuscript to the author when it seems to have problems with common-method bias and the issue has been ignored in the manuscript.

The desk rejection letter asks the writer to perform validity investigations and solve any CMV issues before resubmitting the manuscript. Addressing CMV only after table rejection is not the perfect strategy, of course. The first-best strategy is to prevent potential CMV at the research design stage using treatment 1, that is, by collecting data from multiple sources.

Ex-ante, before running any analyses, the assortment of key information from other resources should be planned, using where possible archival data and multiple respondents. Alternatively, more information can be collected later on. A good example of treatment 1 is Carraher, Sullivan, and Crocitto (2008), who surveyed expatriate employees but obtained their expat performance measure from company records filed by the individual’s supervisor rather than from the expat respondents themselves. Approaches for handling common method variance (CMV). More often than not, a perfect solution is out of reach. Obviously, all the reliability and validity lab tests should also be conducted and reported in the manuscript.

In singling out CMV in this Letter from the Editors we do not need to give JIBS writers the impression that they need to ignore other validity and dependability testing of their quantitative and qualitative research methods – they shouldn’t. We advance the field not only through theory development but also through careful and comprehensive empirical work using guidelines. Another example where CMV may be tolerated is large-scale studies involving multiple countries where obtaining separate data sources for all your countries is impossible. Such large-scale projects typically offer with potential nationwide distinctions in response bias by using within-subject standardization, indicate centering the scores of individuals, or using regression approaches to control for nationwide distinctions in response biases.

These studies also typically web page link some sort of aggregate country scores to a separate source-archival predictor or criterion. Lastly, we want to make it clear to the JIBS community that it is not our intention in this Letter from the Editors to privilege IB research workers who use large, available datasets such as Compustat readily, SDC or Orbis Platinum. Rather, we want to encourage primary and qualitative research in international business – including surveys – but at the same time increase awareness among IB researchers of potential CMV biases so that they can be avoided in the design stage.

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