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Common methods variance detection in business research

2015; Elsevier BV; Volume: 69; Issue: 8 Linguagem: Inglês

10.1016/j.jbusres.2015.12.008

1873-7978

Christie M. Fuller, Marcia J. Simmering, Guclu Atinc, Yasemin Atinc, Barry J. Babin,

Big Data and Business Intelligence

The issue of common method variance (CMV) has become almost legendary among today's business researchers. In this manuscript, a literature review shows many business researchers take steps to assess potential problems with CMV, or common method bias (CMB), but almost no one reports problematic findings. One widely-criticized procedure assessing CMV levels involves a one-factor test that examines how much common variance might exist in a single dimension. This paper presents a data simulation demonstrating that a relatively high level of CMV must be present to bias true relationships among substantive variables at typically reported reliability levels. The simulation data overall suggests that at levels of CMV typical of multiple item measures with typical reliabilities reporting typical effect sizes, CMV does not represent a grave threat to the validity of research findings.