Common Method Variance in IS Research: A Comparison of Alternative Approaches and a Reanalysis of Past Research

TLDR

Common method variance (CMV) is a persistent concern in survey research, yet the information systems (IS) community remains uncertain about its extent. The study aims to systematically examine how CMV affects inferences from IS survey research. The authors describe CMV assessment methods, empirically compare them, and apply the marker‑variable technique to reanalyze correlations and structural relationships in prior studies. The analysis shows CMV exists but is modest (inflation ≤0.10), marker‑variable and conventional tools perform similarly, and after adjustment most correlations and structural relationships remain significant, indicating CMV is not a major threat in IS research.

Abstract

Despite recurring concerns about common method variance (CMV) in survey research, the information systems (IS) community remains largely uncertain of the extent of such potential biases. To address this uncertainty, this paper attempts to systematically examine the impact of CMV on the inferences drawn from survey research in the IS area. First, we describe the available approaches for assessing CMV and conduct an empirical study to compare them. From an actual survey involving 227 respondents, we find that although CMV is present in the research areas examined, such biases are not substantial. The results also suggest that few differences exist between the relatively new marker-variable technique and other well-established conventional tools in terms of their ability to detect CMV. Accordingly, the marker-variable technique was employed to infer the effect of CMV on correlations from previously published studies. Our findings, based on the reanalysis of 216 correlations, suggest that the inflated correlation caused by CMV may be expected to be on the order of 0.10 or less, and most of the originally significant correlations remain significant even after controlling for CMV. Finally, by extending the marker-variable technique, we examined the effect of CMV on structural relationships in past literature. Our reanalysis reveals that contrary to the concerns of some skeptics, CMV-adjusted structural relationships not only remain largely significant but also are not statistically differentiable from uncorrected estimates. In summary, this comprehensive and systematic analysis offers initial evidence that (1) the marker-variable technique can serve as a convenient, yet effective, tool for accounting for CMV, and (2) common method biases in the IS domain are not as serious as those found in other disciplines.