Handling The Common Method Variance/Bias (CMV/CMB) in Cross-Sectional Research: A Systematic Literature Review and Bibliometric Analysis

Concerns over the validity of survey research have grown among editors, reviewers, and authors in recent years. Common method variance (CMV) is one of the main problems. Common method variance (CMV), which measures the amount of misleading covariance shared between variables as a result of the common data collecting method, is one of the main problems. Because CMV has not been extensively studied by IS researchers. Thus, IS experts are still uncertain about this. The current study intends to address the following research issues and add to the ongoing discussion about how common method variance/bias (CMV/CMB) in cross-sectional research is handled. Eight related research studies were analyzed and assessed using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach and VOSviewer's Bibliometric Analysis feature. The findings show that over time, one paper has consistently been published year. This is perhaps because managing common method variance/bias (CMV/CMB) in cross-sectional research is not a topic that most researchers and academics are particularly interested in. As suggested by a number of the above suggestions, common method variance/bias (CMV/CMB) in cross-sectional research can be addressed by (1) employing a large number of respondents; (2) obtaining multiple forms of data (i.e., triangulation approach); and (3) gathering data over multiple periods of time (i.e., longitudinal data).