Assessing omitted variables bias in intention-behavior linkages: Mitigation strategies and research implications

Omitting relevant variables in research models is a significant challenge in academic research involving non-experimental research methods. Omitted variables may bias the empirical findings and lead to erroneous conclusions about relationships between factors underlying information systems phenomena. Using data coded from 128 samples reported in 105 prior studies, this study applies meta-regression methods to quantify the extent to which omitted variables bias the reported effect sizes between behavioral intention and system use. Since a direct examination of omitted variables is not possible, this study quantifies four measures: independent variables common to both behavioral intention and system use, moderators for the relationship between behavioral intention and system use, moderators for relationships between other independent variables and system use, and control variables for system use. Meta-regression results show that the effect size for the relationship between behavioral intention and system use decreases when independent variables common to behavioral intention and system use or moderators for the relationship between behavioral intention and system use are included in research models. This implies that the non-inclusion of relevant variables distorts the effect size for the relationship between behavioral intention and system use resulting in a biased understanding of the relationships. It is crucial for research models to include relevant variables such that the omitted variables bias can be effectively handled. Several mitigation strategies and research implications are described.