How Data Quality Impacts Decision Outcomes: Scoping Review, Methodological Development, and Empirical Demonstration

Public sector value generation increasingly relies on data provided by information systems, yet the impact of data quality (DQ) on decision-making remains underexamined. While prior research has identified associations between DQ and decision outcomes, strong causal evidence remains scarce. This study addresses the issue in two phases: first, through a scoping review of experimental research on the impact of value-critical DQ dimensions—accuracy, completeness, consistency, and timeliness—and second, through methodological development. Searches across Scopus, Web of Science, IEEE, and PsycInfo, supplemented by citation searching and Google Scholar to avoid inclusion bias, identified 20 studies after screening and full-text review. Rather than excluding studies based on methodological quality, the review critically examined existing experimental designs and their findings. The literature revealed substantial weaknesses, including unclear distinctions between objective and subjective variables, weak manipulations, ambiguous operationalizations, and small sample sizes. Consequently, existing understanding of DQ impact remains limited and often inconclusive. To address these limitations, the article proposes a formal model for DQ experiments and presents a proof of concept for completeness using open data. The findings suggest that completeness influences value appraisal and subsequent choice behavior, particularly under higher uncertainty and larger value differences between alternatives.