Decision-Support Systems in the Public Sector: A Systematic Review of Factors Shaping Frontline Decision-Making

Decision-support systems (DSS) are increasingly adopted across the public sector, particularly at the frontline where algorithmic recommendations and predictions is reshaping key decision-making processes. Although frontline workers retain formal discretion, DSS are now embedded in several high-stakes domains, including child welfare, healthcare, and unemployment management. However, despite the growing implementation of DSS in public organizations and the critical importance of frontline workers’ decision-making, we still lack systematic research that synthesizes existing evidence and clarifies what we actually know about how DSSs influence frontline work and the key factors determining DSS influence. This article fills this gap through a systematic literature review of 1,953 publications, of which 96 meet our criteria. We synthesize current knowledge and develop a framework categorizing the macro-, meso-, and micro-level factors shaping frontline workers’ use and perceptions of DSS. Further, the review identifies key research gaps, offers directions for future work, and advances our theoretical understanding in the field.