Decision Support Systems (DSS) are increasingly recognized as vital tools for enhancing organizational decision-making across sectors. This narrative review synthesizes empirical evidence on the effectiveness, challenges, and strategic implications of DSS implementation, focusing on healthcare, education, supply chain, and public policy sectors. By examining these areas, the study highlights sector-specific dynamics and barriers to adoption. Using a systematic narrative approach, the study draws from literature indexed in Scopus, Google Scholar, and Web of Science, applying keyword-based searches and inclusion criteria to select peer-reviewed empirical and theoretical studies published within the last decade. The review finds, for example, that DSS in healthcare reduce sepsis-related mortality by enabling early detection, while in education, DSS support adaptive learning systems that align teaching with student performance data. In supply chains, DSS improve delivery times by up to 30%, and in public policy, they facilitate scenario-based analysis for transparent decision-making. However, systemic barriers such as infrastructure limitations, low digital literacy, and cultural resistance persist, especially in public sector adoption. These barriers impede the full realization of DSS potential and necessitate multi-level interventions. Integration of DSS has shown to not only optimize operational performance but also inform long-term strategic planning and policy development. The discussion underscores the importance of adaptive DSS models, user-centric design, and ethical governance in maximizing system effectiveness. The study concludes that while DSS offer transformative potential, their success depends on addressing institutional readiness and embedding ethical, inclusive frameworks. Future research should prioritize longitudinal studies on DSS adoption in healthcare and education, cross-country comparisons of supply chain DSS, and investigation into governance frameworks that ensure ethical use in public policy
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