Decision Support Systems: Evolution, Architectures, Applications, and Future Directions
Decision Support Systems (DSS) have evolved from stand-alone model-based tools into integrated, data-intensive, and increasingly intelligent systems that assist decision makers in healthcare, finance, supply chains, public administration, and other complex settings. This review article presents a structured integrative synthesis of foundational and contemporary DSS literature. The review examines conceptual origins, major taxonomies, architectural components, domain applications, implementation constraints, and emerging research directions. The synthesis shows that modern DSS combine data management, analytical models, knowledge representation, collaborative functions, and artificial intelligence, but their effectiveness remains contingent on data quality, organisational fit, explainability, interoperability, cybersecurity, and meaningful human oversight. Rather than comparing heterogeneous studies through unsupported aggregate performance statistics, this review evaluates evidence according to study context, DSS function, reported outcome, and methodological limitations. A divergent-convergent process model is presented to explain how multiple analytical perspectives can be generated, integrated, assessed, implemented, and refined through feedback. The review identifies priority directions in real-time adaptive support, interoperable architectures, explainable and accountable AI, longitudinal human-factor evaluation, and governance of increasingly autonomous decision systems. The article contributes an integrated framework that connects DSS evolution, architecture, organisational conditions, risks, and future development.