Decision Support Systems in Electronic Procurement for Public Sector Procurement: A Systematic Literature Review on Machine Learning Integration

This study analyzes the evolution of Decision Support Systems (DSS) and Multi-Criteria Decision Making (MCDM) in public sector procurement between 2020 and 2025. Using bibliometric analysis of Scopus and Web of Science articles, the research focuses on themes such as e-procurement, supplier selection, public procurement, and the integration of intelligent technology. Network visualization, overlays, and density mapping were applied to explore keyword relationships, temporal trends, and research intensity. Findings reveal that in 2020, studies concentrated on transparency and digitalization in public e-procurement, with classical MCDA methods, fuzzy TOPSIS, and semantic DSS dominating the approaches. By 2022–2023, the emphasis shifted toward intelligent technologies, including artificial intelligence, neuro-fuzzy systems, and data mining algorithms. These innovations expanded DSS functions from evaluation to predictive analytics and optimization. Core themes such as supplier selection, optimization, and public procurement remained central, while emerging topics like sustainability and clinical decision support systems pointed to new research directions. A significant gap was identified in the university context. Although public sector e-procurement has been widely studied, no research has specifically addressed DSS–MCDM applications in higher education procurement systems. Consequently, future agendas should prioritize adaptive DSS tailored to universities, blockchain integration for transparency, and AI applications in clinical and humanitarian systems.