What is it about?

This research explores how emerging technologies are shaping the future of public procurement. We analysed scientific publications to identify key trends, technologies, and themes connected to procurement practices worldwide. By using bibliometric and co-occurrence network analysis, we created a map of the research landscape, showing where innovation is happening and where gaps remain. The findings provide useful insights for policymakers, researchers, and practitioners who want to understand how digital transformation can improve transparency, efficiency, and sustainability in public procurement.

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Why is it important?

Public procurement represents a significant share of government spending and has a direct impact on economic growth, innovation, and the delivery of public services. Understanding how emerging technologies can transform procurement is essential for building systems that are more transparent, efficient, and sustainable. Our research provides evidence-based insights that can guide policymakers, practitioners, and researchers in making better decisions and shaping the future of procurement.

Perspectives

Emerging technologies such as artificial intelligence, blockchain, and data analytics will continue to reshape how governments purchase goods and services. These tools can potentially reduce corruption risks, enable smarter decision-making, and foster market innovation. However, their adoption also raises legal, ethical, and governance challenges that must be addressed carefully. By mapping the current research landscape, our work sets the foundation for future studies and policy debates on responsibly integrating technology into public procurement systems.

ARISTOTELIS MAVIDIS
International Hellenic University

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This page is a summary of: Unveiling the Research Landscape of Public Procurement and Emerging Technologies: A Topic Modelling Approach to Bibliometric Analysis and Co-occurrence Network Study, Digital Government Research and Practice, December 2025, ACM (Association for Computing Machinery),
DOI: 10.1145/3746645.
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