Unveiling the Research Landscape of Public Procurement and Emerging Technologies: A Top Modeling Approach to Bibliometric Analysis and Co-occurrence Network Study

This study employs bibliometric analysis, co-occurrence network analysis, and topic modelling techniques to support policymakers, practitioners, and researchers in making informed decisions about resource allocation and research priorities in public procurement and emerging technologies. The goal is to understand the research landscape and emphasise the importance of various themes, thereby guiding efforts to enhance knowledge, foster innovation, and optimise the benefits of emerging technologies in public procurement. The methodology involves collecting bibliographic data from Scopus and constructing a co-occurrence network based on identified themes or keywords. Additionally, topic modelling techniques are applied for a deeper analysis of the co-occurrence network, using tools such as Bibliometrix, LDAShiny, and ggplot2, aided by Biblioshiny and LDAshiny apps for visualisations. The findings emphasise the need to keep researchers updated with advancements in machine learning, data mining, and emerging technologies in public procurement. Subjectivity, the dynamic character of the field, and data quality and availability are among the limits of the approaches, though, which call for domain knowledge and qualitative techniques for validation. In this field, the insights of bibliometric analysis can inform future studies, policy-making, and decision-making. A notable gap identified is the lack of research on digital road mapping transition strategies and technological assessment of emerging technologies in public procurement.