Artificial Intelligence and Decision-Making: A Bibliometric Study Based on the Scopus

This research presents a bibliometric analysis of the relationship between Artificial Intelligence (AI) and decision-making literature from 1971 to 2024, addressing the growing integration of AI solutions in organisational decision processes. Through systematic data collection from the Scopus database, analysing 9,142 papers using bibliometric techniques, the study employs citation analysis, co-authorship networks and keyword analysis to map research evolution and collaboration patterns. The results reveal three distinct periods in the field’s development: an initial period (1971-2007) focused on operational research with 778 papers, an intermediate period (2008–2017) emphasising decision support systems with 1,755 papers and a boom period (2018–2025) marked by exponential growth with 6,609 papers. The analysis identifies leading institutions, with Carnegie Mellon University showing the highest research output and reveals asymmetric collaboration patterns between established and emerging research economies. While countries like the US and UK demonstrate high citation impact and international collaboration, emerging powers like China and India show higher publication volumes but lower international engagement. The findings contribute to understanding research collaboration dynamics in AI, suggesting that effective knowledge production depends not only on publication volume but significantly on the quality of international research networks. This paper offers a comprehensive view of the current state of research but also proves instrumental in guiding future investigations, fostering interdisciplinary collaborations and formulating strategies for the continuous advancement of the respective field of study.