MACHINE LEARNING FOR STRATEGIC AND OPERATIONAL DECISION-MAKING: A BIBLIOMETRIC PERSPECTIVE

Abstract

Besides being a buzzword, machine learning finds new areas of application in organizational decision-making processes by the day. We map the field's intellectual structure, thematic evolution, and application domains through a bibliometric analysis of 1,803 Web of Science and Scopus articles (1990-2024) to elucidate its strategic and operational roles. Six clusters, spanning risk modeling, predictive analytics, strategic intelligence, and human-centered AI, are revealed by co-authorship, keyword co-occurrence, and bibliographic coupling. The findings reveal a fragmented but methodologically diverse landscape, with algorithm adoption differing by decision type and industry. By connecting machine learning methods (like deep learning, natural language processing, and explainable AI) with decision functions (like forecasting, optimization, and classification), we can identify the situations in which machine learning has the biggest influence. We go beyond descriptive enumeration with our integration of conceptual and practical insights.