Prescriptive Analytics: A Bibliometric Analysis of Current Trends in Data - Driven Decision - Making

This study presents a bibliometric synthesis of prescriptive analytics with the aim of charting its intellectual structure, historical trajectory, and emerging frontiers in data-driven decision making. We delineated the review scope, implemented systematic searches in Scopus and Web of Science (WoS), extracted and harmonized records, and undertook descriptive trend analysis alongside thematic mapping. The corpus evidence marked growth in the field: Scopus records increased from a single item in 1970 to 39 in 2024 (reported as 19.4% of the total), while WoS expanded from one publication in 1976 to 107 in 2024 (reported as 10.7%). More than 83% of the outputs have appeared since 2013, accompanied by substantial citation accumulation. The analysis identified leading authors, institutions, and recurrent topics spanning “prescriptive analytics,” “decision science” and “decision intelligence.” It also surfaces conceptual inconsistencies and underexplored areas, motivating the development of adaptive, context-aware frameworks that reconcile competing objectives and enable practical, interdisciplinary AI solutions. Given the rapid technological change and database coverage effects, the observed trends should be interpreted as time- and source-dependent. Overall, the bibliometric perspective clarifies temporal patterns, disciplinary distribution, and high-frequency keywords and delineates a research agenda to address the most salient gaps. Keywords: Scopus; Web of Science (WoS); Prescriptive Analytics; Decision Intelligence; Decision Science.