Artificial Intelligence in decision making: thematic evolution and research agenda

Espinosa M.R.M.; Moya L.P.; Cardona-Acevedo S.; Vanegas J.C.P.; Valencia-Arias A.; Henríquez J.L.; Rodriguez E.M. · RISTI - Revista Iberica de Sistemas e Tecnologias de Informacao (2024)

Resumen

This review examines research on the convergence of artificial intelligence and decision-making. Employing the PRISMA-2020 methodology for bibliometric analysis, trends were identified. Notably, 2018, 2020, and 2022 evince increasing interest in leveraging AI for decision-making. Scientific production tracks a cubic polynomial curve, emblematic of significant expansion. Pioneering figures include Edwards and Dwivedi. Thematic evolution has shifted from teleological explanations to more contemporary concepts such as prediction and topsis. Emerging keywords such as “data science” and “Covid-19,” along with the burgeoning importance of “Big data,” reflect current trends. Functional keyword categorization highlights key areas for future research, providing a comprehensive view of developments in this evolving field.

Instituciones: Universidad Ricardo Palma