Artificial intelligence techniques in business decision support systems: thematic evolution and research agenda

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

Resumen

This study examines research trends in the integration of artificial intelligence (AI) in business decision support systems, with a focus on enhancing organizational decision making through data and pattern analysis. Please note that no changes were made as the text already adheres to the principles or lacks context. To assess the evolution and impact of the field, a bibliometric analysis is carried out using the PRISMA 2020 methodology. The study findings emphasize the ongoing scientific significance, where influential authors such as Khan MA and Antoniadi AM have been notable. There has been a thematic shift from “Model Management” towards “Machine Learning” and “Artificial Intelligence”, corresponding to emerging trends. Emerging terminologies encompass “Feature Extraction”, “Random Forest”, and “Critical Decision Support System”, indicating the current research direction. This study contributes to understanding the use of AI in developing business decision support systems.

Instituciones: Universidad Ricardo Palma