The future avenues of artificial intelligence and decision-making in business management

Abstract

Purpose: In response to the growing integration of artificial intelligence (AI) and decision-making (DM) in business management, this study endeavors to provide a comprehensive understanding of the theoretical foundations, research trajectories, and emergent themes within this transformative intersection. By elucidating the evolving landscape of AI-driven decision-making, the research aims to offer valuable insights for scholars and practitioners, fostering informed decision-making practices and strategic advancements in contemporary business contexts. Originality/value: Methodologically, the study casts elements for AI and DM by conceptualizing, examining, and reviewing the field’s integration. The study also highlights the theoretical roots and classifies the main research themes in the literature strand. Design/methodology/approach: The study conducted a bibliometric analysis of 494 journal articles at the intersection of AI and DM in business management. It conducted two bibliometric analyses: co-citation analysis and co-occurrence analysis. The study also performed a qualitative review to criticize the obtained quantitative results. Findings: This research contributes to the domain’s understanding in three major ways. First, the theoretical roots by showing the most cited references. Second, the meta-analysis shows five pioneering studies in the literature suggesting the following research stages. Third, four distinct research themes are identified: 1. industry and society impact, 2. business strategies, 3. technological applications, and 4. decision systems. Lastly, the results highlighted research topics for future qualitative, quantitative, and mixed methods studies and provided recommendations for future research agendas alongside methodological theoretical and empirical guidelines for further investigations.