<![CDATA[Purpose - This study systematically reviews the development, intellectual structure, theoretical foundations, and future research directions of artificial intelligence (AI) in economic business research. Design/methodology/approach - The study employs a hybrid systematic literature review design that combines bibliometric and content analyses. Bibliometric analysis was conducted on 1,970 articles indexed in the Web of Science Core Collection from 1990 to June 2024. In-depth content analysis was then performed on 110 empirical articles published in Q1/Q2 journals. The analysis covers AI application categories, research topics, guiding theories, methodologies, and research contexts. Findings - The findings identify four dominant AI application domains: market prediction and risk management, marketing and customer analytics, process automation and supply chain optimization, and strategic decision-making and HR analytics. The literature is highly concentrated in data-rich sectors such as banking, fintech, e-commerce, and retail. Most studies emphasize system design, algorithmic performance, and predictive accuracy, while theory-driven, longitudinal, governance-oriented, and context-sensitive research remains limited. Only a minority of empirical studies explicitly applies established theories, indicating a need for stronger integration between computational performance and business, organizational, and socio-technical mechanisms. Originality/value - This study contributes by integrating large-scale bibliometric mapping with manual content analysis to provide a comprehensive synthesis of AI-economic business research. It proposes a future research agenda focused on generative AI, explainable AI, human-AI collaboration, theory-driven inquiry, and long-term societal impacts.]]>
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