Bibliometric Analysis of Data-Driven Decision Making in Business Intelligence
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Abstract
This study conducts a bibliometric analysis to explore the research landscape of data-driven decision-making (DDDM) in Business Intelligence (BI). By examining 748 papers published between 2000 and 2024, the study identifies key themes, influential authors, collaborative networks, and emerging trends. The findings highlight the evolution of research from foundational topics like big data and analytics to application-driven themes such as sustainability, policy, and operational efficiency. The United States emerges as the central hub in global research collaboration, with significant contributions from India, China, and European countries. Challenges, including data quality, integration, cultural resistance, and ethical concerns, are identified as barriers to BI adoption. The study emphasizes the need for future research on emerging technologies, industry-specific applications, and ethical frameworks to advance the field. These insights provide valuable guidance for researchers and practitioners aiming to optimize decision-making processes through BI.
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