Data-Driven Decision-Making in Financial Management: A Comprehensive Bibliometric Analysis of Global Trends, Knowledge Structures, and Emerging Frontiers - Gusti Kayla Fadilah Hafsyari; Indri Kalila Hardayani; Raisyah Adindaffa Putri; Faras Putri Sabina; Arjuna Fauzan Ahmad; Lusiana Desy Ariswati; Muhammad Ramadhani Kesuma (Indo Publishing, 2026)
This study examines the intellectual evolution, knowledge structure, and global research trends of data-driven decision-making (DDDM) as applied to financial management, providing a systematic mapping of the field from its origins through its contemporary frontiers. A quantitative bibliometric analysis was conducted using publication data retrieved from Scopus spanning from the 2000s to the present. The dataset was analyzed using VOSviewer software, applying co-authorship, co-occurrence, and citation mapping techniques.The results reveal a significant upward trajectory in publications over the study period, reflecting the growing salience of DDDM in academic discourse. Collaborative research networks are dense and predominantly anchored by the United States, though emerging economies are increasingly participating. Core thematic clusters center on big data, machine learning, and predictive analytics, while the most recent trends signal a convergence toward the Internet of Things and digital twin technologies. Critical concerns around data security, ethics, and regulatory compliance are identified as cross-cutting challenges.Financial managers and policymakers can leverage these findings to identify underexplored research niches and anticipate the next generation of analytical tools required for sustainable financial decision-making. This study offers the first comprehensive bibliometric mapping of DDDM specifically situated at the nexus of financial management and advanced analytics, extending prior reviews by incorporating temporal overlay analysis and identifying the IoT-digital twin frontier as the emerging research horizon.