This paper presents a bibliometric analysis and literature review of methodologies for optimal capital structure decision making, focusing on research published between 2000 and 2024. This study reviews current research, identifies gaps, and outlines a plan to support with financial decisions. A mixed-methods approach was employed, combining data from the Web of Science and Scopus databases using the search string “capital structure” AND (“decision making” OR “optimal structure”). The study used Bibliometrix(R), VOSviewer, and NVivo tools, and followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flowchart for choosing studies. The findings show that this field is well-developed but still changing. The intellectual structure is organized around two main clusters: one focused on testing classical theories and another oriented toward optimization and managerial applications, revealing a clear theory–practice divide. The mapping also highlights the dominance of Chinese and U.S. scholarship and the central role of practitioner-oriented journals such as Managerial Finance, indicating both a shift toward emerging markets and a strong demand for applicable research. The study provides three key contributions. First, it identifies important countries, authors, outlets, and themes. Second, it uses a method that combines bibliometric and text-mining tools. Third, it introduces a new decision-support framework that is thorough, context-sensitive, and flexible. There are some limitations. These include relying on Scopus and Web of Science, language limits, and the fact that bibliometrics cannot judge the quality of methods. Future research should empirically validate the proposed framework in different contexts, expand studies in emerging markets, test emerging theories such as Brusov–Filatova–Orekhova (BFO) theory, and develop more dynamic and stochastic models to better capture financial uncertainty.
Passos et al. (Thu,) studied this question.
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