Mapping the research landscape of corporate strategy and capital structure: a bibliometric analysis
Bibliographic record
Abstract
Purpose The aim of this study is to analyze the existing literature available on corporate strategy and capital structure with the help of a bibliometric analysis. Design/methodology/approach A total of 133 studies indexed in the Scopus database over the period from 1979 to 2024 are included and analyzed using the Biblioshiny package in RStudio along with VOSviewer for network visualization. Additionally, this study used biblioMagika and OpenRefine to harmonize and clean the data. Findings This study identified the leading contributors in terms of countries, authors, sources, and documents and used various analysis techniques. The USA, Canada, and the UK exhibited the most significant level of contribution. Furthermore, Bradford’s Law is applicable to the results of this study. The bibliographic coupling resulted in the five clusters indicating emerging themes in the field. Research limitations/implications The study’s findings will contribute to the academic landscape by providing an exhaustive examination of the concerned research field and will guide potential researchers for future research avenues. This study will also highlight the need for managers and policymakers to factor in diverse corporate strategies when shaping an organization’s capital structures. Originality/value To the best of the authors’ knowledge, this study represents the first attempt to map the landscape of this field through the presentation of insights derived from bibliometric analysis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.210 | 0.200 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".