Bibliographic record
Abstract
This study examines the impact of capital structure on firm performance, highlighting industry-specific differences and the influence of economic and regulatory environments. Capital structure, particularly the debt-to-equity (D/E) ratio, plays a critical role in financial management, affecting both profitability and financial risk. The Modigliani-Miller theorem serves as a theoretical foundation, positing that in the absence of taxes, a firm’s value is unaffected by its capital structure. However, real-world applications reveal significant variations due to tax considerations, market imperfections, and industry-specific factors. Developed countries, with mature financial markets and stable economic conditions, allow firms to optimize their capital structures using diverse financing instruments. In contrast, firms in developing countries face higher financial risks and rely more on internal and short-term financing due to economic instability, high interest rates, and underdeveloped financial markets. Industry characteristics further influence capital structure; capital-intensive industries often have higher D/E ratios due to the need for significant investment in technology and infrastructure. The study underscores the importance of tailoring capital structure strategies to specific market conditions and industry needs to enhance financial stability and performance. Policymakers and business leaders must navigate these complexities to foster sustainable growth and minimize financial risks.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".