The Influential Factors on Capital Structure: A Study on Portuguese High Technology and Medium-High Technology Small and Medium-Sized Enterprises
Bibliographic record
Abstract
Using the panel data model, this paper studies the influential factors on the capital structure of small and medium-sized enterprises (SMEs) in high and medium-high technology manufacturing sectors in Portugal. In particular, the total sample is further classified into young SME group and mature SME group for observing the similarities and differences. The research results show that firm size, profitability, firm age, and industry sector impact much on the capital structure and debt ratios; on the other hand, the impacts of tangible assets, intangible assets, and growth are not as strong as the previous factors. The differences of the impacts on young and mature SMEs are mainly shown by growth, intangible assets and industry sector. In particular, intangible assets show more statistical significance in young SMEs compared to mature SMEs, and intangible assets tend to be positively related to long-term debt especially in young SME group; this may reflect the positive attitude of financial institutions on the value of intangibles in generating future benefits for high and medium-high technology young firms. Besides, the findings tend to support the pecking order theory more than the trade-off theory regarding the high and medium-high technology manufacturing SMEs here.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".