Factors Affecting Business Failure of Small and Very Small Greek Family Enterprises
Bibliographic record
Abstract
This article investigates the factors that lead small and very small Greek businesses to financial failure using financial and accounting ratios as well as corporate governance characteristics. Our data set consists of 136 small and very small firms that went bankrupt, which were matched with a sample of 472 non bankrupt enterprises formed by random selection based on year, sector and sub-sector determinants, from 2003 to 2014. The total firm-year observations for bankrupt and non bankrupt companies were 940 and 5,041 respectively. Applying a Logit model for panel data, the results showed a significant impact on the likelihood of small and very small firms failing due to factors such as the type and the amount of bank lending, the level of profitability, cash flows, and liquidity.The data also support a statistically significant correlation of the probability of failure with non-financial factors such as Duality on the Board and CEO gender.The results of this paper will be useful for both banks and managers of small and micro businesses.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".