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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".