A New Approach for Risk of Corporate Bankruptcy Assessment during the COVID-19 Pandemic
Bibliographic record
Abstract
The consequences of COVID-19 will aggravate existing multidimensional risks and reveal new ones. The research gap allows contributing to recognizing the exogenous risk factors of corporate bankruptcy during the COVID-19 pandemic in EU countries. This study aims at revealing how to evaluate the risk of corporate bankruptcy phenomenon in the COVID-19 times. The question arises as to whether Schumpeter’s creative destruction approach is still accurate. The article concentrates on implementing the fsQCA (fuzzy set Qualitative Comparative Analysis) method to identify and evaluate the main exogenous drivers of corporate bankruptcy in EU countries based on Fragile States Index data. This new approach focuses on fuzzy sets theory. The fsQCA method is a globally recognized alternative to quantitative analysis (in which the causal complexity is ignored) and qualitative methods for examining individual cases (which do not have the tools to generalize on their basis). The research indicates and examines the main external factors that would increase the risk of corporate bankruptcy in EU countries: namely, economic decline, uneven economic development, unemployment rate, demographic pressure, and government debt. The study discusses the influence of zombie companies on economies during the COVID-19 pandemic. Identifying risk factors that determine the threat of corporate bankruptcy may constitute practical recommendations for business and restructuring practitioners, financial institutions, and banking and public sector representatives in creating warning and recovery measures during the COVID-19 pandemic.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".