Effects of the COVID-19 Global Crisis on the Working Capital Management Policy: Evidence from Poland
Bibliographic record
Abstract
The paper aims to investigate the effects of the COVID-19 pandemic on working capital management policies among Polish small and medium-sized enterprises operating in Group Purchasing Organizations (GPOs). The results show that the firms adopted a moderate–conservative strategy for their working capital management. Moreover, the evidence confirms that the COVID-19 pandemic crisis did not change Working Capital Management (WCM) strategies significantly. The companies that have high financial security as a result of the high ratio of Liquidity, Quick, and cash conversion cycle (CCC) have tried to attract more new customers in the market by increasing the due date of accounts receivable so they can improve their sales performance, and also reduce the liabilities turnover to be able to work with more suppliers in the market. Moreover, among the various WCM strategies, the companies with a higher CCC ratio, along with those whose bulk of current assets consisted of accounts receivable and short-term investments, managed to have higher sales returns. Finally, our outcomes indicate that the firms operating in large cities have lower sales returns, meaning even Polish small and medium-sized enterprises’ ability within GPOs with the aid of the central unit can also get high return on sales (ROS) results.
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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 | 0.003 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".