The Role of ERM and Corporate Governance in Managing COVID-19 Impacts: SMEs Perspective
Bibliographic record
Abstract
SMEs are perceived as more exposed to the consequences of external shocks. The purpose of our work is to examine whether the ERM sophistication or corporate governance mechanisms could be relevant in resistance to COVID-19 shock in the SMEs. In particular, we hypothesize that the SMEs with greater degree of ERM sophistication and stronger CG mechanisms will have a clearer understanding about the severity of the impacts from COVID-19. Our empirical evidence is based on the results of a survey conducted within a large sample of SMEs operating in Poland and in Germany within different experimental settings. We have found that the ERM and CG sophistication influence the perception of COVID-19 interruptions and will alert companies to adjust their business strategy and organizational structure to better cope with effects of the current crisis. The proposed framework can also be a valuable tool for consultants to use to enhance the ERM systems in SMEs.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".