Post-Pandemic Risk Management Strategies in the Aviation Industry: Case study of HNA Group & Aegean Airlines
Bibliographic record
Abstract
As the global economy gradually recovers from the impact of COVID-19, the aviation industry is also ushering in long-awaited development opportunities. However, the challenges facing the post-pandemic aviation industry should not be ignored, including increased market competition, an uncertain global economic environment, frequent policy adjustments, regional conflicts, and external risks. This study analyzed the risk management practices of Hainan Airlines Group and Aegean Airlines, among other typical cases, to explore how airlines can strengthen risk management during the post-pandemic recovery period. This study analyzed the risks faced by airlines during the recovery process and their causes from multiple dimensions, such as market demand forecasting, cost control, operational efficiency improvement, external environment analysis, and safety management, and put forward corresponding management suggestions and strategies. Through systematic analysis and research, this study aims to provide a useful reference for airlines’ risk management in the post-epidemic era and help airlines achieve stability and sustainable development in a complex and ever-changing market environment.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".