Analysing the Impact of Crises on Financial Performance: Empirical Insights from Tourism and Transport Companies Listed on the Bucharest Stock Exchange (during 2005–2022)
Bibliographic record
Abstract
To adapt to the business environment, organisations adhere to management strategies capable of removing the effects of negative events, transforming themselves into resilient organisations. Physical and mental difficulties are the consequences of recent corporate developments, and protecting these organisations is a significant concern for managers. Using regression analysis of panel data, we evaluate the effectiveness and performance of 34 tourism and transport companies listed on the BSE in the 2005–2022 period by testing the effect of leverage on financial performance. Then, we focus on identifying the effects of recent crises (the global financial crisis of 2007–2008 and the COVID-19 pandemic) on financial performance and, implicitly, on organisational resilience. The findings suggest that the research hypotheses were partially validated, noting that the indicators included in the study registered significant decreases for the COVID-19 crisis period compared to the global financial crisis period. The paper provides information on measuring the resilience of companies through their ability to withstand the global financial crisis and the crisis triggered by the COVID-19 pandemic. This study is also among the first to examine the role of financial crises in the leverage and financial performance relationship in Romania.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".