ANALYSIS OF THE FINANCIAL PERFORMANCE OF AIRLINE COMPANIES IN STAR ALLIANCE IN THE PERIOD 2018-2022 USING LOPCOW-TOPSIS METHODS
Bibliographic record
Abstract
This study aims to comparatively evaluate the financial performance of the airlines included in Star Alliance for the period 2018-2022 for pre-COVID-19, COVID-19 and post-COVID-19 periods. For the performance evaluation, 5 criteria and a total of 9 financial performance ratios were used. LOPCOW method was used to determine the criteria (importance) weights of the calculated financial performance ratios and TOPSIS method was used to determine the performance rankings. According to the results of LOPCOW analysis, the most important criterion was determined as net profit/total assets (PR3) for 2018, net profit/total equity (PR2) for 2019 and 2021, short-term debt/total assets (FSR3) for 2020 and net profit/net sales (PR1) for 2022. The criterion with the lowest importance weight is net balance sheet position/equity (CA) for 2018, 2019 and 2021, net sales/ current assets (AT) for 2020, and short-term debt/total assets (FSR3) for 2022. According to TOPSIS performance evaluation results, the best performing airline was Shenzhen Airlines in 2018, 2019 and 2020, Thai Airways International in 2021 and Aegean in 2022. The airline that ranked last in the performance ranking was Croatia Airlines in 2018, Asian Airlines in 2019, Thai Airways International in 2020, Air Canada in 2021 and Air China in 2022.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| 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".