Financial Efficiency Analysis the Malmquist TFP Method: An Application on Star Alliance Member Airlines
Bibliographic record
Abstract
The purpose of this study is to analyze the financial efficiency of 15 airlines that are members of the Star Alliance which is considered the largest international strategic airline network for the period 2016-2019 using the Malmquist Total Factor Efficiency method. In addition, other purposes include the comparison of the change in technical efficiency (TE), technological change (TD) and total factor productivity (TFP) values of airlines that are members of the Star Alliance As a result of the analysis, it was found that the average technological change and total factor productivity values of the airlines in question increased in the period 2016-2017, and the average technical efficiency values decreased. On the other hand, in the period 2017-2018, the opposite situation was observed. In the period 2018-2019, technical efficiency, technological change and total factor productivity values decreased. It was found that the technical efficiency values of the Air Canada and Turkish Airlines increased during the entire period, while the average technical efficiency, technological change and total factor productivity values of the Air New Zealand, Asiana Airlines, Avianca, Lufthansa and Thai Airways decreased.
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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 | 0.013 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".