Measuring production and consumption efficiencies using the slack‐based measure network data envelopment analysis approach: the case of low‐cost carriers
Bibliographic record
Abstract
SUMMARY One of the most important outcomes of the deregulation of air transportation policy is the emergence of low‐cost carriers (LCCs) around the world. Although LCC airlines have been in operation for more than 30 years, not every LCC is successful. In order to reduce the inefficiencies of LCCs, this paper measures the performance of LCCs by using slack‐based measure network data envelopment analysis. This model combines both the production process with input orientation and the consumption process with output orientation into a unified model. Furthermore, envelopment map analysis LCCs are performed to determine the reasons for the LCCs' inefficiency and how improvements can be made. A sample of 16 low‐cost airlines from Europe, the USA, and Asia were selected for operational performance analysis. The results show that the main reason for easyJet's, US Airways', and Virgin Blue's inefficiency is production inefficiency, so these LCCs should reduce their input quantities to increase efficiency. Jet2, Aer Lingus, and JetBlue were, by contrast, found to be consumption inefficient; these LCCs should increase their output quantities in order to enhance performance. Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".