Revisiting the airline business model spectrum: The influence of post global financial crisis and airline mergers in the US (2011−2013)
Bibliographic record
Abstract
This paper re-examines the airline business model spectrum first proposed by Lohmann and Koo in 2013. The paper analyses the period of 2011–2013, where the US airline industry was no longer affected by the global financial crisis and after a few major US airlines went through a merging process. This study examines eight US carriers i.e. Alaska, American, Delta, Hawaiian, JetBlue, SkyWest, Southwest and United. These eight airlines were placed along a continuum of business models, i.e. full-service network carrier (FSNC), hybrid and low-cost carriers (LCC), as proposed in the incipient study by Lohmann and Koo (2013). Data from the airlines were used to delineate and review the indices labelled as ‘revenue’, ‘connectivity’, ‘convenience’, ‘comfort’, ‘aircraft’ and ‘labour’, forming the framework of the business model used. The results of this study continue to highlight the characteristics of each of the US carriers examined for the six proposed indices. A comparison is made between both studies, with the results suggesting that both convergence and divergence from the original FSNC and LCC positions in the spectrum occurred, with a tendency of merged airlines to move towards the FSNC end of the spectrum.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".