New Business Models in Aviation Subscription-Based Aviation applied to the 2030 World Cup
Bibliographic record
Abstract
The emergence of subscription services in all sectors of the economy, whether between the end user and the company or between companies, has extended to the airline industry in recent years. It is therefore important to study their expansion into airline's yield and revenue management systems. The terminology of the Flight Pass recurs in airline marketing terms, although there are few references to it in academic production. This study therefore aims to provide a framework for business models in aviation. It describes the business model concept and the analysis tool: the Business Model Canvas. It also describes the current state of commercial airline business models, subscription business models, and yield and revenue management systems (also focusing on loyalty and mobility-as-a-service programmes). To examine the business and Flight Passes of Alaska Airlines, Frontier Airlines, Air Canada, TAP Air Portugal, and Ryanair, the Flight Pass Canvas tool was developed. The results are presented in the Canvas framework, distinguishing between low-cost and fullservice models; and how these influence the Flight Pass aspect, suggesting that low-cost airlines use it as a tool to increase load factors, rather than legacy airlines using it to segment customers. The conclusions of Flight Pass are then subject of consultation with subject matter experts through a survey. Finally, the findings of the empirical study are used to design a Flight Pass for the 2030 World Cup. The findings suggest that unbundling the business is necessary to ensure its economic viability.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".