Bibliographic record
Abstract
Airlines have been recently debated the management of some of their non-core divisions, such as the Frequent Flyer Program (FFP). A spinoff is a form of corporate contraction that many companies have recently chosen. Through a spinoff, both the parent company and the divested subsidiary can each focus on their own activity, which translates into a better performance of both entities. This paper studies the circumstances in which a spinoff is a good strategy to pursue, along with some important issues that must be considered when reaching agreements. Spinoffs are basically a "downsizing" of the parent firm; therefore, the smaller firm must be economically more viable by itself than as a part of its parent company. The motivation for analyzing this particular topic comes from a question of current interest: Under what circumstances is it advantageous for an airline to spin off its Frequent Flyer Program, or other divisions that are not related with the airline's operation? In this paper, an extensive literature review introduces the reader to the different forms of corporate contraction and their performance under different circumstances. Three cases related to the airline industry follow: the spinoffs of TripAdvisor from the web agency Expedia, of Air Canada's FFP Aeroplan, and of American Airline's distribution system Sabre. These three cases illustrate some of the key issues that must be carefully considered when spinning off a subsidiary. The paper concludes that spinoffs are a smart strategy when the focus of the spun off division is different from that of the parent company. However, to safeguard future business relationships, the two entities must negotiate detailed agreements that are robust enough to perform successfully in all foreseeable circumstances.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".