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Record W2460255110 · doi:10.1016/j.rtbm.2016.06.002

Revisiting the airline business model spectrum: The influence of post global financial crisis and airline mergers in the US (2011−2013)

2016· article· en· W2460255110 on OpenAlex
Darren A. Jean, Gui Lohmann

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueResearch in Transportation Business & Management · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicAviation Industry Analysis and Trends
Canadian institutionsAir Canada
Fundersnot available
KeywordsRevenueLow-cost carrierBusiness modelFinancial crisisBusinessDivergence (linguistics)Convergence (economics)Air transportFull serviceService (business)MarketingFinanceTelecommunicationsIndustrial organizationEconomicsComputer scienceCommerceEngineeringAeronauticsEconomic growth

Abstract

fetched live from OpenAlex

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 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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.599
Threshold uncertainty score0.511

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.044
GPT teacher head0.291
Teacher spread0.247 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it