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Record W2944535965

Pondhopping: Changes in Airline Competition and Service Patterns on the North Atlantic

2018· article· en· W2944535965 on OpenAlexaboutno aff
Dennis Nickson, David Pitfield

Bibliographic record

VenueWestminsterResearch (University of Westminster) · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicAviation Industry Analysis and Trends
Canadian institutionsnot available
Fundersnot available
KeywordsConsolidation (business)AllianceCompetition (biology)BusinessDeregulationEconomyInternational tradeGeographyMarket economyEconomicsFinance
DOInot available

Abstract

fetched live from OpenAlex

This paper draws upon a range of research and data sources to examine recent developments in the North Atlantic air transport market. \n\nOver the last decade, major changes to the industry structure have taken place including mergers between United-Continental, Delta-Northwest, American-US Airways, British Airways-Iberia-bmi-Aer Lingus (IAG). The alliance between British Airways and American has gained anti-trust immunity and acquired the former services of US Airways while Delta has formed a new Joint Venture with Virgin Atlantic. Icelandair and Wow have developed budget hub operations between the two regions via Reykjavik and in the last two years, further disruption has been led by Norwegian’s low-cost transatlantic services, joined now by low-cost offerings from WestJet, Air Canada Rouge and IAG’s Level. The extent to which the network carriers are trying to tap a new market through these offerings versus merely frustrating the efforts of their upstart rivals is discussed. \n\nCapacity and frequency data from Innovata and OAG is used to examine the development of air services along with traffic data from ICAO, UK CAA and US DoT. It is shown that the alliances have curbed capacity, increased load factors and reduced the influence of secondary hubs in favour of the major cities and gateways. New non-stop routes have been facilitated by technological developments such as the 787 and 737Max.\n\nA case study is made of London and New York (the two largest markets) and the impact of airline consolidation through mergers and alliances on market concentration is assessed using the Herfindahl - Hirschman Index (HHI). This is applied to both frequency (flights/week) and capacity (seats/week). Capacity is shown to be more concentrated as the stronger players are able to utilise larger aircraft. Traffic and load factors on North Atlantic routes from these cities are also analysed in parallel. It is found that concentration in London has increased through the growing dominance of British Airways-American although tempered by the growth of Delta-Virgin Atlantic as a strong second force on the North Atlantic while United, which is one of the legacy operators through its acquisition of Pan Am’s London rights, has lost ground. The New York market demonstrates a different trend with the move of Continental from SkyTeam to the Star Alliance (and subsequent merger with United) creating a more even split between the three alliance groups on North Atlantic services from that city. The price advantage of connections through hubs over direct flights has often diminished since these all became controlled by the same three major airline and alliance groups.\n\nIt is concluded that the North Atlantic has become an oligopolistic market following recent industry consolidation but there are some incipient signs that this is being challenged by the new entrants with point to point services. Although there is a potential niche for direct transatlantic service from secondary airports and regional cities, it is not necessarily a large or low-cost one and the challenge will be whether the required combination of capacity, traffic volume and yields can be achieved to compete with the network carriers and the high frequencies from their reinvigorated hubs.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.070
GPT teacher head0.230
Teacher spread0.160 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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