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Enregistrement W3039631324 · doi:10.46783/smart-scm/2020-1-9

Trends and Prospects of the Development of the Global and National Air Transport Markets

2020· article· en· W3039631324 sur OpenAlexaboutno aff
Sergiy Lytvynenko, Iryna Panasiuk

Notice bibliographique

RevueElectronic Scientific Journal Intellectualization of Logistics and Supply Chain Management #1 2020 · 2020
Typearticle
Langueen
DomaineEconomics, Econometrics and Finance
ThématiqueAviation Industry Analysis and Trends
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésAir transportAviationInternationalizationGlobalizationMultinational corporationTourismBusinessWorld economyQuarter (Canadian coin)Open skiesInternational tradeEconomyIndustrial organizationMarket economyEconomicsFinancePolitical scienceGeographyEngineeringTransport engineering

Résumé

récupéré en direct d'OpenAlex

The article identified the preconditions for the development of the world economy which included the processes of globalization and internationalization. The role of air transport in the world economy is emphasized because its weight grows every year, because it provides the development of tourism, international programs and cooperation between individual regions of the world. There has been an increase in the number of multinational corporations, increasing the mobility of business passengers what is very important for the airlines. The critical analysis of scientific publications made it possible to state that in Ukraine and abroad the development of the air transportation market was studied by many scientists and their contribution to solving a number of scientific problems related to identifying prospects and opportunities for international and national air transportation markets as well as with the optimization of air carriers is important. However, it was found that insufficient research on recent trends and prospects for the development of global and national air transport markets is in the face of new challenges including the outbreak of coronavirus infection COVID-19. It was found that air passenger traffic in the global market has grown steadily in recent years by 7-8% every year and a quarter of sales depend on the regularity of air transportation of which 70% of this type of business determine the vectors of market expansion. The analysis of aviation accidents revealed that they have a very significant impact on the performance of the carrier whose aircraft suffered them. This applies to both reputational and purely financial losses. The problems with the Boeing 737 Max type also became a serious challenge for the aviation industry due to a number of incorrect design decisions of the world’s leading aviation concern and attempts to save on the training of pilots of this type of aircraft. It is noted that modern passengers are trying to minimize the time spent on travel, the trend of fragmentation of the holiday period is growing rapidly. As a result of the analysis of the domestic air transportation market, it was revealed that during 2019 there was an expansion of the market in general, as well as the activities of foreign airlines, travel from Ukraine to Europe and other parts of the world increased rapidly. A radical change in market trends was observed in mid-March 2020, when due to the spread of the COVID-19 coronavirus in the world, quarantine was introduced and regular flights were stopped. The national air carrier Ukraine International Airlines has declared a two-stage period of resumption of work after the end of quarantine. The authors found signs of hybridization of the airline’s business models at the first stage of the restart and the transition to the airline’s business model which has features of both point-to-point models and obvious features of the model of low-cost carriers. Whereas in the second stage the transition to a partial network model with low-cost models is most likely followed by the emergence of a new already stable hybrid business model based on network principles but with stable features of a low-cost airline.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,674
Score d'incertitude au seuil0,290

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,028
Tête enseignante GPT0,229
Écart entre enseignants0,201 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2020
Routes d'admission1
Résumé présentoui

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