International Tourism and the Olympics: The Legacy Effect
Notice bibliographique
Résumé
INTRODUCTION This study examines international tourism as a legacy of hosting the Olympics. In this research, international tourism is measured by the number of international air passengers enplaning and deplaning each month at the international airport(s) serving the Olympic host city. Hypothetically, increases in tourism to the host city is composed of four major components: (1) increased tourism at the time of the Olympics as a direct result of tourists coming to watch the Olympics, (2) Olympic visitors returning for an additional visit, (3) Olympic tourists encouraging friends back home to visit the host city, and (4) visitors who are generated by the media coverage of the Olympics and the Olympic host city. The most important of these is the extensive media coverage (Preuss, 2004). The US is exposed to more media coverage of the Olympics than any other country (Short, 2004). The US comprised 20.9 percent of the viewers watching the opening ceremonies of the 2006 Olympics (ETOA, 2006). AC Neilson estimated that 40.7 million people watched the opening ceremonies for the London 2012 Olympics, an all-time high number of viewers. The 1996 Atlanta Olympics held the record previously (Collins, 2012). An overall audience of 219.4 million viewers for the games made the London Olympics the most watched event in American history (IOC, 2013). Although the US is the largest source of international visitors for many of the host cities at any time, a recent study shows that exposure in the US to the games does not produce sustained increases in international tourism from the US to the Olympic host city (Gruben, 2012). The cost of hosting the Olympics has dramatically escalated since the 1984 games in Los Angeles (Malfas, 2004), yet cities wanting to host the event form long lines years in advance to put their names in the pool of those to be considered as a host city. Although short term profit may be a motivating factor, Los Angeles was the first city in modern times to generate a profit from hosting the games (Holloway, 2006; Yongjian, 2008). Few Olympic host cities have shown a profit since the 1984 Los Angeles games. London, host site of the 2012 Olympics, spent 2.38 billion[pounds sterling] over an eight-year period to hold the games, yet generated revenue of only 2.41 billion[pounds sterling] over the same period (Owen, 2013). Regardless of the profitability, hosting the games is considered a prestigious honor for the host city. This paper examines the changes in international tourism at Olympic venues during the games as well as the time just prior to and after the event. Increasing international tourism is the largest economic justification for hosting the Olympics. Measuring the change in international tourism has also been one of the more difficult research issues related to the Olympics. The time series methodology used in this research will improve upon the estimation process by controlling for existing trends in international tourism to the host city. The methodology and data used will also improve on prior studies by controlling for the displacement effect. Another improvement on prior studies is the use of international tourism (measured by international air passengers) from all originations versus domestic visitors or visitors from one country to the Olympic site. This paper is organized as follows. First, a review of the literature pertaining to Olympic tourism will be presented. A description of the data will come next. A discussion of the methodology will follow. Fourth, the results for six Olympic host cities (Atlanta, London, Salt Lake City, Sydney, Turin, and Vancouver) will be presented. These six cities were selected based on data availability for the primary international airports serving the region. Finally, concluding remarks and implications of the study are discussed. LITERATURE REVIEW The Olympic bidding process is expensive and time consuming. …
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,012 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».