Segmentation of the Aruban Tourism Market: Classification of Visitors’ On-Island Activities
Notice bibliographique
Résumé
Travel and tourism offices in destinations throughout the world are keenly aware of the importance of attracting visitors to their locale. This is especially true in places where tourism is an important component of the local economy. Nowhere is tourism more important than for the island nations of the Caribbean, an area that has been called one of the most tourism dependent regions in the world. With approximately 1.22 million people visiting each year, the Caribbean island of Aruba “The World Tourism Council (WTTC) reports Aruba’s GDP is more reliant on travel and tourism than any other nation, relative to size, in the world”. In fact, Aruban tourism is the island’s main economic pillar contributing 88% of the nation’s GDP. Tourism supports not only direct commerce such as retail stores, hotels and restaurants, travel agents, transportation, etc., but also indirect commerce to support these industries including artisans, farming, manufacturing, etc.. Although the Aruba Tourism Authority’s website declares that “Aruba’s popularity has remained constant, due not only to sun, sand and sea, but also to other factors including the hospitality and friendliness of its people, safety, political stability, and various niches such as activities, nightlife, shopping, restaurants” there has been no academic research investigating what tourists do while visiting Aruba. Segmenting tourists according to the activities, nightlife, and shopping they have been involved in during their stay can be a valuable tool used by local governments and business owners in anticipating consumer demand and attracting potential tourists. Therefore, the main objective of this research was to determine if tourists can be segmented based on the activities they enjoy. A total of 503 tourists were sampled using an intercept data collection method at the Oranjestadt International Airport. Approximately 87% of the sample were from the U.S. with the remaining tourists coming from the Netherlands, the U.K., Spain, Italy, Canada and Brazil. Respondents included 187 females (37.2%) and 311 males (61.8%). Factor analysis was performed to determine if tourists could be segmented according to groups of activities in which they participated. Three distinct salient segments of tourists emerged and were labeled as: 1) Active Newlyweds, 2) Cultural Explorers, and 3) Social Entertainment Seekers. Active Tourists were those tourists who were more likely to have been married while on Aruba or honeymooning on the island and were interested in participating in more active sports such as wind-surfing, golf, land-sailing, horseback riding, etc. The Cultural Explorer group was composed of respondents who were more interested in vising Aruban historic or cultural sites or visiting festivals, art galleries, museums, etc. Lastly, the respondents in the Social Entertainment Seekers latent group wanted activities that had a social aspect to them such as dining out, going to casinos, meeting new people, and going out to enjoy the nightlife of the island. Using the information from this project can be used to more effectively target groups of tourists interested in visiting Aruba. This type of marketing tool can be especially useful for the smaller, yet tourism-dependent countries of the Caribbean with limited resources.
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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 | 0,000 | 0,000 |
| 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 | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 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 ».