Segmentation of the Aruban Tourism Market: Classification of Visitors’ On-Island Activities
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".