Segmenting the Market of First-Time Visitors to an Island Destination
Bibliographic record
Abstract
Purpose: The main purpose of this study is to segment the market of first-time visitors based on the activities travelers engage in while at a destination. The various segments are then profiled by demographics, socio-economic variables, and trip-related characteristics.Design/methodology/approach: This study is based on 1,104 exit surveys completed by first-time visitors to the Canadian province of Prince Edward Island, a major island tourist destination. Clustering analysis is used to develop the segments. In addition, analysis of variance (ANOVA), multivariate analysis of variance (MANOVA), discriminant analysis, and Chi-Square analyses are completed.Findings: The results indicate that there are three distinct segments of first-time visitors based on travel activities: “culture-oriented” (26% of the market), “active” (37%), and “casual” (37%). The key differences among the three segments are illustrated using demographics, socio-economic variables, trip-related characteristics, and spending patterns. Based on these results, it is clear that the three segments are sustainable and profitable.Practical implications: Segmenting markets for products or services, in any industry, is vital to gain a better understanding of the customer, and to better allocate scarce tourism resources to product development, marketing, service, and delivery. Therefore, all tourism industry stakeholders must be aware of the market segments that are currently visiting the destination.Originality/value: Tourist segments based on activities are not absolutes; they should be viewed on a continuum. The majority of first-time visitors to a destination engage in a variety of travel activities. The continuum of highlighted activities across the segments is from more to less involved. Successful tourism destinations are those that meet the various activity needs of their segments in both their marketing and “on the ground.”
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".