Gender and Age Differences in Choice of Holiday Destination: Case of Langkawi, Malaysia
Bibliographic record
Abstract
While gender and age are considered as important demographic factors in tourism segmentation, lack of attention has been given by tourism researchers. Moreover, gender and age analysis within tourism studies are still limited, particularly in the context of choice of destination. The aim of this paper is to examine the role of gender and age in determining the destination choice. Langkawi has been chosen as a location for the study due to its popularity among the local and international tourist. Survey questionnaire is used as a tool for data collection. A total of 529 Langkawi holidaymakers participated in the study. T-test and ANOVA has been employed to analyse the data. The findings indicate that gender and age both influence Langkawi being chosen as a holiday destination. Male and female consumers place different emphasis on the selection of Langkawi as a destination of choice. These findings suggest that tourism advertisers and destination promoters need to be aware of different needs and wants of both males and females. However, tourists of different ages evaluate Langkawi similarly, which marketers can use a variety of promotion packages for all age group. The study's practical implications and limitations are discussed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".