Tourists’ Satisfaction with a Destination: An Investigation on Visitors to Langkawi Island
Bibliographic record
Abstract
This study attempts to investigate the antecedents of tourist satisfaction. The focus of the study is Langkawi Island, a well-known tourist destination in Malaysia. Questionnaires were distributed to 500 tourists in Langkawi Island. Descriptive statistic, factor analysis and multiple regressions were run on the 482 useable data. The results indicate that 295 (61.2%) of the respondents were repeat visitors and the remaining 187(38.8%) were first-timers. More than half (56.8%) of the respondents had high levels of satisfaction with the mean items score of 3.90 and above. When factor analysis was run, seven factors emerged from the 33 items used to measure the contructs. Apart from tourist expectations, perceived quality, destination image, cost and risks, and perceived value, a new variable known as social-security was identified as a predictor. Regression analysis revealed that destination image, tourist expectations, costs and risks, and social-security have positive and significant influence on tourist satisfaction. Social-security was found to be the most important predictor of tourist satisfaction, followed by tourist expectations, destination image, and costs and risks. The findings of this study could provide guidelines for tourism managers and destination operators to further develop better strategies to satisfy travellers to Langkawi.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".