Tourists’ Satisfaction with a Destination: An Investigation on Visitors to Langkawi Island
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<p>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.</p>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it