Rural Tourism Sustainable Management and Destination Marketing Efforts: Key Factors from Communities’ Perspective
Bibliographic record
Abstract
Rural tourism is seen as a potential sector in promoting country to the world and at the same time generates incomes to local communities. However, due to the lucrative economic benefits, tourism destination’s sustainability and quality of services is often being ignored. Thus, this study highlights the importance of sustainable management and destination marketing efforts in rural tourism destinations with identified significant contributively factors from local communities’ perspective. A total of 168 respondents comprising of local communities from Kampung Telaga Air and Kampung Semadang, Kuching, Sarawak took part voluntarily in this study. To assess the developed model, SmartPLS 2.0 (M3) is applied based on path modelling and bootstrapping. Interestingly, the findings revealed that local communities believed factors like climate change, carrying capacity of a destination, and environmental education are significantly affect both tourism destination sustainable management and destination marketing efforts. Furthermore, community support is also found to be important too for tourism destination marketing efforts. Surprisingly, community support was found no relations with destination sustainable management from local communities’ point of view. This study further discussed on the implications of the findings, limitations, and direction for future research.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".