Innovative and Sustainable Tourism Management: The Role of Local Wisdom in 'Jelajah Danau Ranau'
Bibliographic record
Abstract
This study aims to enhance sustainable tourism management in 'Jelajah Danau Ranau,' Lampung Barat, by examining the roles of stakeholder collaboration, entrepreneurial innovation, local community empowerment, and digital adaptation. It also investigates the moderating effect of local wisdom on these relationships. A mixed-method approach was employed, combining quantitative and qualitative data collection. Surveys were distributed to stakeholders, including local community members, tourists, and business owners, and analyzed using SmartPLS for structural equation modeling. In-depth interviews with key informants were analyzed using NVivo for thematic analysis. The results indicate that stakeholder collaboration, entrepreneurial innovation, local community empowerment, and digital adaptation significantly enhance sustainable tourism management. However, local wisdom did not significantly moderate these relationships, suggesting that its impact is context-dependent and varies based on integration with modern sustainable practices. This research contributes to the literature on sustainable tourism by integrating multiple factors and highlighting the importance of stakeholder collaboration, entrepreneurial innovation, local community empowerment, and digital adaptation. It offers valuable insights for policymakers and practitioners in developing culturally appropriate and environmentally sustainable tourism strategies. The study's novelty lies in its comprehensive approach, combining stakeholder collaboration, entrepreneurial innovation, local community empowerment, and digital adaptation with the analysis of local wisdom. This provides a holistic understanding of sustainable tourism management in a developing country context.
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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 | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".