Exploring the Potential of Digital Platform-Based Tourism Markets Towards International Tourism Markets to Realize the Green Economy Concept
Bibliographic record
Abstract
The tourism industry is a rapidly growing economic sector, especially in the digital era, where digital platforms play a significant role in expanding market reach.This study aims to explore the potential of digital platform-based tourism markets in increasing international tourism and promoting the concept of green economy in Central Buton and South Buton, Indonesia.This study uses a qualitative approach with triangulation analysis and SWOT analysis.Data were collected through observation, document analysis, surveys, and interviews with 63 respondents from Central Buton and South Buton.Triangulation content analysis was conducted to verify the validity of the data and identify patterns and relationships between relevant variables.The findings of the study indicate that digital platforms play a significant role in expanding the tourism market by increasing accessibility, promotion effectiveness, and service efficiency.Digitalization also supports economic sustainability by creating local jobs, optimizing tourism revenues, and encouraging environmentally friendly tourism practices.However, there are challenges in the form of limited infrastructure, low digital literacy among local stakeholders, and lack of regulatory support.To address these challenges, this study recommends strategic interventions, such as enhancing digital literacy programs, investing in tourism infrastructure, and developing policies that integrate digital marketing with sustainable tourism principles.By effectively utilizing digital platforms, the tourism industry in Central Buton and South Buton can improve its global competitiveness while supporting sustainable development goals.This study is limited to the Central Buton and South Buton regions, so the results may not be fully applicable to other tourism destinations in Indonesia.Future research can expand the scope by examining the implementation of digital strategies in other tourism destinations that have similar challenges and further explore the impact of green economy policies on tourism management.
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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 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".