The role of DMOs in sustainability transitions. A smart network approach
Bibliographic record
Abstract
Addressing sustainability transitions in tourism destinations requires a comprehensive understanding of path-dependance, lock-in factors, trigger events and path shaping processes. To initiate sustainability transitions in tourism destinations, it is crucial to analyze the roles of various governance actors, particularly destination management organisations (DMOs). While the role of DMOs is undisputable for sustainability in tourism, there is a need to pay more attention to their ability to harness knowledge and contribute to path shaping processes. Therefore, this study combines quantitative and qualitative methods, including importance-performance analysis, content analysis and social network analysis, to explore the role of DMOs as spatially embedded structures in sustainable transitions of destinations. The results show that in terms of lock-in factors in unsustainable development, DMOs face challenges related to data, management, and methodology issues. However, by triggering the sustainability transitions as leaders of stakeholders´ network, DMOs can break the historical development path and shift destinations to new paths. The study concludes that DMOs can adopt smart roles as data miners, hubs, and boundary spanners, managing knowledge distribution and leading sustainability transitions in tourism destinations. The findings provide contributions to the new roles of DMOs and link sustainability transitions to a smart network approach.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".