A network perspective on managing stakeholders for sustainable urban tourism
Bibliographic record
Abstract
Purpose This study aims to examine the current network of inter‐relationships of stakeholders representing government, the community and the tourism and hospitality industry, and their perceptions of critical stakeholders in destination development. Design/methodology/approach While the network analysis enabled examination of the interconnectedness of stakeholders, the stakeholder approach identified the critical stakeholders in destination development. These two approaches helped determine how the existing relationship structures of destination stakeholders might influence sustainable destination development. Findings The destination marketing/management organizations (DMOs) and stakeholders with access to or possession of critical resources have the highest centrality in urban destinations. In all three clusters, local government and DMOs are perceived to hold the greatest legitimacy and power over others in destination development. It is also found that there is a lack of “bridges” between the three clusters of industry, government and the community. Research limitations/implications The study demonstrates the use of a network analysis methodology as a potential tool for researchers and managers in examining destination stakeholder relationships. Practical implications DMOs, hotels and attractions stakeholders have the most crucial roles in achieving inter‐stakeholder collaboration for sustainable destination development, particularly because the many and diverse industry actors trust or depend on them. Originality/value There are very few studies that have applied both network and stakeholder perspectives to destinations to examine the structure of inter‐stakeholder relationships and the potential influence of this relational structure on sustainable destination development.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".