A network perspective on managing stakeholders for sustainable urban tourism
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it