The Use of Intelligence in Tourism Destination Management: An Emerging Role for DMOs
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Over time, the acronym DMO has evolved from a meaning centered on marketing (i.e. Destination Marketing Organization) to a meaning centered on management (i.e. Destination Management Organization). Expanding the role of DMOs to one of management implies a greater need to engage stakeholders both within the destination and external to the destination. We assert that this places the DMO in a fundamentally unique position of being a boundary spanner between the internal destination environment and the external competitive environment. This boundary‐spanning role requires higher capabilities in knowledge management. The successful DMO of the future will be an intelligent agent of the destination that is able to identify, engage and learn from disparate stakeholders both within and outside the destination. It must acquire, filter, analyze and prioritize data and information from various sources to create knowledge that can be used to fulfill its role in destination management. Our paper is conceptual in nature, advocating an organizing framework to help understand the DMO's role as an intelligent agent that acts as a boundary spanner between the destination and the external competitive environment generating and disseminating knowledge. Outside the destination, the DMO must gain knowledge about the competitive environment, opportunities and threats, and trends that will change the future competitive landscape. Within the destination, the DMO must use this knowledge to strategically assess the strengths and weaknesses of the destination, align the resources of stakeholders and develop adequate competencies to formulate a strategy that is both competitive and sustainable. We conclude with a set of prescriptions for destination managers seeking to create an intelligent DMO that maximizes their knowledge management capabilities. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.009 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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