Managing DMOs through Storytelling: A Model Proposal for Network and Value Co-creation in Tourism
Bibliographic record
Abstract
The rise of new business models based on shared content and experience has required tourism destinations to adopt appropriate tools for the construction and promotion of their identity based on sociality, emotions, interaction and connectivity. The aim of this paper is to analyse actors, actions, processes and relations related to the adoption and development of storytelling practices in tourism destination management, analysing critical aspects linked to the generation of content and the narration of territories. As an attempt to understand the processes of innovation and value-creation underlying the development of storytelling in destination management (“destination telling”), the Service Dominant Logic, and the actor-network theory interpretative framework have been adopted. The study was conducted following the qualitative methodology of multiple case studies. In view of the interviews and the analyses conducted, Destination Telling preconditions, contents, managerial criteria and outcomes have been identified, in reference to each of the three stages (“planning”, “narration” and “assessment”) the process has to be split. Finally, managerial implications for an involving construction and sharing of stories to happen have been examined and discussed.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".