A Framework for Tourism Destination Marketing in Network Destination Structures
Notice bibliographique
Résumé
Tourism destinations are an essential part of the tourism system and are the place where most tourism consumption occurs. In order to achieve a competitive advantage in the market place, individual destinations need to market themselves and provide a service that fulfils the guests needs. This is complicated by the fact that destinations are made up of a multitude of heterogeneous actors that provide the complete tourism experience together. The management of the destination system is facilitated through networks, which provide the governance structure or framework for the destination to function. This thesis analyses the structure of these networks at the normative, strategic and operative management levels to determine the effect they have on the destination. \n \nThe major bodies of theory used in this thesis are the destination marketing and management literature, drawing heavily on the Swiss tourism and management perspectives, and network theory to examine relationships between actors within the destinations. The Swiss school of tourism management uses an integrated systems approach to tourism planning, applying managerial models to tourism firms and regions. These are complemented by the networks literature, which can be used to analyse the interaction between different components or actors within a given system. Network analysis provides a foundation on which the destination system can then be analysed. \n \nQualitative theory building research allowed for more accurate delineation of destination network types for both research and managerial purposes. The empirical research examined three case studies; Wanaka in New Zealand, Åre in Sweden, and St Moritz in Switzerland to determine how the networks affect the destination management. Interviews with relevant actors in each destination were used to collect data. Secondary documents provided further insight into the cases. Each case was analysed individually first and then they were compared across cases. \n \nThe findings show that different network structures can be found at the three levels of destination management. The thesis presents new insights into destination networks that take into account the relationships between actors within the destination at the normative, strategic and operative management levels. This provides the framework for destination marketing and other destination wide activities. These activities provide the basis for a sustainable competitive position for the destination. \n \nThis thesis contributes to the destination marketing literature in three ways. First, The thesis integrates the Swiss tourism and management literature with English literature to suggest a new framework for analysing destinations, based on three levels of management. Secondly, it operationalises this model in three international case studies and clearly differentiates between different types of destination networks, providing criteria for their analysis. Thirdly, the results of the research distinguish key success factors for operating in networks at the three different management levels. \n \nIn addition, the sources of influence for actors in these networks and success factors for operating at each of the three levels provide a resource for tourism managers to improve the marketing and management of their destination.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,004 | 0,009 |
| Communication savante | 0,010 | 0,012 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,004 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,032 | 0,004 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».