A best practice framework: the processes of repositioning destination brands for cities impacted by a natural disaster
Notice bibliographique
Résumé
This study develops an understanding of the processes involved in repositioning destination brands for cities impacted by natural disasters, by exploring the empirical experiences of destination brand leaders. This study frames a best practice approach to repositioning destination brands for cities impacted by natural disasters and proposes a conceptual model for testing. The research aims are achieved through an exploratory, qualitative analysis of three case studies: Brisbane (Australia) following the floods of 2010-2011, Christchurch (New Zealand) following the 2010-2011 earthquake, and St. John’s (Canada) following Hurricane Igor in 2010. These case studies meet key selection criteria: the city was impacted by a natural disaster, the natural disasters occurred across the three case studies within a six-month timeframe, and the natural disaster received international media attention. Qualitative data is collected through a review of government literature and through expert, in-depth interviews with nine senior destination brand leaders who had decision-making involvement in destination brand repositioning in the three case studies. This study finds that repositioning destination brands for cities impacted by a natural disaster involves a range of complex and dynamic processes that occur throughout the disaster management lifecycle, including the early stages of disaster response. Part of the complexity and dynamics of post-disaster destination branding relates to how the government and community disaster response contributes to the reformation of destination brand identity and image. A best practice approach to repositioning destination brands for cities impacted by natural disasters is framed as involving the processes and activities associated with the reformation of a destination’s image and identity. This includes processes and activities associated with leadership and communication, governance, investment, community and industry confidence, stakeholder engagement and strategic promotional messaging. This study adopts a global and multidisciplinary approach and brings the subject of destination branding outside of the conventional domains of tourism marketing and destination development, to advance the practice and study of destination branding. This study also demonstrates the multidisciplinary nature of post-disaster destination brand repositioning by drawing on concepts and theories from the fields of crisis communication, natural disaster management and recovery, place branding, brand positioning and tourism marketing. Linkages are developed between existing concepts and theories and the practical applications of destination brand repositioning processes and activities in a post-disaster context. Stemming from these linkages, this study frames a best-practice approach to destination brand repositioning for cities impacted by natural disasters and proposes a post-disaster destination brand repositioning model for testing. One of the most valuable outcomes of this study is in the documentation of historical accounts and perspectives of destination brand leaders, who led their communities through the challenges of natural disaster recovery. In all three cases, destination brand leaders worked closely with their communities to re-imagine their cities and achieve social and economic stability. As a result, this study highlights how destination brand repositioning can be leveraged as a form of place management and provides reference material for governments seeking guidance on how to reposition destination brands for cities impacted by natural disasters in the future.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,000 |
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 tête enseignante, 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 ».