A Framework for Tourism Destination Marketing in Network Destination Structures
Bibliographic record
Abstract
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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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.032 | 0.004 |
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".