The road to successful green destination branding
Bibliographic record
Abstract
One of the largest and most lucrative industries within tourism, the meetings industry, is experiencing an increasing trend towards clients demanding green practices. There are a few destinations world-wide which are trying to capitalize on this trend and brand their destination as green to differentiate and achieve a competitive advantage.\nDespite its increasing importance, the research conducted on green destination branding is limited. There is an evident gap in literature not covering the actual process, drivers, challenges, stakeholders, and the key success factors in a business tourism context. For these reasons this research study has provided in-depth information and therefore contributed to filling these gaps in literature.\nThis research conducted a multiple case study where a conceptual model was tested empirically on five best practice destinations in green destination branding, targeting business tourism. These were: Cape Town, Copenhagen, Portland, Vancouver and Melbourne. Green destination branding was found to be a highly complex procedure which relies on the collaboration of multiple stakeholders in order to succeed.\nThe main drivers found to why destinations wanted to rebrand as green was a way to differentiate, take social responsibility and be role models. The study found that both the government and destination marketing organizations (DMO) could take leadership of the branding process. When dealing with green branding it must be credible. This has great implications on the process as there is a necessity to ensure a green destination identity before communication of the brand, if not the destination could be accused of greenwash. As neither the DMO nor the government has enough power to control individual participants, this is a challenging task to secure. Despite this, the formation of partnerships, were found important as to secure consensus and commitment from powerful players. Involving the wider community was absolutely necessary as broad collaboration was needed and the provision of incentives and education was found to overcome lack of knowledge and commitment challenge. Another factor for success was found to get stakeholders engagement, and that they lived by example.
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How this classification was reachedexpand
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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; both teacher heads agree on what is shown here.
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".