Entrepreneurship and Sustainability in Tourism: An Interpretative Model
Bibliographic record
Abstract
Especially in recent years, the attention to sustainability is even more felt in the tourism sector where the consequences of indiscriminate behavior in the exploitation of resources on the environment, on human beings and on their economic activities have become increasingly evident (Jaremen, Nawrocka, & Żemła, 2019). Tourism is often considered as a source of natural and cultural resources’ exploitation, but it also contributes to GHG emissions, being one of the main reasons that pushes the world population to move. On the other hand, tourism-related activities, when correctly designed, can be a strong source of sustainable development. Indeed, tourism products should be sustainable as they depend on local area resources: they are complex products which, on the one hand, should use local resources as a differentiation strategy, on the other hand, hey should factor in the needs of several territory’s stakeholders. Researchers and institutions have developed many tools to assess tourism environmental impacts focusing both on the local area as a whole or on a given product. For the tourism sector, social and environmental impacts, responses and indicators fall into five categories (Buckley, 2012): population, peace, prosperity, pollution and protection. Moreover, these tools and measures have not been able to increase sustainability of tourism products and the industry is not yet close to sustainability. In this chapter, we proposed an approach, built around Elkington’s three pillars model (1994), to assess sustainability (Lehtonen, 2004) of tourism products; we focus on products design processes to create a model that help entrepreneurs in assessing if their products are sustainable and where they are their main weaknesses. In order to show how such a simple model can be used to evaluate sustainable tourism initiatives and highlight their weaknesses we have used a multiple case studies approach and we have analyzed three different cases.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".