Choosing Ecotourism Destinations for Vacations: A Decision-Making Process
Bibliographic record
Abstract
Although ecotourism is fast growing industry information on travels to different ecotourism destinations are often not easily accessible. This paper reports reviews of literature on eco-tourists behaviour regarding choice of destination for ecotourism and factors influencing the choice. The importance of information in marketing of ecotourism and eco-tourists’ satisfaction are discussed. The eco-tourists who are visiting a destination for the first time go through all stages in the decision-making process and extensive information search before choosing the destination to visit. Eco-tourists who have visited the destination in the past go through only some of the stages and limited information search. Eco-tourists’ choice of an ecotourism destination are influenced by factors such as, the family, friends, societal values, preferences, safety and promotions related to the destination. Decision regarding re-visiting an ecotourism destination depends on the level of satisfaction that the eco-tourist experienced during his or her first time visit to the destination. Eco-tourists who are satisfied with the ecotourism destination during their first time visit are likely to re-visit the destination but those who are not satisfied are not likely to re-visit. For ecotourism managers to sustain the inflow of eco-tourists to different ecotourism destinations and revenue in the ecotourism industry it is important for the managers to strive towards meeting expectations of eco-tourists and make information regarding the destinations more accessible.
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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 | 0.016 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".