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Record W2168910041 · doi:10.5539/ass.v10n2p10

Choosing Ecotourism Destinations for Vacations: A Decision-Making Process

2013· article· en· W2168910041 on OpenAlexvenueno aff
Eugene E. Ezebilo

Bibliographic record

VenueAsian Social Science · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Aspects of Tourism Research
Canadian institutionsnot available
Fundersnot available
KeywordsEcotourismDestinationsProcess (computing)BusinessTourismTourist destinationsDecision-makingProcess managementMarketingComputer scienceGeography

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.937
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0060.001
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.029
GPT teacher head0.407
Teacher spread0.378 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2013
Admission routes1
Has abstractyes

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