MétaCan
Menu
Back to cohort
Record W3043470214 · doi:10.1080/08865655.2020.1792799

The Relevance of the Souvenirs, Food, Experiences and Facilities of a Bordered Destination on the Key Relationship of Perceived Value, Attitudes and Satisfaction

2020· article· en· W3043470214 on OpenAlexvenueno aff
Francisco Orgaz Aguëra, Salvador Moral Cuadra

Bibliographic record

VenueJournal of Borderlands Studies · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Aspects of Tourism Research
Canadian institutionsnot available
Fundersnot available
KeywordsTourismStructural equation modelingValue (mathematics)Socioeconomic statusMarketingSustainable developmentDestinationsBusinessQuestionnaireSample (material)Sustainable tourismGeographyPsychologySociologyPolitical science

Abstract

fetched live from OpenAlex

Borders are geographic areas with great potential for the development of tourism activity, and tourism can contribute to socioeconomic development and the conservation of resources, both cultural and natural. The study addresses the influence of aspects such as food, souvenirs, experiences and facilities on the perceived value, attitudes and satisfaction of visitors towards a border destination. A questionnaire was administered to a sample of 583 tourists visiting the northern border of the Dominican Republic and the Republic of Haiti. This geographical area being the main point of flow of visitors between both countries. Using variance-based structural equation modeling based on the partial least squares method, the food, experiences at the destination, and facilities of a border destination have a positive influence on their perceived value. It has also been verified a positive influence of tourist attitudes on the perceived value and satisfaction. Results are very useful for local stakeholders, for the improvement of elements such as souvenirs sale, which can increase tourist satisfaction and contribute to the sustainable development of the region, the creation of stores and/or local businesses.

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)0.001
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.166
Threshold uncertainty score0.935

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.071
GPT teacher head0.342
Teacher spread0.271 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations5
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Borderlands StudiesSame topicDiverse Aspects of Tourism ResearchFrench-language works237,207