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Record W2133309282

Implications of host-guest interactions for tourists' travel behaviour and experiences

2010· article· en· W2133309282 on OpenAlexaff
Ming Su, Geoffrey Wall

Bibliographic record

VenueUniversity of Zagreb University Computing Centre (SRCE) · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Aspects of Tourism Research
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsTourismDestinationsBeijingHost (biology)ChinaPerceptionMarketingGeographyAdvertisingQuality (philosophy)Destination managementBusinessPsychologyEcology
DOInot available

Abstract

fetched live from OpenAlex

Implications of host-guest interactions for tourists' travel behaviour and experiencesTh rough their multiple roles in tourism, residents of destination communities interact with tourists at destinations.Th e consequences of these host-guest interactions are bi-directional.Much research has been done on impacts of host-guest interactions on the local community.On the other hand, few studies have evaluated how tourists' on-site behaviour and experiences are aff ected by such interactions.Such information could enhance tourism planning and management.Th us, this paper explores tourists' perceptions and opinions on host-guest interactions and the impacts of such interactions on their experiences.Th rough a survey of domestic travelers residing in Beijing China conducted in 2008, tourists' opinions on the infl uence of destination community members on their previous domestic travel behaviour and experiences were obtained.Most respondents acknowledged that interactions with local people infl uence their assessments of the destination, the quality of their experiences, future destination choice and on-site expenditures, particularly those with higher educations and of a younger age.Th e importance of impacts of host-guest interactions on tourists' travel behaviour and experiences, and their evaluations of a destination are confi rmed.Practical implications are suggested for the planning and management of tourism destinations.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0010.000
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 head0.016
GPT teacher head0.276
Teacher spread0.261 · 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

Citations30
Published2010
Admission routes1
Has abstractyes

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