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Record W2806875727 · doi:10.1177/0047287518776805

Investigating Tourists’ Fun-Eliciting Process toward Tourism Destination Sites: An Application of Cognitive Appraisal Theory

2018· article· en· W2806875727 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Travel Research · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Behavior in Brand Consumption and Identification
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsExperiential learningTourismPsychologyMarketingValue (mathematics)CognitionCompetitor analysisDestinationsConsumer behaviourService (business)Consumption (sociology)AdvertisingSocial psychologyBusinessSociology

Abstract

fetched live from OpenAlex

Previous studies have shown that destinations must distinguish themselves from competitors and develop experiential offerings that deliver memorable value to consumers. More and more consumers want experiential service during their travel. Despite the gradual increase in research on experiential consumption in tourism, no consensus has yet emerged on what factors of experiential value lead to positive behavioral outcomes in consumer cognitive appraisals. This study used the cognitive appraisal theory (CAT) to investigate the determinants of consumer emotional responses, as well as how evoked emotions affect behavior in tourism. Study findings contribute to the existing body of literature on the ability of CAT to illustrate how the experiential value of “fun” influences on-the-spot behavior. This study also helps tourism destination marketers by providing a clear picture of how to elicit positive emotions among tourists for a tourism destination that leads to positive behavioral outcomes.

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.

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)0.004
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.848
Threshold uncertainty score0.450

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.143
GPT teacher head0.422
Teacher spread0.279 · 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