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Record W2137913452 · doi:10.5539/ijms.v4n6p10

The Relationships among Tourist Novelty, Familiarity, Satisfaction, and Destination Loyalty: Beyond the Novelty-familiarity Continuum

2012· article· en· W2137913452 on OpenAlexvenueno aff
Masaki Toyama, Yuichi Yamada

Bibliographic record

VenueInternational Journal of Marketing Studies · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Aspects of Tourism Research
Canadian institutionsnot available
Fundersnot available
KeywordsNoveltyLoyaltyTourismStructural equation modelingPerceptionPsychologyCognitionMarketingBusinessSocial psychologyComputer sciencePolitical science

Abstract

fetched live from OpenAlex

Novelty and familiarity play an important role in tourists’ perceptions, and these have been regarded as contrasting concepts for a long time. However, recent cognitive science literature suggests that novelty and familiarity are separate concepts, independently influencing consumer behavior. Based on this, our study aims to examine the differences between the effects of novelty and familiarity on satisfaction and destination loyalty. These relationships are explored by analyzing the responses of tourists in Takayama City, Japan. The research model was tested using structural equation modeling (SEM) techniques. The results show that both novelty and familiarity contribute to destination loyalty; however, only novelty has an effect on the formation of satisfaction. Theoretical and managerial implications of these are also discussed.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.058
GPT teacher head0.367
Teacher spread0.309 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations99
Published2012
Admission routes1
Has abstractyes

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