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Record W3001369638 · doi:10.3968/11482

Examining the Influence Mechanism of Customer Perceived Food Authenticity and Loyalty in the Ethnic Restaurant: Cultural Identity as a Moderation

2019· article· en· W3001369638 on OpenAlexvenueno aff
Yafen Huang, Ying Tang, Chen Zhen

Bibliographic record

VenueCanadian social science · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCulinary Culture and Tourism
Canadian institutionsnot available
Fundersnot available
KeywordsModerationEthnic groupLoyaltyPerceptionStructural equation modelingSocial psychologyPsychologyAdvertisingTasteMarketingSociologyBusinessMathematics

Abstract

fetched live from OpenAlex

The paper examines the structural relationships of customers’ authenticity perception on satisfaction and loyalty in the ethnic restaurant, cultural identity as moderation. A survey was conducted and the structural equation modeling analysis method was adopted. The research displays the following results: the perception of authenticity has significant direct impact on satisfaction and loyalty. The perception of authenticity has indirect impact on loyalty through satisfaction as well. And the directly effect on destination is more significant than indirect effect. Cultural identity has moderation between perceived authenticity and satisfaction. In order to realize the sustainable development of the ethnic restaurants, this paper put forward some suggestions: grasp the dimension of customers’ perception of authenticity and improve the satisfaction and loyalty of ethnic restaurants; maintain the authenticity, realize the cross - local operation of ethnic restaurants; highlight the sense of cultural identity and strengthen the “taste of hometown” in ethnic restaurants.

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)0.003
metaresearch head score (Gemma)0.008
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 score0.988
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.000

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 head0.037
GPT teacher head0.262
Teacher spread0.225 · 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

Citations4
Published2019
Admission routes1
Has abstractyes

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