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Record W2274969710 · doi:10.5539/ibr.v9n3p25

The Effect of Tourists’ National Culture on Perceived Performance of Restaurants in Petra, Jordan

2016· article· en· W2274969710 on OpenAlexvenueno aff
Ma’moun A. Habiballah, Jebril A. Alhelalat, Naseem Mohammad Twaissi

Bibliographic record

VenueInternational Business Research · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCustomer Service Quality and Loyalty
Canadian institutionsnot available
Fundersnot available
KeywordsAccommodationEntertainmentProduct (mathematics)BusinessSanitationService (business)MarketingQuality (philosophy)AdvertisingService qualityPerceptionTourismPsychologyGeographyPolitical scienceEngineering

Abstract

fetched live from OpenAlex

The primary aim of this research is to study the influence of customers’ national culture on Perceived Performance of Restaurant Services (PPRS) in Petra city. Restaurant service quality links to server behavior in relation to customers’ cultural background were researched thoroughly considering the attributes of servers’ accommodation, sanitation, product knowledge, entertainment, professionalism, and cordiality. The present research was carried out using a self-administrated questionnaire which was surveyed on 155 tourists from different nationalities. Results of data analyses applied in the current study support the impact of national culture on tourists’ PPRS. The culture influence was significantly direct on some of the service quality factors (entertainment, sanitation and product knowledge) and indirect on other factors (professionalism, accommodation and cordiality). A detailed explanation was provided in the present study to contribute in wider understanding of how national culture and its dimensions may shape the perceptions of food service quality.

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)0.002
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 score0.018
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.041
GPT teacher head0.346
Teacher spread0.305 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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