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Record W4385992437 · doi:10.5267/j.uscm.2023.6.008

Towards recreational travel modelling in non-western countries: An empirical study using a structural equation modeling approach

2023· article· en· W4385992437 on OpenAlexvenueno aff
Nidal Alzboun, Nour Al Okaily, Hamzah Khawaldah, Ayman Harb, Muhammad Turki Alshurideh

Bibliographic record

VenueUncertain Supply Chain Management · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Aspects of Tourism Research
Canadian institutionsnot available
Fundersnot available
KeywordsStructural equation modelingRecreationTourismConfirmatory factor analysisLoyaltyMarketingBridge (graph theory)Perspective (graphical)PsychologyEmpirical researchVariety (cybernetics)AdvertisingSocial psychologyBusinessComputer scienceGeographyPolitical science

Abstract

fetched live from OpenAlex

Recreational tourism as a niche market has prompted researchers to take a variety of approaches to investigate the elements that influence travelers’ decisions to engage in leisure activities. The reasons why Muslim visitors select leisure-based travel are commonly discussed in the tourism literature, although little research has been done in this area. By analyzing the significance of travel motivation, emotional response, and satisfaction in determining attitudinal loyalty for Muslim leisure travelers, this study aims to bridge this knowledge gap. This study employed Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM) using AMOS version 23 to answer the research questions. According to the findings, emotional experiences and extrinsic motives have a good direct impact on satisfaction and an indirect impact on the inclination to return. Having a deeper understanding of leisure-based travel from the perspective of Muslim tourists will benefit recreation managers.

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)0.003
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.120
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.152
GPT teacher head0.399
Teacher spread0.248 · 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 designSimulation or modeling
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

Citations3
Published2023
Admission routes1
Has abstractyes

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