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Record W4385971252 · doi:10.5267/j.uscm.2023.8.003

Halal supply chain and halal tourism industry in forming economic growth

2023· article· en· W4385971252 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueUncertain Supply Chain Management · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicHalal products and consumer behavior
Canadian institutionsnot available
Fundersnot available
KeywordsNonprobability samplingTourismBusinessStructural equation modelingSupply chainMarketingAffect (linguistics)Chain (unit)Geography

Abstract

fetched live from OpenAlex

The purpose of this research is to investigate the influence of the halal supply chain on the halal tourism industry and economic growth. The idea was tested using quantitative methodology in this study. The research instrument was a questionnaire filled out by the respondents. The participants in this study were MSME actors in the tourism industry in Banten Province, Indonesia. For this study, 113 participants were selected using purposive sampling, and data were analyzed using a structural equation model (SEM). The findings from this study are that the halal supply chain has a positive and statistically significant effect on the halal tourism industry. In addition, the halal supply chain and the halal tourism industry affect economic growth. These findings contribute as additional literature for further research that the halal supply chain is an essential factor for the halal tourism industry in Indonesia.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.455
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.023
GPT teacher head0.285
Teacher spread0.261 · 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