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Record W4379280398 · doi:10.5267/j.uscm.2023.3.023

Promoting tourism governance and supply chain management in the competitiveness of tourism sector

2023· article· en· W4379280398 on OpenAlexvenueno aff
Suparman Suparman, Muzakir Muzakir, Wahyuningsih Wahyuningsih, Patta Tope, Ponirin Ponirin

Bibliographic record

VenueUncertain Supply Chain Management · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicSMEs Development and Digital Marketing
Canadian institutionsnot available
Fundersnot available
KeywordsCompetitive advantageTourismBusinessSupply chain managementCorporate governanceSupply chainIndustrial organizationStructural equation modelingMarketingInformation technologyDestinationsComputer science

Abstract

fetched live from OpenAlex

The purpose of this study was to determine and analyze the effect of accessibility, tourism governance, and information technology on competitive advantage mediated by supply chain management at tourist destinations in Central Sulawesi Province. The research was conducted using a quantitative approach. A total of 205 respondents were used as a simple sampling technique. The data analysis technique used was Structural Equation Modeling (SEM) with Partial Least Square with the help of SmartPLS software. The results of this study indicate that accessibility, tourism governance, and information technology had positive effects on supply chain management and competitive advantage. Furthermore, supply chain management was able to mediate the effect of accessibility, tourism governance, and information technology on competitive advantage. This would practically imply that tourist attraction with easy access is more likely to increase the capability of supply chain management and competitive advantage.

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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.017
GPT teacher head0.259
Teacher spread0.241 · 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

Citations7
Published2023
Admission routes1
Has abstractyes

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