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Record W4404599311 · doi:10.5267/j.uscm.2024.7.028

The mediating role of passenger safety in the relationship between safety procedures, ship crew competency, and operational performance

2024· article· en· W4404599311 on OpenAlexvenueno aff
Asep Suparman, Dinar Dewi Kania, Prasadja Ricardianto, Endri Endri, Neng Sri Komala, Sunit Agus Tri Cahyono, Chatarina Rusmiyati, E Kuntjorowati, Anung Trijoko Wasono

Bibliographic record

VenueUncertain Supply Chain Management · 2024
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Management
Canadian institutionsnot available
Fundersnot available
KeywordsCrewAeronauticsSample (material)Transport engineeringStructural equation modelingPort (circuit theory)BusinessOperations managementEngineeringComputer science

Abstract

fetched live from OpenAlex

The main problems found mainly in the ferry transportation in the port of Merak-Bakauhuni in the Province of Banten, Indonesia, is the high occupational accident due to safety procedures and low ship crew competency which finally influence the company's operational performance. The aim of this research was to know and analyze the influence of safety procedures and ship crew competency on passenger safety that has impacts on the company's operational performance of ferriage at the port. This research used Structural Equation Model – SmartPLS4 with a research sample of as many as 150 ship crew. The results of this research indicated that safety procedures and ship crew competency positively and significantly influence passenger safety. Besides, safety procedures, ship crew competency, and passenger safety positively and significantly influence operational performance. These results also confirmed that passenger safety could mediate the influence of safety procedures on operational performance and the influence of ship crew competency on operational performance.

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)0.002
metaresearch head score (Gemma)0.007
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 score0.016
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.359
Teacher spread0.317 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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