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Record W4379280418 · doi:10.5267/j.uscm.2023.4.007

Safety based dynamic uncertainty reduction to increase safety performance in aviation industry

2023· article· en· W4379280418 on OpenAlexvenueno aff
Asri Santosa, Suharnomo Suharnomo, Mirwan Surya Perdhana

Bibliographic record

VenueUncertain Supply Chain Management · 2023
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsnot available
Fundersnot available
KeywordsAviationAviation safetySafety cultureStructural equation modelingWork (physics)Occupational safety and healthComputer scienceRisk analysis (engineering)BusinessEngineeringManagementEconomicsMechanical engineering

Abstract

fetched live from OpenAlex

There is a lot of evidence regarding air flight accidents caused by human error, especially air traffic controllers (ATC). On the other hand, the principle of work safety through safety performance can help organizations reduce the number of work accidents and create zero accidents in the aviation industry. This research aims to analyze the effect of leadership on safety culture and safety performance by investigating the mediating role of Safety Based Dynamic Uncertainty Reduction (SDUR) as an integral aspect in safety research. A total of 214 respondents were involved in this research. The analysis technique used in this study is Partial Least Square-Structural Equation Modeling (PLS-SEM). The results showed significant effects of leadership on safety culture and safety performance. The mediating analysis also reveals the significant effects of SDUR in strengthening the impact of leadership to safety performance. As implications, SDUR can be considered as an effective strategy in improving Safety Performance in the workplace.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.393
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.002

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 head0.037
GPT teacher head0.391
Teacher spread0.354 · 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; both teacher heads agree on what is shown here.

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

Citations4
Published2023
Admission routes1
Has abstractyes

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