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Record W4285185653 · doi:10.5267/j.uscm.2022.3.010

Impact of loading and unloading productivity on service user satisfaction

2022· article· en· W4285185653 on OpenAlexvenueno aff
Prasadja Ricardianto, Esterlinus Edwin Lermatan, Muhammad Thamrin, Edi Abdurachman, Heri Subagyo, Antoni Arif Priadi, Tri Iriani Eka Wahyuni, Rosliawati Achyani Kosman, Endri Endri

Bibliographic record

VenueUncertain Supply Chain Management · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and Coastal Ecosystems
Canadian institutionsnot available
Fundersnot available
KeywordsPort (circuit theory)ProductivityService (business)BusinessWork (physics)Operations managementMarketingEngineering

Abstract

fetched live from OpenAlex

This study is aimed at analyzing the effect of port service performance, occupational safety, and health, and work safety on loading and unloading productivity and its impact on service user satisfaction at Yos Sudarso Tual Port, Maluku province, in Eastern Indonesia in 2020. What was found was the limited-service performance delivered to service users, resulting in dissatisfaction. This could have implications for the less-than-optimal loading and unloading performance at Yos Sudarso Tual Port. The study uses a quantitative method, with a path analysis model, with a total of 40 samples. Research respondents are users of loading and unloading services. The findings, in general, indicate that there is an effect of port service performance, occupational safety, and health and work security on loading and unloading productivity which in turn has an impact on increasing user satisfaction of Yos Sudarso Tual Port services. The key finding is that new investments are needed which will require the ongoing capacity building and development of several port authorities who are civil servants who will oversee port planning and operations and regulate access to key port services and facilities.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.140
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.010
GPT teacher head0.229
Teacher spread0.219 · 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.

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

Citations6
Published2022
Admission routes1
Has abstractyes

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