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Record W3046226949 · doi:10.54297/sjeb.vol1.iss1.126

PENGARUH KUALITAS LAYANAN TERHADAP KEPUASAN PELANGGAN DEPOT ISI ULANG AIR GALON ANDERE

2020· article· en· W3046226949 on OpenAlexaff
Sahyunu, Surlan, Safuruddin

Bibliographic record

VenueSultra Journal of Economic and Business · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Optimization Techniques
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsGallon (US)Customer satisfactionService qualityService (business)Operations managementData collectionAgency (philosophy)Agricultural scienceBusinessStatisticsMarketingEngineeringEnvironmental scienceMathematicsWaste managementSociology

Abstract

fetched live from OpenAlex

This study aims to determine the service quality of the Andere Gallon Water Refill Depot, Angata Sub-district, South Konawe Regency to customer satisfaction. The type of this study is a quantitative descriptive. Data collection techniques carried out through literature study and field research in the form of observations, questionnaires, agency data, and documentation. The number of samples used 100 people chosen at random. The analytical method used Linear Regression method. The results of this study showed that the service quality indicator at the Andere gallon water refill depot, Angata Sub-district, South Konawe Regency was good because of high percentage, especially the responsiveness indicator (responsiveness). Customer satisfication at the Andere gallon water refill depot, Angata Sub-district South Konawe Regency was good because the quality of service was higher than customer satisfication which means customers of the Andere water refill gallon, Angata Sub-district of South Konawe regency was very satisfied about the service. Effect of service quality to customer satisfaction, from the regression model the result was obtained significant.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.325
Threshold uncertainty score0.714

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.020
GPT teacher head0.218
Teacher spread0.197 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations0
Published2020
Admission routes1
Has abstractyes

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