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Record W2916007858 · doi:10.5267/j.msl.2019.2.007

Does a workload influence the performance of bank employees?

2019· article· en· W2916007858 on OpenAlexvenueno aff
Siswanto Siswanto, Achmad Sani Supriyanto, Ulfatun Ni’mah, Nur Asnawi, Ismail Suardi Wekke

Bibliographic record

VenueManagement Science Letters · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicEmployee Performance and Management
Canadian institutionsnot available
Fundersnot available
KeywordsWorkloadBusinessComputer scienceOperations managementProcess managementEconomicsOperating system

Abstract

fetched live from OpenAlex

A workload is one of contradictive variables, whether it influences the performance of employees or not. The purpose of this study is to analyze whether or not the workload influences the performance of bank employees. It employs quantitative approach and path analysis is employed to analyze the effect of workload on the performance of bank employees. The research samples include 74 bank employees around the City of Blitar, East Java, Indonesia. The result shows that a workload had a significant positive effect on the performance of bank employees. It also has greater influence on employee performance through motivation variables. The managerial implication of the current research is that the provision of workload which is in accordance with the competence and comfort of employees may improve their performance. In addition, employees who have higher education level are able to adjust more on the workload because they have higher-achievement motivation.

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)0.007
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.008
GPT teacher head0.254
Teacher spread0.246 · 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

Citations41
Published2019
Admission routes1
Has abstractyes

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