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Record W3046296137 · doi:10.5267/j.msl.2020.7.039

The role of work satisfaction as a mediation leadership on employee performance

2020· article· en· W3046296137 on OpenAlexvenueno aff
A. Ratna Pudyaningsih, Joes Dwiharto

Bibliographic record

VenueManagement Science Letters · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEmployee Performance and Leadership
Canadian institutionsnot available
Fundersnot available
KeywordsMediationJob satisfactionEmployee engagementWork (physics)PsychologyEmployee researchEmployee moraleSocial psychologyBusinessPublic relationsOrganizational commitmentPolitical scienceEngineering

Abstract

fetched live from OpenAlex

Leadership is an important factor in providing direction to employees and it can improve employee performance, significantly. When a leader can grow the employees' confidence in carrying out their respective duties, he/she contributes to the performance of the organization. In addition to leadership, job satisfaction is also an important factor which influences on employee performance. To be able to maintain existing resources, companies are required to increase employee satisfaction, increase employee organizational commitment and provide job security for employees. This study aims to analyze the influence of leadership on job satisfaction, analyze the influence of leadership on employee performance, study the effect of job satisfaction on employee performance and analyze job satisfaction as a mediate leadership on employee performance based on path analysis. The result of the analysis shows that leadership influenced job satisfaction and employee performance while job satisfaction affects performance. Finally, the results indicate that job satisfaction mediated the effect of leadership on employee 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)0.008
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 score0.012
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.001

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 head0.221
Teacher spread0.183 · 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

Citations20
Published2020
Admission routes1
Has abstractyes

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