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Record W3023000011 · doi:10.5267/j.msl.2020.4.016

Effect of discipline on employee performance through total quality management as mediation variables

2020· article· en· W3023000011 on OpenAlexvenueno aff
Stanley Ferdinandus

Bibliographic record

VenueManagement Science Letters · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicEmployee Performance and Management
Canadian institutionsnot available
Fundersnot available
KeywordsMediationTotal quality managementBusinessQuality (philosophy)Employee engagementOperations managementBusiness administrationPsychologyProcess managementMarketingManagementPolitical scienceEconomicsService (business)

Abstract

fetched live from OpenAlex

The purpose of this study was to analyze the effect of work discipline on the performance of Ambon State Polytechnic education staff and analyze the effect of work discipline on performance through Total Quality Management (TQM) in Ambon State Polytechnic education staff. Respondents in this study were 100 Ambon State Polytechnic education staff. The analytical model used to determine the effect between variables is a structural model with the Partial Least Square (PLS) approach. The results show that work discipline directly affected the performance of educational staff by 20.6% and the indirect effect of work discipline affected the performance of the education staff through Total Quality Management of 38.7% which means that the practice of TQM is able to provide a considerable influence in improving the performance of educational staff. To improve the performance of educational staff, work discipline should be directed at the TQM process in advance so that the work process is more directed.

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)0.008
metaresearch head score (Gemma)0.028
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 score0.008
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.028
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.000

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 head0.021
GPT teacher head0.328
Teacher spread0.307 · 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

Citations10
Published2020
Admission routes1
Has abstractyes

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