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Record W3158877092 · doi:10.31219/osf.io/gtdkm

The Impacts of Competence, Work Motivation, Job Satisfaction and Organizational Commitment on Lecturers’ Performance

2018· article· en· W3158877092 on OpenAlexaff
H. Mursalim Umar Gani

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicEmployee Performance and Management
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsOrganizational commitmentJob satisfactionCompetence (human resources)PsychologyWork motivationStructural equation modelingAffective events theorySocial psychologyJob performancePopulationApplied psychologyBusiness administrationJob attitudeWork (physics)BusinessMathematicsSociologyEngineeringStatistics

Abstract

fetched live from OpenAlex

This study aims to examine and analyze the effect of the competence, motivation and job satisfaction and organizational commitment of lecturer’s performance. Research was conducted in private Islamic university in Makassar. The population in this research is the foundation of the entire faculty as many as 1,264 people using Slovin formula samples were obtained 176 respondents. Data were analyzed using the Structural Equation Model using AMOS aid 18. The results prove that the competence and no significant negative effect on lecturers commitment. The other variables effect shows that between work motivation and job satisfaction positive and significant impact on organizational commitment. At the end found that the competence, motivation, job satisfaction and organizational commitment has a positive significant effect on the lecturer’s 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)0.006
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.016
GPT teacher head0.261
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

Citations4
Published2018
Admission routes1
Has abstractyes

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