Effect of recruitment, selection and culture of organizations on state personnel performance
Bibliographic record
Abstract
This research was carried out in fourteen ministries and one national staff agency, which was the largest and most comprehensive study of government institutions that examined the importance of the recruitment, selection and organizational culture of employee performance which is still an important issue in developing countries.The purpose of this study was to analyze the effect of recruitment, selection and organizational culture on the performance of civil servants, especially at the National Personnel Agency and 14 Ministries.This research was conducted in the Democratic Republic of Timor Leste (RDTL) with an area of 15,410 KM 2 .The total population is 1,261,072 people.The study began in September 2018 until February 2019, using quantitative (positivism) methods.The study was conducted using proportional random sampling, so that from each work unit a total of 1000 target populations and 286 samples were obtained using a questionnaire.This research used structural equation modeling (SEM) method with partial least square (SEM-PLS) approach.The results of the study found that well-programed recruitment was not able to provide significant results either directly or through organizational culture, but recruitment could have a significant effect on job performance through mediation selection.Moreover, well-programmed recruitment backed by selection quality could improve employee performance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".