Factors affecting e-procurement division employee performance
Bibliographic record
Abstract
In facing business competition in the cement industry, PT Semen Baturaja (Persero) Tbk is making creative and innovative breakthroughs. Poor procurement planning, competence in the procurement of goods and services, hard skills and soft skills, ineffective coordination between divisions, low culture and work discipline, as well as ineffective education and training are the main causes of the ineffective implementation of the e-Procurement system in the company. Employee performance in the e-procurement division is a concern in this study. This study aims to determine the influence of competence, education, training, and employee placement partially and simultaneously on employee performance at PT. Semen Baturaja (Persero) Tbk. This study uses a quantitative approach with a confirmative survey method that is descriptive and uses verification. The population and sample of this study is all employees in e-procurement division, as it uses a census sampling technique, amounting to 105 respondents. The data is gathered using questionnaire, documentation, and observation method. Furthermore, the data is then processed using SPSS 24 application. The results of this study show that employee competence, education, training, and employee placement had positive effects on employee performance. The strategy to improve employee performance will be effective by first providing technical training to improve competence in the e-Procurement division, then rearranging the placement by paying more attention to the suitability of individual competencies, expertise, and abilities in carrying out the e-Procurement Standard Operating Procedure.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".