The effect of competency management on organizational performance through supply chain integration and quality
Bibliographic record
Abstract
Synergy is built by manufacturing companies with suppliers and customers in the supply chain to improve organization performance. The research provides simultaneous testing of competency management, supply chain integration, supply chain quality, operational capability as a strategy to improve company performance. Collecting data for medium and large manufacturing companies in Indonesia are performed by sending a questionnaire link via email and WhatsApp. 625 respondents received the questionnaires and 152 respondents filled them with a response rate of 24.32%. Data analysis were performed using partial least squares to test the hypotheses and found that competency management had a direct impact on supply chain integration (0.598), supply chain quality (0.387) and operational capability (0.346). Supply chain integration affects increasing supply chain quality (0.428), operational capability (0.619) and organizational performance (0.255). Supply chain quality impacts increasing operational capability (0.260) and does not significantly affect organizational performance (0.018). The operational capability of a manufacturing company has an impact on improving organizational performance (0.584). Practical contribution is that managers who manage the supply chain must continue to enhance skills and knowledge and supply chain components in quality for increased 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 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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".