The effect of competency management on organizational performance through supply chain integration and quality
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it