Enterprise risk management and supply chain effectiveness: Evidence in the Indonesian electricity project
Bibliographic record
Abstract
The research aimed to know the influence of long-term relationships, information sharing, Cooperation, and integration process in partial on the supply chain effectiveness of the EPC Steam Power Plant project in the Province of North Sulawesi. It was also to know whether enterprise risk management moderates the influence, long-term relationship, information sharing, Cooperation, and integration process toward supply chain effectiveness. The employees who became the sample in the supply chain activities of the steam power plant project in North Sulawesi were 250 people, 149 of whom were proportionally from the project owner. The research uses the data analysis technique using Structural Equation Modelling-Partial Least Square. The result of the study indicated that long-term relationships, information sharing, Cooperation, and integration process partially have a positive and significant influence on supply chain effectiveness. In addition, enterprise risk management proved to moderate the impact of information sharing. Still, it needed to moderate the effect of a long-term relationship, Cooperation, and integration process on the supply chain effectiveness of the EPC Steam Power Plant Sulut-3 project.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".