Supply chain management, supply chain flexibility and firm performance: an empirical investigation of agriculture companies in Indonesia
Bibliographic record
Abstract
The purpose of this research is to better understand the impact of supply chain management (SCM) and flexibility on firm performance, as well as the role of competitive advantage in mediating the model in Indonesian agriculture companies. Companies must apply supply chain management and supply chain flexibility (SCF) to boost industrial competitiveness, which impacts firm performance. To ensure that supply chain management supports the company's strategy, companies must evaluate supply chain concerns. From the literature search, researchers have not found any published studies or articles on SCM and SCF in their influence on firm performance through competitive advantage, specifically for corn companies in Indonesia. The population in this study includes agriculture companies in Indonesia. Sampling was carried out using probability sampling technique, the total population of 200 obtained a sample size of 133.333 which can be rounded up to 134 research samples. The inferential statistical method used in the data analysis of this study was the Partial Least Square Version 3 program. The study found that SCM influenced firm performance and SCF had a direct influence on firm performance. However, competitive advantage variable failed in being a mediator in SCM and SCF on firm 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".