Critical evaluation of policies in supply chain performance: Quality assurance, continuous process improvement and environmental regulation and policies
Bibliographic record
Abstract
The primary objective of this study is to find the role of different policies on supply chain performance (SCP).To achieve this objective, quality assurance policies (QAP) and continuous process improvement (CPI) are selected as independent variables.Additionally, the moderating effects of environmental regulations and policy (ERP) are examined between various policies and SCP.ERP is taken as moderating variable because the role of ERP in logistics is crucial.Logistics transport has serious effect on environment due to the emission of CO2.Primary data are collected from supply chain companies of Indonesia.Three hundred questionnaires are used in this study and they are analyzed through statistical tests.Conclusion of the study shows that QAP and CPI had major role in SCP.Better implementation of QAP and CPI increase the SCP among Indonesian supply chain companies.Moreover, ERP is a moderating variable between the relationship of QAP and CPI and SCP.Therefore, Indonesian supply chain companies should enhance the policies related to quality assurance, process improvement and environmental policies to enhance SCP.. ,
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 | 0.028 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".