Investigating the role of reverse supply chain performance and the government policy on the performance of the manufacturing firms
Bibliographic record
Abstract
The purpose of this study is to analyze the relationships between supply chain leadership and reverse supply chain performance, government policy and reverse supply chain performance, and finally, government policy and supply chain leadership. The research methodology is analytical, namely survey research that aims to collect, compile, analyze, interpret, and finally draw conclusions. The approach used is quantitative, which includes the development of an empirical model and its measurement based on theoretical studies. Research respondents are managers who are responsible to manage the reverse supply chain operations such as supply chain, warehouse, transportation/distribution, production, planning and control of production and inventory planning and control, procurement, and marketing in manufacturing companies. The research data was obtained by distributing online questionnaires to 560 supply chain managers of manufacturing companies who were determined using the simple random sampling method and the questionnaires were designed using a Likert scale. Data analysis used structural equation modeling (SEM) with SmartPLS 3.0 software tools. The stages of data analysis are validity test, reliability test and hypothesis testing. The results indicate that supply chain leadership had a significant effect on reverse supply chain performance, government policy had a significant effect on reverse supply chain performance and, finally, government policy had a significant effect on supply chain leadership.
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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 | 0.003 | 0.012 |
| 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) | 0.002 | 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".