The Influence Path of Industry Collaboration Network and Policy Support on the Optimization of Sugarcane Bagasse Packaging Value Chain: An Empirical Study Based on Structural Equation Modeling
Bibliographic record
Abstract
The rapid transition toward sustainable packaging highlights the need to understand how external enablers drive value chain optimization (VCO) in emerging green industries.This study systematically examined the influence paths of industry collaboration networks (ICN) and policy support (PS) on the optimization of the sugarcane bagasse packaging value chain, focusing on the mediating roles of technology integration capability (TIC) and green innovation (GI), and the moderating effect of environmental responsiveness.Drawing on Resource-Based View (RBV), Collaborative Network Theory, and Institutional Theory, primary data were collected from 463 industry participants and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM).Results indicated that ICN significantly enhance TIC (β = 0.593, p < 0.001) and GI (β = 0.256, p < 0.001), while PS more strongly promotes GI (β = 0.464, p < 0.001).Both technology integration (β = 0.338, p < 0.001) and GI (β = 0.416, p < 0.001) positively affect value chain optimization.Environmental responsiveness (ER) significantly moderated these relationships (β = 0.124 and 0.104, both p < 0.05), and mediation analyses confirmed both internal capabilities as key pathways.These findings clarified the mechanisms by which external collaboration and policy support optimize value chains through strengthening internal capabilities, with ER amplifying these effects.This research provided robust empirical evidence and actionable insights for advancing sustainable transformation in the agricultural by-product packaging sector.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".