Linking forward and reverse supply chain investments: The role of business uncertainty
Bibliographic record
Abstract
Abstract This paper explores managerial efforts in reverse supply chains (RSC), where the focus is on the capture and exploitation of used products and materials. The RSC can potentially reduce negative environmental impacts of extracting virgin raw materials and waste disposal. If so, investment in the reverse supply chain should not be made in isolation, but instead must be integrated with investments selected to improve the forward supply chain. After defining and operationalizing these constructs, a survey of plant managers was used to empirically assess the linkages between supply chain investments, organizational risk propensity (i.e., willingness to take risk) and business uncertainty. Reverse supply chain investment had two primary dimensions: reconditioning (i.e., high‐value recovery) and recycling and waste management (i.e., low‐ or no‐value recovery). Ongoing investment in the forward supply chain was significantly related to investment in recycling and waste management, but not to investment in reconditioning. Moreover, risk propensity was found to mediate the relationship between the external business uncertainty and investment in the forward and reverse supply chain.
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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 | 0.007 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".