Supply chain management, electronic collaboration tools and organizational innovativeness
Bibliographic record
Abstract
The paper focuses on three critical but under-investigated issues for supply chain management: (i) the inherent dynamics of a multi-layered supply chain, (ii) the deployment, use and relative efficiency of e-collaboration tools within the supply chain, and (iii) the impact of these tools on the innovativeness of individual firms acting at different layers of the supply chain. The research design covers multiple layers of one supply chain and provides empirical evidence obtained from a multiplecase study and an electronic mail survey. Results point to the intrinsic relationships between supply chain structure and the deployment of e-collaboration tools. Further, results indicate that the level of perceived efficiency of e-collaboration tools is lower at the upstream end of the supply chain and that supply chain execution (SCE) e-collaboration tools are more efficient than the supply chain planning (SCP) tools. The overall findings also suggest that e-collaboration tools can improve supply chain members' ability to innovate in terms of processes and relationships but not yet in terms of products.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".