The role of joint collaboration planning actions in a demand‐driven supply chain
Bibliographic record
Abstract
Abstract Purpose – Drawing on the operations and information systems literature as well as concepts tied to buyer‐seller relationships, the objective of this exploratory research is to measure the influence of joint collaboration planning actions on the strength of relationships, interorganizational information systems (IOISs) use and firm flexibility. The path model proposed in this study also posits that joint collaboration planning actions and the strength of relationships positively affect IOISs use, which in turn positively affects firm flexibility. Design/methodology/approach – Empirical evidence is gathered through an electronic survey conducted with 53 suppliers in a single supply network in the telecommunications equipment industry. Findings – The present study demonstrates that joint collaboration planning actions positively and significantly impact the strength of relationships. The results also show that IOISs use mediates the impact of joint collaboration planning actions and of the strength of relationships on firm flexibility. Practical implications – This study contributes to managers' understanding of the critical role played by joint collaboration planning actions between partners and IOISs in a demand‐driven supply chain. Originality/value – This research is amongst the few that have examined the preparation or other activities that precede the actual collaboration between partners.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| 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".