The Manufacturer’s Strategic Responses to the Imbalance of Power in Supply Chain
Bibliographic record
Abstract
The extant literature on power in supply chain has mainly focused on more powerful players that control and influence behaviors of the weaker actors. The weaker actors have been portrayed as passive targets that are dominated by more powerful players but them serving as key decision makers has been left largely unexamined. Taking weaker actors’ perspective, we find that the weaker manufacturers would use distinctive strategies to counteract the dominance of more powerful supply chain partners. Specifically, they often pursue an exploration strategy to countervail the power dominance of suppliers, and adopt an exploitation strategy to deal with more powerful customers. In dealing with both dominant suppliers and customers, the weaker manufacturers are prone to simultaneously pursue exploration and exploitation and hence become ambidextrous. We also find a positive and negative moderating role of competitive intensity in the relationships of the power with exploration and exploitation strategies, which indicates that the weaker manufacturers would strike a balance between exploration and exploitation in periods of high competitive rivalry. In addition, weaker manufacturers who pursue an exploration strategy often acquire tacit technological know-how from suppliers and their internal new product development unit, whereas those pursuing an exploitation strategy are more likely to learn explicit sales/marketing expertise from customers and their internal sales unit. Finally, we reveal that design- manufacturing integration is effective in improving operational and business performance, and customer integration is positively related to operational performance.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".