Manufacturers’ tailored responses to powerful supply chain partners
Bibliographic record
Abstract
Purpose This study aims to investigate how relatively weaker manufacturers respond to the dominance of stronger suppliers and/or customers. The study also analyzes how the competitive intensity perceived by manufacturers moderates their responses to powerful chain partners. Design/methodology/approach Using hierarchical regression, data from 1,417 manufacturing companies sampled from the fifth and sixth versions of the International Manufacturing Strategy Survey were analyzed. Findings This study found that relatively weaker manufacturers often adopt exploration strategies to countervail the dominance of suppliers and adopt exploitation strategies to deal with more powerful customers. In dealing with both dominant suppliers and customers, relatively weaker manufacturers are prone to adopt exploration and exploitation strategies simultaneously and hence become ambidextrous. Furthermore, the link between dominance in supply chains and the exploration (exploitation) strategy is strengthened (weakened) as market competition perceived by manufacturers intensifies. Originality/value The contribution of this paper is multi-folds. First, this paper develops and test a novel theoretical model on how relatively weaker manufacturers create tailored strategies to defend their positions in the supply chain. Second, it integrates resource dependence theory and organizational learning theory to propose that relatively weaker manufacturers could use a unique configuration of exploration and exploitation strategies to counteract the dominance of their suppliers and customers. Third, it investigates supply chain power by considering the manufacturers’ upstream and downstream powerful partners together, rather than individually and fourth, it reveals that relationships linking supply chain power to manufacturers’ tailored strategies are contingent on competitive intensity.
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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 | 0.003 | 0.022 |
| 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 | 0.001 | 0.001 |
| 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".