Toward a structural view of co‐opetition in supply networks
Bibliographic record
Abstract
Abstract Co‐opetition, or simultaneous competition and cooperation, in the supply chain management literature has been treated as a dyadic relational phenomenon where the buyer's strategy is considered to be the primary driver. In this paper, we move beyond the dyadic view and propose a theory of co‐opetition in supply networks. We argue that as firms within a supply network interact over time to access, share, and transform resources, new ties between firms are formed and existing ties dissolve, giving rise to co‐opetition dynamics at the network level. Taking a configurational approach, we employ the inter‐related dimensions of ties between firm, firm‐level task, network‐level objective, and governance to specify four practical supply network archetypes that cover a wide range of economic activities. We then explain how coopetitive relationships may evolve in these supply network archetypes. Specifically, we discuss how relationships form or dissolve in these archetypes and how local structural changes lead to co‐opetition dynamics at the network level. We also discuss the implications of such dynamics from a managerial perspective.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".