Combating copycatting from emerging market suppliers in global supply chains
Bibliographic record
Abstract
We examine how different strategies can be used to protect global manufacturers from the prevalent issue of supplier copycatting in emerging markets. In particular, using a game‐theoretical model, we consider a manufacturer that sells a product to an emerging market, which requires the completion of multiple tasks. The manufacturer can perform any of these tasks in‐house or outsource any of them to an emerging market supplier. The former approach carries a higher cost, while the latter puts the manufacturer's intellectual property (IP) at risk of supplier copycatting. Either the manufacturer or the emerging market government can exert enforcement effort to protect the IP rights within the supply chain. Our results show that, surprisingly, there are cases where the manufacturer should outsource fewer tasks when in‐house production is more costly. Further, even though the supplier is the target of the enforcement, we show that the manufacturer's enforcement effort can help the supplier but hurt customers and the emerging market. Concerning whether the government or the manufacturer should take responsibility for IP protection, we recommend that the government enforce IP protection when the manufacturer has a weak brand quality and that the manufacturer enforce IP protection when it has a strong brand. This managerial insight provides a theoretical framework for the recent practitioners’ debate about who should be responsible for protecting IP rights within the supply chain.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".