Deceptive counterfeits and anti-counterfeiting: a blessing in disguise?
Bibliographic record
Abstract
Despite various anti-counterfeiting measures, counterfeits thrive as one of the biggest threats to product brand innovation and sales. This study employs an analytical game theoretical model to examine the strategic interactions between an authentic brand firm and a deceptive counterfeiter to provide managerial implications for anti-counterfeiting. Firstly, we find the authentic product firm can be worse off with a heavier penalty for counterfeits. Secondly, the authentic product firm benefits from more costly anti-counterfeiting with sufficiently cheap quality improvement and counterfeiting. Next, the authentic product firm benefits from deeper counterfeit penetration when the penalty is sufficiently high and counterfeiting is costly. Finally, we reveal that the counterfeiter only benefits from deeper counterfeit penetration when the counterfeit imitation is sufficiently costly, or the existing level of counterfeit penetration is not too high. To verify the robustness of the results, we further extend the main model to multiple scenarios including consumers’ brand loyalty, counterfeit penetration uncertainty, etc.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".