Blockchain Adoption for Combating Deceptive Counterfeits
Bibliographic record
Abstract
Counterfeiting is a severe problem in many sectors. There are two types of counterfeits: non‐deceptive and deceptive. While both types are important business challenge, deceptive counterfeit has an additional negative impact—customers have a post‐purchase regret if they expect to purchase a real product but ended up with a fake. The focus of this study is on the setting that relates to deceptive counterfeits. Our paper is one of the first that examines the effectiveness of blockchain as a solution to a supply chain challenge. Specifically, the unique feature of blockchain that we model, which none of the traditional strategies studied in the literature is capable of, is that blockchain adoption changes the analysis from a deceptive counterfeit setting to a non‐deceptive counterfeit setting. We also consider government being a decision maker and customers' privacy concern from blockchain adoption, two features that are not examined in the existing literature. We consider a market with a manufacturer and a deceptive counterfeiter. The manufacturer can signal product authenticity either with blockchain technology or through pricing. The government can provide subsidy to encourage blockchain adoption. Blockchain should be used when the counterfeit quality is intermediate or when customers have intermediate distrust about products in the market. If government provides subsidy, blockchain can be more effective than differential pricing strategy in eliminating post‐purchase regret. Our results advocate for government providing subsidy because it benefits both customers and the society and could be a better approach than government enforcement efforts.
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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 | 0.010 |
| 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 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".