Neither an Authentic Product nor a Counterfeit: The Growing Popularity of Shanzhai Products in Global Markets
Bibliographic record
Abstract
Abstract Counterfeits have been a longstanding concern to global brand manufactures. However, recently, a new product category that partly imitates and partly innovates under the term shanzhai has entered into market. Shanzhai products mimic original leading brands through visual or functional similarities and may also provide additional features. Given this new copycat phenomenon, our study for the first time conceptually distinguishes shanzhai products from counterfeits, theoretically compares the values of consumers choosing shanzhai products versus counterfeits, and empirically tests such differences in one integrative model. Specifically, shanzhai buyers value product functional benefits more than counterfeit buyers, while counterfeit buyers value status consumption, yet experience less self‐clarity than shanzhai buyers. Our findings offer important implications for imitative innovation literature as well as for practitioners .
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".