Bibliographic record
Abstract
Purpose – The purpose of this paper is to create a framework for broadly understanding categories and motivations behind purchasing different counterfeit products. Design/methodology/approach – Focus groups provided qualitative data from 509 counterfeit purchases incidents by 95 informants. Findings – The most frequently mentioned motivation was the utility (35 percent) received from the good over the genuine article. The second, but negative, motivation was the perceived risk involved in the purchase (22 percent), whether it is physical or social risk. Social norms, confusion, and ethical concerns each represented about 10 percent of the motivations toward the purchase of counterfeit items. The least mentioned motivations to purchase, at less than 4 percent each, were culture, habit, and desire to explore. These factors were evident across a variety of 15 product categories, headed by electronics, such as DVDs and computer software. Practical implications – Through targeting negative motivations, such as perceived physical and social risks, businesses can devise strategies from a demand side perspective to overcome the problem of counterfeit consumption. Originality/value – Qualitative responses, over many product categories, provide a unique overview to the perception of counterfeit consumption. The finding that consumer ethics may depend on whether the activity benefits the society as a whole is worthy of additional discussion. The authors learn that when consumers thought their counterfeit consumption caused little or no harm, they do not see much ethical concern in their actions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".