Safety hazard and time to recall: The role of recall strategy, product defect type, and supply chain player in the U.S. toy industry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract This research identifies and tests key factors that can be associated with time to recall a product. Product recalls due to safety hazards entail societal costs, such as property damage, injury, and sometimes death. For firms, the related external failure costs are many, including the costs of recalling the product, providing a remedy, meeting the legal liability, and repairing damage to the firm's reputation. The recent spate of product recalls has shifted attention from why products are recalled to why it takes so long to recall a defective product that poses a safety hazard. To address this, our research subjects to empirical scrutiny the time to recall and its relationship with recall strategies, source of the defect and supply chain position of the recalling firm. We develop and verify our conceptual arguments in the U.S. toy industry by analyzing over 500 product recalls during a 15‐year period (1993–2008). The empirical results indicate that the time to recall, as measured by difference between product recall announcement date and product first sold date, is associated with (1) the recall strategy (preventive vs. reactive) adopted by the firm, (2) the type of product defect (manufacturing defect vs. design flaw), and (3) the supply chain entity that issues the recall (toy company vs. distributor vs. retailer). Our results provide cues that could trigger a firm's recognition of factors that increase the time to recall.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 it