Safety hazard and time to recall: The role of recall strategy, product defect type, and supply chain player in the U.S. toy industry
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.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".