Bibliographic record
Abstract
Purpose This study aims to investigate the effects of firm characteristics and crisis characteristics on remedies offered to consumers by firms in the event of a product recall crisis. Design/methodology/approach Published data on 868 product recalls in the US toy industry from 1988 to 2011 have been used to investigate the effects of firm experience in product recalls, type of firm (company versus intermediary) and product recall severity in predicting remedies offered to consumers in the event of a product recall. Findings The findings show that firm recall experience, firm type and recall severity are negatively associated with recall remedies offered. Specifically, firms offer lower remedies if they have higher recall experience, if they are upstream firms in the supply chain (farther from consumers) and if the recall is more severe. Research limitations/implications This study focuses on the toy industry and does not consider product complexity, firm reputation and the role of external regulatory agencies in the prediction of remedies offered by firms. Future research may extend this study to include the above factors. Practical implications Offering a high remedy to consumers of a recalled product may be a responsible decision by a firm, but it may also attract shareholder wrath. The study has implications for managing multiple goals in product recall crisis management. Originality/value Studies focused on issues of interest to consumers during a recall crisis, such as swift recalls and appropriate remedies, are limited. This study contributes to the understanding of the antecedents of recall remedies.
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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 | 0.004 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".