Product Recall Contagion in the Supply Chain
Bibliographic record
Abstract
Following a manufacturer's large product recall, its supplier's shareholders may perceive uncertain future demand for the supplier's products and react punitively, causing a drop in the supplier's stock return—that is, a contagion (or negative spillover). Moreover, shareholders’ information asymmetry may cause them to “screen” the supplier's information cues to determine the supplier's extent of demand uncertainty. The ideal screen is the supplier's proportion of sales revenue from the recalling manufacturer. However, not all suppliers disclose this information. Therefore, we propose that shareholders use a two-stage screening. The first screen is whether the supplier demonstrates transparency by voluntarily disclosing information about its customer portfolio. The second screen—available only to the subset of suppliers that disclose customer information—is the supplier's sales revenue from the recalling manufacturer. We used a sample of 896 U.S. public manufacturer–supplier dyads impacted by 27 large manufacturer recalls. An event study followed by cross-sectional regressions provides evidence of contagion. In addition, it reveals that the supplier's voluntary disclosure of customer information mitigates contagion, whereas revenue dependence aggravates it. Contextual (i.e., recall) variables also impact contagion. Our research study contributes to the supply-chain contagion literature, screening theory, and customer information disclosure literature. The findings inform supplier firm managers that their prior customer-related disclosures and the contextual variables can moderate contagion.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".