Price and quality competition while envisioning a quality-related product recall
Bibliographic record
Abstract
Many product recalls are caused by quality-related product failures. When such recalls occur, the effects may not only be limited to the firm selling the product but also extend to competing firms in the category. This paper analyzes quality and pricing strategies for competing firms facing the risk of a severe quality-related recall making the product hazardous and leading to its removal from the market. We develop a two-stage Nash game where the probability of recall depends on the firms’ chosen quality investments, and either firm can experience a recall. We consider a utility-based model where consumers’ sensitivity to price and quality can change following the recall. Our results indicate that the competitor should lower its price after a recall if consumers’ price sensitivity changes enough and may increase or keep its price the same otherwise. Surprisingly, considering the risk of a recall does not always lead firms to enhance their product quality. If the change in consumer quality sensitivity is low enough, firms adopt an inferior product quality level than when they overlook the product recall risk, even if consumer quality sensitivity increases and/or consumer price sensitivity decreases after the recall. These results can help companies plan their pricing and quality decisions in competitive industries with potential product quality failures leading to recalls.
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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 | 0.023 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".