How Can Companies Recover from Liability-Invoking Failures? Exploring the Role of Uncertainty Avoidance in Facilitating Consumer Compliance Across National Cultures
Bibliographic record
Abstract
A company often faces incidents in which its offerings cause bodily (e.g., product safety defects) or psychological (e.g., data breach) harm to its consumers. Such incidents may invoke product liability lawsuits against the company. The company may try to recover from the liability-invoking failure by notifying the affected consumers, offering a remedy, and persuading them to comply with the company message. The authors theorize and experimentally demonstrate that, on average, a prevention-focused message receives greater compliance than a promotion-focused message. Further, a prevention-focused message is more effective with consumers from high-uncertainty-avoidance cultures, whereas a promotion-focused message is more effective in low-uncertainty-avoidance cultures. Perceived compatibility of prevention or promotion goals with low or high values of uncertainty avoidance mediates the interaction effect on compliance. The findings can help companies overcome consumer apathy to product recall or data breach notices and offer managers ways to promote consumer safety and protection.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".