How Do Brands Change Their Advertising Spending in Response to a Rival's Product Recall?
Bibliographic record
Abstract
A brand manager can interpret a rival's product recall as an opportunity to preempt sales and/or signal superior quality by raising their brand's ad spending. Conversely, they may interpret the recall as a threat that may harm their brand's image and/or lead buyers to draw unfavorable comparisons between their brand and the recalling brand. This interpretation nudges the manager to suppress their brand's ad spending. The authors test the interpretations empirically in the context of 62 substitute car models’ responses to the recall of a competing model. They assess the response over 31 weeks and 308 geographical regions, leading to 591,976 model-week-region observations. Regression discontinuity in time analysis reports that, on average, a substitute brand responds by lowering its ad spending by 50%, suggesting that the threat interpretation dominates the opportunity interpretation. A decomposition of spending by type suggests that substitute brands increase their spending on price advertising by 25%, decrease spending on quality advertising by 71%, but make no adjustment to brand advertising. This nuanced analysis suggests that substitutes attempt sales preemption, avoid quality signaling, and are not worried about brand spillover. A follow-up analysis reports that this advertising strategy strengthens the positive spillover effect of a brand's recall on its substitute brands’ sales volume. The key findings hold for another major automobile recall event in the same market. The findings contribute to the literature on the management of quality perceptions while informing about substitute brands’ managers responses to a rival brand's quality failure and whether the response helps or hurts the substitutes’ sales. Furthermore, the findings build an empirical foundation for future analytic investigation on strategic interactions among brands when a quality defect occurs.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".