Slicing vs chunking product-harm crisis: antecedents and firm performance implications
Bibliographic record
Abstract
Purpose The high prevalence of product-harm crises (PHC) represents a continuing challenge to which firms sometimes react by announcing several smaller recalls (i.e. slicing) but at other times by announcing the recall of all faulty products at once (i.e. chunking). The slicing vs chunking phenomenon has not been identified by prior literature; this study aims to explore two research questions: Why do firms sometimes slice and other times chunk PHC? Do slicing and chunking affect firm performance differently? Design/methodology/approach The authors examined recall guidelines from the US National Highway Traffic Safety Administration (NHTSA) and conducted expert interviews as well as a quantitative analysis of 378 product recalls to determine the antecedents of slicing vs chunking. The authors further performed an event study to examine the impact of slicing vs chunking PHCs on firms’ financial performance. Findings The authors find that slicing vs chunking is not a deliberate strategy but rather the consequence of firms’ resource availability and constraints. Furthermore, the authors show that larger firms have a lower likelihood of slicing versus chunking. By contrast, larger R&D expenditures, and greater reputation, as well as larger recall sizes, increase the likelihood of slicing versus chunking. Finally, the results reveal that, compared to chunking, slicing PHC has a strong negative impact on firms’ stock value. Research limitations/implications The authors relied on recalls in the US automobile industry. A possible extension would be to study the same phenomenon in other industries or other geographical areas. In addition, the results need to be generalized to other types of negative news that can be either decoupled (slicing) or coupled (chunking), especially negative news for which firms have more discretion regarding the timing of their announcements than for product recalls. Practical implications As shown by prior research (Eilert et al. , 2017), firms should aim to announce recalls quickly in the wake of a PHC. Importantly though, the results indicate that speed should not come at the expense of comprehensiveness in identifying all defective products, so that only one recall is needed. As suggested by our findings about PHC, investors may react negatively to the slicing of other types of negative news; thus, the results suggest how to best communicate to external stakeholders during crises in general. Originality/value To the best of the authors’ knowledge, this is the first study that examines why firms sometimes slice and at other times chunk PHC and identifies the performance implications of these two types of recalls in response to PHC.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".