Slicing vs chunking product-harm crisis: antecedents and firm performance implications
Notice bibliographique
Résumé
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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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».