The DOI matches its Crossref record; 110 of the 110 references it cites were found in a registry or an index.
Chain liability in multitier supply chains? Responsibility attributions for unsustainable supplier behavior
Open accessAbstract
When it becomes publicly known that products are associated with suppliers that engage in unsustainable behaviors, consumers protest, as Nestlé, Zara, and Kimberly Clark, among others, have learned. The phenomenon by which consumers hold firms responsible for the unsustainable behavior of their upstream partners suggests the notion of “chain liability.” This study aims to generate insights into the antecedents and consequences of such consumer responsibility attributions. Using data from four vignette‐based survey experiments, the authors find that the chain liability effect increases if an environmental degradation incident (1) results from supplier behavior rather than force majeure, (2) results from a company decision rather than the decision of an individual employee, and (3) is more severe. Responsibility attributions do not differ with varying organizational distance from the supplier, firm size, strategic importance of the supplied product, or the existence of environmental management systems. The chain liability effect also creates strong risks for the focal firm; higher responsibility attributions increase consumers’ anger and propensity to boycott. Therefore, firms should work to ensure sustainable behavior throughout the supply chain, to protect them from chain liability.
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Metadata
- Authors
- Hartmann, J., & Moeller, S.
- Venue
- Journal of Operations Management
- Publisher
- Wiley
- Published
- 2014-02
- Volume
- 32
- Issue
- 5
- Pages
- 281-294
- ISSN
- 0272-6963 (ISSN-L)
- 1873-1317
- OpenAlex
- W2041428553
- Cited by
- 433
- References
- 110
- FWCI
- 42.26
- Type
- journal article
- Language
- en
- Open access
- hybrid
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
