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Cited by 272

The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.

Accepting Inequality Deters Responsibility: How Power Distance Decreases Charitable Behavior

Winterich, Karen Page, Zhang, Yinlong·2014·Journal of Consumer Research

Abstract

Could power distance, which is the extent that inequality is expected and accepted, explain why some countries and consumers are more likely to engage in prosocial behavior, including donations of both money and time? This research proposes that higher power distance results in weaker perceptions of responsibility to aid others, which decreases charitable behavior. Both correlational and causal evidence is provided in a series of five studies that examine country-level power distance as well as individual and temporarily salient power distance belief. Consistent with the mediating role of perceived responsibility, results reveal that uncontrollable needs and communal relationship norms are boundary conditions that overcome the negative effect of power distance on charitable behavior. These results explain differences in charitable giving across cultures and provide implications for nonprofit organizations soliciting donations.

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Metadata

Authors
Winterich, K. P., & Zhang, Y.
Venue
Journal of Consumer Research
Published
2014-03-19
Volume
41
Issue
2
Pages
274-293
ISSN
  • 0093-5301 (ISSN-L)
  • 1537-5277
OpenAlex
W2054370045
Cited by
272
References
18
FWCI
38.91
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Accepting Inequality Deters Responsibility: How Power Distance Decreases Charitable Behavior. https://app.cochat.ai/papers/10.1086/675927
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Winterich, K. P., & Zhang, Y. (2014). Accepting Inequality Deters Responsibility: How Power Distance Decreases Charitable Behavior. Journal of Consumer Research. https://doi.org/10.1086/675927

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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.