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Cited by 288

The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.

Cross‐Cultural Differences in Risk Perception: A Model‐Based Approach

Bontempo, Robert N., Bottom, William P., Weber, Elke U.·1997·Risk Analysis

Abstract

The present study assessed cross‐cultural differences in the perception of financial risks. Students at large universities in Hong Kong, Taiwan, the Netherlands, and the U.S., as well as a group of Taiwanese security analysts rated the riskiness of a set of monetary lotteries. Risk judgments differed with nationality, but not with occupation (students vs. security analysts) and were modeled by the Conjoint Expected Risk (CER) model.(1) Consistent with cultural differences in country uncertainty avoidance,(2) CER model parameters of respondents from the two Western countries differed from those of respondents from the two countries with Chinese cultural roots: The risk judgments of respondents from Hong Kong and Taiwan were more sensitive to the magnitude of potential losses and less mitigated by the probability of positive outcomes.

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Crossref · 10.1111/j.1539-6924.1997.tb00888.x

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Metadata

Authors
Bontempo, R. N., Bottom, W. P., & Weber, E. U.
Venue
Risk Analysis
Publisher
Wiley
Published
1997-08
Volume
17
Issue
4
Pages
479-488
ISSN
  • 0272-4332 (ISSN-L)
  • 1539-6924
OpenAlex
W2074982085
Cited by
288
References
31
FWCI
3.40
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Cross‐Cultural Differences in Risk Perception: A Model‐Based Approach. https://app.cochat.ai/papers/10.1111/j.1539-6924.1997.tb00888.x
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Bontempo, R. N., Bottom, W. P., & Weber, E. U. (1997). Cross‐Cultural Differences in Risk Perception: A Model‐Based Approach. Risk Analysis. https://doi.org/10.1111/j.1539-6924.1997.tb00888.x

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

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.