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Cited by 297

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

A Causal Model Explaining the Perception and Acceptance of Gene Technology1

Siegrist, Michael·1999·Journal of Applied Social Psychology

Abstract

This study examined factors affecting perceived risks and acceptance of gene technology. Based on the reviewed literature, a causal model was proposed. The plausibility of the postulated model was tested using structural equation modeling procedures. Participants were randomly selected students from a Swiss university (N= 837). Results indicated that trust in companies and scientists performing gene manipulations have a strong effect on the benefits and risks perceived. When trust was controlled for the inverse relationship between perceived risk and perceived benefit vanished. Furthermore, world views, perceived benefit, and perceived risk each are found to contribute independently to the prediction of acceptance of gene technology. World views and trust appear to play an important role in perceptions of gene technology.

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Crossref · 10.1111/j.1559-1816.1999.tb02297.x

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Metadata

Authors
Siegrist, M.
Venue
Journal of Applied Social Psychology
Publisher
Wiley
Published
1999-10
Volume
29
Issue
10
Pages
2093-2106
ISSN
  • 0021-9029 (ISSN-L)
  • 1559-1816
OpenAlex
W2128892457
Cited by
297
References
3
FWCI
17.09
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: A Causal Model Explaining the Perception and Acceptance of Gene Technology1. https://app.cochat.ai/papers/10.1111/j.1559-1816.1999.tb02297.x
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Siegrist, M. (1999). A Causal Model Explaining the Perception and Acceptance of Gene Technology1. Journal of Applied Social Psychology. https://doi.org/10.1111/j.1559-1816.1999.tb02297.x

Data sources

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.