TY - JOUR
T1 - Public opinion outweighs knowledge
T2 - A dual-process framework for understanding acceptance of genetic modification among scientists and laypeople
AU - Chen, Anfan
AU - Zhang, Xing
AU - Jin, Jianbin
N1 - Hong Kong Baptist University, Grant/Award Number: 163102; National Social Science Foundation of China, Grant/Award Number: 21CXW018; Science Popularization and Risk Communication of Transgenic Biotechnologies, Grant/Award Number: 2016ZX08015002
Publisher Copyright:
© 2025 Society for Risk Analysis.
PY - 2025/1/17
Y1 - 2025/1/17
N2 - Communication research on scientific issues has traditionally relied on the deficit model, which posits that increasing scientific knowledge leads to public acceptance. However, this model's effectiveness is questioned due to inconclusive impacts of knowledge on acceptance. To address this, we propose a dual-process framework combining the deficit model (with scientific knowledge as a key predictor) and a normative opinion process model (where perceived majority opinion plays a crucial role) to predict people's risk/benefit perceptions and their support for genetic modification (GM). Using two national surveys in mainland China—Study 1 with 5145 laypeople and Study 2 with 12,268 scientists—we found positive and significant correlations between scientific knowledge or perceived majority opinion and GM support, mediated by risk/benefit perceptions. Importantly, the normative pathway—represented by perceived majority opinion—exerts a stronger direct and indirect impacts on GM support than scientific knowledge across both scientists and laypeople. Moreover, while the normative process shows a greater influence than the informative process on individuals’ perceptions of both benefits and risks associated with GM, its prominence differs between scientists and laypeople depending on the types of perceptions—scientists are more sensitive to risk-related social norms, whereas laypeople are more concerned with norms related to benefits. The paper concludes with a discussion on the theoretical and practical implications of these findings.
AB - Communication research on scientific issues has traditionally relied on the deficit model, which posits that increasing scientific knowledge leads to public acceptance. However, this model's effectiveness is questioned due to inconclusive impacts of knowledge on acceptance. To address this, we propose a dual-process framework combining the deficit model (with scientific knowledge as a key predictor) and a normative opinion process model (where perceived majority opinion plays a crucial role) to predict people's risk/benefit perceptions and their support for genetic modification (GM). Using two national surveys in mainland China—Study 1 with 5145 laypeople and Study 2 with 12,268 scientists—we found positive and significant correlations between scientific knowledge or perceived majority opinion and GM support, mediated by risk/benefit perceptions. Importantly, the normative pathway—represented by perceived majority opinion—exerts a stronger direct and indirect impacts on GM support than scientific knowledge across both scientists and laypeople. Moreover, while the normative process shows a greater influence than the informative process on individuals’ perceptions of both benefits and risks associated with GM, its prominence differs between scientists and laypeople depending on the types of perceptions—scientists are more sensitive to risk-related social norms, whereas laypeople are more concerned with norms related to benefits. The paper concludes with a discussion on the theoretical and practical implications of these findings.
KW - deficit model
KW - genetic modification
KW - normative opinion process
KW - perceived majority opinion
KW - risk-benefit perceptions
KW - scientific knowledge
UR - http://www.scopus.com/inward/record.url?scp=85215521675&partnerID=8YFLogxK
UR - https://onlinelibrary.wiley.com/doi/10.1111/risa.17704
U2 - 10.1111/risa.17704
DO - 10.1111/risa.17704
M3 - Journal article
AN - SCOPUS:85215521675
SN - 0272-4332
JO - Risk Analysis
JF - Risk Analysis
ER -