Evidence map›Paper›PMID 39991211›Full record

ArticleHeliyon2025

A two-layer network model of the evolution of public risk perception of emerging technologies.

Xiaqun Liu, Xiaoyue Qiu, Yaming Zhuang

Abstract read
In one paragraph

Article in Heliyon, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Xiaqun LiuSchool of Economics and Management, Southeast University, Nanjing, 211189, China.
Xiaoyue QiuSchool of Economics and Management, Southeast University, Nanjing, 211189, China.
Yaming ZhuangSchool of Economics and Management, Southeast University, Nanjing, 211189, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To fully comprehend public understanding of emerging technologies and their risks, from the stakeholder perspective, this study categorizes the public into primary and secondary stakeholders. Considering the impact of the spread of risk events on public risk perception of emerging technologies, this study adopts complex network theory to construct a two-layer network model consisting of "the spread of risk events-the evolution of public risk perception" of emerging technologies. The evolutionary threshold of public risk perception is analysed using the microscopic Markov chain approach. The influence of public composition and the spread of risk events on the evolution of risk perception is further verified through a simulation. The results of the study are as follows: First, public risk perception of emerging technologies tends to stabilise quickly. Second, the threshold and steady state of the evolution of public risk perception are influenced by the spread of risk events. Third, as the proportion of primary stakeholders or risk perception gap between primary and secondary stakeholders increases, the threshold for the evolution of public risk perception of emerging technologies increases, and the proportion of the public who think that emerging technologies are risky gradually decreases.

Indexed as

Emerging technologiesEvolution modelRisk eventsRisk perceptionTwo-layer network

Identifiers

PMID39991211
PMCPMC11847281

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.