Evidence map›Paper›PMID 32120821›Full record

ArticleHealthcare (Basel, Switzerland)2020

A Study on the Preference for the Defensive Attribute under Environmental Risk in China.

Xiaohong Chen, Li Zhou

Abstract read
In one paragraph

Article in Healthcare (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

2 authors.

Xiaohong ChenCollege of Economics and Management, Nanjing Agricultural University, Nanjing 210095, China.
Li ZhouCollege of Economics and Management, Nanjing Agricultural University, Nanjing 210095, China.

Funding

Chinese National Funding of Social Sciences 19ZDA117Nanjing Agricultural University China Center for Food Security StudiesNational Natural Science Foundation of China 71773052National Natural Science Foundation of China 71773053Priority Academic Program Development of Jiangsu Higher Education Institutions PAPD
6 · The paper itself

Abstract

Little is known about the preference for defensive consumption goods and their defensive attributes under environmental risks in developing countries. The paper takes the water purifier as an example of defensive consumption goods against heavy metal pollution risk from drinking water. Using a survey data in China, the discrete choice experiment method is employed to investigate rural farmers' preferences for a water purifier. The scientific knowledge and risk perception effects are used to determine farmers' preferences for the defensive attribute on a water purifier. Using a mixed logit model, rural farmers are found to prefer a water purifier with low price, free installation, a longer warranty period, and a heavy metal filter (i.e. the defensive attribute). Farmers' neighborhood norm perception dominates the defensive preference while scientific knowledge do not work significantly. More specifically, the more the neighborhood norm perception is recognized, the more likely farmers are to improve their own preferences for the defensive attribute. Affected by the neighborhood norm perception, rural farmers' preferences for the defensive attribute are found to be increased significantly as average income gaps narrow. The future defensive health policies should be devoted to improving environmental risk awareness and utilizing informal social networks in areas with high environmental risks.

Indexed as

defensive health behaviordiscrete choice experimentdrinking water safetyenvironmental pollutionrisk perceptionsocial networks

Identifiers

PMID32120821
PMCPMC7151335

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.