Evidence map›Paper›PMID 39841630›Full record

ArticlePloS one2025

The impact of social networks on rural residents' engagement in living environment upgrade: An integrated analysis drawing on social network theory and the theory of planned behavior.

Gulsanam Amat, Jing Wang

Abstract read
In one paragraph

Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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.

Gulsanam AmatSchool of Public Administration, Hebei University of Economics and Business, Shijiazhuang, China.
Jing WangSchool of Public Administration, Hebei University of Economics and Business, Shijiazhuang, China.ORCID 0000-0001-8174-405X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The issue of rural living environment is a critical concern for China and the global community, intricately intertwined with regional economic development. The proactive involvement of rural residents, who are both beneficiaries and influencers of the environment, constitutes a cornerstone in improving environmental standards. Therefore, the factors that shape rural residents' participation in living environment upgrade must be explored. Drawing upon the theory of planned behavior and social capital theory, this study employs a structural equation model (SEM) to comprehensively examine the determinants of rural residents' engagement in living environment upgrade, utilizing data from 334 households in Hebei Province, China. The findings can be summarized as follows: (1) Social networks, subjective norms, perceived behavioral control, and attitudes towards behavior significantly and positively impact rural residents' engagement in upgrading the living environment, (2) Attitudes towards behavior act as mediators between perceived behavioral control and rural residents' engagement in living environment upgrade, as well as between subjective norms and rural residents' engagement in living environmental upgrade. (3) Social networks could indirectly influence rural residents' engagement in living environment upgrade through the independent mediating effect of subjective norms and attitudes towards behavior, as well as through the chain mediating effect of the two. In conclusion, several managerial recommendations are proposed to stimulate rural residents' active participation in living environment upgrade.

Indexed as

Rural PopulationSocial NetworkingAdultAttitudeChinaFemaleHumansMaleMiddle AgedSocial SupportTheory of Planned Behavior

Identifiers

PMID39841630
PMCPMC11753695

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.