Evidence map›Paper›PMID 38023057›Full record

ArticleFrontiers in psychology2023

Exploring the inherent mechanism of residents' participation behavior in neighborhood regeneration projects: an empirical study using an extended IMB model in China.

Xinyue Fu, Taozhi Zhuang, Ruopeng Huang, Yaxian Dong

Abstract read
In one paragraph

Article in Frontiers in psychology, 2023. 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

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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

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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
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4 · The record

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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

4 authors.

Xinyue FuSchool of Management Science and Real Estate, Chongqing University, Chongqing, China.
Taozhi ZhuangSchool of Management Science and Real Estate, Chongqing University, Chongqing, China.
Ruopeng HuangManagement in the Built Environment, Faculty of Architecture and the Built Environment, Delft University of Technology, Delft, Netherlands.
Yaxian DongEngineering Unit A, Department of Architectural Engineering, The Pennsylvania State University, University Park, PA, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Resident participation has gained increasing prominence and significance in the pursuit of sustainable neighborhoods regeneration. However, the current state of resident participation practices remains beset by several challenges, which present formidable impediments to the initiation and execution of neighborhood regeneration initiatives. This study aims to investigate the underlying mechanisms of residents' participation behavior to enhance resident participation in neighborhood regeneration projects. Methods: The present study employs the extended Information-Motivation-Behavioral (IMB) model to examine the determinants and mechanisms influencing residents' willingness and participation in neighborhood regeneration projects, with a specific focus on the Chinese context. Drawing upon data from 477 meticulously validated questionnaires administered to residents, the study applies structural equation modeling (SEM) to unravel the intrinsic dynamics of residents' participation behavior. Results: The empirical findings of this research reveal that information, motivation, and the perceived local government support all exert a significant impact on residents' participation willingness. Notably, motivation emerges as the most influential factor. Discussion: This study uncovers a direct influence of local government organizations on both residents' willingness and their actual participation, suggesting that government organizations can spearhead innovative strategies to bolster residents' willingness and furnish avenues for translating willingness into tangible participation. The outcomes of this study furnish an indispensable theoretical framework and offer policy recommendations that hold paramount importance for the deployment of novel interventions geared toward stimulating active involvement of residents in neighborhood regeneration.

Indexed as

information-motivation-behavioral modelinherent mechanismneighborhood regenerationresidents’ participation behaviorstructural equation modeling

Identifiers

PMID38023057
PMCPMC10666756

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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.