Evidence map›Paper›PMID 36909975›Full record

ArticleComputational intelligence and neuroscience2023

Analyzing the Relationship among Social Capital, Dynamic Capability, and Farmers' Cooperative Performance Using Lightweight Deep Learning Model: A Case Study of Liaoning Province.

Simeng Zhang, Dongli Wu

RetractedAbstract readRetracted Publication
In one paragraph

Article in Computational intelligence and neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. 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. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

2 authors.

Simeng ZhangSchool of Economics and Management, Shenyang Agricultural University, Shenyang 110000, China.
Dongli WuSchool of Economics and Management, Shenyang Agricultural University, Shenyang 110000, China.ORCID https://orcid.org/0000-0003-1676-2264

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The purpose of this study is to understand the relationship between social capital and the performance of Farmers' Cooperatives (Cooperatives) and explore the internal mechanism of social capital affecting the performance of Cooperatives. This work selects two dimensions: cognitive social capital (CSC) and structural social capital (SSC), as indexes to measure the social capital of Cooperatives. An analytical framework is proposed: "Social capital-Dynamic capabilities-Organizational performance." First, according to the characteristics of Cooperatives, it determines the most appropriate index values and preprocesses the original data. Statistical Product and Service Solutions (SPSS) and Analysis of Moment Structure (AMOS) 25.0 software are used for factor analysis. A financial performance evaluation model of Cooperatives based on backpropagation neural network (BPNN) is constructed. Then, based on the survey data of 212 Cooperatives in Liaoning Province, the structural equation model (SEM) is used to test the interaction path between "Social capital-Dynamic capacity-Organizational performance." The results show that SSC's standardized regression coefficients (SRCs) on Cooperatives' economic benefits and member satisfaction are 0.208 and 0.095, respectively, significant at 1%. The actual case analysis concludes that the larger the scale of the structural network embedded in Cooperatives is, the more conducive it is to obtaining extensive resources. As such, Cooperatives can absorb the advanced experience and compensate for the weakness of lack of internal resources and experience. The SRC of CSC on Cooperatives' economic benefits is 0.336, and the

Indexed as

Deep LearningSocial CapitalChinaFarmersHumansSurveys and Questionnaires

Identifiers

PMID36909975
PMCPMC9995197

What OpenQuestion holds

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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.