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Record W4389373099 · doi:10.1108/cfri-07-2023-0187

Shaping corporate ESG performance: role of social trust in China's capital market

2023· article· en· W4389373099 on OpenAlexaff
Tiantian Tang, Liyan Yang

Bibliographic record

VenueChina Finance Review International · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsUniversity of Toronto
FundersNational Social Science Fund of ChinaNational Natural Science Foundation of China
KeywordsCorporate governanceBusinessLeverage (statistics)Social trustSocial capitalCapitalizationProfitability indexCorporate social responsibilityAccountingNexus (standard)OriginalityPropensity score matchingPanel dataEconomicsFinanceEconometricsPublic relations

Abstract

fetched live from OpenAlex

Purpose This study investigates the influence of social trust on the attainment of corporate environmental, social and governance (ESG) objectives. Design/methodology/approach This study conducts panel regression analysis on a distinctive dataset for 2009–2017 on Chinese firms. Findings The analysis reveals a significant positive association between social trust and firm-level ESG practices. Moreover, the impact of social trust on shaping ESG outcomes is further amplified by factors such as economic growth, corporate governance standards and institutional quality. This relationship remains statistically positive when the authors employ alternative measures and methodologies, such as the instrumental variables, propensity score matching and difference-in-differences approaches. Notably, the results of heterogeneity tests indicate that the Trust–ESG nexus is more prominent for state-owned enterprises and firms with substantial market capitalization, superior profitability and higher leverage. Originality/value This study expands the comprehension of the determinants of ESG and underscores the influential role of social trust as an informal institution in enhancing a firm's ESG performance.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.192
Threshold uncertainty score0.784

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.028
GPT teacher head0.269
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations67
Published2023
Admission routes1
Has abstractyes

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