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Record W4406374061 · doi:10.3390/jrfm18010031

ESG Performance and Corporate Governance—The Moderating Role of the Big Four Auditors

2025· article· en· W4406374061 on OpenAlexvenueno aff
Puji Handayati, Yeut Hong Tham, Yuni Yuningsih, Zhiyue Sun, Tatas Ridho Nugroho, Sulis Rochayatun

Bibliographic record

VenueJournal of risk and financial management · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessCorporate governanceAccountingAuditFinance

Abstract

fetched live from OpenAlex

The purpose of this study is to investigate the impact of corporate governance on ESG performance in large publicly listed firms in Indonesia from 2016 to 2023. The study adopts both stakeholder-agency theory and resource dependency theory to explore the relationship between sustainability assurance, board governance characteristics, and the extent of ESG performance. Fixed effects regression controlling both industry and year fixed effects is used to measure the relationship between sustainability assurance, corporate governance characteristics, and ESG performance. We find a positive significant relationship between assurance sustainability reports and ESG performance. Additionally, we also document a positive association between sustainability committees and ESG performance. Adopting the Big Four auditors as a moderating variable, we find a positive relationship between gender-diverse boards and firms audited by the Big Four auditors and sustainability performance. This result suggests that firms with gender-diverse boards audited by the Big Four auditors enhance sustainability performance. Additional robustness tests using GMM estimation, conducted to address endogeneity concerns, corroborated the main test results.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.620
Threshold uncertainty score0.343

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 communication0.0000.000
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 head0.011
GPT teacher head0.197
Teacher spread0.186 · 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

Citations16
Published2025
Admission routes1
Has abstractyes

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