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Record W6929729033 · doi:10.5267/j.ac.2025.3.001

The effect of CEO’s social relational and moral capital on board process and performance of socially responsible company

2025· article· en· W6929729033 on OpenAlexvenueno aff

Bibliographic record

VenueAccounting · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Research and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsRelational capitalStakeholderSocial capitalSocial responsibilityMediationStock exchangeCorporate social responsibilityMoral disengagementCapital (architecture)

Abstract

fetched live from OpenAlex

This study investigates the effects of stakeholder relations quality, and the social and moral capital of CEOs on the board processes and performance of socially and environmentally responsible companies. Data is collected from 40 companies listed on the Sri-Kehati Index of the Indonesia Stock Exchange and evaluated under the PROPER program by the Ministry of Environment Indonesia. Using GeSCA for analysis, results show that CEO’s relational and moral capital significantly impact board processes and performance. The quality of stakeholder relationships is more pronounced at the individual CEO level than the company level. Further analysis indicates that CEO’s relational capital strengthens the relationship between their moral capital and stakeholder relationship quality at the company level. Additionally, while the CEO’s relational capital significantly affects both board processes and performance, the CEO’s moral capital and the company’s responsible status only significantly impact board performance. Mediation analysis reveals that the CEO’s relational capital significantly mediates the relationship between the CEO’s moral capital and the company’s responsible status, affecting board processes. The findings underscore the importance of CEO’s relational capital at both individual and company levels for socially and environmentally responsible companies.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.199
Threshold uncertainty score0.139

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.006
GPT teacher head0.288
Teacher spread0.281 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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