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Record W4321093457 · doi:10.3390/jrfm16020131

Board Gender Diversity and Carbon Proactivity: The Influence of Cultural Factors

2023· article· en· W4321093457 on OpenAlexvenueno aff
Haifei Wang, Qingliang Tang, Ting Guo

Bibliographic record

VenueJournal of risk and financial management · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsnot available
Fundersnot available
KeywordsProactivityIndulgenceDiversity (politics)IndividualismGender diversityCorporate governanceBusinessEntrepreneurshipCultural diversityHofstede's cultural dimensions theorySocial psychologyPsychologyPolitical science

Abstract

fetched live from OpenAlex

Due to inadequate studies, our knowledge of the effect of female directors and national culture on the corporate response to climate change is still limited. To address this gap, the purpose of this paper is to investigate the dynamic relationship between gender diversity on the board of directors and corporate carbon proactivity and how two dimensions of national culture (individualism and indulgence) moderate this relationship. This study focuses on large companies that disclosed carbon-related information via the CDP survey in 2011–2017. Our findings show that gender diversity promotes corporate carbon proactivity. Furthermore, the positive effect of gender diversity on carbon proactivity is weaker when firms are in countries marked by a higher level of individualism and indulgence. As far as we know, this study is the first to explore and document the empirical evidence on the dynamic impact of gender diversity in the corporate governance body and national culture on managers’ climate change behaviors in terms of green proactivity.

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)0.000
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 score0.021
Threshold uncertainty score0.332

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.001
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 head0.029
GPT teacher head0.234
Teacher spread0.205 · 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

Citations8
Published2023
Admission routes1
Has abstractyes

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