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Record W2943449940 · doi:10.1002/csr.1762

The impact of women leaders on environmental performance: Evidence on gender diversity in banks

2019· article· en· W2943449940 on OpenAlexaff
Giuliana Birindelli, Antonia Patrizia Iannuzzi, Marco Savioli

Bibliographic record

VenueCorporate Social Responsibility and Environmental Management · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsBlackberry (Canada)
FundersFuture Farm Industries Cooperative Research Centre
KeywordsHomophilyGender diversityDiversity (politics)Chief executive officerPerspective (graphical)SustainabilityOfficerBusinessPublic relationsMarketingPolitical scienceDemographic economicsAccountingCorporate governancePsychologyManagementSocial psychologyEcologyEconomicsFinance

Abstract

fetched live from OpenAlex

Abstract This study analyses the impact of women leaders on environmental performance in a sample of 96 listed banks in the EMEA (Europe, Middle East and Africa) region from 2011 to 2016. Gender diversity in leadership positions is explored through women in the board of directors, chief executive officer gender, and the interaction between these two aspects. This study sheds light on inconsistent results in prior literature by testing three theoretical perspectives: gender difference, critical mass, and homophily. The main results suggest that there is nonlinear relationship between women directors and the environmental performance of banks and that female chief executive officers play a strategic role in shaping this relationship, by confirming the homophily perspective for the banking sector. Therefore, leader gender diversity is an important driver of environmental sustainability in banks, which are increasingly involved in environmental issues either directly, as companies, or indirectly, through their lending activity.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
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 head0.065
GPT teacher head0.260
Teacher spread0.195 · 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.

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

Citations332
Published2019
Admission routes1
Has abstractyes

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