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Record W4385075136 · doi:10.1177/00076503231183690

Corporate Social Responsibility and Directors’ and Officers’ Liability Risk: The Moderating Effect of Risk Environment and Growth Potential

2023· article· en· W4385075136 on OpenAlexafffund
Hao Lu, M. Martin Boyer, Anne Kleffner

Bibliographic record

VenueBusiness & Society · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsHEC MontréalUniversity of CalgarySaint Mary's University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsCorporate social responsibilityBusinessUnderwritingLiabilityRisk managementLiability insuranceActuarial scienceEmpirical evidenceAccountingFinancePublic relations

Abstract

fetched live from OpenAlex

Theoretical arguments regarding the effect of corporate social responsibility (CSR) on firm liability risk are abundant; however, empirical evidence about this relationship is scarce. We investigate the relationship between CSR and the personal liability risk of a firm’s directors and officers. We argue that companies with better CSR performance represent a better underwriting risk for directors’ and officers’ (D&O) insurance providers and, therefore, have a lower cost of insurance. Our results show that firms with better CSR performance are more likely to purchase D&O insurance and have a lower premium-to-coverage ratio, known as the insurance rate-on-line. We also show that this risk-reduction effect is stronger for firms that operate in a high-risk environment and have higher sales growth. These results provide evidence that CSR can be used as a risk management tool to mitigate liability risk and suggest which firms benefit most from this effect.

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)0.006
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.016
GPT teacher head0.224
Teacher spread0.208 · 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

Citations6
Published2023
Admission routes2
Has abstractyes

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