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Record W2739088856 · doi:10.5465/ambpp.2017.136

Corporate Governance and the Rise of Integrating CSR Criteria in Executive Compensation

2017· article· en· W2739088856 on OpenAlexaff
Caroline Flammer, Bryan Hong, Dylan Minor

Bibliographic record

VenueAcademy of Management Proceedings · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsKellogg's (Canada)
Fundersnot available
KeywordsCorporate social responsibilityCorporate governanceBusinessExecutive compensationStakeholderAccountingCompensation (psychology)ShareholderAgency (philosophy)Value (mathematics)Stakeholder engagementEnterprise valuePublic relationsFinancePolitical science

Abstract

fetched live from OpenAlex

This study examines the antecedents and consequences of integrating corporate social responsibility (CSR) criteria in executive compensation, a relatively recent practice in corporate governance. Using a novel database of CSR contracting, we find that CSR contracting is more prevalent in emission-intensive industries and has become more prevalent over time. We further find that the adoption of CSR contracting leads to i) a reduction in short-termism; ii) an increase in firm value; iii) an increase in social and environmental performance; iv) a reduction in emissions; and v) an increase in green innovations. These findings are consistent with our theoretical arguments highlighting a new form of agency conflict–the misalignment between shareholders' and managers' preferences for stakeholder engagement–and suggest that CSR contracting can enhance corporate governance.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.055
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.015
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.055
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.007
Scholarly communication0.0050.004
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.043
GPT teacher head0.289
Teacher spread0.246 · 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 source (direct Gemma or distilled Codex), 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

Citations4
Published2017
Admission routes1
Has abstractyes

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