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Record W3118572261 · doi:10.1002/csr.2107

How do firms achieve corporate social performance? An integrated perspective

2021· article· en· W3118572261 on OpenAlexaff
Walid Ben‐Amar, Claude Francœur, S. Marsat, Aida Sijamic Wahid

Bibliographic record

VenueCorporate Social Responsibility and Environmental Management · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsUniversity of TorontoHEC MontréalUniversity of Ottawa
Fundersnot available
KeywordsCorporate social responsibilityIncentiveCorporate governanceBusinessPerspective (graphical)Context (archaeology)Sample (material)Compensation (psychology)Industrial organizationAccountingPublic relationsEconomicsMicroeconomicsFinancePolitical science

Abstract

fetched live from OpenAlex

Abstract This study provides an integrated view of the combined direct and indirect effects of the main drivers of CSR performance, at country, firm and CEO levels respectively. We extend prior literature by showing that the institutional context, firm CSR governance practices, and CSR‐related compensation incentives have impacts of different magnitudes on CSR performance, as well as significant combined effects. Using an international sample of 1272 observations over 20 countries, we document significant indirect cascading effects of the institutional setting and firm‐specific governance practices on CSR performance. From a managerial perspective, we find that firms operating in countries that are less oriented towards satisfying the needs of the stakeholders still have the ability to counterbalance this institutional impact and achieve relatively high CSR performance by implementing sound firm‐level CSR governance practices and incentives.

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)0.005
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.003
Science and technology studies0.0010.007
Scholarly communication0.0110.008
Open science0.0010.007
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.039
GPT teacher head0.237
Teacher spread0.198 · 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 designTheoretical or conceptual
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

Citations24
Published2021
Admission routes1
Has abstractyes

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