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Record W2799605321 · doi:10.1002/csr.1517

Do socially responsible firms provide more readable disclosures in annual reports?

2018· article· en· W2799605321 on OpenAlexaffabout
Walid Ben‐Amar, Inès Belgacem

Bibliographic record

VenueCorporate Social Responsibility and Environmental Management · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsUniversity of OttawaInstitute on Governance
Fundersnot available
KeywordsCorporate social responsibilityAccountingAgency (philosophy)Perspective (graphical)BusinessNarrativeStakeholderImpression managementSample (material)Social responsibilityPrincipal–agent problemStock exchangePublic relationsMarketingCorporate governanceFinancePsychologySociologyPolitical scienceSocial psychology

Abstract

fetched live from OpenAlex

Abstract This paper examines the relationship between the adoption of corporate social responsibility (CSR) practices and the syntactic complexity of the management's discussion and analysis (MD&A) section of the annual report. Based on stakeholder and agency perspectives, we offer an empirical test of two competing hypotheses. First, we may expect socially responsible firms to provide transparent disclosures because this reflects a firm's commitment to high ethical standards. In contrast, the agency perspective predicts that managers engage in CSR for self‐interest purposes and that CSR‐oriented firms will be more likely to attempt to mislead stakeholders about the firm's actual performance through complex narrative disclosures. Based on a sample of large firms listed on the Toronto Stock Exchange, our results show a positive association between corporate social performance and the MD&A's textual complexity. Consistent with the agency perspective, our findings suggest that managers may engage in CSR opportunistically and use complex narrative disclosures in an impression management strategy.

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)0.005
metaresearch head score (Gemma)0.001
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 score0.132
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0000.002
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 head0.024
GPT teacher head0.251
Teacher spread0.227 · 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

Citations87
Published2018
Admission routes2
Has abstractyes

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