MétaCan
Menu
Back to cohort
Record W2601315742 · doi:10.1111/1911-3846.12542

The Influence of Corporate Social Responsibility Measures on Investors' Judgments when Integrated in a Financial Report versus Presented in a Separate Report

2019· article· en· W2601315742 on OpenAlexvenueno aff
Anthony C. Bucaro, Kevin Jackson, Jeremy B. Lill

Bibliographic record

VenueContemporary Accounting Research · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsnot available
Fundersnot available
KeywordsCorporate social responsibilityDimension (graph theory)Perspective (graphical)BusinessAccountingRelevance (law)FinancePublic relationsPolitical science

Abstract

fetched live from OpenAlex

ABSTRACT This study examines the effect on investors' judgments of corporate social responsibility (CSR) measures when integrated with financial information in a single report versus when presented in a separate CSR report. Advocates for integrated reports argue that CSR information will be perceived as more relevant and have a greater impact on users when observed in an integrated report. However, we provide experimental evidence that CSR measures have greater influence on investors' judgments when investors observe the CSR information and financial information depicted in separate reports. We also provide evidence that this greater influence of CSR measures is caused by investors' evaluations taking on a “multidimensional perspective” that includes both a social responsibility and a financial dimension, which is triggered by observing the separate CSR report. Activating a social responsibility dimension elevates the perceived relevance of CSR measures, increasing their influence on investors' judgments. Our study contributes to practice by highlighting a potential unintended consequence of issuing integrated versus separate CSR reports: that investors incorporate CSR information less when it is integrated with financial information versus separately reported.

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)0.035
metaresearch head score (Gemma)0.056
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0350.056
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.005
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0000.002
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 head0.130
GPT teacher head0.355
Teacher spread0.226 · 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; both teacher heads agree on what is shown here.

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

Citations96
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueContemporary Accounting ResearchSame topicCorporate Social Responsibility ReportingFrench-language works237,207