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Record W2945024919 · doi:10.1111/1911-3846.12521

What Drives Investor Response to CSR Performance Reports?

2019· article· en· W2945024919 on OpenAlexvenueno aff
Andrés Guiral, Doocheol Moon, Hun‐Tong Tan, Yao Yu

Bibliographic record

VenueContemporary Accounting Research · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsnot available
Fundersnot available
KeywordsCorporate social responsibilityHeuristicAffect (linguistics)Value (mathematics)BusinessProcess (computing)Stock (firearms)PsychologyPublic relationsPolitical scienceComputer scienceEngineeringMechanical engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

ABSTRACT Recent research finds that investors' assessments of a stock's fundamental value are influenced by corporate social responsibility (CSR) performance through the affect‐as‐information heuristic. We extend prior research by examining two boundary conditions for the use of this heuristic: (i) whether the CSR performance relates to activities that are integrated in a firm's core business practices (material CSR issues) or not (immaterial CSR issues), and (ii) whether the CSR performance is positive or negative. Employing an experimental method, we find that the affect‐as‐information heuristic applies only to immaterial CSR issues but not to material CSR issues, and only to positive but not negative CSR performance. Our findings suggest that investors likely use a heuristic approach to process immaterial and positive CSR issues, and a more deliberate and systematic approach to process material or negative CSR issues. Our study has both practical and theoretical implications.

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)0.022
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.706
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0220.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0030.010
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.004

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 head0.076
GPT teacher head0.335
Teacher spread0.259 · 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

Citations99
Published2019
Admission routes1
Has abstractyes

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