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Record W2955427312 · doi:10.3390/su11133698

The Effect of Corporate Visibility on Corporate Social Responsibility

2019· article· en· W2955427312 on OpenAlexaff
Zhichuan Li, Taylor R. Morris, Brian Young

Bibliographic record

VenueSustainability · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsWestern University
Fundersnot available
KeywordsVisibilityCorporate social responsibilityProxy (statistics)StakeholderNewspaperBusinessPublic relationsAccountingMarketingAdvertisingPolitical scienceComputer science

Abstract

fetched live from OpenAlex

Outside of direct ownership, the general public may feel it is an implicit stakeholder of a firm. As the public becomes more vested in a firm’s actions, the firm may be more likely to engage in Corporate Social Responsibility (CSR) activities. We proxy for the public’s stake in a firm with public visibility. Based on 3400 unique newspaper publications from 1994–2008, we measure visibility for the S&P 500 firms with the frequency of print articles per year concerning the firm. We find that visibility has a signficant, positive relationship with the CSR rating. Evidence also suggests this relationship may be causal and working in one direction, from visibility to CSR. While the existing literature provides other factors that influence CSR, visibility proves to have the most significant impact when tested alongside those other factors. Visibility also has a mediating effect on the relationship between CSR rating and firm size. CSR rating and firm size relate negatively for the lowest visibility firms and positively for the highest. This paper provides strong evidence that visibility is an important factor to consider for studies on corporate social performance.

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)0.003
metaresearch head score (Gemma)0.036
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 score0.006
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.036
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0000.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.018
GPT teacher head0.271
Teacher spread0.253 · 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

Citations30
Published2019
Admission routes1
Has abstractyes

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