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Record W4309548443 · doi:10.3390/jrfm15110543

CSR and Firm Risk: Is Shareholder Activism a Double-Edged Sword?

2022· article· en· W4309548443 on OpenAlexvenueno aff
Konstantinos Bozos, Timothy King, Dimitrios Koutmos

Bibliographic record

VenueJournal of risk and financial management · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsnot available
Fundersnot available
KeywordsVotingShareholderCorporate social responsibilityBusinessSystematic riskSWORDAccountingCorporate governancePublic relationsFinancePolitical science

Abstract

fetched live from OpenAlex

Few can argue with the notion that corporations should at least consider corporate social responsibility (CSR) to better understand the impact of their operations on society. However, recent empirical tests suggest CSR has an ambiguous impact on firm performance. To shed new light on this debate, we examine the extent to which voting support for nonbinding shareholder-initiated CSR proposals is empirically linked to changes in firms’ underlying systematic risks. Using a rich dataset of proposals in the US from 1998 to 2011, we contribute several novel findings. First, we show that shareholder voting support is nonlinearly linked to changes in systematic risk. Specifically, proposals with low voting support increase risk while those with high support decrease risk. This nonlinearity is particularly pronounced for consumer-sensitive firms that cater primarily to individual consumers rather than for firms in non-consumer-sensitive industries that produce goods or services meant for industrial or governmental use. Second, the 2007–2009 financial crisis exacerbated increases in firms’ systematic risks for proposals with low voting support. Our results, which highlight asymmetry regarding firms’ CSR initiatives, remain robust when controlling for firm-specific factors as well as shifts in investor sentiment. From a risk management perspective, our findings suggest that CSR initiatives need strong shareholder support to realize benefits from the so-called ‘risk-reduction hypothesis’.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.761
Threshold uncertainty score0.742

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.019
GPT teacher head0.234
Teacher spread0.214 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations7
Published2022
Admission routes1
Has abstractyes

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