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Record W2925908476 · doi:10.5430/ijba.v10n3p1

Does Ethics Reward on Public Markets: Empirical Evidences Ten Years After the Great Recession

2019· article· en· W2925908476 on OpenAlexvenueno aff
Catello Giovanni Landi, Valerio Rapone, Danilo Tuccillo, Andrea Rey

Bibliographic record

VenueInternational Journal of Business Administration · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsnot available
Fundersnot available
KeywordsCeteris paribusCorporate social responsibilityRecessionLeverage (statistics)BusinessSustainabilityStock exchangeMonetary economicsExternalityReputationMarket liquidityEconomicsFinanceMicroeconomics

Abstract

fetched live from OpenAlex

In the aftermath of the last Great Recession in 2007, firms’ commitment to social responsibility and sustainability started to be considered a corporate leverage to make extra-returns as well as to improve corporate reputation on institutional markets. This in turn has implied a lower uncertainty among investors and a higher trust from stakeholders’ categories, rising virtuous firms’ returns to over-perform their less responsible peers. Hence, this paper investigates the positive externalities of CSR on Italian stock exchange market, focusing on Blue Chips’ financial performance over the ten years post-crisis. In particular, we examined whether a listed company has been rewarded by its stakeholders over a high volatility periods, leveraging on CSR and Sustainability issues. Empirical findings highlight, ceteris paribus, two implications in regards to the impact of sustainability rating on corporate financial health. Indeed, the effect of CSR and corporate sustainability improves significantly companies’ earning performance (Return on Asset), although firms do not benefit from economic outperformances (Earning per Share) on stock exchange market.

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)0.005
Version: codex-gemma-dda1882f352aValidation 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 score0.068
Threshold uncertainty score0.875

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0000.000
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 head0.064
GPT teacher head0.347
Teacher spread0.283 · 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 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

Citations2
Published2019
Admission routes1
Has abstractyes

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