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Record W4378083236 · doi:10.5539/jms.v13n1p215

Can Organizational Culture Encourage Corporate Social Responsibility and Improve Environmental Performance in [B]³ Companies?

2023· article· en· W4378083236 on OpenAlexvenueno aff
Kilvia Cristina Amaral da Luz, Nayane Thais Krespi Musial

Bibliographic record

VenueJournal of Management and Sustainability · 2023
Typearticle
Languageen
FieldDecision Sciences
TopicBusiness and Management Studies
Canadian institutionsnot available
FundersCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsOrganizational cultureCorporate social responsibilityBusinessSustainabilitySocial responsibilityCompetitive advantageBusiness administrationStock exchangeAccountingMarketingPublic relationsPolitical scienceFinanceEcology

Abstract

fetched live from OpenAlex

Organizational culture is one of the resources used by companies to obtain competitive advantage and organizational sustainability, whether by financial, social or environmental efficiency (Chatman & O’Reilly, 2016; Dyck et al., 2019). Another resource that is being encouraged to try and achieve sustainability is Corporate Social Responsibility (CSR), which can be facilitated or inhibited by organizational culture (Leandro & Rebelo, 2011; Shanak et al., 2020). Therefore, this study aims to analyze the relationship of organizational culture and CSR practices on environmental performance in companies listed on [B]³ (Brasil, Bolsa, Balcão), São Paulo’s stock exchange. The results indicated that there are no direct relations between organizational culture, environmental performance and the mediating effect; however, at the significance level of 10%, companies with high polluting potential become more harmful to the environment when there is a predominance of market and hierarchical culture.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0030.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.030
GPT teacher head0.291
Teacher spread0.261 · 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

Citations4
Published2023
Admission routes1
Has abstractyes

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