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Record W2802718282 · doi:10.1002/csr.1502

Improving CSR performance by hard and soft means: The role of organizational citizenship behaviours and the internalization of CSR standards

2018· article· en· W2802718282 on OpenAlexaff
Francesco Testa, Olivier Boiral, Iñaki Heras Saizarbitoria

Bibliographic record

VenueCorporate Social Responsibility and Environmental Management · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsCorporate social responsibilityBusinessOrganizational citizenship behaviorCitizenshipKnowledge managementPublic relationsOrganizational commitmentPolitical scienceComputer science

Abstract

fetched live from OpenAlex

Abstract This study analyzes ‘hard’ (i.e. formal structures) and ‘soft’ (i.e. values) determinants of corporate social responsibility (CSR) performance, such as the effectiveness of management systems for CSR and the role of managers' organizational citizenship behaviours. Based on a sample of 130 Italian organizations that adopted management systems according to an international standard, this study shows that CSR performance depends on employees' commitment and the internalization of formalized CSR practices. The study also underlines the role of managers' organizational citizenship behaviours and the importance of leading by example in the substantial implementation of CSR practices. The integrative model proposed in the paper provides an overall picture of both the ‘hard’ and ‘soft’ factors that can explain the effectiveness of CSR management standards. The paper also contributes to the literature on the internalization and key success factors of certifiable management standards. Managerial implications of the main findings and avenues for future research are discussed.

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)0.003
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.110
Threshold uncertainty score0.827

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.012
GPT teacher head0.208
Teacher spread0.196 · 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

Citations61
Published2018
Admission routes1
Has abstractyes

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