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Record W1976078374 · doi:10.5539/ibr.v6n12p145

The Impact of Corporate Social Responsibility on Human Resource Management: GDF SUEZ’s Case

2013· article· en· W1976078374 on OpenAlexvenueno aff
Claire Dupont, Perrine Ferauge, Romina Giuliano

Bibliographic record

VenueInternational Business Research · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsnot available
Fundersnot available
KeywordsCorporate social responsibilityBusinessHuman resource managementEconomic shortageHuman resourcesContext (archaeology)Social responsibilityPopulationTalent managementMarketingSustainable developmentDiversity (politics)Public relationsManagementEconomicsPolitical scienceSociology

Abstract

fetched live from OpenAlex

This paper aims to analyse GDF SUEZ’s sustainable development report, focusing on its involvement in socially responsible Human Resource Management practices. We seek to know how Corporate Social Responsibility (CSR) affects HR functions, roles, and activities. According to Fortune magazine, GDF SUEZ ranks first among companies in the world in terms of social responsibility and is among the top 10 global companies across all sectors. Our research focuses on the following practices: recruitment and employment access, training and career development, and well-being in the workplace. These HRM practices seem important to analyse given the context in which companies will have to evolve: ageing of the population, risks of labour shortage, or the war for talent. Our results imply that CSR has a positive influence on employees’ advocacy role (Ulrich & Brockbank, 2005) because the Group integrates concerns regarding equal treatment, health and safety, and diversity. We also believe that GDF SUEZ Group desires to develop its brand further by presenting itself as a responsible employer to harvest the benefits that flow from that label.

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)0.005
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.518
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.159
GPT teacher head0.418
Teacher spread0.260 · 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.

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

Citations33
Published2013
Admission routes1
Has abstractyes

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