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Record W3038432419 · doi:10.5430/bmr.v9n2p25

Exploring New Ways of Communicating CSR to the Relevant Stakeholders: An Empirical Study

2020· article· en· W3038432419 on OpenAlexvenueno aff
Mohammad Abul Bashar

Bibliographic record

VenueBusiness and Management Research · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsnot available
Fundersnot available
KeywordsCorporate social responsibilityBusinessSustainabilityPublic relationsMarketingPolitical science

Abstract

fetched live from OpenAlex

A flower must be pollinated properly to produce seeds as final outcome of next germination to create new flowers and so on. Accordingly Corporate Social Responsibility (CSR) actions and practices of an enterprise should be communicated with its stakeholders. CSR and CSR communication should go hand-by-hand. The recent business trend shows that businesses are becoming increasingly aware of CSR and CSR communication systems. Existing studies, firm’s case study and real world phenomenon also reveal that business houses and society are benefiting from CSR actions and communicating those with their stakeholders. This research explores CSR and CSR communicating strategies and finds that stakeholders have in-depth concerns about CSR. It’s interesting that popular CSR practices like charity and philanthropic actions have been replaced by environmental (carbon footprint, air and water pollution), legal (complying regulatory imperatives) and ethical (promoting corporate ethics, norms and values) etc. Moreover, the study also shows that communicating CSR actions through CSR reporting, company annual reports or firm’s sustainability reports and advertising have become less fashionable means of exchanging CSR efforts while academic books (companies are cited as examples or extracted as referred case studies), newspaper, internet and third party (social, political, local government authorities) association have become more trusted ways of communicating CSR motives, practices and actions.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.692
Threshold uncertainty score0.560

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.003
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 head0.734
GPT teacher head0.421
Teacher spread0.313 · 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

Citations3
Published2020
Admission routes1
Has abstractyes

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