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Record W2084547737 · doi:10.5539/ibr.v8n2p28

Corporate Social Responsibility Reporting through the Lens of ISO 26000: A Case of Malawian Quoted Companies

2015· article· en· W2084547737 on OpenAlexvenueno aff
Andrew Munthopa Lipunga

Bibliographic record

VenueInternational Business Research · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsnot available
Fundersnot available
KeywordsCorporate social responsibilityAccountingBusinessPromotion (chess)Corporate governanceSocial responsibilityAnnual reportPublic relationsPolitical scienceFinance

Abstract

fetched live from OpenAlex

The paper examines the status of corporate social responsibility (CSR) reporting in the annual reports of Malawian quoted companies through the lens of ISO 26000 - Guidance on social responsibility. The study used content analysis methodology. A CSR disclosure index was developed based on ISO 26000’s seven core subjects of social responsibility to measure the level of CSR information disclosure in the annual reports for 2012 and 2013. The results indicate that all the sampled companies were making some CSR disclosures in their annual reports; however the disclosure levels are generally low. Out of the seven CSR themes, the companies scored highly only on organisational governance category and above average on community involvement and development and labour practices categories. On the other hand, they scored poorly on human rights, consumer issues, fair operating practices and environment categories. Furthermore, low individual company scores and the overall score suggest that much more effort is needed to enhance CSR reporting among Malawian quoted companies. The study seems to highlight that there is low awareness amongst the preparers of annual reports regarding all relevant categories that make up CSR. Thus the study recommends promotion of ISO 26000 among them in order to promote holistic approach to CSR that may lead to holistic CSR reporting.

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)0.011
metaresearch head score (Gemma)0.036
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.062
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.036
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.333
GPT teacher head0.426
Teacher spread0.093 · 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

Citations12
Published2015
Admission routes1
Has abstractyes

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