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Record W4388689124 · doi:10.1002/csr.2677

The impact of sustainability governance attributes on comprehensive <scp>CSR</scp> reporting: A developing country setting

2023· article· en· W4388689124 on OpenAlexaff
Waris Ali, Zeeshan Mahmood, Jeffrey Wilson, Hina Ismail

Bibliographic record

VenueCorporate Social Responsibility and Environmental Management · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsSustainabilitySustainability reportingCorporate governanceBusinessSustainability organizationsAccountingOfficerSustainability scienceSocial sustainabilityCorporate social responsibilityPublic relationsFinancePolitical science

Abstract

fetched live from OpenAlex

Abstract This research examines the influence of sustainability governance attributes such as the sustainability department, sustainability committee, sustainability officer, sustainability policy, sustainability strategy, and sustainability assurance on comprehensive CSR reporting. Content analysis of corporate annual reports and sustainability reports of 280 non‐financial listed companies in Pakistan was used to determine comprehensive CSR reporting score and to capture sustainability governance attributes. Multivariate regression analysis technique was used to test the relationships. The results revealed that all the sustainability governance mechanisms except the sustainability officer and the sustainability department positively influenced comprehensive CSR reporting. This study highlights the usefulness of implementing sustainability governance mechanisms in promoting comprehensive reporting in developing countries and sets implications for shaping future CSR‐related policies and practices. This research extends the literature on the importance of internal corporate governance mechanisms in driving sustainability reporting agenda by providing empirical evidence in favor of sustainability governance mechanisms in improving comprehensive reporting in developing countries.

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)0.006
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science 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 score0.121
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0020.001
Scholarly communication0.0000.001
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 head0.041
GPT teacher head0.283
Teacher spread0.242 · 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

Citations21
Published2023
Admission routes1
Has abstractyes

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