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Record W4391063582 · doi:10.5267/j.uscm.2024.1.014

Accounting conservatism, accounting measurement, social capital disclosure, quality of accounting information: The moderating role of corporate social responsibility

2024· article· en· W4391063582 on OpenAlexvenueno aff
Nabil Ahmed Mareai Senan

Bibliographic record

VenueUncertain Supply Chain Management · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsnot available
FundersPrince Sattam bin Abdulaziz University
KeywordsAccountingAccounting information systemBusinessContext (archaeology)Corporate social responsibilityConsistency (knowledge bases)Positive accountingQuality (philosophy)Financial accountingPublic relationsPolitical science

Abstract

fetched live from OpenAlex

The paper aims to investigate the direct impact of accounting measurement, Social Capital Disclosure (SCD), and Accounting Conservatism (AC) on the Quality of Accounting Information (QAI), focusing on consistency in the context of Corporate Social Responsibility (CSR). The study involves industrial companies in the Yemeni capital, Sana’a. Hypotheses are developed and tested through the collected data from a questionnaire distributed to 178 employees. Results indicate a positive impact of accounting measurement, SCD, and AC on the quality and reliability of accounting information. However, when considering CSR as a moderating variable, these factors do not show a significant positive effect. The study is limited to industrial companies in Sana’a, and broader implications may require consideration of various other sectors.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.030
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.052
GPT teacher head0.279
Teacher spread0.227 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2024
Admission routes1
Has abstractyes

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