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Record W3037041118 · doi:10.5539/ibr.v13n7p93

Ownership Structure, Board Composition and Voluntary Disclosure by Non-financial Firms Listed in )ASE)

2020· article· en· W3037041118 on OpenAlexvenueno aff
Dana Adel Alqatameen, Mahmoud Alkhalaileh, Mohammad Nadeem Dabaghia

Bibliographic record

VenueInternational Business Research · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessAccountingVoluntary disclosureAuditTurnoverTransparency (behavior)Panel dataAudit committeeStock exchangeQuality (philosophy)Foreign ownershipFinanceEconomics

Abstract

fetched live from OpenAlex

This study aims to examine the impact of ownership structure and board composition on the level of voluntary disclosure by non-financial firms listed in the Amman Stock Exchange (ASE). The study uses panel hand-collected data from 443 annual reports for a 5-year period (2012 – 2016) and employs an OLS-regression to test the study predictions. Compatible with the study predictions and most prior related studies’ findings, both higher managerial ownership and the CEO-duality produce low levels of voluntary disclosure, while foreign ownership is positively associated with the level of voluntary disclosure. Findings also indicate that larger firms deemed to provide higher levels of voluntary disclosures than smaller firms. Besides, companies audited by big4 firms disclose more voluntary information than those audited by others. The study findings have implications for policymakers and regulators. Policymakers and regulators may encourage, emphasize and enforce, if necessary, the regulation that enhances the quality of financial disclosures including the separation between the Chairman of the board of directors and CEO roles to improve the level of control and supervision and enhance the transparency of financial reporting by Jordanian firms.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.225
Threshold uncertainty score0.524

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.031
GPT teacher head0.287
Teacher spread0.257 · 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

Citations13
Published2020
Admission routes1
Has abstractyes

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