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Record W3041544551 · doi:10.5430/ijfr.v11n4p195

Effects of Board Size, Board Composition and Dividend Policy on Real Earnings Management in the Jordanian Listed Industrial Firms

2020· article· en· W3041544551 on OpenAlexvenueno aff
Yousef Shahwan, Tareq Hammad Almubaydeen

Bibliographic record

VenueInternational Journal of Financial Research · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsnot available
FundersZarqa University
KeywordsAccountingShareholderBusinessDatabase transactionDividendAuditDividend policyEarningsComposition (language)On boardValue (mathematics)Dividend payout ratioAudit committeeBridging (networking)Earnings managementFinanceCorporate governanceDatabaseStatistics

Abstract

fetched live from OpenAlex

Earning manipulation has been a normal transaction among the global businesses, in which business organization sees it as beneficial, thereby turning black eyes to its negative impact on the general economy. This study aimed at examining the impact of board size, Board composition and dividend policy on real earnings management in the listed Jordanian industries. 8 years data (2010 to 2018) was extracted from the audited financial reports of the selected firms. Data was analyzed using Structural Model via AMOS version 26 and SPSS version 21. The findings revealed a positive and significant effect between board size, board composition and real earning management at p-value<0.05 and 0.001 (two-tailed) respectively. While negative of dividend policy on REM was recorded at p-value>0.05 (two-tailed). This study has immensely contributed towards bridging the gap in the existing knowledge as it documented a new finding. The benefits of these findings cross over the managers, shareholders, board of directors, investors, the Jordanian government and all other relevant institute for the buildup of the healthiest industrial sector and better economy.

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)0.002
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.804
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.034
GPT teacher head0.315
Teacher spread0.281 · 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

Citations23
Published2020
Admission routes1
Has abstractyes

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