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Record W3011477161 · doi:10.5430/afr.v9n2p11

Corporate Governance Mechanisms and Firm’s Performance: Evidence from Jordan

2020· article· en· W3011477161 on OpenAlexvenueno aff
Mohammad Abdullah Fayad Altawalbeh

Bibliographic record

VenueAccounting and Finance Research · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCorporate governanceBusinessAccountingStock exchangeSample (material)Government (linguistics)Empirical evidenceQuality (philosophy)Test (biology)Finance

Abstract

fetched live from OpenAlex

The purpose of this study is to investigate the effect of corporate governance mechanisms on the firm’s performance. Corporate governance practices were divided into two groups; board structure and ownership structure. The sample of the study consists of 60 companies from industrial and service sectors that are listed on Amman stock exchange (ASE). Data was gathered manually through the annual financial reports for the period from 2012-2017 results in 366 year-observation. Stata statistical software was used to test the study hypotheses. The results revealed that board meetings frequency and government ownership positively and significantly impact the firm’s performance, these results suggest that board meetings frequency is considered an indicator of the board effectiveness that enhances decision making quality and thus the firm performance, the results suggest that government ownership is providing a helping hand that improves the firm’s performance. The findings also showed that board independence negatively and significantly impact the firm’s performance, this result suggests that independent board members do not guarantee to improve the performance of a firm, and it stays the firm’s responsibility to choose independent board members who are able to exercise effective oversight function for the purpose of enhancing the performance of a firm. This study contributes to the literature by providing empirical evidence from developing countries about the impact of corporate governance measures and practices on firms’ performance.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.464
Threshold uncertainty score0.795

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.002
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 head0.081
GPT teacher head0.278
Teacher spread0.197 · 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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