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Record W4361802830 · doi:10.55365/1923.x2023.21.13

The Effect of Board Attributes on Firm Performance: Evidence from Post MCCG 2007 and Post-MCCG 2012 in Malaysia

2023· article· en· W4361802830 on OpenAlexvenueno aff
Rina Ismail, Hasan Saifuddin, Haslinda Yusoff, Hamezah Md Nor

Bibliographic record

VenueReview of Economics and Finance · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCorporate governanceAccountingBusinessEmerging marketsPrincipal–agent problemDiversity (politics)Stewardship theorySample (material)On boardStewardship (theology)Gender diversityFinancePolitical scienceEngineering

Abstract

fetched live from OpenAlex

This study aims to examine the effect of board attributes on the firm performance of public listed companies in Malaysia during the periods of post-Malaysian Code on Corporate Governance (2007) and post-Malaysian Code on Corporate Governance (2012).Based on the Agency and Stewardship theories, the relationships between CEO duality, board composition, board size, gender diversity, and firm performance on PLCs in the main board of Bursa Malaysia were examined.A sample of 688 companies from 2011, 2012, 2016, and 2017 was observed.Findings indicate that the CEO duality, board size, and gender diversity significantly influence the firm performance in the study periods.Such findings offer interesting insights to the relevant authorities towards designing the bestsuited governance measures that may lead to a successful implementation of corporate governance practice.This study also signals the need for an enhanced role of relevant institutional agencies in strategising and strengthening the corporate governance framework in an emerging country such as Malaysia.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.038
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.013
GPT teacher head0.221
Teacher spread0.209 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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