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Record W2801903637 · doi:10.5539/ibr.v11n5p102

Directors’ Remuneration and Firm’s Performance: A Study on Malaysian Listed Firm under Consumer Product Industry

2018· article· en· W2801903637 on OpenAlexvenueno aff
Mohd Waliuddin Mohd Razali, Ng Sue Yee, Josephine Yau Tan Hwang, Akmal Hisham Tak, Norlina Kadri

Bibliographic record

VenueInternational Business Research · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsnot available
FundersUniversiti Malaysia Sarawak
KeywordsRemunerationBusinessReturn on assetsProfitability indexIncentiveLeverage (statistics)ShareholderAccountingProduct (mathematics)Sample (material)MarketingCorporate governanceFinanceEconomicsMicroeconomics

Abstract

fetched live from OpenAlex

Remuneration is broadly used as an incentive that affects decisions made and strategies planned by directors which cause great impact on firm performance and profitability. This study aims to investigate the directors’ remuneration of the consumer products sector focusing particularly on Malaysian listed companies under Consumer Product Industry. These firm’s performances are measured by return on assets (ROA) and return on equities (ROE). This study consists a sample of 40 Malaysian listed companies for the period of 2012 to 2014. After controlling for board size, CEO duality, firm size, firm age, and leverage; the regression results show director remuneration has positive relationship with firm performance (measured by ROA and ROE). This suggests that high remuneration is able to motivate and retain directors in order to perform their duty and work harder for the best interest of shareholders. The result also shows all variables affect firm performance differently. For future research, we recommend that this study be expanded using more samples from other industries and other measurement of firm performances such as growth and ratings.

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)0.000
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 score0.024
Threshold uncertainty score0.644

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.001

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 head0.097
GPT teacher head0.345
Teacher spread0.248 · 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

Citations18
Published2018
Admission routes1
Has abstractyes

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