MétaCan
Menu
Back to cohort
Record W2945816347 · doi:10.5430/ijfr.v10n3p239

Examining the Relationship Between Enterprise Risk Management and Firm Performance in Malaysia

2019· article· en· W2945816347 on OpenAlex
Mazurina Mohd Ali, Nur Shazwani Ab Hamid, Erlane K Ghani

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueInternational Journal of Financial Research · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicRisk Management in Financial Firms
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessLeverage (statistics)AccountingEnterprise risk managementProfitability indexPublic enterpriseAudit committeeSample (material)GuidelineAuditRisk managementFinance

Abstract

fetched live from OpenAlex

This study aims to examine the relationship between enterprise risk management (ERM) implementation and firm performance in Malaysia. Using the sample from 2010 to 2016, this study examines the relationship between ERM and firm performance among Malaysian top 100 public listed firms registered on the Index FTSE Bursa Malaysia 100 (FBM100) KLSE. This study also provides comparisons before and after the introduction of Bursa Malaysia Guidelines 2013. This study shows a positive and significant coefficient between profitability and firm performance towards ERM implementation. However, this study shows insignificant relationship between firm size, financial leverage and audit firm with firm performance. This study also shows that there is an increase in the mean score and standard deviation of these variables after the implementation of Bursa Malaysia Guideline 2013. The findings in this study provides an understanding to the Malaysian public listed firms on the importance of ERM and subsequently, maximise the benefits of ERM especially after the introduction of Bursa Malaysia Guidelines 2013 for the benefits of their stakeholders and regulatory improvement in future.

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.

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)0.004
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.070
Threshold uncertainty score0.394

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
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 science0.0010.001
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 head0.074
GPT teacher head0.325
Teacher spread0.252 · 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