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Record W2889438466 · doi:10.5539/ibr.v11n9p86

Corporate Social Responsibility Disclosure and Firm Performance of Malaysian Public Listed Firms

2018· article· en· W2889438466 on OpenAlexvenueno aff
Mohd Waliuddin Mohd Razali, Winnie Hii Sin Sin, Janifer Lunyai, Josephine Yau Tan Hwang, Irma Yazreen Md Yusoff

Bibliographic record

VenueInternational Business Research · 2018
Typearticle
Languageen
FieldDecision Sciences
TopicImpact of AI and Big Data on Business and Society
Canadian institutionsnot available
FundersUniversiti Malaysia Sarawak
KeywordsReturn on assetsReturn on equityBusinessCorporate social responsibilityAccountingLeverage (statistics)Market liquidityEnterprise valueControl (management)FinanceProfitability indexEconomics

Abstract

fetched live from OpenAlex

Corporate Social Responsibility (CSR) disclosure has become a rising concern for the public listed firms worldwide due to its ability to enhance firm’s market performance and financial performance. The main objective of this study is to investigate the relationship between CSR disclosure and firm performance of Bursa Malaysia’s listed companies based on their market value added (MVA), return on equity (ROE) and return on assets (ROA). 324 samples of public listed companies’ annual report for the period of 2014 to 2016 were obtained from Bursa Malaysia and examined. The extent of their CSR disclosure were measured and analyzed. After accounting for control variables such as firm size, firm age, firm leverage and firm liquidity, the result shows that there is a positive significant relationship between CSR disclosure and firm performance in terms of ROA and ROE. This reveals that high level of CSR disclosure helps firms to achieve optimum performance through increased competitiveness, improved firm’s image amongst society, and creates new opportunities in the marketplace. The findings also showed mix results among the control variables towards firm performance. For future research, this paper recommends to extend the study by using different CSR disclosure measurement, different firm performance measurement such as return on investments (ROI) and Tobin’s Q and different samples.

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)0.006
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.368
GPT teacher head0.467
Teacher spread0.099 · 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

Citations19
Published2018
Admission routes1
Has abstractyes

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