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Record W4398164469 · doi:10.1016/j.heliyon.2024.e31580

Corporate social responsibility and firm performance: Case of Kazakhstan

2024· article· en· W4398164469 on OpenAlexaff
Maya Katenova, Hassan Qudrat‐Ullah

Bibliographic record

VenueHeliyon · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsYork University
Fundersnot available
KeywordsCorporate social responsibilityProfit marginOperating marginReturn on assetsStock exchangeReturn on equityBusinessEmerging marketsAccountingSample (material)Equity (law)Net profitKazakhProfit (economics)MarketingEconomicsFinancePolitical sciencePublic relations

Abstract

fetched live from OpenAlex

The research seeks to find a relationship between Corporate Social Responsibility (CSR) and companies' performance. Studied variables were measured and analysed using a sample of companies listed on the Kazakhstan Stock Exchange (KASE). The study employed the regression model and least squares technique as the primary analytical tools. CSR is examined in conjunction with variables such as Return on Assets, Return on Equity, Market Value, and Net Profit Margin. As a result of the research, it was found that firm performance and CSR relate to each other in the studied companies. The research found a positive correlation between CSR practices and Net Profit Margin in Kazakh companies. While this study focused on a single country, its methodology can be applied to research in other emerging and developing nations. The primary contribution of this research is the examination of the relationship between firm performance and CSR practices in the post-Soviet emerging market of Kazakhstan.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.440
Threshold uncertainty score0.616

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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)0.0000.000

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 head0.045
GPT teacher head0.285
Teacher spread0.240 · 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

Citations14
Published2024
Admission routes1
Has abstractyes

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