MétaCan
Menu
Back to cohort
Record W4407811859 · doi:10.3390/jrfm18030110

Effect of Financial Indicators on Corporate Social Responsibility: Evidence from Emerging Economies

2025· article· en· W4407811859 on OpenAlexvenueno aff
Assem Orazayeva, Muhammad Arslan

Bibliographic record

VenueJournal of risk and financial management · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsnot available
Fundersnot available
KeywordsCorporate social responsibilityBusinessEmerging marketsSocial responsibilityFinancial systemAccountingMonetary economicsEconomicsFinancePublic relationsPolitical science

Abstract

fetched live from OpenAlex

The relationship between corporate social responsibility (CSR) and financial performance remains a subject of ongoing debate, particularly regarding the determinants of CSR in emerging economies. This study examines the effect of financial indicators on the level of corporate social responsibility. This study presents an integrated perspective to determine factors that can impact socially responsible behavior in developing and emerging countries. We drew our sample from 110 firms from 20 emerging economies from 2016 to 2020. We applied an instrumental variable estimation technique to address potential endogeneity and heterogeneity issues. The results revealed that financial performance is a weak determinant of socially responsible behavior in developing and emerging regions. Additionally, weak enforcement mechanisms and regulatory frameworks play a significant role in shaping CSR behaviors. Contrary to conventional assumptions, firms with higher organizational slack do not necessarily allocate additional resources toward CSR initiatives. This study contributes to the literature by providing empirical insights into the financial and institutional drivers of CSR in emerging markets and offers implications for policymakers, regulators, and corporate decision-makers aiming to enhance socially responsible business practices.

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)0.004
metaresearch head score (Gemma)0.005
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 score0.673
Threshold uncertainty score0.919

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.014
GPT teacher head0.259
Teacher spread0.245 · 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

Citations6
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueJournal of risk and financial managementSame topicCorporate Social Responsibility ReportingFrench-language works237,207