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Record W4412598295 · doi:10.5267/j.ac.2025.5.004

The financial trade-offs of corporate social responsibility: A simultaneous equation approach in the Nigerian context

2025· article· en· W4412598295 on OpenAlexvenueno aff
Olabamiji Atanda, Abiodun Daniel Aikomo, Idorenyin John Okon

Bibliographic record

VenueAccounting · 2025
Typearticle
Languageen
FieldComputer Science
TopicEconomic Growth and Development
Canadian institutionsnot available
Fundersnot available
KeywordsCorporate social responsibilityContext (archaeology)BusinessStructural equation modelingSocial responsibilityAccountingFinanceEconomicsPublic relationsPolitical scienceMathematicsStatisticsGeography

Abstract

fetched live from OpenAlex

We explore the bidirectional relationship between corporate social responsibility (CSR) and firm performance among non-financial firms in Nigeria from 2010 to 2022, addressing the financial trade-offs associated with CSR in an emerging market. Given mixed evidence on CSR’s impact on profitability and the limited research in resource-constrained environments, this study aims to clarify how CSR affects profitability, as measured by return on assets, and vice versa. Employing a simultaneous equation modeling framework, estimated via System Generalized Method of Moments (System GMM), the study addresses endogeneity and heterogeneity to produce robust results. Findings reveal a significant negative impact of CSR disclosure on profitability, implying that CSR may impose immediate costs on firms, reducing short-term returns. Furthermore, lower profitability drives firms to intensify CSR efforts, potentially as a strategic response to enhance reputation and stakeholder support. These results suggest that CSR engagement involves financial trade-offs, with firms balancing short-term costs against long-term reputational benefits, particularly relevant in contexts like Nigeria where CSR lacks regulatory backing. This study offers valuable insights into the CSR-profitability dynamics in an emerging market context, providing actionable information for managers, policymakers, and investors on navigating the complexities of CSR in financially constrained settings.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.512
Threshold uncertainty score0.236

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.026
GPT teacher head0.233
Teacher spread0.207 · 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 designTheoretical or conceptual
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

Citations1
Published2025
Admission routes1
Has abstractyes

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