Corporate Social Responsibility Reporting, Intellectual Capital, and Financial Performance of Listed Firms in Ghana
Bibliographic record
Abstract
This study examines the moderating effect of intellectual capital (IC) on the relationship between corporate social responsibility (CSR) reporting and financial performance of 36 listed firms in Ghana from 2013 to 2022. We employed the system generalized method of moment and the dynamic panel threshold regression. We used data from BankScope, the Refinitiv database, and unconsolidated financial statements. We discovered a significant negative effect of CSR reporting on financial performance. Conversely, the IC components including value-added intellectual coefficient (VAIC), human capital efficiency (HCE), and structural capital efficiency (SCE) have a positive effect on financial performance. However, capital employed efficiency (CEE) negatively impacts financial performance of listed firms in Ghana. Our study further revealed that VAIC, HCE, and SCE exhibit positive and statistically significant moderating effects on the relationship between CSR reporting and financial performance. Our analysis also reported a negative and significant effect of CEE on the relationship between CSR reporting and financial performance. We also found that CSR reporting positively affects financial performance of listed firms in Ghana when IC (VAIC, HCE, and SCE) exceeds a certain threshold. Firms must carefully consider the interplay between CSR reporting, IC, and environmental engagement to optimize their financial performance. This research contributes to the theoretical understanding by demonstrating how IC components, particularly VAIC, HCE, and SCE, positively moderate the relationship between CSR reporting and financial performance, offering fresh perspectives on the role of IC in enhancing the financial outcomes of CSR activities among listed firms in Ghana.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".