The Effects of Corporate Social Responsibilities on the Financial Performance of RCBs in Ghana: The Moderating Role of Board Quality
Bibliographic record
Abstract
Abstract This chapter aims to examine the moderating role of corporate governance on the relationship between corporate social responsibility (CSR) and the financial performance of some selected rural and community banks (RCBs) in Ghana. Using cluster and convenient sampling, the researchers distributed questionnaires to all senior staff and board members of the sampled 27 RCBs in the Bono, Bono East, Ahafo, and Ashanti Regions of Ghana. In analyzing the research data, stakeholder and agency theories were used combined with the ordinary least square (OLS) regression modeling method and proxies of the moderator variable to ensure robustness. CSR, among the sampled RCBs, tends to be high on philanthropic and economic responsibilities due to its potential economic returns. Hence, there is a positive relationship between engagement in CSR activities and the financial performance of RCBs in Ghana. Nonetheless, in the short term, there is a negative relationship between the expenditure on CSR activities in a given year and RCBs’ financial performance. Again, there is a complementary moderating effect of corporate governance quality on the relationship between CSR and the financial performance of RCBs in Ghana. This study provides vital information to policymakers in ensuring board quality and CSR as a vessel for improving financial performance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 source (direct Gemma or distilled Codex), 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".