Exploring the impact of Corporate Social Responsibility on the Financial Performance of Rural and Community Banks in Ghana
Bibliographic record
Abstract
The interest of Rural and Community Banks (RCBs) in CSR activities which include education and leadership development, Health, Community development are geared towards ensuring the wellbeing of community members. This means that CRS is key to the success of RCBs. Based on the above reasons the study attempts to examine the influence of CSR on the financial performance of selected Ghanaian Rural and Community Banks. RCBs sampled for this study were fifteen (15) from the Kumasi Metropolis in Ghana using annual reports for a six-year-period from 2012 to 2017. Regression analysis was employed to measure the effect of CSR, financial indicators, bank age, and size of the board of directors using the Data Envelope Technique on the performance of the RCBs. Findings showed that technical efficiency and productivity were low in some RCBs over the six-year-period. The results also showed that technical change, technological change, and Total Factor Productivity affected performance. However, the size of the board of directors was inversely related to the performance of RCBs. There is therefore the need for RCBs to improve input savings and also ensure an efficient allocation of monetary resources to corporate social responsibility activities as a way of enhancing their overall productivity. Not much has been written about the impact of CSR on the financial performance of RCBs in Africa. This study thus is among the first significant attempts to explore the impacts of CSR on the financial performance of RCBs in Africa.
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 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 | 0.001 | 0.004 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".