Corporate Social Responsibility Expenditures and Bank Performance: Role of Size Among Listed Banks in Ghana
Bibliographic record
Abstract
This study investigates the relationship between listed Ghanaian banks’ financial performance and corporate social responsibility (CSR), given the anticipated increase in businesses’ social duties. This study utilizes a panel autoregressive distributive lag model (Panel ARDL) to examine the impact of CSR on bank financial performance, as well as the moderating effect of bank size on CSR and financial performance, using return on assets as the measure of financial performance. All banks listed on the Ghana Stock Exchange (GSE) whose financial statements are readily accessible online, in print, or on their websites are chosen using convenience sampling. The sample spans 14 years, from 2010 to 2023. The results are shown for both the long and short run. Contrary to the expectations of many proponents of CSR, we find that firms incorporating CSR in their undertakings have negative financial performance. Additionally, the study finds that, relative to smaller banks, larger banks are able to alleviate this negative effect of CSR on performance by a certain magnitude. Therefore, not only should banks be strategic in their CSR implementation, but they should strive to grow their assets to the level where the negative effects of undertaking CSR could be reduced, if not entirely eliminated. To achieve this growth, the level of assets to keep is found to be above GHC 3922.52 million.
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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 | 0.001 | 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 | 0.000 | 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) | 0.004 | 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".