Corporate Involvement in the Sustainable Development Goals: The Case of Banks in Sub‐Saharan Africa
Bibliographic record
Abstract
ABSTRACT This study examines the extent of banks' involvement in the Sustainable Development Goals (SDGs) and factors influencing their participation in Sub‐Saharan Africa (SSA). Previous studies have highlighted the dearth of research conducted in developing countries. The study employed an explanatory sequential mixed‐methods approach across six countries underpinned by legitimacy and corporate sustainability theories. The quantitative phase involved 61 banks, utilizing content analysis, followed by a qualitative phase with 14 interviewees. The findings reveal a low depth of SDG involvement but a relatively higher breadth of engagement and awareness. Banks prioritize SDGs 13, 8, 5, 4 and 3, aligning with their core business and allowing them to make a positive impact, consistent with corporate sustainability and legitimacy theories. The results reveal challenges that impede progress among banks in SSA. The findings can serve as a guide for policymakers and practitioners in creating an enabling environment that supports the banking sector to increase its contribution to the SDGs.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".