Social Media Cost and the Levels of Cash Flow Among Listed Banks in Emerging Economies in Africa
Bibliographic record
Abstract
We examine the effect of social media advertising on the operating, financing and investing level of cash flow based on listed banks in emerging economies in Africa. We employ control variables such as board size and financial leverage to control other factors not captured in our model. Results obtained reveal that social media costs aid all levels of cash flow in Ghana and South-Africa. Social media costs in Botswana have a significant impact with operating and financing cash flow and an insignificant effect with investing cash flow. The results obtained from Kenya revealed a significant relationship between the independent variable and the financing and investing cash flow while an insignificant relationship was statistically obtained when social media cost was regressed against the operating level of cash flow. The Tanzanian results reveal a significant impact with the financing and investing level of cash flow but an insignificant value was obtained from the operating level of cash flow. The Nigerian results yielded an insignificant value, from the operating and financing level of cash flow, while investing cash flow generated a significant relationship.
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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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".