Mobile Network Operators’ Agency Banking Quality, Financial Inclusion Practices and the Sustainable Development Goals: Evidence from Cameroon
Bibliographic record
Abstract
The Sustainable Development Goals were launched by the United Nations in 2015. Empirical evidence suggests that despite the excitement at their launching by the international community and other stakeholders, their attainment by enterprises is still poor. Thus, using the technology of agency banking and mediated by financial inclusion practices, this study sought to determine the role played by Telecommunication Companies (MNO) in the attainment of the Sustainable Development Goals in Cameroon. To attain this objective, the study mobilized the Baron and Kenny (1986) model in the analysis of partial mediation effect using Ordinary Least Squares Regression. The purposive sampling technique was used to engage 1,420 users of MNOs’ services from the Cameroon’s 10 regional capitals in the study. The results showed that the effect of agency banking quality on Sustainable Development goals via the financial inclusion practices (Adjusted R2) is higher for the agents of Commercial banks and Micro, Small and Medium Sized Enterprises (MSMEs) than Microfinance Institutions. Thus, we suggest that Cameroon’s public authorities should focus more on Commercial banks and MSMEs than Microfinance Institutions to increase financial inclusion and ease the attainment of the Sustainable Development Goals in Cameroon.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| 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".