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Record W4393054057 · doi:10.5539/jsd.v17n2p96

Mobile Network Operators’ Agency Banking Quality, Financial Inclusion Practices and the Sustainable Development Goals: Evidence from Cameroon

2024· article· en· W4393054057 on OpenAlexvenueno aff
Serge Messomo Ellé

Bibliographic record

VenueJournal of Sustainable Development · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMicrofinance and Financial Inclusion
Canadian institutionsnot available
Fundersnot available
KeywordsFinancial inclusionBusinessAgency (philosophy)Inclusion (mineral)Quality (philosophy)Sustainable developmentFinanceFinancial systemFinancial servicesPolitical scienceSociology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.867
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.035
GPT teacher head0.286
Teacher spread0.251 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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