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Electronic Banking and Deposit Money Bank’s Performance in Nigeria

2021· article· en· W4285330643 on OpenAlex
Leelee N. Deekor

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueCross Current International Journal of Economics Management and Media Studies · 2021
Typearticle
Languageen
FieldComputer Science
TopicEconomic Growth and Development
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)Net interest marginMobile bankingPoint (geometry)Margin (machine learning)BusinessNet interest incomePoint of saleInterest rateFinanceComputer scienceMathematicsMarketingGeography

Abstract

fetched live from OpenAlex

The study investigates the impact of electronic banking on the performance of deposit money banks in Nigeria using quarterly data from the first quarter of 2010 the quarter of 2018 from the Central Bank of Nigeria statistical bulletin. It tested the response of the variables of interest, automated teller machine (ATM), point of sale (POS), mobile banking (MOBE), and web pay (WEPA) on net interest margin (NIMA).It employed the use of Jarque Bera normality and diagnostic tests, Augmented Dickey fuller unit root test for stationarity. The result shows that ATM, POS and WEPA are not significant to NIMA while MOBE is positively related and significant to NIMA. The study recommended that customers should be encouraged to use mobile banking platform.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.215
Threshold uncertainty score0.406

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.023
GPT teacher head0.270
Teacher spread0.247 · 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