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Record W3197377114

Financial Innovation Strategy and Financial Performance of Deposit Taking Sacco’s in Nairobi City County

2019· article· en· W3197377114 on OpenAlexaff
Nancy Mbesa Moki, Stephen Kanini, Godfrey Kinyua

Bibliographic record

VenueSSRN Electronic Journal · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMicrofinance and Financial Inclusion
Canadian institutionsSaint Paul University
Fundersnot available
KeywordsFinancial intermediaryFinancial inclusionFinanceBusinessDescriptive researchFinancial servicesDescriptive statisticsPopulationFinancial system
DOInot available

Abstract

fetched live from OpenAlex

Deposit taking SACCO’s continue to play a significant role in the lives of the poor in Kenya by responding to their needs, concerns and voices by providing easy access of financial services. Financial inclusion is seen as a solution to include on a large-scale previously excluded poorer groups without access to capital into the financial system. The objective of the study was to determine the effect of financial innovation strategy on performance of savings and credit co-operative society in Nairobi City County. The study was guided by open systems theory, financial intermediation theory and the Life cycle of saving theory. This study adopted both descriptive research design and causal research design. The study population comprised of the 40 registered deposit taking SACCO’s in Nairobi County and the study used descriptive inferential analysis of data collected. The study identified that financial innovation was significant in increasing financial performance of SACCO’s. The study concluded that firms that have not effectively implemented financial innovation may fail or collapse or otherwise can be absorbed by other well managed SACCO’s. The study recommended the need to invest in financial innovation strategy to reduce cost and increase efficiency in the sector.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.337
Threshold uncertainty score0.868

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.013
GPT teacher head0.210
Teacher spread0.196 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations7
Published2019
Admission routes1
Has abstractyes

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