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Record W2373019582 · doi:10.5539/jas.v8n6p159

Effects of Microcredit on the Financial Performance of Small Scale Cooking Oil Processors in Central Malawi

2016· article· en· W2373019582 on OpenAlexvenueno aff
Lovemore Mtsitsi, Joseph Dzanja, Sera Gondwe, Bonnet Kamwana

Bibliographic record

VenueJournal of Agricultural Science · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMicrofinance and Financial Inclusion
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessMicrofinanceDebt-to-equity ratioDebtScale (ratio)Production (economics)Asset (computer security)Financial systemFinanceEconomicsEconomic growth

Abstract

fetched live from OpenAlex

<p>The study was conducted to determine the effect of microcredit on financial performance of small scale cooking oil processors in central Malawi. Adopting a mixed research approach, the DuPont identity was used to compare the financial strengths and weaknesses between businesses that acquired a microcredit and those that did not. First, the study found that small scale cooking oil processing is a profitable business, regardless of their status in microcredit acquisition. However, microcredit had mixed effects on the financial performance of businesses. Microcredit improved the level of business capital for the businesses translating into better production efficiency, competitiveness and acquisition of a market share thus positively contributing to financial performance. On the other hand, microcredit increased the debt equity ratio hence increasing the businesses’ risk of default. The study recommends the businesses to further improve production efficiency and net asset turnovers. In addition, small and medium scale businesses ought to prudently contract microcredit in order to enhance their financial performance whilst checking for their risk of financial distress.</p>

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.647
Threshold uncertainty score0.227

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.011
GPT teacher head0.183
Teacher spread0.172 · 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 designBench or experimental
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
Published2016
Admission routes1
Has abstractyes

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