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Record W4316468359 · doi:10.3390/jrfm16010052

Strengthening Formal Credit Access and Performance through Financial Literacy and Credit Terms in Micro, Small and Medium Businesses

2023· article· en· W4316468359 on OpenAlex

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.

venuePublished in a venue whose home country is Canada.
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

VenueJournal of risk and financial management · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy and Behavior
Canadian institutionsnot available
Fundersnot available
KeywordsFinancial literacyBusinessAffect (linguistics)Stratified samplingSample (material)LiteracySmall and medium-sized enterprisesTest (biology)FinanceEconomicsEconomic growthPsychologyStatisticsMathematics

Abstract

fetched live from OpenAlex

This study aims to test financial literacy and credit conditions in determining formal credit access to determine the performance of MSMEs. This research includes the type of associative research that is accompanied by hypothesis testing. This research was conducted on MSMEs of as many as 324 creative industry players in four cities in East Java (Mojokerto, Pasuruan, Gresik, and Sidoarjo) with a sample size of 100 actors who had accessed formal credit using the stratified random sampling method for data collection. The results of Smart PLS analysis show that financial literacy and credit terms directly and significantly affect access to formal credit and MSME performance; formal credit access directly and significantly affects MSME performance. Likewise, financial literacy and credit terms indirectly affect the performance of MSMEs. These results mean that financial literacy and credit terms have a strategic role in explaining why access to formal credit is growing and is attracting MSMEs to strengthen capital to improve performance.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.405
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.004
Open science0.0000.001
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 head0.014
GPT teacher head0.239
Teacher spread0.225 · 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