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Record W4394610332 · doi:10.3390/jrfm17040150

Perceived Risk and External Finance Usage in Small- and Medium-Sized Enterprises: Unveiling the Moderating Influence of Business Age

2024· article· en· W4394610332 on OpenAlexvenueno aff
Nawal Abdalla Adam

Bibliographic record

VenueJournal of risk and financial management · 2024
Typearticle
Languageen
FieldDecision Sciences
TopicImpact of AI and Big Data on Business and Society
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessSmall and medium-sized enterprisesFinance

Abstract

fetched live from OpenAlex

The attainment of adequate finance remains a substantial hindrance for small- and medium-sized enterprises (SMEs) across many countries. This study aim to investigate the association between SMEs’ external finance utilization and perceived risk (PR). Additionally, it intends to explore the moderating role of business age (BAge) in the relationship between SMEs’ external finance utilization and PR. The study employed a structured online questionnaire to gather data from 711 SME owners/managers in Saudi Arabia. SmartPLS 4 software was utilized to analyze the research data. The results of the partial least squares structural equation modeling confirmed that the decision of SMEs to use external financing is significantly and negatively impacted by the PRs associated with external finance. Furthermore, BAge moderates the relationship between PR and SMEs’ external finance usage (EFU). However, the study found that BAge does not significantly affect both the PRs and the SMEs’ EFU. This study highlights the intricate dynamics of PR, BAge, and an SME’s decision to employ external finance. The practical and theoretical implications of the study findings are thoroughly discussed.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.823
Threshold uncertainty score0.446

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
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 integrity0.0000.000
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 head0.029
GPT teacher head0.294
Teacher spread0.265 · 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
Published2024
Admission routes1
Has abstractyes

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