Perceived Risk and External Finance Usage in Small- and Medium-Sized Enterprises: Unveiling the Moderating Influence of Business Age
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".