The Role of Loan-Related Risk Appetite in the Relationship between Financial Risk Considerations and MSME Growth Decision: A Mediation Analysis
Bibliographic record
Abstract
While many studies have focused on assessing performance, studies that pivot on growth itself are limited. To contribute in this area, this study used the Stimulus-Organism-Response (SOR) Model as its foundation in order to explore how inflation and access to finance affected loan-related risk appetite, also known as their willingness to bear either debt-related or opportunity-related risks arising from loan acceptance or avoidance, respectively. Subsequently, the mediating effect of loan-related risk appetite between inflation and access to finance and growth decision was also investigated. The analysis of links between variables under scrutiny was premised on the utilization of partial least squares-structural equation modeling (PLS-SEM), with the data resulting from a purposive sampling method comprising 80 respondents who are owners and/or managers of their MSME business operating for at least two (2) years. The findings present that access to finance, as well as loan-related risk appetite, has direct links to growth decision. Access to finance was also found to have direct effects to loan-related risk appetite. On the other hand, it was found that loan-related risk appetite functions as a partial mediator between access to finance and growth decision. Contrarily, the aforementioned circumstances cannot be observed for inflation.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".