Bank Loan Financing Decisions of Small and Medium-Sized Enterprises: The Significance of Owner/Managers’ Behaviours
Bibliographic record
Abstract
The objective of this study is to highlight the influence of entrepreneurs’ behaviour on the decisions to apply for bank loans. A mixed research methodology known as triangulation was employed in order to achieve the objective of the study. Data were sourced from a stratified randomly selected sample of 450 Cameroonian SMEs and analysed using logistic regression. The result of the study revealed that both control aversion and overconfidence behaviours of the owner/managers influence significantly the decisions of SMEs to apply for bank loans. From the result, it is found that behavioural finance theory explains the decisions of SMEs to seek for bank credits. Contrary to the predictions of the pecking order theory, managerial behaviours such as the fear to loss the control of the firm, and overconfidence provide explanations on the decisions of SMEs to seek for bank loans. For instance, the fact that debt does not entail any loss of business control urges SMEs to prefer debt than external equity.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".