Growing SMEs and internal financing: the role of business practices
Bibliographic record
Abstract
Purpose The purpose of this paper is to identify business practices that may promote internal financing of growing SMEs. The authors expand the literature on entrepreneurial finance that reduces business practices to either financial management or bootstrapping, by exploring all management practices that may have an impact on liquidities. This study enriches the literature on business practices. This is an important consideration for managers of SMEs who intend to preserve their financial independence and their capacity to survive different crises. Design/methodology/approach The empirical study involved a sample of 235 growing Canadian SMEs. The sample was extracted from a private database using a questionnaire that covered a wide range of business practices. Variance testing of business practices between SMEs with a line of credit and those without (and lower overall debt) was supplemented by a logistic regression. Findings SMEs which make use of efficiency-promoting technology, carry out preventive maintenance and control their costs and turnover during their growth are more inclined to use less external financing. Originality/value This is the first study that associates business practices, beyond bootstrapping, with financing and which answers a critical question posed by SME executives on how to preserve their financial and decision-making autonomy through growth stages. In addition, the desire to retain control of the company does not compel the SME manager to limit the size of the company.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| 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".