Bibliographic record
Abstract
Purpose– This paper explores the main determinants of working capital management in the Egyptian SMEs and explains its effect on working capital management. Also, I examine the relation between working capital management and SMEs' profitability and capital structure.Design/methodology/approach – The study sample include data for 138 SMEs working in Egypt and financed by the national bank of Egypt from 2010 to 2013. Data have been collected from SMEs financial statements for four years for each company. OLS regression models have been used to examine the effect of working capital determinants on working capital level measured by CCC. We used firm size and industry as control variables and robustness our results using full regression models for every year of analysis. All regression models were checked for normality, multicollinearity and hetroscedisticity. Findings – The main results reflect a negative and significant effect of SMEs profitability, tangible fixed assets, and leverage. Also, the industry represents a significant factor in determining the level of working capital in the Egyptian SMEs. Moreover, the effect of working capital management and SMEs profitability and capital structure decisions has been examined. The results reflect that the Egyptian SMEs follow an aggressive policy as businesses hold low level working capital which leads to high return and high degree of risk (measured by LEVERAGE).Research limitations/implications – The study limited to the Egyptian SMEs and financed by the National Bank of Egypt. Originality/value – The study explores the main determinants of working capital in the Egyptian SMEs and examined its effect on working capital management. Also, we examined the effect of working capital determinants on each component of working capital measured by cash conversion cycle. Moreover, I examined the effect of working capital on SMEs' profitability and capital structure decisions.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".