Do Corporate Governance Practices Influence Working Capital Management Efficiency? Evidence From Listed Manufacturing Companies in Sri Lanka
Bibliographic record
Abstract
The purpose of the study is to investigate the influence of corporate governance practices on working capital management efficiency in the listed companies of the manufacturing sector in Sri Lanka. Board meeting, board size, CEO tenure and size of the audit committee are used as corporate governance practices and the cash conversion cycle is calculated to measure the working capital management efficiency. Sales growth and firm size are considered as control variables to evaluate the influence of corporate governance practices on working capital management efficiency. Relevant data are extracted from the annual reports of 30 listed manufacturing companies for the period from 2013 to 2017. Finally, 150 observations are used for the data analysis. Pearson correlations are executed to determine the relationship between corporate governance practices and working capital management efficiency. OLS regression analysis is performed to determine the explanatory power of the combination of corporate governance practices on the efficiency of working capital management. The correlation analysis shows that board meeting, CEO tenure and firm size have a significant positive relationship with cash conversion cycle. The regression results suggest that board meetings and CEO tenure have a significant positive influence on cash conversion cycle. Generally, the shorter the cash conversion cycle is better for the business, therefore, according to this result the increase in a board meeting and CEO tenure have the considerable decreasing in liquidity position in an organization. Therefore, the outcome of the study may be useful to the top management of the firms and practitioners when they are implementing governance mechanisms in order to enhance the working capital efficiency.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".