The Impact of Corporate Governance on Working Capital Management Efficiency: Evidence from the Listed Companies in the Consumer Services Sector in Botswana
Why this work is in the frame
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Bibliographic record
Abstract
The paper presents the findings of the analysis of the impact of corporate governance mechanisms on working capital management efficiency in the listed companies of the Consumer service sector in Botswana. Eight corporate governance elements and seven working capital components were extracted from the annual reports of a sample of six companies for the period 2012 to 2017 for the analysis. Thirty six observations were obtained. Pearson correlations were executed to determine the relationship between corporate governance elements and working capital components. OLS regression analysis was performed to establish the explaining power of the combination of corporate governance elements on each of the working capital components. The correlation analysis shows that number of non-executive directors has a significant negative but moderate relationship with cash conversion cycle and number of board subcommittees has significant positive but moderate relationship with Debt ratio. The regression results suggest that corporate governance mechanisms have a significant impact on working capital management, the highest impact being reflected on inventory conversion period. The implications of these findings are that boards of directors have a significant role to play in working capital management efficiency of the companies they govern. They should therefore continue providing attainable policies on working capital management and remain vigilant on demanding feedback on their implementations.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it