The impact of corporate governance on working capital management efficiency of American manufacturing firms
Bibliographic record
Abstract
Purpose The purpose of this study is to investigate the impact of corporate governance on working capital management efficiency. This study also seeks to extend the findings of Gill and Shah. Design/methodology/approach This study applied a co‐relational research design. A sample was selected of 180 American manufacturing firms listed on the New York Stock Exchange (NYSE) for a period of 3 years (from 2009‐2011). Findings The findings of this study indicate that corporate governance plays some role in improving the efficiency of working capital management. Research limitations/implications This is a co‐relational study that investigated the association between corporate governance and working capital management efficiency. There is not necessarily a causal relationship between the two, although the paper provides some conjectures to the findings. The findings of this study may only be generalized to firms similar to those that were included in this research. Originality/value This study contributes to the literature on the factors that improve the efficiency of working capital management, and in particular on the association between several features of corporate governance and the efficiency of working capital management. The findings may be useful for financial managers, investors, financial management consultants, and other stakeholders.
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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 | 0.003 | 0.015 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".