Herding Trend in Working Capital Management Practices: Evidence from the Non-Financial Sector of Pakistan
Bibliographic record
Abstract
Working capital management requires careful attention from corporate managers because it plays an important role in corporate stability. The social belongingness of managers induced them to learn from their society, colleagues, and overall industrial movement. They also learn from their peers that have more strategic efficiency. In line with these arguments, the objective of the current study is to explore the peer influence on corporate working capital management practices. For regression analysis, we utilized ten years of data (2009–2018) of non-financial publicly listed firms at PSX (Pakistan Stock Exchange). We used the cash conversion cycle (CCC) as a proxy variable to measure working capital management (WCM). We employed panel fixed effect and system GMM (generalized method of moments) models to estimate regression between the variables of the study. The empirical findings suggest the significant impact of peer WCM on corporate WCM. They also suggest the significant impact of other variables that determine the WCM. This study recommends social learning policy for corporate managers. They can learn from their peers to manage the working capital. Most previous studies discuss peer influence on investment decisions, corporate cash holding, financing policy, etc., but no study explores such a relationship specifically in the case of Pakistan.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".