Working capital management and managerial talent
Bibliographic record
Abstract
Purpose Talented managers arguably remain quintessential to firm value and performance. While the literature offers evidence for the long-term orientation of talented managers, there is a paucity of evidence on the short-term performance of managers. Here, we examine the relationship between managerial talent and working capital management (WCM). Design/methodology/approach This study primarily employs a panel fixed-effect method controlling for firm-year and firm-industry for non-financial and non-utility firms for the years 1980 through 2016. Also, the authors control of potential bias that may impact the result. These controls include social capital, financial constraints and tests for endogeneity and spurious correlation. Findings The authors find the association between managerial talent and WCM to be positive and significant. The results indicate that talented managers have a higher cash conversion cycle. The empirical evidence still holds after controlling for social capital, religiosity and financial constraints. Also, the evidence still holds by employing an interaction term between Tobin's Q as a proxy for investment opportunities and talented managers. Practical implications The finding may lend credence to executive contracts. Human nature, by default, is only vested on a net benefit for self-aggrandization. Self-aggrandization can be evident through structures in managerial contracts. These contracts usually tie consequences to long-term growths. If a benefit is offered based on short-term operational goals, talented managers may do more to the management of working capital. Originality/value In the managerial talent literature, talents reflect a holistic picture of one that can succeed in both the short-term and long-term goals of a company. Here, the authors show that talented managers are inefficient in meeting short-term goal – working capital management. Thus, the authors add to the research by providing evidence that talented managers are myopic.
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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 | 0.005 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".