CEO Personal Characteristics and Investment-Cash Flow Sensitivity: An Analysis of Indian Independent (Non-Business-Group-Affiliated) Firms and Business Group-Affiliated Firms
Bibliographic record
Abstract
This study investigates the relationship between the CEO characteristics and investment–cash flow sensitivity (ICFS) of Indian manufacturing firms. By using the GMM technique, this study finds that CEO characteristics reduce ICFS. Further, this study examines the moderating role of business group-affiliated firms, independent firms (non-business group-affiliated firms), and firm size on the relationship between CEO characteristics and ICFS. The results reveal that group affiliation moderates the effectiveness of CEO characteristics in reducing ICFS. In addition to this, independent firms rely more heavily on the individual capabilities of CEOs to overcome financial constraints and mitigate ICFS, whereas group firms benefit from structural advantages that diminish the relative impact of CEO characteristics on ICFS. Additionally, this study finds that firm size also moderates the relationship between CEO characteristics and ICFS. The results reveal that CEO characteristics significantly reduce ICFS, with a more pronounced effect in small-sized independent firms compared to their larger counterparts. However, in group-affiliated firms, CEO characteristics have a minimal effect on ICFS, and this impact remains consistent across small and large group firms. These findings offer valuable insights for firms, lending institutions, and investors, emphasizing the role of CEO characteristics in shaping financial decision making, especially in independent and smaller firms.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".