Bibliographic record
Abstract
Purpose – The purpose of this paper is to survey managers of dividend-paying firms listed on the National Stock Exchange (NSE) in India to learn their views about the factors influencing dividend policy, dividend issues, and explanations for paying cash dividends and repurchasing shares. The authors compare the results to other dividend surveys based on firms in Indonesia, Canada, and the USA. Design/methodology/approach – The authors use questionnaire to gather primary data from a sample of 500 firms listed on the NSE. Findings – The most important determinants of dividends involve earnings (the stability of earnings as well as the level of current and expected future earnings) and the pattern of past dividends. Comparing the overall rankings of the 21 factors by respondents from Indian firms to those of Indonesian, Canadian, and US firms reveals statistically significant correlations. Respondents also perceive that dividend policy affects firm value. Respondents also view maintaining an uninterrupted record of dividends as important. The most highly supported explanations for paying cash dividends concern signaling, the firm life cycle, and catering. Although none of the theories of repurchasing shares is dominant, respondents provide little support for the agency explanation. Research limitations/implications – Although the tests suggest that the sample does not suffer from non-response bias, the findings should be viewed as suggestive rather than definitive because of the relatively low response rate. Originality/value – The paper presents new evidence about dividend policy of Indian firms. To the knowledge, this is the most comprehensive survey of Indian firms to date that captures managerial perceptions on both cash dividends and share repurchases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".