Bibliographic record
Abstract
Purpose – The purpose of this paper is to survey managers of firms listed on the Casablanca Stock Exchange (CSE) to learn their views about the factors influencing dividend policy, dividend issues, and explanations for paying dividends. It compares the results to similar dividend surveys in the USA, Canada, Indonesia, and India. Design/methodology/approach – The study uses a mail survey of CSE listed firms that paid one or more cash dividends to common stock holders between June 1, 2010 and September 30, 2014 as the primary means of collecting data. Findings – The evidence shows that the most important determinants of a firm’s dividend policy are the level of current earnings, stability of earnings, and needs of current shareholders. A significant correlation exists between the overall rankings of the 25 factors influencing dividend policy between managers of Moroccan firms and those of USA, Canadian, Indonesian, and Indian firms. Managers of Moroccan firms perceive that dividend policy affects firm value. Managers view multiple theories including signaling, agency, catering, and life cycle explanations as credible and contribute in explaining why their firms pay dividends. Research limitations/implications – Despite a high response rate of almost 55 percent, the number of responses limits the ability to divide the sample firms by size, industry, and other characteristics and to test for statistically significant differences between various groups. Originality/value – This is the first survey-based research designed to document how Moroccan managers view dividends. Although evidence suggests that some factors are consistently more important than others, no universal set of factors is likely to be applicable to all firms.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".