Management Practices of SMEs Owners in Emerging Economies: A Gender Comparative Study
Bibliographic record
Abstract
The literature on management practices indicates that the company’s performance depends largely on the skills of its leader, when the intuition of the latter is based on the instruments and rational management methods. The aim of this study is to analyse the relationship between the gender and management practices in terms of current operations (Production, marketing, finance, …), identify the characteristics of the owner-manager of SMEs (male and female specific), and detect the points of divergence and convergence between women's and men's management. To do so, we conducted a theoretical analysis of the main concepts and indicators that allowed us to develop a research model. The analysis of the answers was based on a survey adressed to a sample of owner-managers. Our findings confirm that the personal characteristics of the owner-manager influence the management practices. The results of the comparison between the Moroccan ruling woman and man, show that there are no real differences in management style, but rather some shared values between them. This paper provides a theorical contribution on the link between the profile of owner-managers and management practices including the gender parameter. In terms of pratical contribution, it contribute to understand behavior of Moroccan SMEs owners and to show the importance of this two dimensions, the profil of owner managers and gender approach, it can be also considered as a recent study of the typical profile of owner-managers in an emerging country such as Morocco. We try, through this work, to contribute to this field of research which remains very fertile.
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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 | 0.001 |
| 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".