THE MULTI-FACETED IDENTITY OF FEMALE ENTREPRENEURS IN THE MOROCCAN DIRECT SELLING BUSINESS
Bibliographic record
Abstract
This study explores how the identities of female independent direct sellers (IDS) influence brand performance in Morocco’s direct selling industry and highlights the entrepreneurial opportunities offered by this sector. Direct selling has become a vital pathway for women in Morocco to achieve economic empowerment and entrepreneurial growth. Through in-depth, semi-structured interviews with 21 direct sellers in the Moroccan context, this research explores how various facets of IDS identity (personal, social, professional, entrepreneurial and self-projection) contribute to brand performance. The findings reveal that IDS are integral in co-creating the brand’s identity, and that their personal and professional identities significantly affect brand success. However, challenges such as limited educational backgrounds and inconsistent representation can hinder effective brand communication and trust-building with customers. Despite these challenges, IDS have developed critical entrepreneurial skills, including interpersonal communication, negotiation and self-confidence, which are essential for their success. The study also highlights that although IDS have made strides in personal development, their entrepreneurial vision and long-term self-projection remain underdeveloped. This research underscores the importance of aligning IDS identities with brand strategies and provides actionable insights for brand managers to enhance brand performance through targeted support and identity-driven training for female IDS.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".