Gender composition of ownership and management of firms and the gender digital divide in Africa
Bibliographic record
Abstract
Purpose: This study analysed the determinants of firms’ adoption and utilisation of digital technologies in Africa, with specific attention to the gender structure of firms’ ownership and management, in the interest of closing the gender digital divide. Design/methodology/approach: Logistic and Poisson regression techniques were used to analyse firm-level data from the World Bank’s Enterprise Survey in 48 African countries for the period 2006–2019. Findings/results: (1) Representation: The descriptive analysis shows very low representation of women in the ownership and management of firms in Africa. Whilst just over a quarter of the firms were partly women-owned, less than 10% are majority- or all-women-owned and only 12% have women as a top manager. The results are a comparison of firms according to gender composition. (2) Adoption: The regression estimates suggest that firms that are partly women-owned are more likely to adopt digital technologies, but all-women-owned and firms with women as top managers are less likely to adopt digital technologies for their business activities. These results on the adoption of digital technologies remained consistent with the results on utilisation of digital technologies for business activities. (3) Utilisation: Partly women-owned or women-led firms are less likely to use digital technologies for business activities such as using the Internet for research and placing orders. However, these firms are more likely to use e-mail for business communication. Partly women-owned firms are more likely to use digital technologies more intensively, whilst the opposite was observed for majority- or fully women-owned and women-led firms. Practical implications: This study highlights the need for initiatives focussed on developing women in Africa’s knowledge and use of digital technologies in business. Based on the results, women are urged to enhance their skills in this domain. This may present greater opportunities in terms of employment of women to increase women’s representation. Originality/value: The article contributes to knowledge on the nexus between gender digital divide and gender inequality in ownership and management of firms. The results may also inform initiatives to narrow the digital divide in Africa.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".