Why Do Women Professionals Leave the IT Field? Ten Insights and Recommendations From the World IT Project
Bibliographic record
Abstract
This article presents 10 insights explicating the reasons why women information technology (IT) professionals leave the IT field. This study analyzes the data obtained from 10 386 IT employees in 37 countries, collected during the World IT Project, the largest academic IT study ever conducted. The findings indicate that at the highest risk of permanently leaving the IT profession are women who 1) are employed part-time, have less education, and, as a result, work in supporting and liaison roles rather than in traditional core (i.e., men-dominated) IT positions; 2) are between 21 and 29 years old; 3) belong to an organization in a non-IT industry that has not reached a high level of organizational IT maturity and employs fewer than 200 people; and 4) exhibit high uncertainty avoidance and low individualism. Women occupying middle- and senior-level managerial positions are also more likely to leave IT than their nonmanagerial counterparts. The insights reveal an archetype of a woman IT employee who is at the highest risk of permanently leaving the IT profession and lead to practical recommendations for IT managers and policymakers.
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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 | 0.018 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".