Measuring gender disparities in the intentions of startups to adopt artificial intelligence technology: A comprehensive multigroup comparative analysis
Bibliographic record
Abstract
This study examines gender differences in attitudes and intentions to adopt artificial intelligence among startup professionals. Utilizing a survey methodology encompassing responses from male and female participants, key constructs including attitude, perceived ease of use, perceived usefulness, and intention to use were analyzed through a comparative lens. The results reveal nuanced disparities between male and female perspectives on AI adoption. While minor differences were observed in the influence of attitude and perceived ease of use on adoption intentions, a significant gender gap emerged in the perception of how ease of use impacts perceived usefulness. These findings underscore the importance of recognizing gender dynamics in shaping attitudes and intentions towards AI adoption, highlighting the need for gender-inclusive strategies in fostering technology adoption among startups. This study contributes to the understanding of gender-specific considerations in AI adoption processes and offers insights for policymakers and industry stakeholders seeking to promote equitable and inclusive technological advancement.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".