Mind the Gap: Gender Differences in Generative AI Adoption at Work

Despite the growing relevance of generative AI in the workplace, a significant gender gap in its adoption persists. This study investigates why women are less likely than men to use generative AI tools at work and identifies predictors that explain this difference. Combining a cross-sectional survey (n = 200) with a one-week diary study (n = 76, 266 daily observations), we examine both the intention to use and actual daily use of generative AI. Across both studies, women reported lower usage intentions and spent significantly less time using generative AI. Drawing on the UTAUT, we find that performance expectancy is the strongest predictor—particularly among women—followed by social influence. In contrast, effort expectancy and facilitating conditions appear less relevant. Additional factors such as AI literacy and job demands further explain AI use. Our results highlight the need for gender-sensitive interventions to reduce the gender gap in generative AI use.

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Publication Details

Journal
ScholarSpace (University of Hawaii at Manoa)
Published
2025-12-23
Primary Topic
Ethics and Social Impacts of AI
Type
article
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article

Mind the Gap: Gender Differences in Generative AI Adoption at Work

Marius Wehner, Lynn Schmodde, Dominik Zahs
ScholarSpace (University of Hawaii at Manoa)
Ethics and Social Impacts of AI
article

Mind the Gap: Gender Differences in Generative AI Adoption at Work

Marius Wehner, Lynn Schmodde, Dominik Zahs
article en

Abstract

Despite the growing relevance of generative AI in the workplace, a significant gender gap in its adoption persists. This study investigates why women are less likely than men to use generative AI tools at work and identifies predictors that explain this difference. Combining a cross-sectional survey (n = 200) with a one-week diary study (n = 76, 266 daily observations), we examine both the intention to use and actual daily use of generative AI. Across both studies, women reported lower usage intentions and spent significantly less time using generative AI. Drawing on the UTAUT, we find that performance expectancy is the strongest predictor—particularly among women—followed by social influence. In contrast, effort expectancy and facilitating conditions appear less relevant. Additional factors such as AI literacy and job demands further explain AI use. Our results highlight the need for gender-sensitive interventions to reduce the gender gap in generative AI use.

ScholarSpace (University of Hawaii at Manoa)
Quality Education
Openalex Percentile: Top 27%
Ethics and Social Impacts of AI
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