The gender gap in AI change: exploring disparities in emotional responses

Purpose This paper examines how gender shapes employees' emotional reactions to workplace AI, and whether these differences are associated with epistemic legitimacy, explained by perceived AI knowledge and conditioned by psychological safety. Design/methodology/approach We analyse survey data from 104 employees using a conditional process framework. Multiple regression models test direct gender effects on cognitive and emotional reactions to AI, mediation via perceived AI knowledge, and moderation by team-level psychological safety. Findings Women report less favourable cognitive and emotional reactions to AI than men. These differences are largely attributable to lower perceived AI knowledge. Psychological safety attenuates the direct gender effect: in high-safety climates, gender gaps in reactions diminish; in low-safety climates, they re-emerge. Overall, the pattern supports a contextualised partial-mediation model in which perceived knowledge is pivotal, but its explanatory power depends on climate. Research limitations/implications The cross-sectional design limits causal inference and the generalisability is bounded by the organisational context studied. Future research should use longitudinal or experimental designs, examine additional inequality dimensions (e.g. age, role), and unpack how AI literacy interventions reshape appraisal dynamics over time. Practical implications AI initiatives should build employees' perceived understanding (how AI works, limits, and human–AI complementarity) and foster psychological safety so that questions and uncertainty are acceptable. Monitoring gendered participation and confidence during roll-out helps prevent AI from amplifying existing inequalities. Social implications Managing AI adoption as an inclusion challenge—rather than solely a technical one—can reduce uneven emotional costs of digital transformation and support fairer access to AI-enabled opportunities. Originality/value The study integrates gender, appraisal (perceived AI knowledge), and climate (psychological safety) into a single framework explaining both cognitive and emotional reactions to AI. It reframes gender gaps as contextual, highlighting levers – literacy and climate – that organisations can use to enable more equitable AI adoption.