Examining The Role Of Gender In Reactions To AI-Augmentation In Negotiation

Abstract

Recent years have seen rapid growth in the development and organizational use of artificial intelligence (AI), including in negotiation training, preparation, and decision support. This dissertation explores the unintended social consequences of AI awareness in negotiation. Specifically, how knowing that a negotiation partner has used AI systems influences negotiators’ behaviour and outcomes in gendered ways. Drawing on research on stereotype threat and masculinity effects in negotiation, I theorize that awareness of a partner’s AI augmentation serves as a socially meaningful signal that alters evaluative dynamics, leading women and men to respond differently and achieve different outcomes. Across two experimental studies, I investigated these ideas by manipulating whether negotiators are informed that their opponent has used AI systems for negotiation training or assistance. Study 1 involved human–human negotiations in a controlled laboratory setting, and Study 2 expanded the design to a larger online sample negotiating against standardized AI confederates while believing they were interacting with human partners. The results showed partial support for interaction effects between gender and AI awareness as independent variables and negotiation outcomes as the dependent variable. The simple effects revealed mixed results such that women tended to achieve lower results when a partner's AI use was made salient, while men’s outcomes remained relatively stable, with some directional increases. No main effects of AI awareness or gender were observed. Contrary to theoretical expectations, the proposed mediating mechanisms, stereotype threat and masculinity threat, were not supported. Overall, this research contributes to scholarship on negotiation, gender, and AI by demonstrating that AI impacts social interaction not only through what it enables people to do but also through what its presence signals to others, with significant implications for fairness and transparency in technology-augmented work.