When Gender Meets Super-Expertise: Perceptual Bias and Behavioral Effects in AI–Human Hybrid Recommendations

Consumers increasingly rely on algorithmic and human recommendations to navigate complex purchase decisions, yet research has predominantly examined either human or AI recommendation sources in isolation. This study focuses on AI–human hybrid recommendation systems, in which algorithmic outputs are combined with human expertise. Specifically, it examines whether recommender gender and expertise level jointly shape consumer perceptions and behavioral responses. Using a 2 × 2 between-subjects experiment (N = 169), participants evaluated an AI–human hybrid recommendation for a virtual reality headset. Gender was treated as a social cue and expertise as a boundary condition. Results show a differentiated pattern across expertise conditions. When the recommender is described as an expert, perceived transparency does not differ significantly between male and female recommenders. When the recommender is described as a super-expert, the female recommender is perceived as more transparent than the male recommender. In addition, perceived transparency was negatively associated with purchase intention, indicating that greater informational openness may activate caution rather than commitment in high-technology experience contexts. The paper provides preliminary evidence relevant to the debate on AI–human hybrid systems, suggesting that gender and expertise may function as joint determinants of perceived transparency, and offering initial insights into the conditions under which social identity cues shape consumer responses within algorithmic recommendation environments.