Gen AI for metal help-seeking - Survey Data

The deployment of artificial intelligence (AI) in mental health services has increased substantially, yet the specific AI characteristics that influence mental health help-seeking behaviors remain unclear. Drawing on dual-process theory and the Heuristic-Systematic Model (HSM), this study examines the mechanisms by which various characteristics of generative AI influence the intention to seek help. Analyzing data from 619 generative AI users in China, the findings indicate that systematic cues, including perceived intelligence and AI compatibility, along with heuristic cues, including anthropomorphism and likability, positively affect help-seeking intentions through both cognitive and emotional trust pathways. Notably, emotional trust exerts a stronger influence on help-seeking intentions than cognitive trust and can enhance cognitive trust, introducing a potential bias effect. Moreover, perceived privacy risks negatively moderate the mediating effects of cognitive and emotional trust between heuristic and systematic cues and help-seeking intention. Additionally, while users' trust positively correlates with help-seeking behavior, the perceived threat posed by AI can negatively moderate this relationship. This study not only enriches the theoretical discourse surrounding the attributes of generative AI and their impact on mental health help-seeking but also offers practical insights for the design and utilization of AI technologies to enhance mental health support.