Survey analysis finds distinct user types in Shanghai, highlighting the influence of privacy risks and education.
This study explores the influencing factors of generative AI usage behavior among residents in Shanghai. Based on 208 questionnaire responses and analyzed using SPSS, the findings reveal that users can be categorized into three types: technology-dependent, ethics-sensitive, and function-oriented. High-frequency users (80.19%) exhibit weaker awareness of privacy risks, while low-frequency users (19.8%) pay more attention to technological transparency. Age and education level significantly influence usage intention in specific scenarios. Recommendations are provided to optimize technology design and policy regulation for different user groups.
No takes yet. Share an insight, caveat, or question.
Zhang et al. (2025) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: