Journal of Information Resources Management ›› 2025, Vol. 15 ›› Issue (1): 54-68.doi: 10.13365/j.jirm.2025.01.054

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A Multinational Comparative Study of Regulatory Policies for Generative AI from the Perspective of “Tools-Structure”

Deng Shengli Ding Weiwei Wang Fan Wang Haowei   

  1. School of Information Management, Wuhan University, Wuhan, 430072
  • Online:2025-01-26 Published:2025-02-19
  • About author:Deng Shengli, Ph.D., professor, doctoral advisor, research interests include big data security and information service; Ding Weiwei, master candidate, research interests include information behavior and user experience; Wang Fan(corresponding author), Ph.D. candidate, research interests include false health information and generative artificial intelligence, Email: 1161252028@qq.com; Wang Haowei, master candidate, research interests include generative artificial intelligence and data governance.
  • Supported by:
    This paper is supported by the Major Program of the National Social Science Foundation of China "Research on the Construction and Application of Knowledge Base for Research Methods in Information Resource Management Discipline"(23&ZD229).

Abstract: Based on the dual perspective of policy tools and structural characteristics, this study takes the regulatory policies of generative AI in different countries as the research object, aiming to explore the structural characteristics and internal connections of the policy elements of generative AI, in order to promote the healthy development of generative AI. A total of 14 effective policy texts were collected, and 327 relevant text units were coded and interpreted using bibliometrics, content analysis, and BERTopic. The structural characteristics were analyzed from three dimensions: policy issuance time, policy issuance subject, and policy theme characteristics. The role paths were discussed by dividing into three types of policy tools: environmental, demand-driven, and supply-driven. The findings show that the regulation of generative AI is still in its infancy, and there are significant differences in the overall characteristics of policies among different countries, with significant differences in the regulatory level. Overall, the role path of policy tools is mainly dominated by the indirect role of environmental policy tools, showing a structural imbalance and bias in the role path. Further comparison of the similarities and differences in regulatory policies among different countries is conducted, and corresponding countermeasures and suggestions are put forward to optimize the policy tool structure, balance the role path of policy tools, assessing the effectiveness of policy implementation, and promote the coordinated development of the generative AI regulatory system.

Key words: Generative artificial intelligence, Regulation, Policy tools, Text analysis, Topic identification, Collaborative governance

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