A quantitative study of disruptive technology policy texts:An example of China’s artificial intelligence policy

Purpose:The transformative impact of disruptive technologies on the restructuring of the times has attracted widespread global attention.This study aims to analyze the characteristics and shortcomings of China’s artificial intelligence(AI)disruptive technology policy,and to put forward suggestions for optimizing China’s AI disruptive technology policy.Design/methodology/approach:Develop a three-dimensional analytical framework for“policy tools-policy actors-policy themes”and apply policy tools,social network analysis,and LDA topic model to conduct a comprehensive analysis of the utilization of policy tools,cooperative relationships among policy actors,and the trends in policy theme settings within China’s innovative AI technology policy.Findings:We find that the collaborative relationship among the policy actors of AI disruptive technology in China is insufficiently close.Marginal subjects exhibit low participation in the cooperation network and overly rely on central subjects,forming a“center-periphery”network structure.Policy tool usage is predominantly focused on supply and environmental types,with a severe inadequacy in demand-side policy tool utilization.Policy themes are diverse,encompassing topics such as“Intelligent Services”“Talent Cultivation”“Information Security”and“Technological Innovation”,which will remain focal points.Under the themes of“Intelligent Services”and“Intelligent Governance”,policy tool usage is relatively balanced,with close collaboration among policy entities.However,the theme of“AI Theoretical System”lacks a comprehensive understanding of tool usage and necessitates enhanced cooperation with other policy entities.Research limitations:The data sources and experimental scope are subject to certain limitations,potentially introducing biases and imperfections into the research results,necessitating further validation and refinement.Practical implications:The study introduces a three-dimensional analysis framework for disruptive technology policy texts,which is significant for formulating and enhancing disruptive technology policies.Originality/value:This study utilizes text mining and content analysis techniques to quantitatively analyze disruptive technology policy texts.It systematically evaluates China’s AI policies quantitatively,focusing on policy tools,policy actors,policy themes.The study uncovers the characteristics and deficiencies of current AI policies,offering recommendations for formulating and enhancing disruptive technology policies.

Thematic Trends in Complementary and Alternative Medicine Applied in Cancer-Related Symptoms

Purpose： The main goal of this study is to discover the scientific evolution of Cancer-Related Symptoms in Complementary and Alternative Medicine research area, analyzing the articles indexed in the Web of Science database from 1980 to 2013.Design/Methodology/Approach： A co-word science mapping analysis is performed under a longitudinal framework（1980 to 2013）. The documental corpus is divided into two subperiods,1980–2008 and 2009–2013. Thus, the performance and impact rates, and conceptual evolution of the research field are shown.Findings： According to the results, the co-word analysis allows us to identify 12 main thematic areas in this emerging research field： anxiety, survivors and palliative care,meditation, treatment, symptoms and cancer types, postmenopause, cancer pain, low back pain, herbal medicine, children, depression and insomnia, inflammation mediators, and lymphedema. The different research lines are identified according to the main thematic areas,centered fundamentally on anxiety and suffering prevention. The scientific community can use this information to identify where the interest is focused and make decisions in different ways.Research limitation： Several limitations can be addressed： 1） some of the Complementary and Alternative Medicine therapies may not have been included; 2） only the documents indexed in Web of Science are analyzed; and 3） the thematic areas detected could change if another dataset was considered.Practical implications： The results obtained in the present study could be considered as an evidence-based framework in which future studies could be built.Originality/value： Currently, there are no studies that show the thematic evolution of this research area.