在有关人工智能对就业、产业融合、经济增长等方面的研究与日俱增背景下,从人工智能对就业、经济增长、产业融合应用等多方面进行相关文献的综述.基于Web of Science和中国知网数据库 2003-2022 年的 1 908 篇中外文文献,从发文量、文献刊载期刊、研究机构、合作网络、核心作者等角度,利用CiteSpace软件对人工智能与经济发展相关研究进行文献计量和知识图谱分析,识别出该领域的研究热点和演进特征.在研究进程上,人工智能与经济发展相关研究于 2017 年后数量大幅增长,呈现跨学科多领域交叉特征,且国外跨学科合作相较于国内而言更加密切,国内相关研究合作网络尚处于初始构建阶段,成效尚不明显.在研究内容上,现有相关研究主要聚焦于探究人工智能对就业、产业融合及经济增长影响等三大领域,且尚未形成一致结论.在研究方法上,相关研究呈现出"定性—建模—实证"的发展脉络,实证研究渐成主流,但对于人工智能指标的选取及度量仍存在较大的局限性.未来可进一步围绕人工智能指标选取及测度、人工智能带来的创新模式改变及人工智能政策分析等开展研究.
The Economic Effects of Artificial Intelligence:Current Situation,Hotspots,and Prospects Based on CiteSpace
Against the backdrop of increasing research on the impact of artificial intelligence(AI)on employment,industrial integration,and economic growth,this paper reviews the literature on AI's impact on employment,economic growth,and industrial integration.Based on 1 908 Chinese and foreign papers from the Web of Science(WoS)and China National Knowledge Infrastructure(CNKI)databases from 2003 to 2022,it conducts bibliometric and knowledge mapping analysis of AI and economic development research from perspectives of publication volume,journals,research institutions,collaboration networks,and core authors,using CiteSpace software to identify research hotspots and evolutionary characteristics in this field.In terms of research progress,a significant increase in AI and economic development research can be seen after 2017,showing interdisciplinary and multi-domain cross-features,and interdisciplinary cooperation in foreigner countries is closer compared to China.China's research on collaboration networks is still at its infancy,failing to take significant effect.In terms of research content,existing studies mainly focus on the impact of AI on three major areas:employment,industrial integration,and economic growth,but a consistent conclusion has not yet been formed.Regarding to research methods,the relevant research shows a development trajectory of qualitative analysis-modeling-empirical research,and empirical research has gradually become the mainstream.However,there are still significant limitations in the selection and measurement of AI indicators.Future research can further delve into aspects such as the selection and measurement of AI indicators,changes in innovation models brought by AI,and analysis of AI-related policy.
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