摘要
人工智能高层次人才是国家重要的科技战略资源和支撑产业发展的重要力量。本文从全球人工智能领域高层次人才的流动角度,构建人才流动对科研绩效影响机制的概念模型,并运用结构方程模型和偏最小二乘算法估计模型参数。研究发现:(1)流动贯穿科研人员职业生涯全生命周期,可以从积累人力资本和社会资本两个维度对个体科研绩效产生影响,但在不同阶段,流动所积累的资本类型有所不同。在教育阶段,流动更多地以不断提升人力资本为主,在工作阶段,流动除了寻求自身人力资本提升外,更重要的是构建科研合作网络,通过增进社会资本提升科研的产出和影响力。(2)产学跨界流动使得科技人力资源在不同部门之间优化配置,对科研人员的科研产出和科研影响力具有促进作用,产学跨界流动虽然能提升人力资本、构建合作网络,但由于不同部门的价值体系不同,进入新环境的适应成本更高,因此对绩效的影响比非跨界流动略低。本研究结论对于合理促进新兴领域科研人员良性有序流动,在产学融合的背景下完善科研评价机制,以及注重新兴领域人才产学协同培养具有一定启示。
High-level talents in the artificial intelligence(AI) field are an essential scientific and technological strategic resource and a vital force supporting industrial development. From the perspective of high-level flow in the global artificial intelligence field, the paper constructs a conceptual model of the impact mechanism of mobility on scientific research performance. It uses the structural equation model and the partial least square algorithm to estimate model parameters. The study shows that:(1) Mobility runs through the entire life cycle of a scientific researcher’s career and can affect individual scientific research performance from the two dimensions: human capital and social capital. Still, the types of capital accumulated by mobility are different at different stages. In the education stage, mobility mainly improves the continuous human capital. In contrast, in the work stage, mobility seeks to improve its human capital and build a scientific research collaboration network to enhance the output and scientific influence by enhancing social capital.(2) The industry-academia intersectoral mobility optimizes the allocation of scientific and technological human resources among the different sectors, promoting the scientific research output and research influence of scientific researchers. Although industry-academia intersectoral mobility can improve human capital and build a collaboration network, due to the different value systems of different sectors, the adaptation costs of entering a new environment are higher, so the impact on performance is slightly lower than that of non-intersectoral mobility. The conclusions of this study have specific implications for rationally promoting the conscience flow of researchers in emerging fields, improving the scientific research evaluation mechanism in the context of industry-academia integration, and focusing on industryacademia collaborative training of talents in emerging fields.
作者
裴瑞敏
程豪
Pei Ruimin;Cheng Hao(Institutes of Science and Development,Chinese Academy of Sciences,Beijing 100190,China;National Academy of Innovation Strategy,China Association for Science and Technology,Beijing 100038,China)
出处
《今日科苑》
2022年第11期55-67,共13页
Modern Science
基金
国家自然科学基金资助项目(项目编号:71904185,72001197)
中国科学院科技战略咨询研究院前沿探索计划项目(项目编号:E2X1261Z01)
国家统计局全国统计科学研究优选项目(项目编号:2021LY052)
2022年度科技智库青年人才计划(项目编号:20220615ZZ07110343)。
关键词
高层次人才
人才流动
科研绩效
人工智能
high-level talents
talent mobility
scientific research performance
artificial intelligence
