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高成长企业网络发展的空间格局与演化特征——以江苏省为例
引用本文:郑琼洁,戴靓.高成长企业网络发展的空间格局与演化特征——以江苏省为例[J].商业经济与管理,2022,42(4):90-100.
作者姓名:郑琼洁  戴靓
作者单位:1. 南京市社会科学院 经济发展研究所    2. 南京财经大学 公共管理学院
基金项目:国家社会科学基金项目“人工智能推动中国制造业全球价值链攀升的影响机理与路径研究”(19CGL021);;江苏省自然科学基金项目“省域、区域、全国尺度下江苏城市创新网络的结构与机理研究”(BK20190797);
摘    要:基于江苏省高成长企业总部分支机构联系,通过链锁网络模型构建城市间的企业网络,从企业布局、网络指数、空间关联层面,分析2018-2020年江苏省高成长企业网络发展的空间格局与演化特征。结果显示:首先,江苏省高成长企业具有高度的空间集聚性,苏州和南京汇聚了超七成的总部,而分支分布于全国各大城市群且偏好省会城市。2018-2020年南京的总部数量涨势迅猛而苏州的增速放缓,苏北的总部经济崛起;企业分支布局由沿海向内陆城市拓展。其次,江苏省各城市的高成长企业指数层级梯度分异,苏州和南京稳居第一、二位,两者差距缩小。2018-2020年苏州的高成长企业指数呈倒U型发展;南京保持稳健涨势,城市首位度提升;镇江和盐城则断崖式下跌;疫情后江苏省省内差距增大,瞪羚企业受疫情冲击最大,而(培育)独角兽企业迎来新发展;贡献度最高的产业为信息技术服务业。最后,江苏省高成长企业网络呈现加密和拓展格局,由单核向双核、由沿海向内陆发展。但省内的网络关联较为稀疏且由南向北递减,全域一体化有待深化。针对此,研究从优化企业布局、统筹区域发展、加强省内联系等方面提出相关政策建议。

关 键 词:高成长企业  企业网络指数  空间关联  链锁网络模型  
收稿时间:2022-03-29

Spatial Patterns and Evolutional Characteristics of the Network Development ofHigh-Growth Enterprises: Evidence from Jiangsu
ZHENG Qiongjie,DAI Liang.Spatial Patterns and Evolutional Characteristics of the Network Development ofHigh-Growth Enterprises: Evidence from Jiangsu[J].Business Economics and Administration,2022,42(4):90-100.
Authors:ZHENG Qiongjie  DAI Liang
Institution:1.Institute of Economic Development, Nanjing Academy of Social Sciences    2.School of Public Administration, Nanjing University of Finance and Economics
Abstract:Based on the linkages between headquarters and branches of high-growth enterprises in Jiangsu, this paper constructs inter--city enterprise networks through the Interlocking Network Model, and analyzes the spatio-temporal evolution characteristics of inter-city high-growth enterprise networks of Jiangsu through the lens of enterprise layout, network indexes, and spatial connections. We find that: (1) The high-growth enterprises show great spatial concentration with more than 70% headquarters located in Suzhou and Nanjing, and branches located in major urban agglomerations and provincial capitals. From 2018 to 2020, the number of headquarters in Nanjing increased rapidly whereas the growth of Suzhou slowed down, and the headquarter economy in North Jiangsu rose. The branches expanded from coastal to inland cities. (2) The high-growth enterprise network indexes of Suzhou and Nanjing ranked the 1st and 2nd, and the gap between them became smaller. During then, Suzhou's high-growth enterprises showed an inverted U-shaped development; Nanjing maintained a steady upward trend but Zhenjiang and Yancheng dropped precipitously. The inter-city gap in Jiangsu increased after the COVID-19 epidemic. In terms of sub-type indexes, Gazelle enterprises were most severely affected by the epidemic, while (cultivating) unicorn enterprises embraced a new development. The industry with the highest contribution is the information technology service industry. (3) The overall networks of Jiangsu's high-growth enterprises became denser, larger, and polycentric. However, their internal connections within the province were relatively sparse and decreased from south to north, thus provincial integration needs to be improved. On this basis, the paper proposes policy suggestions of optimizing enterprise layout, coordinating regional development, strengthening intra-provincial linkages, and so forth.
Keywords:high-growth enterprise  enterprise network index  spatial linkage  Interlocking Network Model  
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