Article(id=1309226320404512876, tenantId=1146029695717560320, journalId=1308397381743640680, issueId=1309226306445865584, articleNumber=null, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1742313600000, receivedDateStr=2025-03-19, revisedDate=1745251200000, revisedDateStr=2025-04-22, acceptedDate=null, acceptedDateStr=null, onlineDate=1790073479032, onlineDateStr=2026-09-22, pubDate=1781452800000, pubDateStr=2026-06-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790073479032, onlineIssueDateStr=2026-09-22, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790073479032, creator=13701087609, updateTime=1790073479032, updator=13701087609, issue=Issue{id=1309226306445865584, tenantId=1146029695717560320, journalId=1308397381743640680, year='2026', volume='44', issue='6', pageStart='1121', pageEnd='1344', issueExtLink='null', onlineDate='null', pubDate='1781452800000', pubDateStr='2026-06-15', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1790073475705, creator='13701087609', updateTime=1790073520980, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1309226496414286647, tenantId=1146029695717560320, journalId=1308397381743640680, issueId=1309226306445865584, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1309226496414286648, tenantId=1146029695717560320, journalId=1308397381743640680, issueId=1309226306445865584, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=1178, endPage=1191, ext={EN=ArticleExt(id=1309226320601645165, articleId=1309226320404512876, tenantId=1146029695717560320, journalId=1308397381743640680, language=EN, title=How does factor agglomeration affect new quality productive forces through dualinnovation?, columnId=1309226307205034609, journalTitle=Studies in Science of Science, columnName=Hot Issues, runingTitle=null, highlight=null, articleAbstract=

Innovation factor agglomeration is a key way to integrate scientific and technological innovation resources, and an important way to promote new quality productive forcesthrough exploratory innovation and exploitative innovation. At present, the development of new quality productive forcesin China is facing challenges such as insufficient driving force of scientific and technological innovation, limited breakthrough of key technologies, and difficulty in obtaining innovative resources in underdeveloped areas. The agglomeration of innovative factors promotes the development of new quality productive forcesby breaking the limitations of traditional innovation models. However, there is still insufficient research on the systematic mechanism of innovation factor agglomeration affecting new quality productive forces. In particular, the synergy of innovation factor agglomeration through dualinnovation is still in the "black box". How does innovation factor agglomeration affect new quality productive forces through dualinnovation mechanism? The existing research mainly discusses the agglomeration of innovation factors from the aspects of spatial distribution characteristics, influencing factors and economic effects, but there are few explorations on the different paths that affect the new quality productive forces. Some scholars have made preliminary explorations on talent agglomeration, capital agglomeration and technology agglomeration as important research dimensions, but these studies focus more on the spatial distribution characteristics and agglomeration effect measurement of innovation elements.

From the perspective of dualinnovation, based on the data of 30 provinces in China from 2014 to 2023, this paper systematically explores the influence mechanism of innovation factor agglomeration on new quality productive forcesaround the four action paths formed by the cross combination of two dimensions of network effect and scale effect and dualinnovation. The results show that the agglomeration of innovation factors has a significant role in promoting new quality productive forces, among which the positive effects of innovation technology and platform agglomeration are the most significant. As an important moderating variable, regional innovation environment significantly strengthens the positive impact of innovation factor agglomeration on new quality productive forces. The agglomeration of innovation elements affects new quality productive forcesthrough the dualinnovation mechanism. Although the short-term network effect of exploratory innovation is negative, it significantly promotes new quality productive forcesafter scale, and can be transformed into exploitative innovation to form a continuous driving force. Further analysis from the factor dimension shows that among the four elements of innovative talents, capital, technology and platform, only innovative technology agglomeration and platform agglomeration have a significant positive impact on new quality productive forcesthrough the scale effect of dualinnovation. The former enhances the technological breakthrough ability of innovation subjects by promoting technology spillover and knowledge diffusion, while the latter reduces innovation barriers and enlarges synergy effects as an important hub for the integration of innovative resources. Although talent agglomeration and capital agglomeration also show a positive relationship in some stages, their separate impact on new quality productive forcesis relatively limited, and they may need to be combined with technological innovation and platform construction to give full play to their effectiveness. This shows that in the current development stage, technological innovation and platform construction may be the core bridge connecting innovation elements and new quality productive forces. The heterogeneity test also finds that the effect of innovation factor agglomeration is more significant in regions with higher levels of marketization, digital economy and transportation infrastructure.

The marginal contribution of this paper is that, first, it reveals the mechanism of innovation factor agglomeration on new quality productive forcesfrom the perspective of dual innovation, and enriches the theoretical research on the formation mechanism of new quality productive forces. Secondly, the two dimensions of network effect and scale effect are combined with dualinnovation to form four action paths, and the system mechanism of factor agglomeration to empower new quality productive forcesis constructed. The third is to introduce adjustment effect and heterogeneity analysis to provide empirical basis for differentiated policies. The research conclusions provide theoretical basis and policy enlightenment for optimizing the spatial layout of innovation elements, improving regional innovation efficiency and promoting the formation of new quality productive forces.

, authors=Cheng-wei ZHAO1, 2, Wen-ya LI1, Hong-qi CHEN1, Xian-hong XIANG1, 2, Yan-yu WANG3, authorsList=Cheng-wei ZHAO, Wen-ya LI, Hong-qi CHEN, Xian-hong XIANG, Yan-yu WANG, authorCompany=null, correspAuthors=Yan-yu WANG, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1309226321356619890, articleId=1309226320404512876, tenantId=1146029695717560320, journalId=1308397381743640680, language=CN, title=要素集聚如何通过双元创新影响新质生产力?, columnId=1309226307364418163, journalTitle=科学学研究, columnName=热点议题, runingTitle=null, highlight=null, articleAbstract=

创新要素集聚是整合科技创新资源的关键方式,是通过探索式创新和利用式创新催生新质生产力的重要途径。基于2014-2023年中国30个省份面板数据,从双元创新视角出发,围绕网络效应和规模效应两个维度,形成4条作用路径,系统探究了创新要素集聚赋能新质生产力的影响机制。研究发现:(1)创新要素集聚对新质生产力具有显著促进作用,其中创新技术和平台集聚的正向效应最为显著,而创新环境能够显著增强创新要素集聚对新质生产力的促进作用。(2)创新要素集聚通过双元创新机制影响新质生产力,探索式创新虽短期网络效应呈负向,但规模化后显著促进新质生产力,且可以转化为利用式创新形成持续驱动力;进一步从要素维度分析,仅创新技术与平台集聚通过双元创新的规模效应对新质生产力产生显著正向影响。(3)创新要素集聚的效果还表现出明显的区域异质性,即在市场化程度、数字经济和交通基础设施水平较高的地区,其对新质生产力的促进作用更为显著。研究结论为优化创新要素空间布局、提升区域创新效率、推动形成新质生产力提供了理论依据和政策启示。

, authors=赵成伟1, 2, 李文雅1, 陈红其1, 向仙虹1, 2, 王砚羽3, authorsList=赵成伟, 李文雅, 陈红其, 向仙虹, 王砚羽, authorCompany=null, correspAuthors=王砚羽, authorNote=

赵成伟(1982-),男,副教授,博士。

李文雅(2001-),女,硕士研究生。

陈红其(2002-),女,硕士研究生。

向仙虹(1990-),女,副教授,博士。

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王砚羽(1986-),女,教授,博士, E-mail:。
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Measurement index table of new quality productivity

, figureFileSmall=null, figureFileBig=null, tableContent=
一级指标 二级指标 三级指标 四级指标
新质生产力 科技生产力 创新生产力 高技术产业业务收入
规上工业企业产业创新经费
技术生产力 规上工业企业劳动生产率
规上工业企业R&D人员全时当量
绿色生产力 资源节约型生产力 能源消费量/国内生产总值
工业用水量/国内生产总值
环境友好型生产力 工业固废物综合利用量/产生量
工业废水排放/国内生产总值
工业SO2排放/国内生产总值
数字生产力 数字产业生产力 集成电路产量
电信业务总量
产业数字生产力 互联网宽带接入端口数
软件业务收入
光缆线路长度/地区面积
电子商务销售额
), ArticleFig(id=1309226327744544934, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1309226320404512876, language=CN, label=表1, caption=

新质生产力衡量指标表

, figureFileSmall=null, figureFileBig=null, tableContent=
一级指标 二级指标 三级指标 四级指标
新质生产力 科技生产力 创新生产力 高技术产业业务收入
规上工业企业产业创新经费
技术生产力 规上工业企业劳动生产率
规上工业企业R&D人员全时当量
绿色生产力 资源节约型生产力 能源消费量/国内生产总值
工业用水量/国内生产总值
环境友好型生产力 工业固废物综合利用量/产生量
工业废水排放/国内生产总值
工业SO2排放/国内生产总值
数字生产力 数字产业生产力 集成电路产量
电信业务总量
产业数字生产力 互联网宽带接入端口数
软件业务收入
光缆线路长度/地区面积
电子商务销售额
), ArticleFig(id=1309226327832625319, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1309226320404512876, language=EN, label=Table 2, caption=

Explanatory variable measurement index table

, figureFileSmall=null, figureFileBig=null, tableContent=
一级指标 二级指标 三级指标
创新要素集聚 创新人才集聚 院士人数
研发人员占就业人员比重
每10万人高等学校学生数
创新资本集聚 研发投入强度
金融机构存贷款余额占GDP比重
固定资产投资额占GDP比重
创新技术集聚 科技论文数
技术市场成交额
新产品销售收入
创新平台集聚 高新区数
国家重点实验室数
高技术产业企业数
), ArticleFig(id=1309226327895539880, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1309226320404512876, language=CN, label=表2, caption=

解释变量衡量指标表

, figureFileSmall=null, figureFileBig=null, tableContent=
一级指标 二级指标 三级指标
创新要素集聚 创新人才集聚 院士人数
研发人员占就业人员比重
每10万人高等学校学生数
创新资本集聚 研发投入强度
金融机构存贷款余额占GDP比重
固定资产投资额占GDP比重
创新技术集聚 科技论文数
技术市场成交额
新产品销售收入
创新平台集聚 高新区数
国家重点实验室数
高技术产业企业数
), ArticleFig(id=1309226327958454441, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1309226320404512876, language=EN, label=Table 3, caption=

Descriptive statistics

, figureFileSmall=null, figureFileBig=null, tableContent=
变量 观测数 均值 标准差 最小值 最大值 25分位 中位数 75分位
新质生产力 300 0.207 0.184 0.028 0.877 0.083 0.154 0.206
创新要素集聚 300 0.126 0.107 0.003 0.484 0.049 0.094 0.164
创新人才集聚 300 0.145 0.114 0.008 0.823 0.078 0.119 0.183
创新资本集聚 300 0.074 0.127 0 0.919 0.013 0.028 0.072
创新技术集聚 300 0.157 0.162 0.002 0.749 0.046 0.104 0.208
创新平台集聚 300 0.130 0.128 0 0.633 0.039 0.081 0.192
税负水平 300 0.080 0.028 0.036 0.188 0.061 0.074 0.091
经济发展水平 300 67137 32970 25101 200278 44901 57391 76771
社会消费水平 300 0.390 0.0670 0.180 0.610 0.354 0.398 0.430
城镇化率 300 0.615 0.130 0 0.893 0.548 0.609 0.678
人口密度 300 475.5 714.7 7.972 3951 131.6 286.9 582.6
), ArticleFig(id=1309226328067506346, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1309226320404512876, language=CN, label=表3, caption=

描述性统计

, figureFileSmall=null, figureFileBig=null, tableContent=
变量 观测数 均值 标准差 最小值 最大值 25分位 中位数 75分位
新质生产力 300 0.207 0.184 0.028 0.877 0.083 0.154 0.206
创新要素集聚 300 0.126 0.107 0.003 0.484 0.049 0.094 0.164
创新人才集聚 300 0.145 0.114 0.008 0.823 0.078 0.119 0.183
创新资本集聚 300 0.074 0.127 0 0.919 0.013 0.028 0.072
创新技术集聚 300 0.157 0.162 0.002 0.749 0.046 0.104 0.208
创新平台集聚 300 0.130 0.128 0 0.633 0.039 0.081 0.192
税负水平 300 0.080 0.028 0.036 0.188 0.061 0.074 0.091
经济发展水平 300 67137 32970 25101 200278 44901 57391 76771
社会消费水平 300 0.390 0.0670 0.180 0.610 0.354 0.398 0.430
城镇化率 300 0.615 0.130 0 0.893 0.548 0.609 0.678
人口密度 300 475.5 714.7 7.972 3951 131.6 286.9 582.6
), ArticleFig(id=1309226328143003819, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1309226320404512876, language=EN, label=Table 4, caption=

Benchmark regression effect results

, figureFileSmall=null, figureFileBig=null, tableContent=
(1) (2) (3) (4) (5)
创新要素集聚 0.112**(2.10)
创新人才集聚 0.032(0.87)
创新资本集聚 -0.020(-0.97)
创新技术集聚 0.144***(3.46)
创新平台集聚 0.231***(5.29)
税负水平 -0.724***(-2.8) -0.667**(-2.57) -0.661**(-2.55) -0.805***(-3.13) -0.854***(-3.43)
经济发展水平 0.000(0.29) 0.000(0.61) 0.000(0.78) -0.000(-0.52) 0.000(0.36)
社会消费水平 0.119**(2.29) 0.133**(2.58) 0.146***(2.81) 0.128**(2.55) 0.125**(2.56)
城镇化率 0.023(1.01) 0.021(0.92) 0.022(0.97) 0.038*(1.68) 0.022(1.03)
人口密度 0.001***(3.62) 0.001***(4.25) 0.001***(4.32) 0.001***(2.86) 0.001**(2.43)
常数项 -0.220**(-2.12) -0.287***(-2.91) -0.300***(-3.02) -0.140(-1.32) -0.078(-0.76)
N 300 300 300 300 300
时间固定 YES YES YES YES YES
个体固定 YES YES YES YES YES
R2 0.225 0.214 0.215 0.247 0.289
), ArticleFig(id=1309226328210112684, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1309226320404512876, language=CN, label=表4, caption=

基准回归结果

, figureFileSmall=null, figureFileBig=null, tableContent=
(1) (2) (3) (4) (5)
创新要素集聚 0.112**(2.10)
创新人才集聚 0.032(0.87)
创新资本集聚 -0.020(-0.97)
创新技术集聚 0.144***(3.46)
创新平台集聚 0.231***(5.29)
税负水平 -0.724***(-2.8) -0.667**(-2.57) -0.661**(-2.55) -0.805***(-3.13) -0.854***(-3.43)
经济发展水平 0.000(0.29) 0.000(0.61) 0.000(0.78) -0.000(-0.52) 0.000(0.36)
社会消费水平 0.119**(2.29) 0.133**(2.58) 0.146***(2.81) 0.128**(2.55) 0.125**(2.56)
城镇化率 0.023(1.01) 0.021(0.92) 0.022(0.97) 0.038*(1.68) 0.022(1.03)
人口密度 0.001***(3.62) 0.001***(4.25) 0.001***(4.32) 0.001***(2.86) 0.001**(2.43)
常数项 -0.220**(-2.12) -0.287***(-2.91) -0.300***(-3.02) -0.140(-1.32) -0.078(-0.76)
N 300 300 300 300 300
时间固定 YES YES YES YES YES
个体固定 YES YES YES YES YES
R2 0.225 0.214 0.215 0.247 0.289
), ArticleFig(id=1309226328268832941, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1309226320404512876, language=EN, label=Table 5, caption=

Robustness test results

, figureFileSmall=null, figureFileBig=null, tableContent=
(1) (2) (3)
缩尾处理 剔除直辖市样本 更换因变量的衡量方式
创新要素集聚 0.165**(2.01) 0.196**(2.14) 0.034**(1.98)
常数项 0.173***(20.94) -0.096(-1.27) 1.250***(7.47)
N 300 260 300
时间固定 YES YES YES
个体固定 YES YES YES
R2 0.083 0.233 0.619
), ArticleFig(id=1309226328335941806, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1309226320404512876, language=CN, label=表5, caption=

稳健性检验结果

, figureFileSmall=null, figureFileBig=null, tableContent=
(1) (2) (3)
缩尾处理 剔除直辖市样本 更换因变量的衡量方式
创新要素集聚 0.165**(2.01) 0.196**(2.14) 0.034**(1.98)
常数项 0.173***(20.94) -0.096(-1.27) 1.250***(7.47)
N 300 260 300
时间固定 YES YES YES
个体固定 YES YES YES
R2 0.083 0.233 0.619
), ArticleFig(id=1309226328403050671, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1309226320404512876, language=EN, label=Table 6, caption=

Endogenous test results

, figureFileSmall=null, figureFileBig=null, tableContent=
2SLS
(1) (2) (3) (4)
第一阶段 第一阶段 第二阶段 第二阶段
工具变量1 0.083***(15.65)
工具变量2 -0.000***(-6.86)
创新要素集聚 1.561***(11.81) 1.416***(5.74)
常数项 0.274***(6.43) -0.121***(-3.10) -0.121***(-3.10) 0.160**(2.35)
N 270 300 270 300
个体固定 YES YES YES YES
时间固定 YES YES YES YES
控制变量 YES YES YES YES
R2 0.991 0.677 0.677 0.690
), ArticleFig(id=1309226328524685488, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1309226320404512876, language=CN, label=表6, caption=

内生性检验结果

, figureFileSmall=null, figureFileBig=null, tableContent=
2SLS
(1) (2) (3) (4)
第一阶段 第一阶段 第二阶段 第二阶段
工具变量1 0.083***(15.65)
工具变量2 -0.000***(-6.86)
创新要素集聚 1.561***(11.81) 1.416***(5.74)
常数项 0.274***(6.43) -0.121***(-3.10) -0.121***(-3.10) 0.160**(2.35)
N 270 300 270 300
个体固定 YES YES YES YES
时间固定 YES YES YES YES
控制变量 YES YES YES YES
R2 0.991 0.677 0.677 0.690
), ArticleFig(id=1309226328625348785, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1309226320404512876, language=EN, label=Table 7, caption=

The test results of the mechanism of innovation factor agglomeration affecting new productivity

, figureFileSmall=null, figureFileBig=null, tableContent=
创新要素集聚对创新类型的影响(第一阶段)
网络效应 规模效应
(1) (2) (3) (4)
探索式创新 利用式创新 探索式创新 利用式创新
创新要素集聚 1.530***(4.62) 0.178***(3.32) 11.712***(3.60) 0.115**(2.10)
常数项 0.254(0.39) -0.209**(-2.09) 15.102**(2.38) -0.216**(-2.06)
创新类型对新质生产力的影响(第二阶段)
网络效应 规模效应
(5) (6) (7) (8)
新质生产力 新质生产力 新质生产力 新质生产力
创新要素集聚 0.708(1.22) 0.096*(1.85) 3.536***(4.88) 0.075(1.36)
网络效应 探索式创新 -0.043***(-4.42)
利用式创新 -0.000(-0.25)
规模效应 探索式创新 0.023***(4.04)
利用式创新 0.010**(2.27)
常数项 6.282***(5.58) -0.362***(-3.40) 8.174***(5.79) -0.304***(-2.79)
N 300 300 300 300
时间固定 YES YES YES YES
个体固定 YES YES YES YES
控制变量 YES YES YES YES
R2 0.776 0.272 0.659 0.240
), ArticleFig(id=1309226328713429170, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1309226320404512876, language=CN, label=表7, caption=

创新要素集聚影响新质生产力作用机制的检验结果

, figureFileSmall=null, figureFileBig=null, tableContent=
创新要素集聚对创新类型的影响(第一阶段)
网络效应 规模效应
(1) (2) (3) (4)
探索式创新 利用式创新 探索式创新 利用式创新
创新要素集聚 1.530***(4.62) 0.178***(3.32) 11.712***(3.60) 0.115**(2.10)
常数项 0.254(0.39) -0.209**(-2.09) 15.102**(2.38) -0.216**(-2.06)
创新类型对新质生产力的影响(第二阶段)
网络效应 规模效应
(5) (6) (7) (8)
新质生产力 新质生产力 新质生产力 新质生产力
创新要素集聚 0.708(1.22) 0.096*(1.85) 3.536***(4.88) 0.075(1.36)
网络效应 探索式创新 -0.043***(-4.42)
利用式创新 -0.000(-0.25)
规模效应 探索式创新 0.023***(4.04)
利用式创新 0.010**(2.27)
常数项 6.282***(5.58) -0.362***(-3.40) 8.174***(5.79) -0.304***(-2.79)
N 300 300 300 300
时间固定 YES YES YES YES
个体固定 YES YES YES YES
控制变量 YES YES YES YES
R2 0.776 0.272 0.659 0.240
), ArticleFig(id=1309226328793120947, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1309226320404512876, language=EN, label=Table 8, caption=

The test results of the influence mechanism of innovation factor agglomeration sub-dimensions on new productivity

, figureFileSmall=null, figureFileBig=null, tableContent=
创新要素集聚对创新类型的影响(第一阶段)
网络效应 规模效应
(1) (2) (3) (4)
探索式创新 利用式创新 探索式创新 利用式创新
创新人才集聚 0.473**(2.03) 0.048(1.35) -1.391(-0.61) -0.006(-0.02)
创新资本集聚 0.326**(2.42) -0.010(-0.46) 6.320***(5.04) -0.024(-1.08)
创新技术集聚 1.229***(4.67) 0.202***(4.85) 6.171**(2.35) 0.146***(3.45)
创新平台集聚 0.793***(2.73) 0.265***(6.24) -1.295(-0.45) 0.231***(5.28)
创新类型对新质生产力的影响(第二阶段)
网络效应 规模效应
(5) (6) (7) (8)
新质生产力 新质生产力 新质生产力 新质生产力
创新人才集聚 0.032(0.90) 0.012(0.02) 0.032(0.87) 0.473**(2.03)
创新资本集聚 -0.072(-0.31) -0.019(-0.92) 1.089***(3.74) -0.036*(-1.67)
创新技术集聚 1.130**(2.48) 0.121***(2.93) 3.337***(5.90) 0.118***(2.66)
创新平台集聚 1.285***(2.62) 0.206***(4.76) 1.441**(2.24) 0.217***(4.97)
网络效应 探索式创新 创新人才集聚 -0.043***(-4.42)
创新资本集聚 -0.033***(-3.45)
创新技术集聚 -0.047***(-4.93)
创新平台集聚 -0.043***(-4.82)
利用式创新 创新人才集聚 -0.000(-0.25)
创新资本集聚 0.001(0.53)
创新技术集聚 -0.000(-0.29)
创新平台集聚 0.000(0.37)
规模效应 探索式创新 创新人才集聚 0.023***(4.04)
创新资本集聚 0.023***(4.15)
创新技术集聚 0.021***(3.73)
创新平台集聚 0.019***(3.52)
利用式创新 创新人才集聚 0.010**(2.27)
创新资本集聚 0.014***(3.10)
创新技术集聚 0.008*(1.72)
创新平台集聚 0.009**(2.19)
N 300 300 300 300
时间固定 YES YES YES YES
个体固定 YES YES YES YES
控制变量 YES YES YES YES
), ArticleFig(id=1309226328906367156, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1309226320404512876, language=CN, label=表8, caption=

创新要素集聚分维度影响新质生产力作用机制的检验结果

, figureFileSmall=null, figureFileBig=null, tableContent=
创新要素集聚对创新类型的影响(第一阶段)
网络效应 规模效应
(1) (2) (3) (4)
探索式创新 利用式创新 探索式创新 利用式创新
创新人才集聚 0.473**(2.03) 0.048(1.35) -1.391(-0.61) -0.006(-0.02)
创新资本集聚 0.326**(2.42) -0.010(-0.46) 6.320***(5.04) -0.024(-1.08)
创新技术集聚 1.229***(4.67) 0.202***(4.85) 6.171**(2.35) 0.146***(3.45)
创新平台集聚 0.793***(2.73) 0.265***(6.24) -1.295(-0.45) 0.231***(5.28)
创新类型对新质生产力的影响(第二阶段)
网络效应 规模效应
(5) (6) (7) (8)
新质生产力 新质生产力 新质生产力 新质生产力
创新人才集聚 0.032(0.90) 0.012(0.02) 0.032(0.87) 0.473**(2.03)
创新资本集聚 -0.072(-0.31) -0.019(-0.92) 1.089***(3.74) -0.036*(-1.67)
创新技术集聚 1.130**(2.48) 0.121***(2.93) 3.337***(5.90) 0.118***(2.66)
创新平台集聚 1.285***(2.62) 0.206***(4.76) 1.441**(2.24) 0.217***(4.97)
网络效应 探索式创新 创新人才集聚 -0.043***(-4.42)
创新资本集聚 -0.033***(-3.45)
创新技术集聚 -0.047***(-4.93)
创新平台集聚 -0.043***(-4.82)
利用式创新 创新人才集聚 -0.000(-0.25)
创新资本集聚 0.001(0.53)
创新技术集聚 -0.000(-0.29)
创新平台集聚 0.000(0.37)
规模效应 探索式创新 创新人才集聚 0.023***(4.04)
创新资本集聚 0.023***(4.15)
创新技术集聚 0.021***(3.73)
创新平台集聚 0.019***(3.52)
利用式创新 创新人才集聚 0.010**(2.27)
创新资本集聚 0.014***(3.10)
创新技术集聚 0.008*(1.72)
创新平台集聚 0.009**(2.19)
N 300 300 300 300
时间固定 YES YES YES YES
个体固定 YES YES YES YES
控制变量 YES YES YES YES
), ArticleFig(id=1309226328981864629, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1309226320404512876, language=EN, label=Table 9, caption=

Moderating effect results

, figureFileSmall=null, figureFileBig=null, tableContent=
(1)
创新要素集聚 0.104*(1.77)
创新环境 -0.243***(-3.36)
创新要素集聚×创新环境 0.119***(3.33)
常数项 -0.064(-0.45)
N 300
时间固定 YES
个体固定 YES
控制变量 YES
R2 0.303
), ArticleFig(id=1309226329057362102, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1309226320404512876, language=CN, label=表9, caption=

调节效应结果

, figureFileSmall=null, figureFileBig=null, tableContent=
(1)
创新要素集聚 0.104*(1.77)
创新环境 -0.243***(-3.36)
创新要素集聚×创新环境 0.119***(3.33)
常数项 -0.064(-0.45)
N 300
时间固定 YES
个体固定 YES
控制变量 YES
R2 0.303
), ArticleFig(id=1309226329141248183, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1309226320404512876, language=EN, label=Table 10, caption=

Heterogeneity test

, figureFileSmall=null, figureFileBig=null, tableContent=
市场化程度 数字经济发展水平 交通基础设施水平
(1)高 (2)低 (3)高 (4)低 (5)高 (6)低
创新要素集聚 0.128**
(2.44)
0.594*
(1.76)
0.461*
(2.16)
0.525
(1.42)
0.272***
(3.98)
0.138**
(2.02)
常数项 0.240***(23.54) 0.077***(5.00) 0.070***(5.80) 0.005(0.03) 0.134***(17.42) 0.201***(18.30)
N 150 150 150 150 150 150
时间固定 YES YES YES YES YES YES
个体固定 YES YES YES YES YES YES
控制变量 YES YES YES YES YES YES
R2 0.398 0.043 0.823 0.325 0.300 0.178
), ArticleFig(id=1309226329220939960, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1309226320404512876, language=CN, label=表10, caption=

异质性检验

, figureFileSmall=null, figureFileBig=null, tableContent=
市场化程度 数字经济发展水平 交通基础设施水平
(1)高 (2)低 (3)高 (4)低 (5)高 (6)低
创新要素集聚 0.128**
(2.44)
0.594*
(1.76)
0.461*
(2.16)
0.525
(1.42)
0.272***
(3.98)
0.138**
(2.02)
常数项 0.240***(23.54) 0.077***(5.00) 0.070***(5.80) 0.005(0.03) 0.134***(17.42) 0.201***(18.30)
N 150 150 150 150 150 150
时间固定 YES YES YES YES YES YES
个体固定 YES YES YES YES YES YES
控制变量 YES YES YES YES YES YES
R2 0.398 0.043 0.823 0.325 0.300 0.178
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要素集聚如何通过双元创新影响新质生产力?
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赵成伟 1, 2 , 李文雅 1 , 陈红其 1 , 向仙虹 1, 2 , 王砚羽 3
科学学研究 | 热点议题 2026,44(6): 1178-1191
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科学学研究 |热点议题 2026 , 44 (6) : 1178 -1191
要素集聚如何通过双元创新影响新质生产力?
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赵成伟1, 2, 李文雅1, 陈红其1, 向仙虹1, 2, 王砚羽3
作者信息
  • 1 新疆大学经济与管理学院, 新疆乌鲁木齐 830046
  • 2 新疆大学新疆创新管理研究中心, 新疆乌鲁木齐 830046
  • 3 北京邮电大学经济管理学院, 北京 100876
通讯作者:
王砚羽(1986-),女,教授,博士, E-mail:。
作者简介:

赵成伟(1982-),男,副教授,博士。

李文雅(2001-),女,硕士研究生。

陈红其(2002-),女,硕士研究生。

向仙虹(1990-),女,副教授,博士。

How does factor agglomeration affect new quality productive forces through dualinnovation?
Cheng-wei ZHAO1, 2, Wen-ya LI1, Hong-qi CHEN1, Xian-hong XIANG1, 2, Yan-yu WANG3
Affiliations
  • 1 School of Economics and Management, Xinjiang University, Urumqi 830046, China
  • 2 Center for Innovation Management Research of Xinjiang, Xinjiang University, Urumqi 830046, China
  • 3 School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing 100876, China
出版时间: 2026-06-15
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创新要素集聚是整合科技创新资源的关键方式,是通过探索式创新和利用式创新催生新质生产力的重要途径。基于2014-2023年中国30个省份面板数据,从双元创新视角出发,围绕网络效应和规模效应两个维度,形成4条作用路径,系统探究了创新要素集聚赋能新质生产力的影响机制。研究发现:(1)创新要素集聚对新质生产力具有显著促进作用,其中创新技术和平台集聚的正向效应最为显著,而创新环境能够显著增强创新要素集聚对新质生产力的促进作用。(2)创新要素集聚通过双元创新机制影响新质生产力,探索式创新虽短期网络效应呈负向,但规模化后显著促进新质生产力,且可以转化为利用式创新形成持续驱动力;进一步从要素维度分析,仅创新技术与平台集聚通过双元创新的规模效应对新质生产力产生显著正向影响。(3)创新要素集聚的效果还表现出明显的区域异质性,即在市场化程度、数字经济和交通基础设施水平较高的地区,其对新质生产力的促进作用更为显著。研究结论为优化创新要素空间布局、提升区域创新效率、推动形成新质生产力提供了理论依据和政策启示。

创新要素集聚  /  新质生产力  /  双元创新  /  网络效应  /  规模效应

Innovation factor agglomeration is a key way to integrate scientific and technological innovation resources, and an important way to promote new quality productive forcesthrough exploratory innovation and exploitative innovation. At present, the development of new quality productive forcesin China is facing challenges such as insufficient driving force of scientific and technological innovation, limited breakthrough of key technologies, and difficulty in obtaining innovative resources in underdeveloped areas. The agglomeration of innovative factors promotes the development of new quality productive forcesby breaking the limitations of traditional innovation models. However, there is still insufficient research on the systematic mechanism of innovation factor agglomeration affecting new quality productive forces. In particular, the synergy of innovation factor agglomeration through dualinnovation is still in the "black box". How does innovation factor agglomeration affect new quality productive forces through dualinnovation mechanism? The existing research mainly discusses the agglomeration of innovation factors from the aspects of spatial distribution characteristics, influencing factors and economic effects, but there are few explorations on the different paths that affect the new quality productive forces. Some scholars have made preliminary explorations on talent agglomeration, capital agglomeration and technology agglomeration as important research dimensions, but these studies focus more on the spatial distribution characteristics and agglomeration effect measurement of innovation elements.

From the perspective of dualinnovation, based on the data of 30 provinces in China from 2014 to 2023, this paper systematically explores the influence mechanism of innovation factor agglomeration on new quality productive forcesaround the four action paths formed by the cross combination of two dimensions of network effect and scale effect and dualinnovation. The results show that the agglomeration of innovation factors has a significant role in promoting new quality productive forces, among which the positive effects of innovation technology and platform agglomeration are the most significant. As an important moderating variable, regional innovation environment significantly strengthens the positive impact of innovation factor agglomeration on new quality productive forces. The agglomeration of innovation elements affects new quality productive forcesthrough the dualinnovation mechanism. Although the short-term network effect of exploratory innovation is negative, it significantly promotes new quality productive forcesafter scale, and can be transformed into exploitative innovation to form a continuous driving force. Further analysis from the factor dimension shows that among the four elements of innovative talents, capital, technology and platform, only innovative technology agglomeration and platform agglomeration have a significant positive impact on new quality productive forcesthrough the scale effect of dualinnovation. The former enhances the technological breakthrough ability of innovation subjects by promoting technology spillover and knowledge diffusion, while the latter reduces innovation barriers and enlarges synergy effects as an important hub for the integration of innovative resources. Although talent agglomeration and capital agglomeration also show a positive relationship in some stages, their separate impact on new quality productive forcesis relatively limited, and they may need to be combined with technological innovation and platform construction to give full play to their effectiveness. This shows that in the current development stage, technological innovation and platform construction may be the core bridge connecting innovation elements and new quality productive forces. The heterogeneity test also finds that the effect of innovation factor agglomeration is more significant in regions with higher levels of marketization, digital economy and transportation infrastructure.

The marginal contribution of this paper is that, first, it reveals the mechanism of innovation factor agglomeration on new quality productive forcesfrom the perspective of dual innovation, and enriches the theoretical research on the formation mechanism of new quality productive forces. Secondly, the two dimensions of network effect and scale effect are combined with dualinnovation to form four action paths, and the system mechanism of factor agglomeration to empower new quality productive forcesis constructed. The third is to introduce adjustment effect and heterogeneity analysis to provide empirical basis for differentiated policies. The research conclusions provide theoretical basis and policy enlightenment for optimizing the spatial layout of innovation elements, improving regional innovation efficiency and promoting the formation of new quality productive forces.

innovation factor agglomeration  /  new quality productive forces  /  dual innovation  /  network effect  /  scale effect
赵成伟, 李文雅, 陈红其, 向仙虹, 王砚羽. 要素集聚如何通过双元创新影响新质生产力?. 科学学研究, 2026 , 44 (6) : 1178 -1191 .
Cheng-wei ZHAO, Wen-ya LI, Hong-qi CHEN, Xian-hong XIANG, Yan-yu WANG. How does factor agglomeration affect new quality productive forces through dualinnovation?[J]. Studies in Science of Science, 2026 , 44 (6) : 1178 -1191 .
2023年9月,习近平总书记在黑龙江考察调研期间提出“新质生产力”,他指出:“要整合科技创新资源,引领发展战略性新兴产业和未来产业,加快形成新质生产力”,而创新要素集聚是整合科技创新资源最重要的方式之一。当前我国新质生产力发展面临科技创新驱动力不足、关键技术突破有限、欠发达地区创新资源获取难等挑战[1,2]。创新要素集聚突破传统创新模式局限,通过网络效应和规模效应促进新质生产力发展,这两个维度与双元创新交织形成四条路径,构建了要素集聚赋能新质生产力的系统机制。因此,研究创新要素集聚对新质生产力的影响有助于深化理论认知,也为各地区优化创新要素布局、促进差异化发展提供政策指引。
当前,学者们将新质生产力视为一种以科技创新引领、数字技术支撑、绿色发展导向的现代生产力形态[3]。研究从创新体系[4]、数字赋能[5]、绿色转型[6]等角度探讨了新质生产力的形成机制,特别强调创新要素优化布局的关键作用。分析表明,提升新质生产力既需要颠覆性技术突破带来质的飞跃,也需要渐进性改进积累量的提升。创新要素的高效集聚能同时支撑突破性和渐进性创新活动,成为提升新质生产力的关键路径。文献梳理表明现有研究已覆盖创新要素集聚对微观层面(企业创新效率、技术突破[7])、中观层面(产业升级[8])与宏观层面(经济增长、区域均衡[9,10]、人才引进[11])的多元影响。学者主要关注了创新要素集聚的空间分布特征、影响因素和经济效应。在空间分布方面,创新要素呈现明显集聚态势且具路径依赖性;在影响因素方面,制度环境、市场条件等因素均影响集聚程度;在经济效应方面,创新要素集聚通过知识溢出、资源共享等提升区域创新效率。此外学者们将人才集聚、资本集聚、技术集聚等作为重要研究维度进行了初步探索,但这些研究更侧重于创新要素的空间分布特征与集聚效应测度。
现有研究虽探讨创新要素集聚效果,但对其影响新质生产力的系统性机制研究不足,尤其是要素焦聚如何通过双元创新的协同作用仍处于“黑箱状态”,故本文的贡献在于:一是从双元创新视角揭示创新要素集聚对新质生产力的作用机理,丰富了新质生产力形成机制的理论研究;二是将网络效应与规模效应两维度与双元创新交叉组合,形成四条作用路径;三是引入调节效应和异质性分析,为差异化政策提供实证依据。基于此,将网络效应与规模效应两维度分别与探索式创新和利用式创新交叉,形成四条作用路径,系统化研究创新要素集聚对新质生产力的作用机制。
创新要素集聚通过空间邻近催生主体间互动交流形成网络效应,同时通过资源汇集达到临界规模产生规模效应,这两种固有特性共同影响新质生产力发展。一方面,从协同互动角度,创新要素集聚形成网络效应,为知识溢出和技术交流创造了便利条件。根据空间经济学理论,地理邻近使创新主体间能以更低成本进行面对面交流,促进信息快速流动与创新资源共享[12]。同时,依据网络外部性理论,集聚环境突破了主体间壁垒,加强了产学研协同和跨区域合作,形成了高效创新网络,网络中的频繁互动与多元合作加速了创新成果的扩散和应用转化,为新质生产力提供了持续动力[13]。另一方面,从资源积累角度,创新要素集聚形成规模效应,显著增强了创新主体的研发实力。基于规模经济理论,创新资源的空间集中降低了单位创新成本,实现了资源的最优布局,使创新主体能够承担更具挑战性的前沿研究[14]。此外,创新要素集聚促进了区域创新平台和专业化创新生态系统建设,提供了先进研发设施和技术支持,吸引更多创新主体参与协同攻关。这些机制共同作用,不仅直接增强了科技创新的突破能力,也为颠覆性创新创造了有利条件,从而系统性地推动形成新质生产力。基于此,本文提出如下假设:
H1:创新要素集聚对新质生产力具有显著的促进作用。
双元创新理论是组织创新研究中的重要理论范式,其核心观点是组织需要在探索式创新与利用式创新之间实现动态平衡,其中,探索式创新强调对新知识和新机会的搜索,表现为对新技术、新市场的探索和突破;利用式创新则注重对现有知识的深化利用,体现为对已有技术和产品的改进优化。组织的持续发展既需要探索式创新带来的长期竞争优势,也离不开利用式创新提供的稳定效益,故需要在两种创新模式间实现均衡。结合双元创新理论,创新要素集聚通过探索式创新和利用式创新两种路径,每种创新类型又分别通过网络效应和规模效应两个维度发挥作用,形成“双元—双维”的复合作用机制。在探索式创新方面,网络效应体现为创新要素集聚促进创新主体间的深度协作,通过跨领域知识整合推动前沿技术突破;规模效应则表现为支持单个创新主体在特定前沿领域形成持续投入,积累创新能力[15]。在利用式创新方面,网络效应主要通过推动创新主体间的经验共享和技术扩散,促进已有技术持续优化;规模效应则支持创新主体在现有技术路径上深化发展,通过专业化分工提升技术改进的效率,这些机制共同作用,最终推动新质生产力的提升。基于此,本文提出如下假设:
H2:创新要素集聚可以通过双元创新赋能新质生产力。
H2a:创新要素集聚可以通过探索式创新—网络效应赋能新质生产力。
H2b:创新要素集聚可以通过利用式创新—网络效应赋能新质生产力。
H2c:创新要素集聚可以通过探索式创新—规模效应赋能新质生产力。
H2d:创新要素集聚可以通过利用式创新—规模效应赋能新质生产力。
构造创新要素集聚对新质生产力的基准回归模型如下:
$Np{q}_{it}={\alpha }_{0}+{\alpha }_{1}In{n}_{it}+{\alpha }_{2}{Z}_{it}+{\mu }_{i}+{\delta }_{t}+{\epsilon }_{it}$
式中,Npqit为省份i在t时期的新质生产力发展水平,Innit为省份i在t时期的创新要素集聚水平,Zit表示一组控制变量,μi为个体固定效应,δt为时间固定效应,随机干扰项用εit表示。
(1)被解释变量
新质生产力(Npq)。运用卢江等[16]与武永超等[17]构建的综合评价指标体系来衡量新质生产力水平。该体系由科技生产力、绿色生产力以及数字生产力3个二级指标和资源节约型生产力等6个三级指标构成,能够较为全面客观地反映出地区新质生产力水平。由于数据可得性的原因,对原有指标体系进行了优化,删除了机器人安装原始密度等数据缺失严重的指标,同时,为避免数据共线性,删除了国内专利授予数,之后运用熵值法确定权重,获得综合得分。
(2)解释变量
创新要素集聚(Inn)。在理论层面,创新体系理论强调创新是多元主体互动、多种要素协同的复杂过程,单一要素难以全面反映创新集聚的系统性特征,而创新要素集聚理论指出,不同要素间存在互补性和协同效应。创新要素集聚是指创新活动所需的各类要素在特定地理空间内的密集程度及其空间组织形式,包括创新人才、创新资本、创新技术等多种要素的汇聚过程与空间格局。参考刘和东和刘繁繁[18]、李治国等[19]等的研究,其指标体系的构建主要从资金、人才、技术等多方面综合衡量。故从创新人才、资本、技术和平台集聚四个维度衡量创新要素集聚,这些要素之间通过双向联系形成互动体系,其中,创新人才与资本互相吸引,与技术形成突破与聚集的良性循环,并与平台相互支持发展;创新资本为技术研发与平台建设提供资金,同时获取技术带来的回报;创新技术既吸引人才又回馈资本,并推动平台升级;创新平台作为核心枢纽整合各要素,降低创新壁垒,强化资源共享(见图2、表2)。
(3)机制变量
双元创新理论强调探索式与利用式创新的平衡发展,这两类创新分别通过网络效应与规模效应形成四条影响新质生产力的机制路径,构成创新要素集聚发挥作用的理论基础[20]。据此,分别从网络效应和规模效应这两个维度,对探索式创新与利用式创新展开分析,进而衍生出4条具体作用路径。借鉴胡山和余泳泽[21]和曹冬勤等[22]的做法,对这4条路径的作用实效进行具体衡量,即:
第一,探索式创新—网络效应,运用联合申请的发明专利数衡量。
第二,利用式创新—网络效应,运用联合申请的非发明专利数(包括实用新型专利和外观设计专利)衡量。
第三,探索式创新—规模效应,运用发明专利授权数衡量。
第四,利用式创新—规模效应,运用非发明专利授权数(包括实用新型专利和外观设计专利)衡量。
(4)控制变量
为准确评估创新要素集聚对新质生产力的影响,选取了关键控制变量,即税负水平(税收收入/地区生产总值)反映地区财政压力与创新资源支持能力;社会消费水平(社会消费品零售总额/地区生产总值)衡量市场需求规模与创新动力;城镇化率(城镇常住人口/总常住人口)显示区域人口集聚基础;人口密度(年末常住人口/土地面积)反映知识溢出与创新互动潜力;经济发展水平(人均GDP对数)代表区域经济实力与创新资源保障。
以2014-2023年中国30个省份为研究样本,数据主要来源于《中国统计年鉴》《中国科技统计年鉴》和国家知识产权局等官方数据库,描述性统计结果显示(见表3),新质生产力均值0.207,标准差0.184,最大最小值差距较大,表明区域发展不均衡。创新要素集聚均值0.126,标准差0.107,呈现右偏分布,反映创新要素在少数地区高度集中。各变量的标准差相对适中,最大最小值分布合理,可以排除异常值对估计结果的显著影响。
表4的基准回归结果得出创新要素集聚对新质生产力的影响显著为正,表明创新要素在空间上的高效集聚能够有力推动新质生产力提升。进一步从分维度视角看,创新要素集聚的各组成部分对新质生产力表现出差异化影响,创新技术集聚和创新平台集聚对新质生产力的促进作用尤为突出,均在高水平上显著为正。这表明技术资源的空间集聚能够促进知识溢出与技术扩散,提升区域创新整体效能;而创新平台作为要素聚合的重要载体,通过降低创新壁垒、强化资源共享,有效放大了创新要素集聚的协同效应。但创新人才集聚和创新资本集聚不显著,表明这两类要素集聚对新质生产力的直接影响相对有限,可能需要通过技术创新和平台建设等机制才能有效转化为生产力的提升。
控制变量中,财政负担水平显著负向影响,社会消费水平正向影响,城镇化率和经济发展水平影响相对有限。验证了假设H1。
(1)稳健性检验
为验证研究结果的稳健性,采用多种方法进行检验(见表5)。一是通过5%双边缩尾处理消除极端值影响,结果显示创新要素集聚系数仍显著为正;二是剔除直辖市样本后,创新要素集聚对新质生产力的促进作用依然显著,排除了特殊区域效应的干扰;三是改变因变量的衡量方式,创新要素集聚维持显著性。综上,三种稳健性检验均证实了创新要素集聚促进新质生产力的核心结论具有较强稳定性,不受样本选择和测量方法变化的影响。
(2)内生性检验
为解决内生性问题,采用双重工具变量策略,一是选择创新要素集聚滞后一期作为工具变量,该变量既不受当期新质生产力影响(满足外生性),又因创新要素集聚的路径依赖性与当期值紧密相关(满足相关性);二是参考赵涛等[23]的研究,选择1984年每百万人邮局数及其与互联网用户数的交互项,这一历史变量既独立于当前新质生产力,又通过影响信息基础设施间接关联创新要素集聚。两个工具变量模型检验结果均显示创新要素集聚系数均显著为正,证实排除内生性干扰后,创新要素集聚促进新质生产力的结论依然稳健(见表6)。
在验证了创新要素集聚对新质生产力具有显著促进作用的基础上,为深入揭示其作用机理,构建具体模型:
$Mediato{r}_{it}={\beta }_{0}+{\beta }_{1}In{n}_{it}+{\beta }_{2}{Z}_{it}+{\mu }_{i}+{\delta }_{t}+{\epsilon }_{it}$
$Np{q}_{it}={\gamma }_{0}+{\gamma }_{1}In{n}_{it}+{\gamma }_{2}Mediato{r}_{it}+{\gamma }_{3}{Z}_{it}+{\mu }_{i}+{\delta }_{t}+{\epsilon }_{it}$
式中,Mediatorit为机制变量,其他变量定义与前文相同。
根据表7的回归结果,创新要素集聚通过双元创新对新质生产力的影响机制呈现出复杂的特征。
(1)网络效应分析
在探索式创新路径方面,列1创新要素集聚对探索式创新的影响显著为正,且影响程度远大于对利用式创新的影响,表明空间集聚更易催生颠覆性技术突破,因为探索式创新需要多领域知识交叉融合,而集聚为创新主体深度交流创造条件。列5探索式创新的网络效应对新质生产力的直接影响为负且显著,反映了其复杂性和时滞性。探索式创新涉及高风险前沿研究,短期内占用资源而未立即转化为生产力;同时颠覆性突破可能导致创造性破坏,形成转型阵痛期;而创新网络构建本身也消耗资源,在效应充分发挥前可能暂时抑制生产力。
从利用式创新路径看,创新要素集聚对其产生的正向影响相对较小(列2),且其网络效应对新质生产力的影响为负但不显著(列6)。这种不显著性可能源于利用式创新的网络协作特性较弱,主要依赖企业内部知识积累,即便外部环境良好,其外部网络效应仍有限。利用式创新的网络协作多为围绕技术整合、改进的功能性联系,缺乏探索式创新那种深度知识融合,使其网络效应难以被显著捕捉。
(2)规模效应分析
在探索式创新方面,创新要素集聚对其影响显著为正(列3),探索式创新的规模效应对新质生产力的作用也显著为正且强于利用式创新(列7)。这表明创新要素集聚通过探索式创新的规模效应显著促进了新质生产力的提升,其原因可能在于,创新要素集聚支持单个创新主体在前沿技术领域形成规模化的创新产出,而这种规模化的探索式创新通过技术溢出和扩散效应,能够广泛应用于多个产业领域,推动整体生产力水平的提升。与网络效应形成鲜明对比的是,探索式创新的规模效应对新质生产力表现出显著的正向影响,其揭示了探索式创新的不同影响机制,即虽然基于网络协作的探索式创新可能因资源分散和协调成本高而暂时抑制生产力提升,但基于规模集聚的探索式创新则能更直接地转化为生产力增长,因为规模效应促使创新主体能够在特定技术方向上形成持续、深入的投入,加速了技术突破和成果转化。
在利用式创新方面,列4显示创新要素集聚对其影响显著为正,列8中利用式创新的规模效应对新质生产力的影响也显著为正,表明创新要素集聚通过利用式创新的规模效应在一定程度上促进新质生产力提升(弱于探索式创新)。与探索式创新侧重于前沿突破不同,利用式创新通过规模效应主要体现在大量持续的技术优化上。创新要素集聚使创新主体能在现有技术基础上广泛改进,这种渐进式改良虽单次提升幅度小,但因低风险易实施,能迅速在产业中推广,从而整体上显著提升新质生产力水平。
为进一步剖析创新要素集聚的内部差异对新质生产力的影响,从网络效应和规模效应两个维度深入分析创新人才集聚、创新资本集聚、创新技术集聚和创新平台集聚这四类要素对新质生产力的影响机制(见表8)。
(1)网络效应
①创新人才集聚
从表8列1和列5看,创新人才集聚对探索式创新的网络效应显著为正,表明人才集聚能够显著促进创新主体间的知识交流与合作,有效推动探索式创新的产生。而列5显示探索式创新的网络效应对新质生产力的影响系数为负且显著,反映了探索式创新的时滞性和转型阵痛特征。虽然人才集聚促进了探索式创新网络的形成,但协作需要磨合,成果转化需时,短期内可能因资源重组而抑制现有生产力发挥。在利用式创新方面(列2和列6),人才集聚的网络效应较弱且不显著,表明人才要素的空间集聚更有利于促进创新主体开展前沿性、探索性的研究活动,而对技术改进和优化的网络协同贡献有限。
②创新资本集聚
从列1和列5看,资本集聚对探索式创新的网络效应显著为正,但其通过探索式创新网络效应对新质生产力的影响同样为负向且系数较小。这再次印证了探索式创新网络在短期内可能面临的转化挑战,资本投入虽促进了探索式创新的网络协同,但这种协同尚未有效转化为新质生产力的提升。在利用式创新方面(列2和列6),资本集聚的网络效应为负且不显著,其对新质生产力的间接影响也不显著,表明资本要素对利用式创新网络的支持相对薄弱。
③创新技术集聚
从列1和列5看,技术集聚对探索式创新的网络效应极为显著,但探索式创新网络效应对新质生产力的影响仍呈现负向且显著的关系,表明即使是技术集聚所促进的探索式创新网络,也面临着从创新成果到生产力提升的转化障碍。相比之下,在利用式创新方面(列2和列6),技术集聚不仅显著促进了利用式创新的网络效应,且对新质生产力表现出显著正向影响。这一对比强调了利用式创新网络在短期内提升新质生产力方面的优势,以及技术要素在促进这一转化过程中的关键作用。
④创新平台集聚
从列1和列5看,平台集聚对探索式创新的网络效应显著为正,但同样面临探索式创新网络对新质生产力的负向影响。而在利用式创新方面(列2和列6),平台集聚不仅显著促进了利用式创新的网络效应,且这种网络效应对新质生产力表现出正向影响但不显著,这表明平台集聚在支持双元创新网络方面具有不同的作用机制和效果。
(2)规模效应
①创新人才集聚
从列3和列7看,人才集聚对探索式创新的规模效应系数不显著,但列7显示探索式创新的规模效应对新质生产力的影响显著为正。这表明虽然人才集聚未能显著促进探索式创新的规模化发展,但规模化的探索式创新一旦形成,却能有效提升新质生产力。在利用式创新方面(列4和列8),人才集聚的规模效应同样不显著,但利用式创新的规模效应对新质生产力的影响显著为正,反映了规模化的渐进式创新对生产力提升的积极贡献。
②创新资本集聚
从列3和列7看,资本集聚对探索式创新的规模效应显著,且探索式创新的规模效应对新质生产力的影响显著为正。这表明资本的规模集聚能够有效支持创新主体在前沿技术领域形成持续、大规模的投入,并最终转化为新质生产力的提升。相比之下,在利用式创新方面(列4和列8),资本集聚的规模效应为负且不显著,但利用式创新的规模效应对新质生产力的影响显著为正,这表明虽然资本集聚不直接促进利用式创新的规模发展,但规模化的利用式创新仍然是提升新质生产力的重要途径。
③创新技术集聚
从列3和列7看,技术集聚对探索式创新的规模效应显著为正,且探索式创新的规模效应对新质生产力的影响显著为正。这表明技术要素的规模集聚能够支持创新主体形成技术积累和规模优势,有效推动探索式创新的大规模开展,并最终转化为新质生产力的提升。同样,在利用式创新方面(列4和列8),技术集聚显著促进了利用式创新的规模效应,且利用式创新的规模效应对新质生产力的影响显著为正,表明技术资源的集中还能够促进渐进式技术改进的系统化推进,支持利用式创新的规模化发展和生产力转化。
④创新平台集聚
从列3和列7看,平台集聚对探索式创新的规模效应系数为负且不显著,但探索式创新的规模效应对新质生产力的影响显著为正。在利用式创新方面(列4和列8),平台集聚表现出极为显著的规模效应,且利用式创新的规模效应对新质生产力的影响显著为正,表明创新平台特别适合支持渐进式技术改进的规模化开展,能够有效促进利用式创新的规模发展和生产力转化。
为深入探究外部环境因素在从创新要素集聚影响新质生产力过程中的调节作用,引入调节变量,一方面,从创新基础条件角度看,创新环境作为创新活动的载体,其优劣直接影响创新要素的吸引力和集聚效应,良好的创新环境能够降低创新成本,提高要素协同效率;另一方面,从创新生态系统角度,完善的创新环境能促进知识流动与技术扩散,优化创新资源布局,为创新要素发挥效能提供保障,进而增强新质生产力发展的内生动力。因此,引入创新环境作为调节变量,选取工业化发展水平、政府教育支持力度、对外开放程度、公共图书馆藏书量和建成区绿化覆盖率为指标构建创新环境综合指数,并构建模型(4)进行检验:
$Np{q}_{it}={\alpha }_{0}+{\alpha }_{1}In{n}_{it}+{\alpha }_{2}Moderato{r}_{it}+{\alpha }_{3}In{n}_{it}\times Moderato{r}_{it}+{\alpha }_{4}{Z}_{it}+{\mu }_{i}+{\delta }_{t}+{\epsilon }_{it}$
式中,Moderatorit为调节变量,其他变量定义与前文相同。
表9的实证结果揭示了调节变量在创新要素集聚影响新质生产力过程中的作用机制。创新要素集聚与创新环境的交互项系数显著为正,表明创新环境能够显著增强创新要素集聚对新质生产力的促进作用。一方面,创新环境降低了各主体创新成本,提高了创新要素集聚的效率;另一方面,良好的创新环境为创新活动提供了直接支持,缓解了创新主体的约束,使得集聚的创新要素能够更好地转化为创新成果。
在前文验证结果的基础之上,关注外部环境因素在创新要素集聚影响新质生产力过程中的作用以及该作用是否因市场化程度、数字经济发展水平和交通基础设施水平不同而存在显著差异。
(1)市场化程度异质性
市场化程度作为区域制度环境的核心指标,对创新要素配置效率具有重要影响,采用樊纲等编制的市场化指数中位数,将样本分为高低两组进行异质性分析。表10列1与2结果显示,高市场化程度和低市场化程度地区创新要素集聚对新质生产力的促进作用均显著,高市场化程度在数量上小于低市场化程度。这源于低市场化程度地区政府对于创新资源的调配更加集中,因此作用的效果更高。
(2)数字经济发展水平异质性
数字经济发展水平作为区域创新环境的重要特征,深刻影响着创新要素的流动与集聚模式。为考察数字经济发展水平的异质性影响,按照数字经济发展指数中位数将样本划分为高低两组。列3与4结果显示,高数字经济水平地区创新要素集聚对新质生产力的促进作用在10%水平显著,而低数字经济水平地区的影响不显著。这源于高数字经济发展地区拥有完善的数字基础设施,降低了创新要素流动成本,促进了远程协作与知识共享。
(3)交通基础设施水平异质性
交通基础设施作为创新要素空间流动的物质载体,其完善程度直接影响创新要素集聚的效率。为分析交通基础设施水平的异质性影响,以公路里程中位数为指标,将样本划分为高低两组进行分组回归。列5与6结果显示,高交通基础设施水平和低交通基础设施水平地区创新要素集聚对新质生产力的促进作用均显著,而高交通基础设施水平地区的促进作用更高。这源于完善的交通网络降低了空间阻隔,促进了面对面交流与合作,加速了知识溢出与技术扩散。
通过构建新质生产力测度指标体系,设置包含创新人才、资本、平台和技术集聚在内的创新要素集聚评价体系,基于网络效应与规模效应两个维度与双元创新交叉形成的4条作用路径框架,并采用多种方法进行实证分析,得出以下主要结论:
(1)创新要素集聚对新质生产力具有显著的促进作用,这一结论在缩尾处理、剔除直辖市样本、改变指标衡量方式等多种稳健性检验下依然成立,从不同维度来看,创新技术集聚和创新平台集聚对新质生产力的促进作用尤为突出。
(2)创新要素集聚通过双元创新路径显著促进新质生产力发展,其中探索式创新对新质生产力的影响幅度大于利用式创新。虽然探索式创新的网络效应在短期内可能因高风险性和调整成本而表现为负向影响,但当其形成规模效应后,能够显著促进新质生产力的提升。同时,探索式创新积累到一定程度后,可转化为利用式创新,形成从技术突破到渐进式改进的创新链条,持续有效地推动新质生产力发展。从要素维度看,技术集聚和平台集聚对新质生产力的促进作用最为显著,前者通过知识溢出和技术扩散增强双元创新效能,后者作为要素整合枢纽降低创新壁垒;而人才集聚主要促进创新主体间协作,资本集聚则支持前沿领域投入,但效果相对有限。
(3)创新要素集聚的效果受外部环境和区域特征的显著影响。创新环境通过优化创新资源布局、提升创新效率等,对创新要素集聚赋能新质生产力产生显著正向影响,有效促进创新要素集聚效果的发挥和新质生产力的提升;另一方面,创新要素集聚的效果在不同发展特征的地区间存在显著差异,即在市场化程度、数字经济发展水平与交通基础设施水平较高的地区,其对新质生产力的促进作用更为显著。
构建多层次创新要素集聚体系是推动新质生产力发展的关键路径。一是应协调双元创新途径,使探索式创新与利用式创新形成互补,前者虽风险大但能创造颠覆性突破,后者风险小能产生稳定效益,两者形成良性循环,实现螺旋上升。通过这种协调,创新链条不断形成和完善,无论是探索式创新本身的效果,还是其转化为利用式创新后的持续影响,共同构成了创新要素集聚赋能新质生产力的完整路径,为区域创新驱动发展提供了系统性解决方案。二是当前应强化规模效应,通过提高专利申请量构建技术基础,以量变促质变,同时,促进网络效应向规模效应转变,实现创新资源的高效配置。三是应着力优化创新环境,提升市场化水平、加快数字经济发展和完善交通基础设施,同时针对不同区域发展特征制定差异化政策,充分释放创新要素集聚对新质生产力的促进作用。
未来研究可从多个维度深化对创新要素集聚影响新质生产力的探索,一是探究数字经济背景下虚拟空间创新要素集聚的新特征和影响机制,尤其是数据要素与传统创新要素的交互作用;二是深入分析创新要素集聚的非线性效应和临界阈值,探索最优集聚规模与密度,并重点考察探索式创新网络效应的时滞性问题,研究其负向效应转化为正向贡献的时间窗口及影响因素,以及探索式创新与利用式创新之间的转化机制,揭示双元创新动态演进的内在规律。
  • 国家社会科学基金教育学一般项目(BIA250147)
  • 国家自然科学基金资助项目(72172016)
  • 新疆维吾尔自治区教育科学规划重点课题资助项目(HEK2024001)
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2026年第44卷第6期
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  • 接收时间:2025-03-19
  • 首发时间:2026-09-22
  • 出版时间:2026-06-15
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  • 收稿日期:2025-03-19
  • 修回日期:2025-04-22
基金
国家社会科学基金教育学一般项目(BIA250147)
国家自然科学基金资助项目(72172016)
新疆维吾尔自治区教育科学规划重点课题资助项目(HEK2024001)
作者信息
    1 新疆大学经济与管理学院, 新疆乌鲁木齐 830046
    2 新疆大学新疆创新管理研究中心, 新疆乌鲁木齐 830046
    3 北京邮电大学经济管理学院, 北京 100876

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王砚羽(1986-),女,教授,博士, E-mail:。
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