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数据分析与知识发现  2022, Vol. 6 Issue (5): 99-111     https://doi.org/10.11925/infotech.2096-3467.2021.0772
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
基于专利合作网络的研发团队识别及创新产出影响研究*
关鹏1,2(),王曰芬2,傅柱3,靳嘉林2
1巢湖学院经济与法学学院 合肥 238024
2天津师范大学管理学院 天津 300387
3江苏科技大学经济管理学院 镇江 212003
Identifying R&D Teams and Innovations with Patent Collaboration Networks
Guan Peng1,2(),Wang Yuefen2,Fu Zhu3,Jin Jialin2
1School of Economics and Law, Chaohu University, Hefei 238024, China
2School of Management, Tianjin Normal University, Tianjin 300387, China
3School of Economics and Management, Jiangsu University of Science and Technology, Zhenjiang 212003, China
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摘要 

【目的】 利用专利发明人合作网络识别技术研发团队,并对团队创新产出的影响因素进行统计分析。【方法】 设计核心研发人员检测算法,提出基于核心研发人员的研发团队识别算法。以专利产出数量作为研发团队创新产出的数量指标,以专利被引数和专利权要求数作为研发团队创新产出的质量指标,利用负二项回归模型分析研发团队特征对团队创新产出的影响。【结果】 在语音识别技术领域的实证研究表明,所提研发团队识别算法可有效识别出研发团队演化序列566个,包含各时间片段的研发团队共1 827个,研发团队平均规模为16.670;研发团队作为子网络,平均聚类系数为0.856,平均最短路径长度为1.646,表现出明显的小世界特性。【局限】 研发团队识别算法对于一些规模较小且缺少技术领域知名发明人的研发团队识别效果不佳;还需进一步扩大实证研究样本,以验证研究结果的普适性。【结论】 基于语音识别技术领域样本数据分析了研发团队特征对创新产出的影响,负二项回归模型结果表明:团队规模、团队网络平均最短路径长度对创新产出数量和质量均有显著正向影响;团队持续时间、团队稳定性、团队网络密度对创新产出数量和质量均有显著负向影响;团队聚类系数对创新产出数量有显著负向影响,对创新产出质量无显著性影响。

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作者相关文章
关鹏
王曰芬
傅柱
靳嘉林
关键词 专利合作网络研发团队创新产出    
Abstract

[Objective] This paper tries to ide.pngy technology R&D teams based on the patent holders’ collaboration networks, aiming to analyze factors influencing these teams’ innovations. [Methods] First, we ide.pngied the core R&D personnel and their team members. Then, we used the number of patents as the quantity index of innovation outputs, and the number of patent citations and claims as the quality index of innovation outputs. Finally, we used the negative binomial regression model to analyze the impacts of team characteristics on their innovations. [Results] We conducted an empirical study in the field of speech recognition technology and the proposed algorithm effectively ide.pngied 566 evolutionary sequences of R&D teams, including 1 827 R&D teams in each snapshot, with an average size of 16.670. These teams form a small world sub-network with an average clustering coefficient of 0.856 and an average shortest path length of 1.646. [Limitations] The proposed algorithm could not effectively find technology R&D teams from the fields with few well-known experts. The sample size also needs to be expanded. [Conclusions] The team size and average shortest path length of team network have significant positive impacts on the quantity and quality of innovations. The persistence, stability and network density of these teams have significant negative effects on the quantity and quality of innovations. The team clustering coefficient has significant negative effects on the quantity of innovations, but no significant impacts on the quality of innovations.

Key wordsPatent Cooperation Network    R&D Team    Innovation Outcome
收稿日期: 2021-07-30      出版日期: 2022-06-21
ZTFLH:  C931  
基金资助:*国家社会科学基金重大项目(16DZA224);安徽省社会科学基金项目(AHSKQ2020D23);安徽省高校优秀青年人才支持计划重点项目的研究成果之一(gxyqZD2019066)
通讯作者: 关鹏     E-mail: guanpeng1983@163.com
引用本文:   
关鹏,王曰芬,傅柱,靳嘉林. 基于专利合作网络的研发团队识别及创新产出影响研究*[J]. 数据分析与知识发现, 2022, 6(5): 99-111.
Guan Peng,Wang Yuefen,Fu Zhu,Jin Jialin. Identifying R&D Teams and Innovations with Patent Collaboration Networks. Data Analysis and Knowledge Discovery, 2022, 6(5): 99-111.
链接本文:  
https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2021.0772      或      https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2022/V6/I5/99
类型 变量名称 符号表示 测量方法
因变量 专利产出数量 P a t e n t s i , t + 1 指研发团队所有成员申请并被授权的专利数量
专利被引用次数 C i t a t i o n i , t + 1 指研发团队所有成员申请并被授权的专利的前项引用数
专利权要求数 C l a i m i + 1 指研发团队所有成员申请并被授权的专利的专利权要求数
自变量 团队网络密度 T e a m _ d e n s i t y i , t 指研发团队作为专利合作网络子网络的网络密度
团队网络平均聚类系数 T e a m _ c l u s t e r i , t 指研发团队作为专利合作网络子网络的平均聚类系数
团队网络平均最短路径长度 T e a m _ a s p l i , t 指研发团队作为专利合作网络子网络的平均最短路径长度
调节变量 团队规模 T e a m _ s i z e i , t 指研发团队的成员数量
团队持续时间 T e a m _ p e r s i s t e n c e i , t 指研发团队当前所在时间段与团队初始时间段的差
团队稳定性 T e a m _ s t a b i l i t y i , t 指研发团队当前成员与上一阶段成员之间的重合度,取值范围为[0,1]
Table 1  变量符号及测量方法
时间片段 节点
数量
边数量 平均聚类系数 平均最短路径长度 网络密度
1990-1994 1 043 1 785 0.653 4.657 3.285E-03
1991-1995 1 416 2 542 0.655 5.437 2.537E-03
1992-1996 1 786 3 401 0.686 8.757 2.134E-03
1993-1997 2 186 4 408 0.685 9.710 1.846E-03
1994-1998 2 728 5 712 0.673 9.712 1.536E-03
1995-1999 3 353 7 604 0.686 9.249 1.353E-03
1996-2000 4 053 9 586 0.699 11.822 1.167E-03
1997-2001 5 008 12 376 0.699 9.293 9.870E-04
1998-2002 5 713 14 302 0.711 9.050 8.770E-04
1999-2003 6 240 16 662 0.724 8.185 8.560E-04
2000-2004 6 535 17 624 0.729 8.500 8.250E-04
2001-2005 6 531 18 036 0.724 8.390 8.460E-04
2002-2006 6 268 17 410 0.729 7.475 8.860E-04
2003-2007 6 303 18 431 0.741 7.314 9.280E-04
2004-2008 6 366 19 105 0.743 7.036 9.430E-04
2005-2009 6 496 20 868 0.749 6.725 9.890E-04
2006-2010 6 478 21 241 0.749 6.698 1.012E-03
2007-2011 6 585 22 878 0.755 6.319 1.055E-03
2008-2012 6 868 24 498 0.753 6.124 1.039E-03
2009-2013 7 317 27 079 0.752 5.871 1.012E-03
2010-2014 7 664 27 914 0.744 5.586 9.510E-04
2011-2015 8 311 30 925 0.740 5.363 8.960E-04
2012-2016 9 012 33 951 0.733 5.365 8.360E-04
2013-2017 9 644 38 848 0.728 5.089 8.350E-04
Table 2  发明人合作网络结构属性
参与合作的次数 程度
中心性
接近
中心性
中介
中心性
参与合作的次数 1
程度中心性 0.501** 1
接近中心性 0.270** 0.329** 1
中介中心性 0.419** 0.720** 0.176** 1
Table 3  相关性分析结果(N=23 567)
Fig.1  各时间片段识别的核心研发人员数量
Fig.2  不同重叠系数下团队数量
Fig.3  不同重叠系数下团队演化轨迹平均跨度
Fig.4  演化轨迹跨度Top10团队的演化轨迹
变量 极小值 极大值 均值 标准差 方差
patents 0 38 1.550 3.580 12.813
citation 0 1 541 26.940 106.878 11 422.998
claim 0 878 22.530 61.496 3 781.813
team_density 0.031 1 0.587 0.290 0.084
team_cluster 0 1 0.856 0.129 0.017
team_aspl 1 6.432 1.646 0.724 0.524
team_size 3 178 16.670 20.251 410.114
team_persistence 0 17 1.590 1.695 2.874
team_stability 0.350 1 0.833 0.221 0.049
Table 4  变量描述性统计分析(N=1 827)
变量 1 2 3 4 5 6 7 8 9
patents 1
citation 0.579** 1
claim 0.806** 0.672** 1
team_density -0.458** -0.251** -0.384** 1
team_cluster -0.279** -0.097** -0.184** 0.645** 1
team_aspl 0.652** 0.356** 0.533** -0.843** -0.507** 1
team_size 0.725** 0.492** 0.679** -0.547** -0.188** 0.792** 1
team_persistence -0.094** -0.056* -0.051* 0.050* 0.028 -0.091** -0.067** 1
team_stability -0.198** -0.107** -0.181** 0.426** 0.243** -0.353** -0.236** -0.511** 1
VIF 5.004 1.887 7.721 3.372 1.554 1.881
Table 5  变量相关性分析(皮尔逊相关系数,N=1 827)
变量 模型1 模型2 模型3 模型4
team_size 0.031***
(20.377)
0.014***
(8.765)
0.027***
(19.642)
0.006**
(2.727)
team_persistence -0.241***
(-7.774)
-0.133***
(-4.456)
-0.190***
(-6.352)
-0.159***
(-5.277)
team_stability -2.016***
(-10.151)
-0.758***
(-3.722)
-1.443***
(-7.329)
-1.286***
(-6.320)
team_density -2.604***
(-14.715)
team_cluster -3.356***
(-12.500)
team_aspl 0.902***
(12.050)
Intercept 1.502***
(7.347)
1.931***
(10.077)
3.792***
(14.065)
-0.375
(-1.452)
AIC 5 126 4 918.2 5 005.7 5 009.5
2 x log-likelihood -5 116.024 -4 906.171 -4 993.713 -4 997.515
Table 6  回归模型结果(N=1 827)(因变量:patents
变量 模型1 模型2 模型3 模型4
team_size 0.036***
(10.049)
0.020***
(5.096)
0.0351***
(9.886)
0.024***
(4.512)
team_persistence -0.237***
(-4.917)
-0.191***
(-3.842)
-0.234***
(-4.784)
-0.214***
(-4.291)
team_stability -2.182***
(-5.773)
-1.588***
(-3.801)
-2.142***
(-5.476)
-2.045***
(-5.026)
team_density -1.652***
(-5.324)
team_cluster -0.313
(-0.565)
team_aspl 0.349*
(2.108)
Intercept 4.354***
(11.135)
4.956***
(12.738)
4.590***
(8.376)
3.798***
(7.246)
AIC 9 504.1 9 482.6 9 505.9 9 502.6
2 x log-likelihood -9 494.123 -9 470.577 -9 493.888 -9 490.582
Table 7  回归模型结果(N=1 827)(因变量:citation
变量 模型1 模型2 模型3 模型4
team_size 0.038***
(13.444)
0.017***
(5.458)
0.035***
(12.757)
0.015***
(3.498)
team_persistence -0.157***
(-4.122)
-0.079*
(-2.059)
-0.133***
(-3.488)
-0.107**
(-2.733)
team_stability -2.131***
(-7.123)
-0.909**
(-2.810)
-1.731***
(-5.646)
-1.571***
(-4.930)
team_density -2.486***
(-10.303)
team_cluster -2.698
(-6.223)
team_aspl 0.820***
(6.316)
Intercept 3.984***
(12.875)
4.525***
(15.022)
5.917***
(13.781)
2.417***
(5.890)
AIC 10 954 10 869 10 928 10 924
2 x log-likelihood -109 43.821 -10 857.271 -10 915.718 -10 912.150
Table 8  回归模型结果(N=1 827)(因变量:claim
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