产学专利合作网络:结构演变与知识流动——合作形式视角
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摘要
产学专利合作主要包括联合申请、转让与许可三种形式。利用专利大数据,构建了不同合作形式下的产学专利合作网络,基于社会网络分析法对网络特征及产学知识流动进行研究,结果表明,2005-2015年联合申请网络呈现边缘融合趋势,网络连通性增强,但转让与许可网络仍表现出较强的分散化格局;联合申请网络中高校节点的合作活跃度较高,企业节点有更强的控制力。基于技术共现网络与区域合作网络进一步探讨了产学合作知识流动状况。结果表明,①产学专利合作具有明显的技术领域选择特征,各领域知识吸收、扩散能力差异较大,但在不同合作形式间表现较统一;②联合申请网络中各省市合作深度与广度较大,而专利转让与许可网络中区域合作有待提高,区域合作程度的差异化导致各省市知识扩散与吸收能力不同。
University-industry patent collaboration pattern mainly includes joint application, transferring and licensing. This paper constructs patent network on different cooperation pattern respectively based on which explores the basic network structure and knowledge flow situation using social network analysis method. Results show that, from 2005 to 2015, joint application network concentrating gradually while transferring and licensing network still scattering. In joint application network, the most activist nodes are universities and corporations are stronger at controlling. Furthermore, this paper studies the knowledge flow situation on technology and district level. Results show that the knowledge absorption and diffusion ability varies in different technology fields while uniform among different patterns. Reginal knowledge absorption and diffusion capability also show heterogeneity. Joint application network performs better in cooperation depth and breadth among regions.
University-industry patent collaboration pattern mainly includes joint application, transferring and licensing. This paper constructs patent network on different cooperation pattern respectively based on which explores the basic network structure and knowledge flow situation using social network analysis method. Results show that, from 2005 to 2015, joint application network concentrating gradually while transferring and licensing network still scattering. In joint application network, the most activist nodes are universities and corporations are stronger at controlling. Furthermore, this paper studies the knowledge flow situation on technology and district level. Results show that the knowledge absorption and diffusion ability varies in different technology fields while uniform among different patterns. Reginal knowledge absorption and diffusion capability also show heterogeneity. Joint application network performs better in cooperation depth and breadth among regions.
引文
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[8] SORENSON O, RIVKIN J W,FLEMING L. Complexity networks and knowledge flow[J]. Research Policy,2006,35(7):994-1017.
[9] SANTORO M D, BIERLY P E. Facilitators of knowledge transfer in university-industry collaborations: a knowledge-based perspective[J]. IEEE Transactions on Engineering Management,2006,53(4):495-507.
[10] RAMANI S V,DE LOOZE M A. Using patent statistics as knowledge base indicators in the biotechnology sectors:an application to France Germany and the UK[J]. Scientometrics,2002,4(3):319-346.
[11] 周磊,杨威,张玉峰. 共现矩阵聚类分析的问题与再思考[J]. 情报杂志,2014,33(6):32-36.
[12] LEYDESDORFF L,VAUGHAN L. Co‐occurrence matrices and their applications in information science: extending ACA to the Web environment[J]. Journal of the American Society for Information Science and Technology. 2006,57(12):1616-1628.
[13] MEYER-KRAHMER F,SCHMOCH U. Science-based technologies: university–industry interactions in four fields[J]. Research Policy,1998,27(8):835-851.
[14] COHEN W M,NELSON R R,WALSH J P. Links and impacts: the influence of public research on industrial R&D[J]. Management Science,2002,48(1):1-23.
[15] SCHARTINGER D,SCHIBANY A,GASSLER H. Interactive relations between universities and firms: empirical evidence for Austria[J]. The Journal of Technology Transfer,2001,26(3):255-268.
[16] 吴翌琳,吴洁琼. 中国科技创新合作网络研究[J]. 统计研究,2017,34(5):94-101.
[17] JAFFE A B,TRAJTENBERG M,FOGARTY M S. Knowledge spillovers and patent citations:evidence from a survey of inventors[J]. American Economic Review,2000,90(2):215-218.
[18] MORENO R,PACI R,USAI S. Spatial spillovers and innovation activity in European regions[J]. Environment and Planning A,2005,37(10):1793-1812.
[2] 陈劲,陈钰芬. 开放创新体系与企业技术创新资源配置[J]. 科研管理,2006,27(3):1-8.
[3] 白俊红,蒋伏心. 协同创新、空间关联与区域创新绩效[J]. 经济研究,2015,50(07):174-187.
[4] NONAKA I,TAKEUCHI H. The knowledge-creating company:how Japanese companies create the dynamics of innovation[M]. New York:Oxford University Press,1995.
[5] 杨仲基,王宏起,王珊珊,等. 基于动态网络方法的产业专利合作态势研究——以中国石墨烯产业为例[J]. 科技进步与对策,2018,35(9):59-65.
[6] 刘凤朝,马荣康,姜楠. 基于“985高校”的产学研专利合作网络演化路径研究[J]. 中国软科学,2011(7):178-192.
[7] 王元地,刘凤朝,潘雄锋. 专利技术许可与中国企业创新能力发展[J]. 科学学研究,2011,29(12):1821-1828.
[8] SORENSON O, RIVKIN J W,FLEMING L. Complexity networks and knowledge flow[J]. Research Policy,2006,35(7):994-1017.
[9] SANTORO M D, BIERLY P E. Facilitators of knowledge transfer in university-industry collaborations: a knowledge-based perspective[J]. IEEE Transactions on Engineering Management,2006,53(4):495-507.
[10] RAMANI S V,DE LOOZE M A. Using patent statistics as knowledge base indicators in the biotechnology sectors:an application to France Germany and the UK[J]. Scientometrics,2002,4(3):319-346.
[11] 周磊,杨威,张玉峰. 共现矩阵聚类分析的问题与再思考[J]. 情报杂志,2014,33(6):32-36.
[12] LEYDESDORFF L,VAUGHAN L. Co‐occurrence matrices and their applications in information science: extending ACA to the Web environment[J]. Journal of the American Society for Information Science and Technology. 2006,57(12):1616-1628.
[13] MEYER-KRAHMER F,SCHMOCH U. Science-based technologies: university–industry interactions in four fields[J]. Research Policy,1998,27(8):835-851.
[14] COHEN W M,NELSON R R,WALSH J P. Links and impacts: the influence of public research on industrial R&D[J]. Management Science,2002,48(1):1-23.
[15] SCHARTINGER D,SCHIBANY A,GASSLER H. Interactive relations between universities and firms: empirical evidence for Austria[J]. The Journal of Technology Transfer,2001,26(3):255-268.
[16] 吴翌琳,吴洁琼. 中国科技创新合作网络研究[J]. 统计研究,2017,34(5):94-101.
[17] JAFFE A B,TRAJTENBERG M,FOGARTY M S. Knowledge spillovers and patent citations:evidence from a survey of inventors[J]. American Economic Review,2000,90(2):215-218.
[18] MORENO R,PACI R,USAI S. Spatial spillovers and innovation activity in European regions[J]. Environment and Planning A,2005,37(10):1793-1812.