|
|
|
| The Behavior of Patent Assignors and Assignees Based on the Network Effect and the Actor Effect |
| Liu Xiaoyan,Sun Lina,Shan Xiaohong,Yang Juan |
| (College of Economics and Management, Beijing University of Technology, Beijing 100124,China) |
|
|
|
|
Abstract As the conversion rate of scientific and technological achievements is less than 30%, technology transaction is regarded as the link between invention and application. It is not only the main channel of technological innovation diffusion, but also an effective way to make the transformation of scientific and technological achievements into practice. This paper focuses on the behavior of patent assignors and assignees, and it aims to analyze the characteristics of transactions, identify the main factors affecting patent transaction, and provide a scientific basis for the government to formulate policies as well as promote technology transactions.#br#At present, the study of patent technology transaction and its behavior has become the focus of attention, but the research is fragmented between the network structure and the attributes of the actors themselves: research on technology transaction networks mainly focuses on the analysis of the factors and evolution characteristics which affect the field of technology transactions at the overall network level. It ignores the influence of the attributes of the network nodes on the evolution of the network. In addition studies on the behavior of patent assignors and assignees are mostly based on case analysis which is greatly influenced by objective factors.#br#The integrated circuit industry is a core and representative industry for the intelligent transformation of manufacturing and is most affected by the U.S. constraints in China in the post-epidemic period. To settle this conflict, this paper begins with the theoretical foundation and divides the factors affecting technology transaction behavior into two dimensions:the network effect and the actor effect. Therefore it proposes the research hypotheses in terms of three dimensions: structural characteristics of the network, actors' own attributes and inter-actor convergence. Then, the paper describes the data sources and processing methods, the research method stochastic actor-oriented model (Siena) and the variable measures. Subsequently, using the patent transfer data of the IC industry from 2002 to 2019, the study analyzes the overall trends and characteristics of patent technology transactions in this industry statistically and divides the industry stages. On this basis, it builds the patent technology transaction network of this industry to measure the characteristics of patent technology transactions in this industry from two aspects: quantity change and network evolution. Meanwhile, combined with research hypotheses, an empirical study of patent technology trading behavior based on the Siena model is conducted to analyze the characteristics of technology transaction behavior.#br#The main findings are as follows. First, in terms of the technological transaction network evolution, the patent technology transaction is mainly in a one-way mode. With the integrated circuit industry entering to the stage of growth, the number of technology transaction entities within the industry increases and the network scale increases. The efficiency of information transmission in the network has been improved, however it is still at a low level; the lock-in effect formed at the initial stage of the network is obviously relieved; the organization in the network is characterized by community, and the scale of community is expanding while the degree is increasing. Second, in terms of the stability of patent technology transaction network, the patent technology transaction network of integrated circuit industry is unstable and has a large variation. Third, in terms of network effect, assignors are more inclined to trade with partners of technology trading partners; as the assignees, firms will attract more new technology trading partners; there is no obvious reciprocal transaction between firms. Fourth, in terms of actor effect, as the industry enters the growth stage, mature firms are more likely to be patent assignors, and start-up firms are more likely to be patent assignees, and firms in the high structural hole position are more likely to be patent assignors; the smaller gap in technological innovation capabilities, the more likely technology transactions will occur between firms; firms tend to choose partners in the same country or in the same enterprise group for patent transaction. The research results have enriched the theoretical system and application scope of social network analysis in the field of patent technology trading, and enables scholars to analyze the factors influencing the transformation of scientific and technological achievements further. Meanwhile, it provides novel insights for the government to formulate industrial policies.#br#
|
|
Received: 24 June 2022
|
|
|
|
|
|
[1] FIGUEROA N,SERRANO C J.Patent trading flows of small and large firms[J].Research Policy,2019,48(7):1601-1616.
[2] 曾婧婧,温永林,毕超.高校技术转移与企业技术转移对区域创新能力的差异性贡献——技术转移中心的调节作用[J].科技进步与对策,2020,37(6):84-91.
[3] HE X J,DONG Y,ZHEN Z,et al.Weighted meta paths and networking embedding for patent technology trade recommendations among subjects[J].Knowledge-Based Systems,2019,184(15):104891-104899.
[4] BAHMANI M,MEHDI N.Trade-based technology transfer and its impact on the Iranian economy:using a CGE model[J].Iranian Economic Review,2015,19(1):107-122.
[5] 任龙,姜学民,傅晓晓.基于专利权转移的中国区域技术流动网络研究[J].科学学研究,2016,34(7):993-1004.
[6] CAVIGGIOLI F,UGHETTO E.The drivers of patent transactions:corporate views on the market for patents[J].R&D Management,2013,43(4):318-332.
[7] LIAN X,GUO Y,SU J.Technology stocks:a study on the characteristics that help transfer public research to industry[J].Research Policy,2021,50(10):104361.
[8] CASTRO I,CASANUEVA C,GALáN J L.Dynamic evolution of alliance portfolios[J].European Management Journal,2014,32(3):423-433.
[9] AHUJA G,SODA G,ZAHEER A.The genesis and dynamics of organizational networks[J].Organization Science,2012,23(4):1211.
[10] 林昭文,张同健,蒲勇健.基于互惠动机的个体间隐性知识转移研究[J].科研管理,2008,29(4):28-33.
[11] LIANG X,LIU A M M.The evolution of government sponsored collaboration network and its impact on innovation:a bibliometric analysis in the Chinese solar PV sector[J].Research Policy,2018,47(7):1295-1308.
[12] SUN Y T,LIU K.Proximity effect,preferential attachment and path dependence in inter-regional network:a case of China's technology transaction[J].Scientometrics,2016,108(1):201-220.
[13] 赵健,尤建新,张同建,等.互惠性视角下的知识型企业知识转化机制[J].同济大学学报(自然科学版),2011,39(2):304-308.
[14] AHUJA G,SODA G,ZAHEER A.Introduction to the special issue:the genesis and dynamics of organizational networks[J].Organization Science,2012,23(2):434-448.
[15] WANG Z Z,ZHU J.Homophily versus preferential attachment:evolutionary mechanisms of scientific collaboration networks[J].International Journal of Modern Physics C,2014,25(5):1440014.
[16] SEBESTYEN T,VARGA A.Research productivity and the quality of interregional knowledge networks[J].Annals of Regional Science,2013,51(1):155-189.
[17] 韩炜,杨俊,陈逢文,等.创业企业如何构建联结组合提升绩效——基于“结构—资源”互动过程的案例研究[J].管理世界,2017,33(10):130-149.
[18] 喻金田,胡春华.技术联盟协同创新的合作伙伴选择研究[J].科学管理研究,2015,33(1):13-16.
[19] 刘璇,汪林威,李嘉,等.科研合作网络形成机理——基于随机指数图模型的分析[J].系统管理学报,2019,28(3):520-527.
[20] 谢建国.市场竞争、东道国引资政策与跨国公司的技术转移[J].经济研究,2007,53(6):87-97.
[21] 朱娜娜,赵红岩,谢敏.基于共生理论下的子母公司合作关系与绩效表现的实证研究——来自上市公司与跨国公司的经验证据[J].预测,2017,36(5):76-80.
[22] 赵超,王铁男.伙伴年龄非对称性对企业战略联盟价值的影响[J].管理评论,2019,31(11):183-194.
[23] HIGGINS G M C.Which ties matter when? the contingent effects of interorganizational partnerships on IPO success[J].Strategic Management Journal,2003,24(2):127-144.
[24] 伊惠芳,吴红.多级需求分析视域下高校专利转移对象识别研究——以石墨烯为例[J].图书情报工作,2020,64(12):118-126.
[25] LAZEGA E,BURT R S.Structural holes:the social structure of competition[J].Revue Francaise de Sociologie,1995,36(4):779.
[26] SUN Y T,GRIMES S.The actors and relations in evolving networks:the determinants of inter-regional technology transaction in China[J].Technological Forecasting and Social Change,2017,125:125-136.
[27] LAVIE D,MILLER S R.Alliance portfolio internationalization and firm performance[J].Social Science Electronic Publishing,2008,19(4):623-646.
[28] SNIJDERS T,BUNT G,STEGLICH C.Introduction to stochastic actor-based models for network dynamics[J].Social Networks,2010,32(1):44-60.
[29] MIRC N,PARKER A.If you do not know who knows what:advice seeking under changing conditions of uncertainty after an acquisition[J].Social Networks,2020,61(8):53-66.
[30] LAZZERETTI L,CAPONE F.How proximity matters in innovation networks dynamics along the cluster evolution.a study of the high technology applied to cultural goods[J].Journal of Business Research,2016,69(12):5855-5865.
[31] WATTS D J,STROGATZ S H.Collective dynamics of 'small-world' networks[J].Nature,1998,393(6684):440-442.
[32] 马永红,杨晓萌,孔令凯.关键共性技术合作网络演化机制研究——以医药产业为例[J].科技进步与对策,2021,38(8):60-69.
|
|
|
|
