|
|
|
| The Growth Mechanism of Core Technology Network Based on Valued ERGMs:A Case Study of Quantum Computing |
| Ren Haiying1,2,Li Zhen1 |
| (1.School of Economics and Management, Beijing University of Technology;2.Research Base of Beijing Modern Manufacturing Development, Beijing 100124, China) |
|
|
|
|
Abstract Core technologies are critical for sustaining the competitive advantage of firms, industries and countries.In any technological field, the core technologies are critical for the super complex technology networks, and their development deserves in-depth investigation.The research on the core technology network (CTN) and its growth mechanism can help understand the dependence and synergy between core technologies, and provide theoretical and methodological support for strategic planning and development.#br#However,the expert-based methods in the current literature are subjective, and cannot quantitatively model the growth mechanism of core technologies;most statistical network models (e.g.,ERGMs) use various proxies of technology, such as firms or institutions, technological classes (e.g.,IPCs) and patents, instead of technological concepts (TCs);most studies model the formation mechanism of technology networks instead of their growth mechanism.#br#This paper addresses these issues with valued exponential random graph models (valued ERGMs) that describe the growth mechanism of TC-based CTNs, because valued ERGMs can test hypotheses on the edge weights in networks, and TCs can explicitly show the technical contents of the core technologies.First, following theories of technological evolution, this study builds a conceptual framework of the growth of CTNs,including the external, internal, and interactive factors of technological concepts (TCs).The external factors of TCs include their network centrality and total technology capacity.The internal factors insist of TRIZ evolution principles and path dependency.The interactive factor is manifested in the assortativity of TCs.Five hypotheses on the growth mechanism of CTN are proposed.Second, all TCs are extracted from a collection of technical texts in a specific technological domain, a domain technology network is constructed with the TCs and their relationships, and a CTN is identified with valued core of the domain technology network.Last, the network effects (variables) from valued ERGMs and the five hypotheses are measured, and a set of growth mechanism models of the CTN is built, with a null model, a main effect model, and a comprehensive (main and interactive effects) model being built for both ERGMs and valued ERGMs.For ERGMs or valued ERGMs, the null models only test the distribution of number of edges or edge weights;the main effect models add the tests for the effects of node attributes on their relationship (formation or weights) with other nodes;the comprehensive model adds extra tests for the interactive effects between two node attributes on their relationship.#br#The above analytical framework is applied to the field of quantum computing.In the CTN construction stage,the Derwent Innovations Index (DII) is used as the data source.To identify appropriate patents in quantum computing, the Derwent Manual Code (DMC) “MAN = T01-E05Q” is searched and the time period was up to January 2, 2021.A total of 2303 patents are obtained.The abstracts of these patents are cleaned, and the TCs in all the patent abstracts are extracted in the form of subject-action-objects (SAOs).The common subjects and objects are merged into nodes, and edge weights are counted, resulting in the technology network in quantum computing domain.The valued core (with a threshold of 5) is identified as the CTN of quantum computing.#br#In the statistical modeling stage, all the ERGMs and valued ERGMs for the CTN are built and tested.It is evident that the valued ERGMs have the smallest AIC and BIC, as well as the highest consistency in the coefficients.The results show that the centrality of technical elements, the R&D capability of patented technologies, the degree of matching with the TRIZ evolution principles and the compatibility of technical elements have positive effects on the growth of the relationship between TCs in CTN.Meanwhile, the growth of its CTN is affected by both the path dependence of technological elements and technological breakthroughs.Finally,the suggestions on the development strategy of quantum computing are proposed from R&D, firm and government perspectives.#br#The contribution of this study is threefold.First, this study is one of the first to apply valued ERGMs as effective tools for analyzing the growth mechanism of technology networks.Second, the core technology network is constructed with technological concepts, and it describes the core technologies explicitly.Last, the effects of two TRIZ evolution principles are empirically verified, which is a novel addition to the theory of technological evolution.#br#
|
|
Received: 30 August 2022
|
|
|
|
|
|
[1] 陈劲,阳镇,朱子钦.“十四五”时期“卡脖子”技术的破解：识别框架、战略转向与突破路径[J].改革,2020,33(12): 5-15.
[2] 远德玉.产业技术界说[J].东北大学学报(社会科学版),2000,2(1): 22-25.
[3] 毛荐其.全球技术链的一个初步分析[J].科研管理,2007,28(6): 85-92.
[4] 杨武,王爽.特征分析视角下核心技术动态趋势识别——以光刻技术为例[J].情报杂志,2021,40(12): 36-44.
[5] 姜军,武兰芬.核心技术及关键产业演变研究——中国专利权人的美国发明专利分析[J].科技进步与对策,2014,31(22): 80-83.
[6] 王曰芬,张露,张洁逸.产业领域核心专利识别与演化分析——以人工智能领域为例[J].情报科学,2020,38(12): 19-26.
[7] 任海英,李真.基于输入输出型SAO网络的核心技术链识别方法研究——以量子计算领域为例[J].图书情报工作,2021,65(19): 117-129.
[8] 高洁,糜仲春,魏久檗,等.企业技术创新网络的形成模式、结构及交互关系研究[J].价值工程,2007,26(8): 30-33.
[9] 李先科.企业技术创新网络的形成与结构——兼论市场与政府作用的边界[J].改革与战略,2020,36(5): 103-110.
[10] ALTSHULLER G.Creativity as an exact science[M].St Petersburg:Science Press,1984.
[11] YOON J,KIM K.Trendperceptor: a property-function based technology intelligence system for identifying technology trends from patents[J].Expert Systems with Applications,2012,39(3): 2927-2938.
[12] 王海花,孙芹,杜梅,等.长三角城市群协同创新网络演化及形成机制研究——依存型多层网络视角[J].科技进步与对策,2020,37(9):69-78.
[13] 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.
[14] 操玉杰,李纲,毛进,等.基于ERGM的学科交叉领域知识连接机制实证研究[J].图书情报工作,2019,63(19): 128-135.
[15] KRIVITSKY P N.Exponential-family random graph models for valued networks[J].Electronic Journal of Statistics,2012,6: 1100-1128.
[16] 李树业,包国光.论产业技术演化的动力机制与规律[J].科学技术与辩证法,2008,25(6): 59-64.
[17] 周文,陈伟,郎益夫.集群创新网络知识动态增长研究:基于过程视角[J].系统工程学报,2015,30(4): 431-441.
[18] 刘星,单晓光,姜南.基于专利信息的中美区块链技术竞争态势分析[J].科技进步与对策,2020,37(18): 1-9.
[19] ARTHUR WB.The nature of technology: what it is and how it evolves[M].New York:Free Press,2012.
[20] CASSI L,MORRISON A,TER WAL A.The evolution of trade and scientific collaboration networks in the global wine sector: a longitudinal study using network analysis[J].Economic Geography,2012,88(3): 311-334.
[21] DOSI G.Sources,procedures,and microeconomic effects of innovation[J].Journal of Economic Literature,1988,26(3): 1120-1171.
[22] FLEMING L.Recombinant uncertainty in technological search[J].Management Science,2001,47(1): 117-132.
[23] YAYAVARAM S,AHUJA G.Decomposability in knowledge structures and its impact on the usefulness of inventions and knowledge-base malleability[J].Administrative Science Quarterly,2008,53(2): 333-362.
[24] WANG C,RODAN S,FRUIN M,et al.Knowledge networks,collaboration networks,and exploratory innovation[J].Academy of Management Journal,2014,57(2): 484-514.
[25] KENDRICK J W.Postwar productivity trends in the United States,1948—1969[J].NBER Books,1973,15(3): 320-335.
[26] DAVID P A.Clio and the economics of QWERTY[J].American Economic Review,1985,75(2): 332-337.
[27] PARK H,REE J J,KIM K.Identification of promising patents for technology transfers using TRIZ evolution trends[J].Expert Systems with Applications,2013,40(2): 736-743.
[28] NEWMAN M E J.Assortative mixing in networks[J].Physical Review Letters,2002,89(20): 208701.
[29] LOMI A,LUSHER D,PATTISON P E,et al.The focused organization of advice relations: a study in boundary crossing[J].Organization Science,2014,25(2): 438-457.
[30] NOLDUS R,MIEGHEM P V.Assortativity in complex networks[J].Journal of Complex Networks,2015,3(4): 507-542.
|
|
|
|
