产业共性技术产学研协同研发策略的微分博弈研究

doi:10.16381/j.cnki.issn1003-207x.2019.12.019

Abstract

Abstract: This article examines the industrial generic technology collaborative research and development of a single research institution (university, research institute) and a single enterprise that are completely rational and have complete information for the conflict between the cooperation motives and behavioral goals caused by the heterogeneity of innovation resources in universities, research institutes, and enterprises during the collaborative R&D of industrial generic technology. Research institution is mainly responsible for basic research of industrial generic technology and company is mainly responsible for the application and development of industrial generic technology. In order to motivate the enthusiasm of research institutions for research and development, enterprises provide research institutions with a certain percentage of R&D investment subsidies. The Hamilton-Jacoby-Berman equation (HJB equation) is used to investigate the respective behaviors of universities, research institutes and enterprises in three cases:Nash non-cooperative R & D game, Stackelberg master-slave R & D game and cooperative R&D game through constructing the differential game model of industry-general-technology and industry-university-research cooperation research and development. The degree of optimal R&D effort, the optimal R&D income and the total optimal R&D income of both sides are analyzed, the effect mechanism of R&D subsidy on industry-university-research collaborative research and development of industrial generic technology is discussed, and the coordination mechanism among the subjects of industry-general-technology industry-university-research collaborative innovation under the dynamic framework is seeked. Through the comparison of the three game results, the following conclusions are drawn:R&D investment subsidy, as an incentive strategy, can promote the research and development efforts of research institutions, the research and development income of both sides and the total R&D income of both sides. Besides, In the case of cooperative game, the degree of respective R&D effort, respective R&D income and total R&D income of both parties are better than that of non-cooperative case, and there is a threshold value of income distribution coefficientα, which can realize the Pareto improvement of individual income of both sides. Specific content as follows:1) In the case of Nash non-cooperative game and Stackelberg master-slave game, the degree of R&D effort of enterprises does not change. In the case of Stackelberg master-slave game, the degree of R&D effort of research institutions is significantly higher than that of Nash non-cooperative game. The raised level is equal to the coefficient of R&D investment subsidy, which indicates that R&D investment subsidy, as an incentive mechanism, can improve the R&D effort of research institutions. In the case of cooperative game, the degree of R&D effort of enterprises and research institutions is better than that of non-cooperative game. 2) In the case of Stackelberg master-slave game, the R&D income of enterprises and research institutions are better than that of Nash non-cooperative game, that is, when enterprises provide R&D investment subsidies to research institutions, the R&D returns of enterprises and research institutions are improved.3) In the case of cooperative cooperation game, the total R&D revenue of enterprises and research institutions is superior to that of Stackelberg's master-slave game. Under the Stackelberg master-slave game, the total R&D revenue of enterprises and research institutions is better than that of Nash non-cooperative games. In order to coordinate the cooperative R&D behavior of industry generic technology between research institutions and enterprises, the range of income distribution coefficient α is discussed and analyzes the revenue coordination mechanism of industry-university-research collaboration R&D of industrial general technology is analyzed. Finally, the results of theoretical derivation are verified by randomly setting parameters within the assumed condition of the article.

Key words: industrial generic technology, industry-university-research, synergetic R&D, differential game, HJB equation

CLC Number:

C934

MA Yong-hong, LIU Hai-jiao, LIU Qing. Differential Game Study on Industry-University Synergetic R&D Strategy of Industrial Generic Technology[J]. Chinese Journal of Management Science, 2019, 27(12): 197-207.

References

[1] 李纪珍. 产业共性技术供给体系[M]. 北京:中国金融出版社,2004.
[2] 马名杰. 共性技术的内涵与评判标准[R]. 国务院发展研究中心调查研究报告,2004,(153):102-118.
[3] Tassey G. Technology infrastructure and competitive position[M]. Norwell,MA:Kluwer Academic Publishers,1992.
[4] Tassey G. The economics of R&D policy[M]. Washington,DC:Baker & Tayor Books,1997.
[5] Tassey G. Standardization in technology-based markets[J]. Research Policy,2000,29(4/5):587-602.
[6] Tassey G. Underinvestment in public good technologies[J]. Journal of Technology Transfer,2005,30(1/2):89-113.
[7] 许瑞阳,徐峰. 产业共性技术的界定及选择方法研究-基于科技计划管理的视角[J]. 中国软科学,2010,(4):73-79.
[8] 樊霞,吴进. 基于文本分析的我国共性技术创新政策研究[J]. 科学学与科学技术管理,2014,35(8):69-76.
[9] 栾春娟. 战略性新兴产业共性技术测度指标研究[J]. 科学学与科学技术管理,2012,33(2):11-16.
[10] 骆正清,戴瑞. 共性技术的选择方法研究[J]. 科学学研究,2013,31(1):22-29.
[11] 黄鲁成,张静. 基于专利分析的产业共性技术识别方法研究[J]. 科学学与科学技术管理,2014,35(4):80-86
[12] 刘波,常珏宁,龙彦召. 基于技术预见视角的共性技术筛选实证研究[J]. 科学学与科学技术管理,2014,35(10):26-34.
[13] 周潇,盛永祥,吴杰,等. 不同类型政府和企业对产业共性技术研发的投资策略研究[J]. 研究与发展管理,2017,29(3):98-109.
[14] 刘洪民,姜黎辉,王中魁. 制造业共性技术研发的知识管理评价体系构建[J]. 科研管理,2016,37(S):379-386.
[15] 郑月龙,周立新,周继祥. 考虑产业共性技术市场化的政府研发支持合同[J]. 科学学研究,2017,35(10):1483-1490.
[16] 陈劲,阳银娟. 协同创新的理论基础与内涵[J]. 科学学研究,2012,30(2):161-164.
[17] 薛捷,张振刚. 国外产业共性技术创新平台建设的经验分析及其对我国的启示[J]. 科学学与科学技术管理,2006,27(12):87-92.
[18] 周国林. 产业共性技术产学研联盟组织模式的评述[J]. 经济学动态2010,(4):90-93.
[19] 樊霞,陈双丽. 产学研合作更有利于产业共性技术研发创新吗-对USPTO中国专利数据的实证检验[J]. 科技进步与对策,2016,33(13):59-65.
[20] 樊霞,黄研,朱桂龙. 产学研合作对共性技术创新的影响效用研究[J]. 科研管理,2018,39(1):34-44.
[21] 殷辉,陈劲. 共性技术下异质企业对学研方合作行为的演化博弈仿真分析[J]. 科技进步与对策,2015,32(8):101-107.
[22] Raesfeld A,Geurts P,Jansen M,et al. Influence of partner diversity on collaborative public R&D project outcomes,a study of application and commercialization of nanotechnologies in the Netherlands[J]. Technovation,2012,33(3):227-233.
[23] Perkmann M,Valentina T. Academic engagement and commercialization:A review of the literature on university-industry relations[J]. Research Policy, 2013,42(2):423-442.
[24] Ankrah S N,Burgess T F,Grimshaw P,et al. Asking both university and industry actors about their engagement in knowledge transfer:What single-group studies of motives omit[J]. Technovation,2013,33(2):55-65.
[25] Tijssen R J W,Leeuwen T N,Wijk E V. Benchmarking university-industry research cooperation worldwide:Performance measurements and indicators based on co-authorship data for the world's largest universities[J]. Research Evaluation,2009,18(1):13-24.
[26] Trigo A,Vence X. Scope and patterns of innovation cooperation in Spanish service enterprises[J]. Research Policy, 2012,41(3):602-613.
[27] 于娱,施琴芬. 产学研协同创新中知识共享的微分对策模型[J]. 中国管理科学,2013, 21(11):684-690.
[28] 朱怀念,刘贻新,张成科,张光宇. 基于随机微分博弈的协同创新知识共享策略[J]. 科研管理,2017, 38(7):17-25.
[29] 洪江涛,黄沛. 基于微分博弈的供应链质量协调研究[J]. 中国管理科学,2016,24(2):100-107.
[30] Basar T,Olsder G J. Dynamic noncooperative games[M]. New York:Academic Press,1995.
[31] Dockner E J,Jorgensen S,Van Long N,et al. Differential games in economics and management acience[M].Cambrige:Cambrige University Press,2000.

Differential Game Study on Industry-University Synergetic R&D Strategy of Industrial Generic Technology

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