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| Innovation Drive and Practice Path of Distributed Ledger R&D |
| Qiao Pengcheng1,Sun Hairong2 |
1.Finance and Economics College,Tibet University for Nationalities,Xianyang 712082,China;
2.School of Journalism and Communication,Northwest University of Political Science and Law,Xi'an 710122,China |
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Abstract After 2016,China's investment in distributed ledger R&D has begun to develop in a spurt manner.At present,the central cities for China's distributed ledger innovation are Beijing,Shenzhen and Shanghai.PingAn Bank,Wanda Network,Weizhong Bank,LeEco Finance,Wanxiang Holdings,Ant Financial and other companies are the main domestic players in the world's seven largest distributed ledger alliances.Accounting firms such as PwaterhouseCoopers,Ernst & Young,Deloitte & Touche also actively invest in research and development,and domestic accounting firms are slightly slow.The balanced and stable R&D group of distributed ledgers has not yet been formed.Through the QCA method,the article proves that R&D innovation performance is the result of multiple factors such as management transparency,talent intensity,financial support,financial risk,and technical topicality.No single factor is a necessary condition for innovation performance.There are three key combination conditions for the development of distributed ledger R&D innovation,which are the alternative to the distributed ledger practice path:Combining the three elements of technical topics and talent strength and financial support;combining the three elements of technical topics and financial support and low financial risks,combining management transparency and technical topics with talent strength and financial support and low financial risk.
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Received: 10 May 2018
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[1] SCHWAB Klaus.第四次工业革命[M].北京:中信出版社,2016.
[2] 工信部.中国区块链技术和应用发展白皮书[R].信息化和软件服务司,2016.
[3] 乔鹏程.分布式账本技术:区块链与经济管理研究的国际演进——Citespace和NVivo计量与多数据比较[J].科技进步与对策,2017(23):33-41.
[4] GREENSPAN G.Payment and exchange transactions in shared ledgers[J].Journal of payments strategy & systems,2016(2):172-180.
[5] 王发明,朱美娟.创新生态系统价值共创行为影响因素分析——基于计划行为理论[J].科学学研究,2018(2):370-377.
[6] 范德成,杜明月.高端装备制造业技术创新资源配置效率及影响因素研究——基于两阶段StoNED和Tobit模型的实证分析[J].中国管理科学,2018(1):13-24.
[7] 黄俊,杨紫锐,殷丽梅,等.国产机器人企业研发效率评价及影响因素研究——基于DEA-Tobit两阶段分析法[J].科技进步与对策,2017(18):101-106.
[8] 余维新,顾新,熊文明.产学研知识分工协同理论与实证研究[J].科学学研究,2017(5):737-745.
[9] 梅海涛,刘洁.区块链的产业现状、存在问题和政策建议[J].电信科学,2016(11):134-138.
[10] 李英,赵越,潘鹤思.技术创新、制度创新与产业演化关系研究综述[J].科技进步与对策,2016(24):154-160.
[11] 李俊江,孟勐.从技术追赶到技术前沿的后发经济增长路径研究[J].云南财经大学学报,2017(2):26-34.
[12] BRYNJOLFSSON E,HITT L M,KIM H H.Strength in numbers:How does data-driven decision making affect firm performance[J].Social science electronic publishing,2011(1):10-16 .
[13] FIGUEIREDO P N.Beyond technological catch-up:an empirical investigation of further innovative capability accumulation outcomes in latecomer firms with evidence from Brazil[J].Journal of engineering & technology management,2014(1):73-102.
[14] 阮建青,王凌,李垚.创新差异的基因解释[J].管理世界,2016(6):107-117.
[15] 彭新敏,郑素丽,吴晓波,等.后发企业如何从追赶到前沿——双元性学习的视角[J].管理世界,2017(2):142-158.
[16] 仲为国,李兰,路江涌,等.中国企业创新动向指数:创新的环境、战略与未来——2017·中国企业家成长与发展专题调查报告[J].管理世界,2017(6):37-50.
[17] 陈晋川,薛云志.区块链核心算法解析[M].北京:中国工信出版集团,2017:15-239.
[18] 腾讯研究组.腾讯区块链应用白皮书——打造数字经济时代信任的基石[R].腾讯研究院,2017.
[19] GAO X.A latecomer′s strategy to promote a technology standard:the case of Datang and TD-SCDMA[J].Research policy,2014(3):597-607.
[20] 陶秋燕,李锐,王永贵.创新网络特征要素配置、环境动荡性与创新绩效关系研究——来自QCA的实证分析[J].科技进步与对策,2016(18):19-27.
[21] VAISEY S.QCA3.0:the “ragin revolution”continues[J].Contemporary sociology,2009(38):308-312.
[22] 杜运周,贾良定.组态视角与定性比较分析(QCA):管理学研究的一条新道路[J].管理世界,2017(6):12-19.
[23] 里豪克斯·伯努瓦,拉金C查尔斯.QCA设计原理与应用:超越定性与定量研究的新方法[M].杜运周,李永发,译.北京:机械工业出版社,2017. |
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2019, Vol. 0 
