|
|
What Kind of Digital Innovation Ecosystem Improves Regional Innovation Performance:An Analysis Based on NCA and QCA |
Lin Yan,Lu Junyao |
(School of Management,Tianjin University of Technology,Tianjin 300384,China) |
|
|
Abstract It is an important path for China to enhance its digital innovation capability and participate in international competition by strengthening the construction of digital innovation ecosystem and improving regional innovation performance. However, the current theoretical research on the digital innovation ecosystem stays in the stage of framework construction, the research results are scattered and there is little research on the relationship between the element coupling of the digital innovation ecosystem and the regional innovation performance. Therefore, this paper first defines the connotation of the digital innovation ecosystem, studies the constituent elements of the system, and analyzes the relationship between each element and innovation performance. Taking 31 provinces and cities in China as the research object, this paper uses NCA method to explore if and to what extent a single element of the system is a necessary condition to improve regional innovation performance. From the perspective of configuration, this paper uses QCA method to explore the complex impact of digital innovation ecosystem on regional innovation performance, and reveals the multiple paths and mechanisms of system element coupling to improve regional innovation performance.#br#This paper believes that the digital innovation ecosystem is a complex system of coexistence between different innovation subjects and innovation environments in a region, relying on digital technology to achieve a new combination of digital resources and non-digital resources to create new products or services, it mainly includes digital enterprises, governments, universities and scientific research institutions, etc., while the innovation environment consists of digital innovation infrastructure, digital talents and financial services. On the basis of analyzing the relationship between various elements and innovation performance, the six elements involved in the digital innovation ecosystem, namely digital enterprises, governments, universities and scientific research institutions, digital innovation infrastructure, digital talents and financial services, are regarded as the antecedent variable, regional innovation performance as the outcome variable, together with 31 provinces and cities in China as samples. Since the impact of system element input on regional innovation performance has a certain time lag effect, this paper sets the lag period to two years, selects the latest available data, that is, the antecedent variable data in 2019 and the outcome variable data in 2021, NCA necessity analysis is carried out, and fsQCA is used to conduct element configuration research.#br#The results show that digital enterprises, governments, universities and scientific research institutions, digital innovation infrastructure, digital talents and financial services are the key elements of the digital innovation ecosystem, but a single element is not a necessary condition for improving regional innovation performance. In addition, four element configurations can improve regional innovation performance, and the digital innovation ecosystems are classified into the'government guidance, intelligence gathering' type, the ‘enterprise development, environmental support' type, thr 'diversified subjects, comprehensive development' type, and 'enterprise-led, capital-driven' type. It is suggested that each region should adapt to local conditions, select a suitable digital innovation ecosystem and cultivate new drivers of regional innovation to promote the new development of digital China.#br#The research conclusions of this paper provide a more detailed explanation for exploring the relationship between digital innovation ecosystem and regional innovation performance. It enriches the research framework of digital innovation ecosystem, provides new ideas for follow-up research, and inspiration for management practice. First, local governments should give full play to their guiding role, strengthen the construction and optimization of the elements of the digital innovation ecosystem, improve the system's radiation capability, and play a positive role in regional innovation performance. Second, local governments should pay attention to the combination and matching of multiple elements of the digital innovation ecosystem, and from an overall perspective, formulate targeted policies to promote the coordinated development of elements and improve regional innovation performance. Finally, local governments should choose from four systems according to their own conditions and resource endowment:'government guidance, intelligence gathering', 'enterprise development, environmental support', 'diversified subjects, comprehensive development', and 'enterprise-led, capital-driven' system types, select a digital innovation ecosystem suitable for development, formulate relevant policies, enhance regional innovation capabilities, and accelerate the construction of an innovative country.#br#
|
Received: 19 November 2021
|
|
|
|
|
[1] MOORE J F. Predators and prey: a new ecology of competition [J]. Harvard Business Review,1993,71(3): 75-86.[2] TATIANA B,MARCOS F,SSACHA K,et al. Dynamics of digital entrepreneurship and the innovation ecosystem: a multilevel perspective[J]. International Journal of Entrepreneurial Behavior & Research,2019,26(2): 266-284.[3] BONGSUG (KEVIN) C. A General framework for studying the evolution of the digital innovation ecosystem:the case of big data[J]. International Journal of Information Management,2019,45(5): 83-94.[4] 张超,陈凯华,穆荣平.数字创新生态系统:理论构建与未来研究[J].科研管理,2021,42(3):1-11.[5] 谷斌,李润宜.基于系统动力学的数字创新生态系统价值创造路径研究[J].系统工程,2022,40(3):56-65.[6] YANJU XU. Digital innovation ecosystem: research context, research hotspot and research trends—— knowledge mapping analysis using Citespace[J]. Journal of Electronics and Information Science,2020,5(1): 237-246.[7] YULIANI S, CHRISTOFER L, NATHALIE S. Assessing value creation in digital innovation ecosystems:a social media analytics approach[J]. Journal of Strategic Information Systems,2018,27(4):335-349.[8] 梁正,李佳钰.商业价值导向还是公共价值导向——对数字创新生态系统的思考[J].科学学研究,2021,39(6):985-988. [9] YOO Y,HENFRIDSSON O,LYYTINEN K.Research commentary:the new organizing logic of digital innovation: an agenda for information systems research[J]. Information Systems Research,2010,21(4): 724-735.[10] ADNER R.Match your innovation strategy to your innovation ecosystem[J].Harvard Business Review,2006,84(4): 98-107,148.[11] SUSENO Y,LAURELL C,SICK N.Assessing value creation in digital innovation ecosystems:a social media analyt- ics approach[J].The Journal of Strategic Information Systems,2018,27(4) : 335-349.[12] SENYO P K,LIU K,EFF AH J. Digital business ecosystem:literature review and a framework for future research [J]. International Journal of Information Management,2019,47 (13):52-64.[13] 张振刚,袁斯帆,李云健,等.高层商业关联、创新合作与创新绩效的关系研究——以产品质量认证为调节变量[J].管理学报,2017,14(6):842-849.[14] 康淑娟,安立仁.政府干预、知识资源与区域创新绩效——基于价值链视角的双重门限效应[J].科技进步与对策,2020,37(1):57-64.[15] 李柏洲,周森.科研院所创新行为与区域创新绩效间关系研究[J].科学学与科学技术管理,2015,36(1) :75-87.[16] 李玲,陶厚永.包容性创新环境对区域创新绩效的影响[J].科技进步与对策,2018,35(19):31-37.[17] 惠国安.高等教育、人才供给与区域创新绩效关系研究[J].中国物价,2019,4(12):16-19.[18] 柳卸林,杨博旭.多元化还是专业化? 产业集聚对区域创新绩效的影响机制研究[J].中国软科学,2020,35(9):141-161.[19] 齐晓丽,郭沛珍,解威,等.政府支持对区域创新绩效的影响:综述及展望[J].华东经济管理,2020,34(3):44-52.[20] TAKMASHEVA I V,ZELINSKAYA A B,BOGOMOLOVA L L. The improvement of the business system of the northern region of Russia on the basis of innovative infrastructure development[J].Turkish Online Journal of Design Art and Communication,2018(8):428-433.[21] 杜运周,刘秋辰,程建青.什么样的营商环境生态产生城市高创业活跃度——基于制度组态的分析[J].管理世界,2020,36(9):141-155.[22] S?DERLUND B, TINGVALL P G. Capital freedom, financial development and provincial economic growth in China[J]. The World Economy, 2017, 40(4): 764-787.[23] RAGIN C C. The comparative method:moving beyond qualitative and qualitative strategies[M]. University of Cafornia Press,1987. [24] DUL J,ERWIN V D L,ROELOF K. A statistical significance test for necessary condition analysis[J].Organizational Research Methods,2020,23(2): 385-395.[25] MEILI ZHANG, BAIZHOU LI, SHI YIN. Configurational paths to regional innovation performance: the interplay of innovation elements based on a fuzzy-set qualitative comparative analysis approach[J]. Technology Analysis and Strategic Management,2020,32(12):1422-1435.[26] 王飞航,本连昌.创新生态系统视角下区域创新绩效提升路径研究[J].中国科技论坛,2021,37(3):154-163.[27] 杜雯秦,郭淑娟.双碳目标下我国绿色能源效率提升路径研究[J].管理现代化,2021,41(6):96-99.[28] 张妮,赵晓冬.区域创新生态系统可持续运行建设路径研究[J].科技进步与对策,2022,39(6):51-61.[29] 李晓娣,张小燕.区域创新生态系统对区域创新绩效的影响机制研究[J].预测,2018,37(5):22-28,55.[30] PARK Y K,FISS P C,SAWY O A E.Theorizing the multiplicity of digital phenomena: the ecology of configurations, causal recipes, and guidelines for applying QCA[J]. MIS Quarterly,2020,44(4):1493-1520.[31] DU Y,KIM P H.One size does not fit all:strategy configurations, complex environments, and new venture performance in emerging economies-science direct[J]. Journal of Business Research,2021,124:272-285. |
|
|
|