富有韧性的创新生态系统可为高质量发展提供更大腾挪空间。因此,研究如何提升创新生态系统韧性具有重要现实意义。本文从演化视角出发考察群落结构变化对区域创新生态系统韧性的影响及作用机制。基于2010—2019年中国发明专利合作申请数据和地理信息数据,运用复杂网络建模方法与面板模型进行实证检验。研究表明:在面临知识源冲击风险时,中国各区域创新生态系统的韧性有差异化表现;群落演化与区域创新生态系统的韧性呈现倒U型关系,即随着群落内紧外松的结构特征深化,韧性先上升后下降;异质性分析结果显示创新水平高、知识喷泉型区域的群落结构对韧性的影响边际效应更强;机制分析结果显示知识多样性是影响群落演化与韧性关系的中介因素。基于上述结论,本文提出创新主体与政策制定者要重视保障群落边界开放性、增强区域技术多样性,避免区域创新生态系统脆弱化发展。
Abstract
A resilient innovation ecosystem can provide greater room for high-quality development. Therefore, it is of great significance to investigate how to improve the resilience of the regional innovation ecosystem. Based on the 2010-2019 Chinese co-application inventions and geographic information data, this study used the complex network methods and panel models to investigate how community dynamics affect the resilience of the regional innovation ecosystem. The results of this study showed that, under the impact of knowledge source risks, community evolution has an inverted U-shaped impact on the resilience of regional ecosystems. With the evolution of the community structure of “tight inside and loose outside” (close connection within the community and sparse connection between communities), the resilience of the regional ecosystem first increased and then decreased. It showed that when the “tight inside and loose outside” of communities increases from a low level to a moderate level, it is beneficial to reduce information transmission costs and transaction costs, increase social capital within the community, and thus have a positive impact on the resilience of the regional innovation ecosystem; but when the community structure dynamics is changed from a moderate level to a higher level, it will lead to “lock-in dilemma”, which will have a negative effect on the resilience of the regional innovation ecosystem. Therefore, the moderate level of community structure is more conducive to the improvement of the resilience of regional innovation ecosystems. Knowledge diversity positively mediates the relationship between community evolution and regional ecosystem resilience. The evolution of community structure further affects the resilience of regional ecosystems by directly affecting the knowledge diversity within the community. As the degree of “tight inside and loose outside” of the community increases, the knowledge diversity within the community will first increase and then decrease. We explain this phenomenon by investigating the learning mechanism within communities. Innovation community theory points out that the learning mechanism of the community has undergone a transition from “learning by localization” to “learning by specialization”. After entering the stage of specialized learning, innovation entities in the community will pay more attention to specific fields with strong technical similarities, which limits the development of knowledge diversification. Knowledge diversity is the guarantee that the innovation ecosystem has more alternative technological paths in the face of external shocks. Therefore, the change in the level of knowledge diversity from high to low will have an inverted U-shaped impact on the resilience of regional innovation systems. On the basis of existing research, this paper has investigated the mechanism by which community evolution affects the resilience of regional innovation ecosystems, and will further complement the research on the evolution mechanism of innovation ecosystems.The community structure of regions with higher levels of innovation performance or acting as knowledge fountains has a stronger marginal effect on regional innovation ecosystem resilience. Regional innovation ecosystems have different characteristics. The results of heterogeneity analysis showed that within the scope of the positive marginal effect of the inverted U-shaped in baseline regression, regions with a higher level of innovation performance or acting as knowledge fountains demonstrate higher marginal effects. This result explained the role of innovation capability and knowledge base in enhancing the resilience of innovation ecosystems and responded to existing research on the operating efficiency of regional innovation ecosystems. For policymakers, we propose that in the context of Chinese innovation ecosystem management, they should focus on how to get close to the “best balance point” in the evolution of the innovation community structure. Possible actions should be taken to monitor and manage the moderate turnover rate of community members in the regional innovation ecosystem.
关键词
创新群落 /
区域创新生态系统 /
知识源 /
韧性
Key words
innovation community /
regional innovation ecosystem /
knowledge source /
resilience
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] 李万,常静,王敏杰,朱学彦,金爱民. 创新3.0与创新生态系统[J]. 科学学研究, 2014, 32(12):1761-1770.
[2] 王伟楠,吴欣桐,梅亮.创新生态系统一个情境视角的系统性评述[J]. 科研管理,2019,40(09):25-36.
[3] Gawer, A., and M. A. Cusumano. Industry Platforms and Ecosystem Innovation[J]. Journal of Product Innovation Management, 2014, 31(2): 417-433.
[4] Oh, D. S., F. Phillips., S. Park., and E. Lee. Innovation Ecosystems: A critical examination[J]. Technovation, 2016, 54: 1-6.
[5] 白俊红,蒋伏心.协同创新、空间关联与区域创新绩效[J]. 经济研究, 2015, 50(07):174-187.
[6] 刘丹,闫长乐. 协同创新网络结构与机理研究[J]. 管理世界, 2013(12):1-4.
[7] 姚艳虹,高晗,昝傲.创新生态系统健康度评价指标体系及应用研究[J]. 科学学研究,2019,37(10):1892-1901.
[8] 雷雨嫣,刘启雷,陈关聚. 网络视角下创新生态位与系统稳定性关系研究[J]. 科学学研究,2019,37(03):535-544.
[9] Holm, J. R., and C. R. ?stergaard. Regional employment growth, shocks and regional industrial resilience: A quantitative analysis of the Danish ICT sector[J]. Regional Studies, 2015, 49(1): 95-112.
[10] ?stergaard, C, R., and E. Park. What makes clusters decline? A study on disruption and evolution of a high-tech cluster in Denmark[J]. Regional Studies, 2015, 49(5): 834-849.
[11] Tsouri, M., and G. Pegoretti. Structure and resilience of local knowledge networks: The case of the ICT network in Trentino[J]. Industry and Innovation, online.
[12] 梁林,赵玉帛,刘兵. 国家级新区创新生态系统韧性监测与预警研究[J]. 中国软科学, 2020(07): 92-111.
[13] 李万,常静,王敏杰,等.创新3.0与创新生态系统[J].科学学研究,2014,(12).1761-1770.
[14] 罗发友,刘友金. 集群内企业创新行为的进化博弈分析[J]. 中国软科学, 2004(09):85-88.
[15] Chen, Z., and J. Guan. The impact of small world on innovation: An empirical study of 16 countries[J]. Journal of Informetrics, 2010, 4(1): 97-106.
[16] Uzzi, B., and J. Spiro. Collaboration and creativity: The small world problem[J]. American Journal of Sociology, 2005, 111(2): 447-504.
[17] 张利飞.创新生态系统技术种群非对称耦合机制研究[J]. 科学学研究,2015,33(07):1100-1108.
[18] Adner R. Match your innovation strategy to your innovation ecosystem[J]. Harvard Business Review, 2006, 84(4): 98-107; 148.
[19] Weng, L., F. Menczer, and Y. Y. Ahn. Virality prediction and community structure in social networks[J]. Scientific Reports, 2013, 3: 2522.
[20] Watts, D. J., and S. H. Strogatz. Collective dynamics of ‘small-world’ networks[J]. Nature, 1998, 393(6684): 440-442.
[21] Fleming, L., and D. M. Waguespack. Brokerage, boundary spanning, and leadership in open innovation communities[J]. Organization Science, 2007, 18(2): 165-180.
[22] Carnabuci, G., and E. Operti. Where do firms’ recombinant capabilities come from? Intraorganizational networks, knowledge, and firms’ ability to innovate through technological recombination[J]. Strategic Management Journal, 2013, 34(13): 1591-1613.
[23] 马荣康,王艺棠. 知识组合多样性,新颖性与突破性发明形成[J]. 科学学研究, 2020(2):10.
[24] Ahuja, G., and C. M. Lampert. Entrepreneurship in the large corporation: A longitudinal study of how established firms create breakthrough inventions[J]. Strategic Management Journal, 2001, 22(6‐7): 521-543.
[25] 梅亮,陈劲,刘洋.创新生态系统:源起、知识演进和理论框架[J].科学学研究,2014,(12).1771-1780.
[26] Moody, J., D. McFarland., and S. Bender-deMoll. Dynamic network visualization[J]. American Journal of Sociology, 2005, 110(4): 1206-1241.
[27] Chen, S. H., M. H. Huang, and D. Z. Chen. Identifying and visualizing technology evolution: A case study of smart grid technology[J]. Technological Forecasting and Social Change, 2012, 79(6): 1099-1110.
[28] 李莉,林海芬, 程露,等. 技术群体耦合对产业创新网络抗毁性的影响研究[J]. 研究与发展管理, 2020, 32(1):12.
[29] Demetrius, L., and T. Manke. Robustness and network evolution—an entropic principle[J]. Physica A: Statistical Mechanics and its Applications, 2005, 346(3-4): 682-696.
[30] Newman, M. E. J, and M. Girvan. Finding and evaluating community structure in networks[J]. Physical Review E, 2004, 69(2): 026113.
[31] 刘凤朝,张淑慧,朱姗姗.技术知识多样性的双重作用:专利受理及创新影响——基于对象—过程视角的研究[J],中国软科学,2018,9:148-159
[32] Xu, G., W. Hu, and Y. Qiao. Mapping an innovation ecosystem using network clustering and community identification: A multi-layered framework[J]. Scientometrics, 2020, 124: 2057-2081.
[33] de Vasconcelos Gomes, L. A., A. L. F. Facin., and M. S. Salerno. Unpacking the innovation ecosystem construct: Evolution, gaps and trends[J]. Technological Forecasting and Social Change, 2018, 136: 30-48.
[34] Baron, R. M., and D. A. Kenny. The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations[J]. Journal of Personality and Social Psychology, 1986, 51(6): 1173-1182.
基金
北京市社会科学基金项目:“数字产业融合背景下京津冀城市群创新生态系统韧性测度与变化机制研究”(22JJC033, 2022.11—2025.11)。