Bulletin of Chinese Academy of Sciences (Chinese Version)
Keywords
new quality productive forces; industrial intelligence; context-driven innovation; modernized industrial system; new pillar industry
Document Type
Policy & Management Research
Abstract
The development of new productive forces hinges on industrial intelligence, which is driven by the deep integration of technological innovation and industrial innovation, and the accelerated construction of a modern industrial system. Industrial intelligence refers to the process of transforming traditional industrial development models and cultivating new pillar industries through the empowerment of intelligent technologies and data elements. It serves as a pivotal engine for promoting new industrialization and developing new productive forces. However, existing research has largely overlooked the critical theoretical and practical issues of how to leverage China’s ultra-large market and its vast contextual advantages to improve the effectiveness of industrial intelligence, and accelerate the development of new productive forces. The study critically reviews extant research and practices on industrial intelligence and new productive forces. Grounded in the theories of context-driven innovation and co-innovation, it explores the connotative characteristics and key elements of context-driven industrial intelligence. It discusses the core logic through which context-driven industrial intelligence accelerates the development of new productive forces from a structural and process perspective, supported by illustrative case studies. The study provides theoretical insights and scientific guidance for seizing the opportunities presented by the context paradigm, advancing the “AI Plus” initiative, promoting new industrialization, improving the construction of a modern industrial system, cultivating new pillar industries, and accelerating the development of new productive forces during the 15th Five-Year Plan period.
First page
1070
Last Page
1081
Language
Chinese
Publisher
Bulletin of Chinese Academy of Sciences
References
1 余江, 马蕾, 张越. 产业智能化:理论机制与中国实践探索. 创新科技, 2024, 24(1): 1-7. Yu J, Ma L, Zhang Y. Industrial intelligence: Exploration of theoretical mechanism and Chinese practice. Innovation Science and Technology, 2024, 24(1): 1-7. (in Chinese)
2 周密, 郭佳宏, 王威华. 新质生产力导向下数字产业赋能现代化产业体系研究——基于补点、建链、固网三位一体的视角. 管理世界, 2024, 40(7): 1-26. Zhou M, Guo J H, Wang W H. Research on digital industry empowering the modern industrial system under the guidance of new quality productivity: Based on the perspective of supplementing nodes, establishing links and fixing networks. Journal of Management World, 2024, 40(7): 1-26. (in Chinese)
3 罗良文, 张郑秋, 周倩. 产业智能化与城市低碳经济转型. 经济管理, 2023, 45(5): 43-60. Luo L W, Zhang Z Q, Zhou Q. Industrial intelligence and urban low-carbon economic transformation. Business and Management Journal, 2023, 45(5): 43-60. (in Chinese)
4 张万里, 宣旸, 睢博, 等. 产业智能化、劳动力结构和产业结构升级. 科学学研究, 2021, 39(8): 1384-1395. Zhang W L, Xuan Y, Sui B, et al. Industrial intelligence, labor structure and industrial structure upgrading. Studies in Science of Science, 2021, 39(8): 1384-1395. (in Chinese)
5 赵培雅, 高煜, 孙雪. “双控”目标下产业智能化的节能降碳减排效应. 中国人口·资源与环境, 2023, 33(9): 59-69. Zhao P Y, Gao Y, Sun X. Energy-saving, carbon emissions-reducing, and industrial pollution emissionsreducing effects of industrial intelligence under the ‘dual control’ system. China Population, Resources and Environment, 2023, 33(9): 59-69. (in Chinese)
6 Yang L, Zou H, Shang C, et al. Adoption of information and digital technologies for sustainable smart manufacturing systems for industry 4.0 in small, medium, and micro enterprises (SMMEs). Technological Forecasting and Social Change, 2023, 188: 122308.
7 尹西明, 陈劲. 产业数字化动态能力:源起、内涵与理论框架. 社会科学辑刊, 2022, (2): 114-123. Yin X M, Chen J. Industrial digital dynamic capability: Origin, connotation and theoretical framework. Social Science Journal, 2022, (2): 114-123. (in Chinese)
8 陈劲, 阳镇. 融通创新视角下关键核心技术的突破:理论框架与实现路径. 社会科学, 2021, (5): 58-69. Chen J, Yang Z. Breakthroughs in key core technologies under the perspective of co-innovation: Theoretical framework and path to realization. Journal of Social Sciences, 2021(5): 58-69. (in Chinese)
9 尹西明, 钱雅婷, 武沛琦, 等. 场景驱动科技成果转化:理论逻辑与过程机理. 科学学研究, 2024, 42(11): 2286-2294. Yin X M, Qian Y T, Wu P Q, et al. Context-driven technology transfer: Theoretical logic and process mechanism. Studies in Science of Science, 2024, 42(11): 2286-2294. (in Chinese)
10 Kamble S S, Gunasekaran A, Parekh H, et al. Digital twin for sustainable manufacturing supply chains: Current trends, future perspectives, and an implementation framework. Technological Forecasting and Social Change, 2022, 176: 121448.
11 Ghazinoory S, Nasri S, Ameri F, et al. Why do we need ‘Problem-oriented Innovation System (PIS)’ for solving macro-level societal problems?. Technological Forecasting and Social Change, 2020, 150: 119749.
12 Poblete L, Kadefors A, Kohn Rådberg K, et al. Temporality, temporariness and keystone actor capabilities in innovation ecosystems. Industrial Marketing Management, 2022, 102: 301-310.
13 Chen Y T, Luo H B, Chen J, et al. Building data-driven dynamic capabilities to arrest knowledge hiding: A knowledge management perspective. Journal of Business Research, 2022, 139: 1138-1154.
14 Jütting M. Crafting Mission-oriented innovation ecosystems: Strategic levers for directing collaborative innovation toward the grand challenges. IEEE Transactions on Engineering Management, 2024, 71: 12053-12067.
15 Barile S, Simone C, Iandolo F, et al. Platform-based innovation ecosystems: Entering new markets through holographic strategies. Industrial Marketing Management, 2022, 105: 467-477.
16 Schade P, Schuhmacher M C. Digital infrastructure and entrepreneurial action-formation: A multilevel study. Journal of Business Venturing, 2022, 37(5): 106232.
17 Rammer C, Es-Sadki N. Using big data for generating firm-level innovation indicators—A literature review. Technological Forecasting and Social Change, 2023, 197: 122874.
18 Akter S, Michael K, Uddin M R, et al. Transforming business using digital innovations: The application of AI, blockchain, cloud and data analytics. Annals of Operations Research, 2022, 308(1): 7-39.
19 Petrescu M, Krishen A S, Kachen S, et al. AI-based innovation in B2B marketing: An interdisciplinary framework incorporating academic and practitioner perspectives. Industrial Marketing Management, 2022, 103: 61-72.
20 Walrave B, Talmar M, Podoynitsyna K S, et al. A multi-level perspective on innovation ecosystems for path-breaking innovation. Technological Forecasting and Social Change, 2018, 136: 103-113.
21 Ghosh S, Hughes M, Hodgkinson I, et al. Digital transformation of industrial businesses: A dynamic capability approach. Technovation, 2022, 113: 102414.
22 Weber K M, Schaper-Rinkel P. European sectoral innovation foresight: Identifying emerging cross-sectoral patterns and policy issues. Technological Forecasting and Social Change, 2017, 115: 240-250.
23 Plantec Q, Deval M A, Hooge S, et al. Big data as an exploration trigger or problem-solving patch: Design and integration of AI-embedded systems in the automotive industry. Technovation, 2023, 124: 102763.
24 李国杰. 智能化科研(AI4R):第五科研范式. 中国科学院院刊, 2024, 39(1): 1-9. Li G J. AI4R: The fifth scientific research paradigm. Bulletin of Chinese Academy of Sciences, 2024, 39(1): 1-9. (in Chinese)
Recommended Citation
YIN, Ximing; SU, Yaxin; CHEN, Tailun; CHEN, Jin; and YU, Jiang
(2024)
"Context-driven: Logic and pathway of industrial intelligence to accelerate development of new quality productive forces,"
Bulletin of Chinese Academy of Sciences (Chinese Version): Vol. 40
:
Iss.
6
, Article 18.
DOI: https://doi.org/10.3724/j.issn.1000-3045.20240718006
Available at:
https://bulletinofcas.researchcommons.org/journal/vol40/iss6/18
Included in
Artificial Intelligence and Robotics Commons, Science and Technology Policy Commons, Technology and Innovation Commons