新质生产力是以科技创新为主导、实现关键性颠覆性技术突破而产生的生产力，攻克关键性颠覆性技术是链主企业主导产业链治理的核心目标。以2010—2022年我国战略性新兴产业A股上市公司为例，定量识别“行业—年份”层面的行政性和经济性链主企业，测算链主企业的新质生产力和产业链治理水平，实证检验链主企业主导的产业链治理对新质生产力的影响。结果表明，链主企业主导的产业链治理能够显著提升新质生产力；在调节效应方面，市场化环境正向调节链主产业链治理对新质生产力的影响。拓展的同群效应分析表明，链主主导的产业链治理存在显著行业同群效应，而地区同群效应具有显著跨层特征，长三角的地区跨层同群效应更显著。研究结论可以为培育产业链链主、培育和发展新质生产力、提升产业链现代化水平及实现经济高质量发展提供参考。

构建产学研深度融合的技术创新体系已成为当前中国制造业打破西方技术封锁、实现科技自立自强的重要路径。制造业领军企业作为引领科技进步和产业发展的核心力量，如何有效主导并管理产学研融合以促进关键核心技术突破，成为当前理论和实践亟需探索的问题。本研究以中国制造业领军企业为研究对象，首次引入管理视角揭示企业产学研融合管理策略对关键核心技术突破的影响机制和边界条件。基于2012~2018年中国制造业500强集团上市企业的数据，研究发现：（1）产学研链路融合和主体融合管理策略均能促进制造业领军企业关键核心技术突破；（2）两者产生互补协同效应；（3）较高的企业权力配置集中度弱化了产学研链路融合对企业关键核心技术突破的促进作用，但强化了主体融合对企业关键核心技术突破的促进作用。本文研究结论为制造业领军企业合理实施产学研融合管理策略，有效发挥不同策略间的互补协同优势以及利用企业权力配置提升关键核心技术突破，提供有益的理论支持和政策建议。

In China’s new development paradigm, sci-tech self-reliance and self-strengthening are important attributes of high-quality development, the core of which is to achieve independent control of key core technologies. At present, although the manufacturing industry in China has seen a substantial improvement in the overall strength of science and technology innovation, the situation with regards to the control of key core technologies has remained essentially unchanged. Building a technological innovation system with deep integration of industry, university and research has become an important path for China's manufacturing industry to break the western technological blockade and achieve technological self-sufficiency and self-improvement. Manufacturing leading enterprises, as the core force leading the scientific and technological progress and industrial development, must learn how to effectively lead and manage the integration of industry-university-research (IUR) in order to enable breakthroughs in core technologies. This is an urgent issue that must be explored both theoretically and practically. Going beyond previous studies that focused primarily on the structural characteristics of IUR integration in the context of alliance portfolios and the traditional corporate innovation performance of general types of firms, this study takes Chinese manufacturing leading enterprises as the research object and introduces the management perspective of IUR integration for the first time to reveal the influence mechanism and boundary conditions of the managerial decisions of enterprises' IUR integration on the key core technology breakthroughs. We divide the managerial decisions of enterprises' IUR integration into two types: the link integration and the subject integration, and investigate the influence and synergy of both on the breakthrough of key core technologies. Meanwhile, we also pay attention to the fact that group control and operation are distinct features of leading manufacturing enterprises, which requires greater effort in setting up an effective corporate power allocation to achieve efficient decision-making, implementation, and coordination of IUR resources at different organizational levels. In this way, we introduce the boundary conditions of corporate power concentration to test its moderating effect on the above relationships. Based on the data of group listed enterprises in China's top 500 manufacturing industries from 2012 to 2018, the study finds that (1) two managerial decisions of IUR integration (link integration and subject integration) can both promote the breakthrough of key core technologies; and (2) they have complementary synergistic effects and can further promote the breakthroughs; (3) the higher corporate power concentration weakens the role of IUR link integration in promoting breakthroughs, while strengthens the role of IUR subject integration in promoting breakthroughs. The findings of this paper provide a new analytical perspective and theoretical logic that contributes to a theoretical understanding of IUR alliances, as well as suggestions for additional quantitative research on key core technological breakthroughs. Furthermore, they offer useful policy recommendations and theoretical support to manufacturing leading enterprises regarding implementing managerial decisions related to IUR integration, maximizing synergy advantages among different decisions, and enhancing key breakthroughs in core technology through corporate power allocation. As part of the institutional mechanism level, the government should also promote establishing IUR integration systems, guide the transfer and gathering of high-quality innovation resources to the key chains and nodes of innovation activities, and pilot and promote new organizational models and institutional forms for IUR integration.

新质生产力是推动企业高质量发展的强劲推动力和支撑力，研究其驱动因素对于企业发展至关重要。本研究基于动态资源基础观，利用2015-2022年中国A股上市公司数据，实证分析了数智化转型对企业新质生产力的影响及异质性特征，并对吸收能力的中介作用和市场竞争强度的调节作用进行了检验。研究发现：（1）数智化转型对企业新质生产力水平的提升有显著影响；（2）吸收能力在数智化转型与企业新质生产力关系间发挥中介作用；（3）市场竞争强度在数智化转型与企业新质生产力关系间发挥正向调节作用；（4）相较于国有、中西部地区、衰退期的企业，非国有、东部地区、成长期和成熟期的企业实施数智化转型更能促进新质生产力水平提升。研究结论为企业数智化转型和提升新质生产力提供了理论支持，并丰富了相关实证研究。

New quality productivity is a powerful driving force and supporting force to promote the high-quality development of enterprises. This view has been widely recognized by the community. Accelerating the development of new quality productivity will help enterprises enhance their competitive advantage and realize sustainable development. However, the existing literature focuses on exploring the connotation characteristics and value significance of new quality productivity, and there are few empirical research models to explore its driving forces. Some studies believe that the digital intelligence transformation is the core driving force to lead the innovation and development of enterprises, and whether the digital intelligence transformation can improve the new quality productivity level of enterprises is an urgent topic to be discussed.Based on the dynamic resource-based view, using the data of China A-share listed companies from 2015 to 2022, this paper empirically analyzes the influence and heterogeneity of digital intelligence transformation on the new quality productivity of enterprises, and tests the mediating role of absorptive capacity and the moderating role of competitive intensity. It is found that: (1) the digital intelligence transformation has a significant impact on the improvement of the new quality productivity level of enterprises, and this conclusion still holds after various robustness tests. (2) the digital intelligence transformation can improve the absorptive capacity and new quality productivity of enterprises, and the absorptive capacity plays a mediating role in the relationship between the digital intelligence transformation and new quality productivity of enterprises. (3) the competitive intensity plays a positive role in moderating the relationship between the digital intelligence transformation and the new quality productivity of enterprises. (4) heterogeneity analysis shows that, compared with state-owned enterprises, enterprises in the central and western regions and in recession, non-state-owned, eastern regions, growing and mature regions can promote the improvement of new quality productivity by implementing digital intelligence transformation. The theoretical contributions of this study are as follows: first, the existing literature focuses on the connotation characteristics and value significance of new quality productivity, while there are few empirical research models to explore its driving factors. This study examines the influence of digital intelligence transformation on the new quality productivity level of enterprises through empirical research, which not only provides theoretical reference for promoting the transformation of digital intelligence and improving the new quality productivity level of enterprises, but also enriches the empirical research on the relationship between digital intelligence transformation and new quality productivity of enterprises. Second, based on the dynamic resource-based view, the theoretical model of "digital intelligence transformation-absorptive capacity-new quality productivity of enterprises" is constructed, and the intermediate transmission mechanism of absorptive capacity is deeply analyzed, which opens the "black box" in the process of digital intelligence transformation and empowerment of new quality productivity of enterprises. Thirdly, by including the industry competitive intensity at the macro level as a moderating variable, the boundary conditions of the impact of digital intelligence transformation on the new quality productivity of enterprises are further clarified, which is helpful to clarify the complex and diverse relationship between them and enrich the relevant research on the dynamic resource-based view.