加快发展“专精特”小巨人企业是提升我国企业整体竞争力、走创新驱动与内生增长道路的重要载体和抓手，是一条符合中国实际、有利于制造业持续健康发展的必由之路，是提高全要素生产率、落实供给侧结构性改革要求的战略举措。本文对“专精特”小巨人企业的重要集聚区域——苏南地区展开专题研究，梳理省市两级政府长期以来培育小巨人企业的思路、政策和成果，为全国范围内“专精特”小巨人企业培育工作提供经验借鉴，为创新驱动和工业强基战略深入实施提供决策参考。

专注细分领域、掌握核心技术的本土“隐形冠军”是我国制造业自主创新的重要支撑。基于自主创新多元动力框架，以98家本土制造业“隐形冠军”为样本，运用模糊集定性比较分析（fsQCA）方法探讨中小企业自主创新的多重动力因素及作用机制。研究发现：①本土“隐形冠军”自主创新存在内部驱动、外部驱动和内外协同驱动3种动力模式，其中，协同驱动模式最为常见；②企业家精神是驱动本土“隐形冠军”自主创新的重要内部因素，在企业研发投入强度不高或未上市时能够联合市场垄断力量、良好区域环境发挥作用，驱动企业自主创新；③在推动本土“隐形冠军”自主创新的外部因素中，行业外资进入与市场垄断地位在特定情境下具有替代关系。

There is a research gap in strategic management regarding the complement from managerial cognition literature to the behavioral theory of firm, as well as linkage between cognitive structure and cognitive process of strategy formulation in the field of managerial cognition, which also calls for further exploration. The purpose of this paper is to construct a model from an integrated view for explaining the process of cognitive reconstruction under incremental changes.

制造业向智能制造转型升级是中国实现制造强国战略的必由之路。本文采用双重差分法，以《中国制造2025》政策出台为准自然实验，利用A股上市公司2010-2019年数据实证检验了智能制造政策对企业全要素生产率的作用效果及机制。研究发现：（1）智能制造政策能有效促进智能制造领域企业全要素生产率的提高；（2）智能制造政策一方面诱导企业增加无意义或低效率的研发投入，降低企业全要素生产率，另一方面引导企业增加有效发明专利数，进而提高企业全要素生产率；（3）智能制造政策作用效果在不同所有制性质、不同市场化程度地区的企业中存在显著差异。上述结果证实了智能制造政策的作用效果，并部分打开其作用机制黑箱，对下一阶段智能制造产业政策的制定与落实具有重要借鉴和启示意义。

The transformation and upgrading of manufacturing industry to intelligent manufacturing is the inevitable choice for China to realize the strategy of manufacturing power. According to the double difference method, this paper takes the introduction of "made in China 2025" policy as the quasi-natural experiment, and empirically tests the effect and mechanism of intelligent manufacturing policy on enterprise total factor productivity by using the data of A-share listed companies from 2010 to 2019. First, "Made in China 2025" has effectively promoted the improvement of total factor productivity of smart manufacturing enterprises. As an incentive policy, this policy will give target companies preferential benefits in terms of capital, taxation, land use, and talents, which can effectively guide and help companies reduce costs and enhance competitiveness, thereby promoting the improvement of corporate total factor productivity. Second, "Made in China 2025" has two opposite mechanisms for the total factor productivity of enterprises. On the one hand, incentive policies induce enterprises to increase meaningless or inefficient R&D investment and reduce the total factor productivity of enterprises. This partly stems from Adverse selection behaviors caused by information asymmetry between enterprises and governments; on the other hand, they guide enterprises to increase the number of effective invention patents, thereby increasing their total factor productivity. Third, compared with state-owned enterprises, the effect of policy implementation on the total factor productivity of non-state-owned enterprises is more obvious. Due to the inherent lack of political resources and derivative advantages of non-state-owned enterprises, they are more eager to get support from policies, and their response speed and degree to policies will be higher. Therefore, policies have a stronger marginal promotion effect on their corporate factor productivity. Fourth, compared with enterprises in low-market areas, the impact of policy implementation on the total factor productivity of enterprises in high-market areas is more obvious. In regions with a high degree of marketization, government policies will be more inclined to consider supporting companies with more competitive market advantages, so as to give full play to the role of policy resources in promoting total factor productivity. The results confirm the effect of intelligent manufacturing policy, and partially open the black box of its mechanism, which has important reference and enlightening significance for the formulation and implementation of intelligent manufacturing industry policy in the next stage.

We examine the relationship between data analytics capabilities and innovation using detailed firm-level data. To measure innovation, we first utilize a survey to capture two types of firm practices, process improvement and new technology development for 331 firms. We then use patent data to further analyze new technology development for a broader sample of more than 2,000 publicly traded firms. We find that data analytics capabilities are more likely to be present and are more valuable in firms that are oriented around process improvement and that create new technologies by combining a diverse set of existing technologies than they are in firms that are focused on generating entirely new technologies. These results are consistent with the theory that data analytics are complementary to certain types of innovation because they enable firms to expand the search space of existing knowledge to combine into new technologies, as well as the theoretical arguments that data analytics support incremental process improvements. Data analytics appears less effective for developing entirely new technologies or creating combinations involving a few areas of knowledge, innovative approaches where there is either limited data or limited value in integrating diverse knowledge. Overall, our results suggest that firms that have historically focused on specific types of innovation—process innovation and innovation by diverse recombination—may receive the most benefits from using data analytics.