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.

制造业智能化是建设制造强国和培育产业竞争新优势的重要推动力。如何优化营商环境促进制造业智能化发展是当前亟需解决的重要问题。本文基于组态视角，采用NCA与QCA相结合的研究方法，以我国30个省份作为研究样本，探究营商环境生态对制造业智能化的多样化驱动路径。研究发现：单个营商环境条件并非是产生高制造业智能化的必要条件，高制造业智能化受到政务环境、市场环境、创新环境、金融服务、公共服务、人力资源共六个条件的“多重并发”影响，形成了三条组态路径，具体归纳为金融与资源驱动型、市场助力下依托创新环境的资源驱动型、政府主导下的创新驱动型。通过进一步探究研究样本的空间情境差异可知，发达地区和欠发达地区实现制造业智能化的驱动路径具有明显的差异性。本文从组态视角系统地探究了营商环境生态驱动制造业智能化的组态路径，为提升制造业智能化发展提供了决策参考和实践指导。

Manufacturing intelligentization is an important driving force for building a powerful manufacturing country and cultivating new industrial competitive advantages. How to optimize the doing environment and promote the intelligent development of manufacturing industry is an important issue that needs to be solved at present. Based on the perspective of configuration, the study adopts the research method of combining NCA and QCA, and takes 30 provinces in China as research samples to explore the diversified driving path of ecosystem of doing business to manufacturing intelligentization. It shows that a single doing business condition is not a necessary condition to produce high manufacturing intelligentization. High manufacturing intelligentization is affected by the "multiple concurrent" of six conditions, namely, government environment, market environment, innovation environment, financial services, public services, and human resources, forming three configuration paths, which can be specifically summarized as the financial and resource-driven path, the market-helping path combining resource-driven,and innovation environment, the innovation-driven path under government-dominant. By further exploring the spatial situation difference of the research samples, it can be seen that the driving path of manufacturing intelligentization in developed and underdeveloped regions is significantly different. Based on the perspective of configuration，the study systematically explores the driving path of doing business to manufacturing intelligentization, providing decision reference and practical guidance for improving the development of manufacturing intelligentization.

We use content analysis to examine the content analysis literature in organization studies. Given the benefits of content analysis, it is no surprise that its use in organization studies has been growing in the course of the past 25 years (Erdener & Dunn, 1990; Jauch, Osborn, & Martin 1980). First, we review the principles and the advantages associated with the method. Then, we assess how the methodology has been applied in the literature in terms of research themes, data sources, and methodological refinements. Although content analysis has been applied to research topics across the subdomains of management research, research in strategy and managerial cognition have yielded particularly interesting results. We conclude with suggestions for enhancing the utility of content analytic methods in organization studies.