In the era of the digital economy，the explosive growth and large'scale application of data have made data elements a crucial factor of production and economic resource Just as traditional elements such as land，labor，capital，management，and technology correspond to land economics，labor economics，financial economics，managerial economics，and technical economics，the recognition of data as a vital economic resource and production factor necessitates the establishment of a similar disciplinary framework for data economics&nbsp;<br>Data possesses unique attributes such as virtuality，nonconsumption，noncompetition，timeliness，scarcity，and high liquidity It also exhibits economic characteristics like economies of scale，high complementarity，scenario dependence，value attenuation，ambiguity of rights confirmation，and economic externalities These characteristics present new opportunities and challenges to the mechanisms by which production factors promote productivity，alter production relations，and advance economic globalization&nbsp;<br>As key production factors，data elements extensively participate in social production，promote the development of new digital productivity，and unleash a “multiplier effect” on economic development The impact of data elements on productivity is mainly reflected in four aspects：direct involvement in the production process，improvement of resource allocation efficiency，promotion of industrial structure upgrading，and enhancement of government decisionmaking quality The impact on production relations is evident in labormanagement relations，exchange relations，consumption relations，and distribution relations Data elements not only profoundly affect human production and lifestyle but also play a key role in reorganizing global factor resources，reshaping the global economic structure，and altering the global competitive landscape&nbsp;<br>Therefore，it is essential to prioritize the research of data economics and strengthen theoretical exploration and empirical research This is fundamentally significant for constructing Chinas independent economic knowledge system and provides a solid theoretical and practical foundation for China to secure a favorable position and assert its voice in the global digital economy competition<br><div> <br></div>

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 &quot;digital intelligence transformation-absorptive capacity-new quality productivity of enterprises&quot; is constructed, and the intermediate transmission mechanism of absorptive capacity is deeply analyzed, which opens the &quot;black box&quot; 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.

We develop a framework for difference-in-differences designs with staggered treatment adoption and heterogeneous causal effects. We show that conventional regression-based estimators fail to provide unbiased estimates of relevant estimands absent strong restrictions on treatment-effect homogeneity. We then derive the efficient estimator addressing this challenge, which takes an intuitive “imputation” form when treatment-effect heterogeneity is unrestricted. We characterize the asymptotic behaviour of the estimator, propose tools for inference, and develop tests for identifying assumptions. Our method applies with time-varying controls, in triple-difference designs, and with certain non-binary treatments. We show the practical relevance of our results in a simulation study and an application. Studying the consumption response to tax rebates in the U.S., we find that the notional marginal propensity to consume is between 8 and 11% in the first quarter—about half as large as benchmark estimates used to calibrate macroeconomic models—and predominantly occurs in the first month after the rebate.