As a new type of production factor in the digital era, data factorization reconstructs the traditional productivity paradigm, and is an important engine to promote the transition of productivity. On the new journey of Chinese path to modernization, it is of great theoretical and practical significance to study how the value of data elements can improve the digital new quality productive forces of enterprises. Using the pilot cities of big data trading centers as a quasi-natural experiment, this paper adopted the data from 287 prefecture level cities in China from 2012 to 2022, combined with a multi period DID model, to evaluate the effectiveness and mechanism of digital new quality productive forces empowered by monetization of data elements. The research found that the establishment of big data trading centers can significantly enhance digital productivity, and that the promoting effect is mainly reflected in the sample group with a higher degree of government intervention and a larger digital divide. The monetization of data can promote the development of new quality productive forces by optimizing the structure of production factors and promoting the development of the digital economy, but the transmission path of optimizing the traditional factor structure is not yet clear. Further research found that data monetization has significant spatial spillover effects. The establishment of big data trading centers under adjacent spatial weights has a significant negative spillover effect on neighboring regions, while under economic spatial weights, the improvement of local data monetization level will have a positive spillover effect on neighboring regions. The conclusion of this study will not only provide new empirical evidence for relevant departments to lay out the construction of big data trading centers, but also provide useful insights for cultivating digital new quality productive forces.

Digital industry clusters are a phenomenon of industrial agglomeration in the context of digital economy with digital native enterprises and digital transformation of incumbent enterprises as important components. Due to differences in the ability of different enterprises to access digital resources, digital industry clusters have the problem of poor industry chain integration, the digital divide faced by cluster enterprises has turned from the "access divide" to the "ability divide". From the perspective of "data elements-digital technology" interaction, the study explored the evolution mechanism of digital industry clusters through an exploratory multi-case analysis of Hefei Artificial Intelligence Industry Cluster, Hangzhou Digital Security Industry Cluster, and Wuhan Optoelectronic Information Industry Cluster. The study revealed that: (1) the deepening of interactive degree and promotion of interactive efficiency between data elements and digital technology, drive the clusters to cluster quickly in cyberspace, which has an impact on the cluster development mode and the building of cluster competitiveness; (2) the dual-wheel drive of "data elements-digital technology" promotes the transformation of cluster network relationships from "imitative network embedding" and "progressive network embedding" to "break through network embedding" by changing the digital connection mode of cluster enterprises; (3) in the process of evolution and development, digital industry clusters choose different development modes according to the level of agglomeration they have reached and the problems they have faced, which promotes the extension of the digital economy industry chain. The findings will provide theoretical basis for the efficient and rational allocation of digital resources in China, and promote the sustainable development of digital industry clusters.

Improving the efficiency of enterprise resource allocation is the key to ensure cost control, enhance productivity and enhance competitiveness. As an important new factor of production in the era of digital economy, it is worth exploring whether enterprises can improve their resource allocation efficiency through marketization of data elements. Based on the data of Shanghai and Shenzhen A-share listed companies from 2011 to 2022, this paper constructed a dual difference model to effectively test the influence effect and action path of the marketization of data elements on the enterprise resource allocation efficiency. The study found that: (1) the marketization of data elements has significantly improved the efficiency of enterprise resource allocation, and the influence on high degree of market segmentation, strong factor flow and high degree of market competition is more obvious in regions and industries; (2) the marketization effect of data element construction on improving the efficiency of enterprise resource allocation is mainly realized through cost saving effect and digital technology innovation; and (3) the marketization of data elements increases the total productivity by affecting the efficiency of enterprise resource allocation, and the overall trend of first decreasing, then decreasing and then increasing. This paper will enrich the relevant research on the economic consequences of the marketization of data elements, reveal the internal mechanism of the marketization of data elements to improve the efficiency of enterprise resource allocation, and provide empirical evidence for further improving the efficiency of enterprise resource allocation and promoting the development of market economy.

In the rapid development of digital economy, data has become the pivotal driver of new quality productive forces (NQPF). However, the existing research lacks systematic integration of the correlation mechanism between data and NQPF. This paper reviewed relevant literature following the logic of "Connotation Evolution—Characteristics Analysis—Mechanism Overview—Framework Integration". The results showed that: (1) The evolution of data's connotation in terms of information resource carrying, value form transformation and factor integration is highly consistent with NQPF's essential requirements; (2) The inherent characteristics of data have reshaped the productivity growth logic, providing a new paradigm for enhancing NQPF; (3) Data's role exhibits multifaceted effects across economic dimensions: at the micro-level, it is the coexistence of productivity enhancement and the "digital paradox"; at the meso-level, it drives industrial restructuring while exacerbating market imbalances; and at the macro level, it raises the challenge of imbalance while promoting economic growth; and (4) Synthesizing existing findings, this study innovatively proposed an integrated framework of "factor—technology—organization—pattern", thus revealing data's transition from "factor empowerment" to "systemic emergence" in driving productivity. Through this review, we have advanced theoretical construction from factor deconstruction to framework integration and will outline future research directions to inform theoretical innovation and policy formulation in this field.

The orderly flow of data elements, as a new type of innovation factor to cultivate new dynamics of economic growth, is the key to stimulating the potential of data elements. From the perspective of data element flow environment, this paper adopted the Laurie gravity model to construct the spatial correlation network of data element flow, examined the circulation of data element flow with the help of social network analysis method, and adopted the stochastic actor model to explore the role mechanism of the driving factors of the evolution of spatial network structure, and measured the relative importance of each driving factor. The results showed that: (1) the flow of data elements is characterized by a network structure, with frequent flow of data elements between provincial-level regions and obvious spatial linkage effects; (2) Under the impetus of policies such as "data in the east and handling capacity in the west"; the sources of plate members are diversified; the roles assumed by regions are dynamic; the flow of data elements is gradually showing polycentric characteristics; and the topology of the spatial correlation network of the flow of data elements is more complex; (3) Endogenous structural factors play a dominant role in the evolution of the network structure, and each provincial-level region promotes the flow of data elements by strengthening the division of labor and cooperation through the industrial chain. Among the exogenous attribute factors, reducing the government's non-market intervention and enhancing regional innovation capability are conducive to accelerating the flow of data elements. This study will provide a new perspective on the construction of a unified national market.

Promoting the market-oriented construction of data elements can give full play to the role of data elements and is of great significance for cultivating and developing new quality productive forces. This paper selected the data of A-share listed companies from 2015 to 2022 as research samples, adopted the establishment of data trading platform as a practical exploration to promote the marketization of data elements, and used the progressive differential model to empirically test the impact of data element marketization on the level of new quality productive forces of enterprises. It was found that the marketization of data elements can significantly improve the level of new quality productive forces of enterprises, and this conclusion is still valid after various robustness tests. The mechanism test showed that the marketization of data elements affects the level of new quality productive forces of enterprises by improving the application level of digital technology and the efficiency of resource allocation. The heterogeneity analysis showed that the marketization of data elements has a more significant impact on enterprises in regions with higher digital economy level, labor-intensive enterprises and high-tech enterprises. The research conclusion will have important implications for further promoting the market-oriented construction of data elements and cultivating and developing new quality productive forces from the enterprise level.

The data element has become an important production factor in promoting the development of the digital economy, and studying the utilization level of data elements is of great significance for advancing the growth of the digital economy. This paper, based on the Technology-Organization-Environment (TOE) theoretical framework, adopting the fuzzy-set qualitative comparative analysis method (fsQCA) and artificial neural network (ANN) approaches, introduced the interpretable machine learning model SHAP to investigate the influencing factors of data element utilization levels. Using the data from 30 provinces (autonomous regions and municipalities) in China, this study empirically analyzed the factors influencing data element utilization levels. The research found that the synergy among technological, organizational and environmental factors forms three configurational paths capable of achieving high data element utilization levels: innovation-organization-openness composite, technology-organization-environment composite, and infrastructure-institution-environment composite. Among these, technological innovation levels and data resource openness have the most significant impact on achieving high data element utilization levels, while a singular focus on data resource openness may have a negative effect on the utilization of data elements. The research conclusions have clarified the influencing factors and configurational paths of data element utilization levels at the theoretical level, enriched the relevant studies on data elements, and proposed a hybrid fsQCA-ANN method, thus offering a new perspective for addressing similar complex configurational problems. At the practical level, the findings will provide some theoretical support and managerial insights for optimizing data element utilization policies and enhancing data element utilization levels in China.

The construction of a unified national market is a key component of the new development paradigm in the new era. Using the panel data from 280 cities between 2010 and 2021, this study created a quasi-natural experiment scenario by establishing data trading platforms. A multi-period difference-in-differences model was employed to systematically examine the impact of market-based allocation of data elements on the development of a unified national market. The findings revealed that the market-based allocation of data elements contributes to the advancement of a unified national market, and the result is robust. The mechanism analysis showed that the market-based allocation of data elements affects the construction of the unified national market by improving circulation efficiency, resource allocation efficiency and the level of external openness. The heterogeneity analysis indicated that the policy effects of data marketization are more pronounced in cities with higher levels of intellectual property protection, talent concentration and technological support. The conclusions of this study will offer valuable policy implications for further promoting the market-based allocation of data elements, fully unleashing the potential of data resources, and accelerating the construction of a unified national market.