New quality productive forces are the productivity driven by future technological innovation. Its formation and development are the result of the continuous impact of many emerging technologies like big data, cloud computing, and artificial intelligence. In the digital economy era, data serves as a new production factor that can be applied across various scenarios and reused by multiple entities, enhancing the input-output efficiency of other production factors. It is becoming a powerful driving force for the formation of new quality productive forces. As crucial micro-entities within the economic system, enterprises are not only producers of goods and services, but also demanders of production factors. In modern enterprises, the value form of data elements is data resources. Ultimately, social productivity manifests its powerful dynamism and effectiveness through various types of enterprises. Therefore, it is necessary to explore how data resources empower the development of enterprises new quality productive forces from the perspective of micro-enterprises.
This paper conducts an analysis on three levels. Firstly, this paper systematically explains the connotations of productive forces, new quality productive forces and enterprise new quality productive forces, as well as analyzes the essence, attributes, and functional forms of data resources. Secondly, by exploring the substitution effect, synergy effect, innovation effect, and connection effect of data resources, this paper analyzes how these effects cultivate new-quality laborers, generate new-quality labor materials, and expand new-quality labor objects, so as to construct a theoretical logic framework for data resources to empower the development of enterprises new quality productive forces. Finally, starting from the three dimensions of cultivating new-quality laborers, generating new-quality labor materials, and expanding new-quality labor objects, this paper proposes practical constraints and enhancement paths for data resources to empower the development of enterprise new quality productive forces.
This paper contributes to promoting research on new quality productive forces from a micro-enterprise level and data element perspective, further enriching the relevant research on Marxist productivity theory and Xi Jinping's theoretical system of economic thought. This paper also aids in promoting the cultivation and implementation of new quality productive forces, providing theoretical research references for guiding enterprises to unleash the data multiplier effect and continuously advance new quality productive forces development.
The main research conclusions are threefold. Firstly, in terms of theoretical logic, data resources drive the development of enterprises new quality productive forces by cultivating new-quality laborers, promoting new-quality labor materials, and expanding new-quality labor objects. The underlying logic includes substitution effect, synergy effect, innovation effect, and connection effect. In the aspect of cultivating new-quality laborers, data resources drive high-quality workers to replace low-skilled workers, enhance employees' competencies, assist enterprises in utilizing various digital-intelligent workers, and help form value and emotional connections between enterprises and high-skilled workers. In the aspect of generating new-quality labor materials, data resources drive highly data-related labor materials to replace traditional labor materials, promote the intelligentization and greenization of traditional labor materials,, foster innovative development of labor materials, and effectively link internal and external labor materials. In the aspect of expanding new-quality labor objects, data resources drive new labor objects to replace traditional labor objects, promote intelligent use and green improvements, foster innovative development of labor materials, and effectively connect internal and external labor objects. Secondly, practical challenges hinder the empowerment of these new productive forces. These include a mismatch in the supply and demand for big data professionals, difficulty in improving workers' data literacy, integrating diverse data sources efficiently, and planning precise data infrastructure. There are also issues with unclear data ownership and the ineffective integration of data resources with application scenarios. Thirdly, in terms of improvement paths, to address challenges of cultivating new-quality laborers, efforts should focus on enhancing big data talent training and improving data literacy among general workers. To overcome constraints in generating new-quality labor materials, methods for integrating multi-source heterogeneous data should be explored, and enterprise-specific data infrastructure development should be promoted. To resolve limitations on expanding new-quality labor objects, efforts should focus on clarifying data ownership and enriching data resource application scenarios.