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The Impact of Data Assetization on the Innovation Efficiency of Manufacturing Enterprises

Author(s)

Miao Wang

Affiliation(s)

Department of Economics, Northwest Normal University, Lanzhou, Gansu, China

Abstract

The In recent years, as data has been recognized as a new type of production factor, how to empower manufacturing industry innovation has become a key issue. Based on the data of A-share manufacturing companies from 2015 to 2023, this paper uses text mining technology to construct indicators for data assetization of manufacturing enterprises, employs the stochastic frontier analysis method to measure innovation efficiency, and builds a theoretical framework of "data capital accumulation - knowledge reorganization - dynamic efficiency optimization". It systematically examines the impact path and heterogeneity characteristics of data assetization on the innovation efficiency of manufacturing enterprises. The research findings are as follows: First, data assetization significantly improves the innovation efficiency of manufacturing enterprises, and this conclusion still holds after controlling for endogeneity and multiple robustness tests; Second, the industry synergy effect is prominent - the data transformation of enterprises is highly dependent on the industry environment, and the overall data level of the industry has a positive spillover effect on the individual innovation efficiency; Third, the effect shows structural differentiation: large-scale enterprises benefit more significantly due to their resource endowment advantages, the response intensity of non-state-owned enterprises is higher than that of state-owned enterprises, and the improvement effect of non-key cities enterprises is better than that of key cities; At the regional level, it presents a gradient feature of "central region > eastern region > western region > northeastern region"; Fourth, the mechanism test shows that data assetization affects the innovation efficiency of enterprises by enhancing the overall value creation ability of enterprises, improving the accuracy of R&D investment, and stimulating the synergy effect between data and human resources and capital. Based on this, it is suggested to establish a classification certification system for enterprise data capabilities to guide differentiated transformation paths, implement regional adaptation strategies to break through institutional transformation barriers, and improve the valuation and circulation infrastructure of data assets, providing systematic support for the "digital-real integration" of manufacturing.

Keywords

Data Assetization; Innovation Efficiency; Text Analysis; Stochastic Frontier Analysis; Simultaneous Equation Model

References

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