The Influence Mechanism of Data Asset Management Regulation and New Quality Productivity on Innovation

DOI: https://doi.org/10.62517/jse.202411106

Author(s)

Yan Zhao*

Affiliation(s)

Economic Management and Electronic Commerce, Huzhou Vocational and Technical College, Huzhou, Zhejiang, China *Corresponding Author.

Abstract

Data asset management can effectively help enterprises to mine data value and enhance their competitiveness, while new quality productivity is a new production mode that can improve production efficiency. Therefore, it is of great significance to study the relationship between data asset management and new quality productivity. This paper adopts normative research method to explore the impact mechanism of data asset management on new quality productivity and innovation. The research conclusions of this paper are as follows. Data asset management plays an important role in promoting the development of new quality productivity of enterprises. Data asset management plays an important role in stimulating innovative thinking. Data asset management can help companies better understand market needs and technology trends. The core driving force of new quality productivity is scientific and technological innovation. The development of new quality productivity is a process of continuous innovation and technology diffusion. The development of new quality productivity requires the establishment of an environment and atmosphere conducive to innovation. The synergy between data asset management and new quality productivity plays an important role in promoting innovation. The mechanism of the impact of data asset management and new quality productivity on innovation is a very complex process. The innovation promotion mechanism of data asset management and new quality productivity is a data-driven innovation model. The research results of this paper can provide reference for theoretical research and enterprise innovation.

Keywords

Data Market Regulation; New Quality Productivity; Enterprise Innovation, Mechanism; Synergy

References

[1]Davenport T H. (2014) How strategists use “big data” to support internal business decisions, discovery and production. Strategy & Leadership, 42(4), 45-50. [2]Marshall A, Mueck S, Shockley R. (2015) How leading organizations use big data and analytics to innovate. Strategy & Leadership, 3(5), 32-39. [3]Nolin J M. (2020) Data as oil, infrastructure or asset? Three metaphors of data as economic value. Journal of Information, Communication and Ethics in Society, 18 (1), 28-43. [4]MayerSchönberger V, Cukier K. (2013) Big data: A revolution that will transform how we live, work, and think. New York: Houghton Mifflin Harcourt. [5]Fisher T. (2009) The Data Asset: How Smart Companies Govern Their Data for Business Success. Hoboken: John Wiley & Sons, Inc., 12‒30. [6]Maroufkhani P, Ismail W K W, Ghobakhloo M. (2020) Big Data analytics adoption model for small and medium enterprises. Journal of Science and Technology Policy Management, 11(4), 483-513. [7]Pinochet L H C, de Camargo Belli Amorim G, Júnior D L, et al. (2021) Consequential factors of big data's analytics capability: How firms use data in the competitive scenario. Journal of Enterprise Information Management, 34(5), 1406-1428. [8]Hariri R H, Fredericks E M, Bowers K M. (2019) Uncertainty in big data analytics: Survey, opportunities, and challenges. Journal of Big Data, 6(1), 44. [9]George G, Haas M R, Pentland A. (2014) Big data and management. Academy of Management Journal,57(2),321-326 [10]Sivarajah U, Kamal M M, Irani Z, et al. (2017) Critical analysis of Big Data challenges and analytical methods. Journal of Business Research, 70, 263-286.