Applied Mathematics and Nonlinear Sciences
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Journal

Applied Mathematics and Nonlinear Sciences


Volume
& Issue

Volume 8, Issue 2


Published
on

October 30, 2023


Pages


DOI

Article

Research on the mechanism of digital economy to enhance the innovation efficiency of high-tech industry in the context of big data

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Authors

Jing Tan Affiliation:
Chongqing Vocational Institute of Safety & Technology, Chongqing, 404020, China.


Abstract

Investigating how the digital economy can improve the innovation efficiency of the high-tech industry in the context of big data. This study uses a three-stage DEA technique to measure the innovation efficiency of high-tech businesses. The index system is created by selecting innovation inputs and outputs and adjusting them with exogenous environmental factors. A thorough and organized method for evaluating digital economy indices has been developed, and weights are allocated to the indexes based on spatial autocorrelation. In the end, the Tobit model is utilized to investigate the impact of the digital economy’s growth on the innovation capacity of high-tech industries. The innovation efficiency of high-tech sectors is positively influenced by the digital economy index, increasing by 0.2102% for every 1% increase. The digital infrastructure regression coefficients for the eastern, central, and western areas are 0.00045, -0.0015, and 0.00076, respectively.


Keywords

Three-stage DEA, Spatial autocorrelation, Tobit model, Digital economy, High technology industry, 97P29


Citation

Tan, J. (2023). Research on the mechanism of digital economy to enhance the innovation efficiency of high-tech industry in the context of big data. Applied Mathematics and Nonlinear Sciences, 8(2). https://doi.org/10.2478/amns.2023.2.00878
3 Total citations
0.94 FWCI
2 Recent citations
(2 years)
17 References
Open Access Yes
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