Applied Mathematics and Nonlinear Sciences
Journal license

Journal

Applied Mathematics and Nonlinear Sciences


Volume
& Issue

Volume 9, Issue 1


Published
on

February 26, 2024


Pages


DOI

Article

Research on the Path of High-Quality Development of Sports Industry Driven by Digital Economy Based on Big Data Analysis

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Authors

Hongzhou Bai Affiliation:
Luoyang Institute of Science and Technology Department of Physical Education, Luoyang, Henan, 471023, China.


Abstract

This paper focuses on how the digital economy drives the high-quality development of the sports industry, explores the new path of development using extensive data analysis, and provides theoretical basis and practical guidance for the innovative development of the sports industry in the digital era. The DEA-Malmquist model and comprehensive data analysis methods are used to measure the development efficiency of the sports industry. The results of the study show that the digital economy has a significant impact on the efficiency improvement of the sports industry, in which the effect on the efficiency improvement of the sports industry is the largest, and the regression coefficient reaches 1.153. In addition, the study also finds that the digital economy can significantly reduce the cost of the sports tourism enterprises, improve the cost profitability, and effectively promote the expansion of consumption. The study’s conclusion shows that the development of digital economy provides new development power for the sports industry, and can effectively promote the optimization of the structure and high-quality development of the sports industry. In this regard, it is recommended to strengthen the application of digital technology in the sports industry and enhance the digitalization level of the sports industry.


Keywords

Digital economy, Extensive data analysis, Sports tourism enterprises, Sports industry, 00A66


Citation

Bai, H. (2024). Research on the path of high-quality development of sports industry driven by digital economy based on big data analysis. Applied Mathematics and Nonlinear Sciences, 9(1). https://doi.org/10.2478/amns-2024-0637
1 Total citations
0.42 FWCI
1 Recent citations
(2 years)
15 References
Open Access Yes
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