<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>To solve various problems in the development of the sports industry and enhance the pulling effect of the sports industry on the national economy. This paper first analyzes the development scale, the number of institutions and the number of people in China’s sports industry at present through sports industry-related indicators. It further constructs an index system to evaluate the comprehensive strength of the regional sports industry. Secondly, the factors affecting the spatial differences in the comprehensive strength of the sports industry are studied, and the spatial effect test and industrial network centrality analysis are conducted. A geographically weighted regression (GWR) power-driven model was further established for empirical analysis. Finally, the estimated parameters of the model were spatially visualized, and the influence of each influencing factor on the comprehensive strength of the local sports industry was analyzed in depth. The results show that the adjusted goodness-of-fit of the GWR model proposed in this paper is 0.8929, which is closer to 1 than 0.8073 of the OLS regression model, indicating that the GWR model has a better fit. Meanwhile, the AICc value of the GWR model is 5.4177, which is also smaller than that of the OLS regression model. The GWR model has a significant positive contribution to the development of the sports industry in each region in terms of the explanatory variables proposed government input, scientific and technological innovation and talent pool, which are all important to enhance the economic benefits of the sports industry in each region.</p>
</abstract>

To solve various problems in the development of the sports industry and enhance the pulling effect of the sports industry on the national economy. This paper first analyzes the development scale, the number of institutions and the number of people in China’s sports industry at present through sports industry-related indicators. It further constructs an index system to evaluate the comprehensive strength of the regional sports industry. Secondly, the factors affecting the spatial differences in the comprehensive strength of the sports industry are studied, and the spatial effect test and industrial network centrality analysis are conducted. A geographically weighted regression (GWR) power-driven model was further established for empirical analysis. Finally, the estimated parameters of the model were spatially visualized, and the influence of each influencing factor on the comprehensive strength of the local sports industry was analyzed in depth. The results show that the adjusted goodness-of-fit of the GWR model proposed in this paper is 0.8929, which is closer to 1 than 0.8073 of the OLS regression model, indicating that the GWR model has a better fit. Meanwhile, the AICc value of the GWR model is 5.4177, which is also smaller than that of the OLS regression model. The GWR model has a significant positive contribution to the development of the sports industry in each region in terms of the explanatory variables proposed government input, scientific and technological innovation and talent pool, which are all important to enhance the economic benefits of the sports industry in each region.

Research on the current situation and development countermeasures of Chinese sports industry companies in the context of big data

Guizhou University of Finance and Economics

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

To solve various problems in the development of the sports industry and enhance the pulling effect of the sports industry on the national economy. This paper...

Research on the current situation and development countermeasures of Chinese sports industry companies in the context of big data

Published Online: Aug 30, 2023

Page range: -

Received: Sep 22, 2022

Accepted: Mar 10, 2022

DOI: https://doi.org/10.2478/amns.2023.2.00266

Keywords
Sports industry, GWR model, Spatial effects, Network centrality, Estimated parameters

© 2023 Xiaoyan Wu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Research on the current situation and development countermeasures of Chinese sports industry companies in the context of big data

Published Online: Aug 30, 2023

Page range: -

Received: Sep 22, 2022

Accepted: Mar 10, 2022

DOI: https://doi.org/10.2478/amns.2023.2.00266

KeywordsSports industry, GWR model, Spatial effects, Network centrality, Estimated parameters

© 2023 Xiaoyan Wu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Keywords
Sports industry, GWR model, Spatial effects, Network centrality, Estimated parameters