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

<p>In order to enhance China’s competitiveness in international ice and snow athletic competitions, this paper proposes a study on the cultivation of ice and snow athletic reserve talents supported by structural equation modeling. The initial selection of the driving factors for the cultivation of ice and snow athletic reserve talents is carried out by means of reviewing the information, and the driving factors for the cultivation model of ice and snow athletic reserve talents in a province are finally determined after expert selection, common factor extraction, and principal component analysis. The fitting index and covariance matrix were solved using the partial least squares method, and then the construction of the structural equation model was completed. Research data were obtained to empirically analyze the cultivation of ice and snow athletic talent reserves using the structural equation model. The results showed that the contribution of the academic training factor to the drivers of ice and snow competitive reserve talent cultivation was 0.891 for the path coefficient of Q6, 0.826 for the path coefficient of Q7, 0.848 for the path coefficient of Q9, and 0.914 for the path coefficient of Q10. This study provides a reference for solving real problems, such as the shortage of competitive ice and snow talents in China, which is of great significance for the strategic development of China’s sports industry.</p>
</abstract>

In order to enhance China’s competitiveness in international ice and snow athletic competitions, this paper proposes a study on the cultivation of ice and snow athletic reserve talents supported by structural equation modeling. The initial selection of the driving factors for the cultivation of ice and snow athletic reserve talents is carried out by means of reviewing the information, and the driving factors for the cultivation model of ice and snow athletic reserve talents in a province are finally determined after expert selection, common factor extraction, and principal component analysis. The fitting index and covariance matrix were solved using the partial least squares method, and then the construction of the structural equation model was completed. Research data were obtained to empirically analyze the cultivation of ice and snow athletic talent reserves using the structural equation model. The results showed that the contribution of the academic training factor to the drivers of ice and snow competitive reserve talent cultivation was 0.891 for the path coefficient of Q6, 0.826 for the path coefficient of Q7, 0.848 for the path coefficient of Q9, and 0.914 for the path coefficient of Q10. This study provides a reference for solving real problems, such as the shortage of competitive ice and snow talents in China, which is of great significance for the strategic development of China’s sports industry.

Study on the Driving Factors and Optimization Path of Ice and Snow Athletics Entering the Cultivation of College Reserve Talents under the New Stage

Applied Mathematics and Nonlinear Sciences

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

In order to enhance China’s competitiveness in international ice and snow athletic competitions, this paper proposes a study on the cultivation of ice and snow...

Study on the Driving Factors and Optimization Path of Ice and Snow Athletics Entering the Cultivation of College Reserve Talents under the New Stage

Published Online: Jun 03, 2024

Page range: -

Received: Feb 03, 2024

Accepted: Apr 18, 2024

DOI: https://doi.org/10.2478/amns-2024-1323

Keywords
Fit indices, Structural equation modeling, Least squares estimation, Covariance matrix, Ice athletics

© 2024 Ming Shi, et al., published by Sciendo

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

Study on the Driving Factors and Optimization Path of Ice and Snow Athletics Entering the Cultivation of College Reserve Talents under the New Stage

Published Online: Jun 03, 2024

Page range: -

Received: Feb 03, 2024

Accepted: Apr 18, 2024

DOI: https://doi.org/10.2478/amns-2024-1323

KeywordsFit indices, Structural equation modeling, Least squares estimation, Covariance matrix, Ice athletics

© 2024 Ming Shi, et al., published by Sciendo

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

Keywords
Fit indices, Structural equation modeling, Least squares estimation, Covariance matrix, Ice athletics