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

<p>Conventional science and technology performance assessment methods and indicators can no longer meet the current needs of urban science and technology innovation performance assessment, and innovative research is urgently needed. In this study, the four elements of innovation input, innovation process, innovation output, and innovation guarantee are taken as the first-level indicators of the urban science and technology innovation performance assessment index system. The G1 entropy value correction method is used to assign weights to the urban science and technology innovation performance assessment index system to reduce subjective arbitrariness. Finally, the specific situation of innovation elements and innovation performance in S city was selected, and the influence relationship between innovation elements and innovation performance assessment was analyzed using a vector autoregressive model. The results show that the method of assessing the city’s science and technology innovation performance based on the VAR model is simple and easy to operate, the confidence coefficient of the assessment model is more than 0.7, and the mean value of the validity CVR is 0.703. The assessment results have a high degree of credibility and validity and effectively promote the region’s scientific and technological self-reliance and self-improvement.</p>
</abstract>

Conventional science and technology performance assessment methods and indicators can no longer meet the current needs of urban science and technology innovation performance assessment, and innovative research is urgently needed. In this study, the four elements of innovation input, innovation process, innovation output, and innovation guarantee are taken as the first-level indicators of the urban science and technology innovation performance assessment index system. The G1 entropy value correction method is used to assign weights to the urban science and technology innovation performance assessment index system to reduce subjective arbitrariness. Finally, the specific situation of innovation elements and innovation performance in S city was selected, and the influence relationship between innovation elements and innovation performance assessment was analyzed using a vector autoregressive model. The results show that the method of assessing the city’s science and technology innovation performance based on the VAR model is simple and easy to operate, the confidence coefficient of the assessment model is more than 0.7, and the mean value of the validity CVR is 0.703. The assessment results have a high degree of credibility and validity and effectively promote the region’s scientific and technological self-reliance and self-improvement.

Optimization of Urban Science and Technology Innovation Performance Assessment Model - Based on VAR Methodology Perspective

Applied Mathematics and Nonlinear Sciences

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

Conventional science and technology performance assessment methods and indicators can no longer meet the current needs of urban science and technology...

Optimization of Urban Science and Technology Innovation Performance Assessment Model - Based on VAR Methodology Perspective

Online veröffentlicht: 03. Juni 2024

Seitenbereich: -

Eingereicht: 22. Feb. 2024

Akzeptiert: 05. Mai 2024

DOI: https://doi.org/10.2478/amns-2024-1307

Schlüsselwörter
Entropy correction G1 method, Innovation performance, Assessment model, Vector autoregression

© 2024 Huimin Zhang, published by Sciendo

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

Optimization of Urban Science and Technology Innovation Performance Assessment Model - Based on VAR Methodology Perspective

Online veröffentlicht: 03. Juni 2024

Seitenbereich: -

Eingereicht: 22. Feb. 2024

Akzeptiert: 05. Mai 2024

DOI: https://doi.org/10.2478/amns-2024-1307

SchlüsselwörterEntropy correction G1 method, Innovation performance, Assessment model, Vector autoregression

© 2024 Huimin Zhang, published by Sciendo

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

Schlüsselwörter
Entropy correction G1 method, Innovation performance, Assessment model, Vector autoregression