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<p>Exploring the path of enterprise management innovation is to help enterprises transform and upgrade faster and better. This paper first explains the principle of logistic regression, introduces the definition of the multiple logistic regression model, and describes the algorithm for estimating regression parameters using the great likelihood method. Then, an extreme gradient boosting XGBoost model is introduced and combined with the multiple logistic regression model; an MLR-XGBoost model is constructed to analyze the enterprise management innovation path. The MLR-XGBoost model is used to analyze the correlation between the indicators and corporate management innovation by using the MLRXGBoost model. From the data on strategic control integration and cultural reconstruction capability, the correlation of infrastructure guarantee construction capability and entrepreneurial leadership accounted for a higher percentage, 79.74%, and 61.32%, respectively. From the data on organizational structure reengineering and business process coordination ability, the correlation of implementation process standardization ability and business operation visualization ability is higher, 76.58% and 70.28%, respectively. The MLR-XGBoost model can effectively analyze the path of enterprise management innovation and help enterprises achieve transformation and upgrading faster.</p>
</abstract>

Exploring the path of enterprise management innovation is to help enterprises transform and upgrade faster and better. This paper first explains the principle of logistic regression, introduces the definition of the multiple logistic regression model, and describes the algorithm for estimating regression parameters using the great likelihood method. Then, an extreme gradient boosting XGBoost model is introduced and combined with the multiple logistic regression model; an MLR-XGBoost model is constructed to analyze the enterprise management innovation path. The MLR-XGBoost model is used to analyze the correlation between the indicators and corporate management innovation by using the MLRXGBoost model. From the data on strategic control integration and cultural reconstruction capability, the correlation of infrastructure guarantee construction capability and entrepreneurial leadership accounted for a higher percentage, 79.74%, and 61.32%, respectively. From the data on organizational structure reengineering and business process coordination ability, the correlation of implementation process standardization ability and business operation visualization ability is higher, 76.58% and 70.28%, respectively. The MLR-XGBoost model can effectively analyze the path of enterprise management innovation and help enterprises achieve transformation and upgrading faster.

Research on the path of enterprise management innovation based on multiple logistic regression model

Applied Mathematics and Nonlinear Sciences

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

Exploring the path of enterprise management innovation is to help enterprises transform and upgrade faster and better. This paper first explains the principle...

Research on the path of enterprise management innovation based on multiple logistic regression model

Online veröffentlicht: 21. Juli 2023

Seitenbereich: -

Eingereicht: 28. Feb. 2022

Akzeptiert: 20. Okt. 2022

DOI: https://doi.org/10.2478/amns.2023.2.00065

Schlüsselwörter
Multiple logistic regression, MLR-XGBoost model, Great likelihood method, Management innovation

© 2023 Daoyang Li et al., published by Sciendo

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

Research on the path of enterprise management innovation based on multiple logistic regression model

Online veröffentlicht: 21. Juli 2023

Seitenbereich: -

Eingereicht: 28. Feb. 2022

Akzeptiert: 20. Okt. 2022

DOI: https://doi.org/10.2478/amns.2023.2.00065

SchlüsselwörterMultiple logistic regression, MLR-XGBoost model, Great likelihood method, Management innovation

© 2023 Daoyang Li et al., published by Sciendo

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

Schlüsselwörter
Multiple logistic regression, MLR-XGBoost model, Great likelihood method, Management innovation