Research on the path of enterprise management innovation based on multiple logistic regression model
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 regre...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Sciendo
2024-01-01
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| Series: | Applied Mathematics and Nonlinear Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.2478/amns.2023.2.00065 |
| _version_ | 1797340890866384896 |
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| author | Li Daoyang Xu Shaofu |
| author_facet | Li Daoyang Xu Shaofu |
| author_sort | Li Daoyang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T10:09:54Z |
| format | Article |
| id | doaj.art-953ffba66494433bb83ad53eb76b70f8 |
| institution | Directory Open Access Journal |
| issn | 2444-8656 |
| language | English |
| last_indexed | 2024-03-08T10:09:54Z |
| publishDate | 2024-01-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Applied Mathematics and Nonlinear Sciences |
| spelling | doaj.art-953ffba66494433bb83ad53eb76b70f82024-01-29T08:52:28ZengSciendoApplied Mathematics and Nonlinear Sciences2444-86562024-01-019110.2478/amns.2023.2.00065Research on the path of enterprise management innovation based on multiple logistic regression modelLi Daoyang0Xu Shaofu11Admission and Employment Office, Wuxi Taihu University, Wuxi, Jiangsu, 214064, China.2Academic Affairs Office, Wuxi Taihu University, Wuxi, Jiangsu, 214064, China.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.https://doi.org/10.2478/amns.2023.2.00065multiple logistic regressionmlr-xgboost modelgreat likelihood methodmanagement innovation62g08 |
| spellingShingle | Li Daoyang Xu Shaofu Research on the path of enterprise management innovation based on multiple logistic regression model Applied Mathematics and Nonlinear Sciences multiple logistic regression mlr-xgboost model great likelihood method management innovation 62g08 |
| title | Research on the path of enterprise management innovation based on multiple logistic regression model |
| title_full | Research on the path of enterprise management innovation based on multiple logistic regression model |
| title_fullStr | Research on the path of enterprise management innovation based on multiple logistic regression model |
| title_full_unstemmed | Research on the path of enterprise management innovation based on multiple logistic regression model |
| title_short | Research on the path of enterprise management innovation based on multiple logistic regression model |
| title_sort | research on the path of enterprise management innovation based on multiple logistic regression model |
| topic | multiple logistic regression mlr-xgboost model great likelihood method management innovation 62g08 |
| url | https://doi.org/10.2478/amns.2023.2.00065 |
| work_keys_str_mv | AT lidaoyang researchonthepathofenterprisemanagementinnovationbasedonmultiplelogisticregressionmodel AT xushaofu researchonthepathofenterprisemanagementinnovationbasedonmultiplelogisticregressionmodel |