Application of Improved AHP-BP Neural Network in CSR Performance Evaluation Model

Li, Wenqin; Xu, Guanghua; Xing, Qiuhang; Lyu, Minghan

doi:10.1007/s11277-019-06981-z

Cited by 21 publications

(15 citation statements)

References 12 publications

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“…Li [14] analyzed and evaluated the competitiveness of China's logistics enterprises, established the evaluation index of the competitiveness of logistics enterprises, and used SPSS (Statistical Product and Service Solutions) software to analyze the dynamic factors. Li [15] proposed an improved analytic hierarchy process-back propagation (AHP-BP) neural network and used it for the evaluation of corporate social responsibility (CSR) performances. Shu [16] combined a multi-level fuzzy evaluation and a BP neural network for the performance evaluation of private enterprises, so that the decision support system has the advantages of a neural network and multi-level dynamic evaluation simultaneously.…”

Section: Related Workmentioning

confidence: 99%

“…Finally, 50 pieces of enterprise collaboration performance evaluation data originating from 25 collaborative enterprises were obtained, and each piece of data contained 21 evaluation indicators. Inspired by the experimental settings of [9,10,15], we randomly select 40 pieces of data as training data and the remaining 10 pieces of data as test data. Matlab_R2021b software is used for programming and operation, the 21 items of data of the enterprise are used as input values, and the corresponding evaluation values are used as target output values.…”

Section: Data Collectionmentioning

confidence: 99%

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Performance Evaluation of Enterprise Collaboration Based on an Improved Elman Neural Network and AHP-EW

Zhang

Ding

et al. 2022

Applied Sciences

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In order to mitigate the influence of human subjectivity on indicator weights in the performance evaluation of enterprise collaboration, and explore the nonlinear relationship between the enterprise collaboration influencing factors and the evaluation results, this paper propose a combined performance evaluation model based on AHP-EW and an improved Elman neural network. Firstly, based on the characteristics of collaboration among manufacturing enterprises, the evaluation system for the collaborative performance of manufacturing enterprises is constructed from three dimensions. Moreover, this study combines subjective and objective weighting methods to obtain comprehensive weights that take into account both expert experience and objective information. Then, an improved Elman neural network is proposed and trained to predict and evaluate the collaborative performance indicator data, which greatly shortens the evaluation time and improves evaluation accuracy. The experimental results show that the proposed model has a faster convergence speed and higher accuracy, which will provide a valuable reference for decision making and the management of enterprise collaboration.

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Section: Related Workmentioning

confidence: 99%

Section: Data Collectionmentioning

confidence: 99%

Performance Evaluation of Enterprise Collaboration Based on an Improved Elman Neural Network and AHP-EW

Zhang

Ding

et al. 2022

Applied Sciences

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“…When summarizing the methodological studies on the quantification of performance evaluation in universities, hierarchical analysis was found to be the most representative, but it is very difficult to test whether the judgment matrix is consistent when studying real-world problems and it is difficult to truly reflect the fuzzy nature of human evaluation [ 12 ]. Therefore, the literature [ 13 ] addresses the shortcomings of expert scoring in the hierarchical analysis method and integrates the principles of fuzzy mathematics to establish a mathematical model to evaluate the training quality more objectively. The literature [ 14 ] established an evaluation system from three aspects: training platform, coaches, and athletes.…”

Section: State Of the Artmentioning

confidence: 99%

An Improved Logistic Regression Method for Assessing the Performance of Track and Field Sports

Songling

2022

Computational Intelligence and Neuroscience

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Track and field is an important part of sports. Track and field athletes are an important reserve force for the development of national sports. An accurate assessment of track and field athletes’ performance can help them develop more appropriate training programs and improve their performance. In order to assess the performance of track and field athletes better, this paper proposes an improved logistic regression method. Firstly, this method uses factor analysis to reduce the data dimensions of the factors that affect the performance of track and field athletes, and uses the principal component analysis to select common factors and their corresponding values. Then, according to the common factors, a binary logistic regression model is established to evaluate the performance of track and field athletes. Experiments show that the method can effectively evaluate the performance of track and field athletes and is suitable for athletes of different track and field sports. It has high accuracy, fast evaluation efficiency, and good universality of performance evaluation. For different numbers of athletes, the proposed method has a lower error evaluation index, higher evaluation accuracy, and better evaluation quality. Compared with the other two methods, the proposed method has the shortest evaluation time and is more effective for the performance evaluation of track and field athletes.

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“…It is capable of learning and storing a large number of input-output pattern mapping relationships without disclosing the mathematical equations describing the mapping relationships beforehand. In the previous research from the Web of Science index database, there are only two papers applying the BP neural network to the CSR's field [50,51], the application of BP in the field of economics is still in the initial stage, so this part is an experimental test.…”

Section: Empirical Analysis Csr To Fdsmentioning

confidence: 99%

Application of BP Neural Network and Fixed Effect in the Relationship between CSR and Female Directors

Lyu

Gao

Salam

2022

Journal of Mathematics

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This study is to illustrate the two-way influencing mechanism of female directors’ gender and corporate social responsibility (CSR) of China’s listed companies. According to an empirical test of 3723 enterprises from 2013 to 2020, it reveals that the presence of female directors—especially female executive directors—contributes to the improvement of CSR. In the stage of back propagation (BP) neural network, the model included the overalls of CSR and macroscopic strategic, fulfillment content, and disclosed technology three dimensions. It randomly selects 100 samples, and via BP neural network, it predicts that the RKS-CSR rating can effectively show that high-CSR lever enhances the presence of female directors in Chinese listed companies and promotes gender balance in directors’ team, thus achieving gender balance and CSR two-way sustainable development.

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Application of Improved AHP-BP Neural Network in CSR Performance Evaluation Model

Cited by 21 publications

References 12 publications

Performance Evaluation of Enterprise Collaboration Based on an Improved Elman Neural Network and AHP-EW

Performance Evaluation of Enterprise Collaboration Based on an Improved Elman Neural Network and AHP-EW

An Improved Logistic Regression Method for Assessing the Performance of Track and Field Sports

Application of BP Neural Network and Fixed Effect in the Relationship between CSR and Female Directors

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