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

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Abstract

The evaluation of corporate social responsibility (CSR) performance may enhance companies’ willingness to undertake social responsibilities, so it is very important to improve the quality of CSR performance evaluation. Based on the three factors of economic performance, social performance and environmental performance, this paper proposed an improved analytic hierarchy process-back propagation (AHP-BP) neural network algorithm, and introduced the improved AHP-BP neural network algorithm into CSR performance evaluation model. In the stage of improved AHP, the model included the importance of the knowledge and experience of the experts by expert scoring, and reduced the subjective influence of expert judgment on the results by introducing a personality test scale. In the stage of BP neural network, trained models have been used for CSR performance evaluation. The results showed that the prediction result of improved AHP-BP neural network model was better than that of BP neural network model. Therefore, the improved AHP-BP neural network algorithm can be used as a good predictor for CSR performance evaluation.

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Acknowledgements

This work was supported by the Project of the National Natural Science Foundation of China (Grant No. 71472088). Special thanks should be given to the authors of references. Any errors or shortcoming in the paper are the responsibility of the authors.

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Correspondence to Guanghua Xu.

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Li, W., Xu, G., Xing, Q. et al. Application of Improved AHP-BP Neural Network in CSR Performance Evaluation Model. Wireless Pers Commun 111, 2215–2230 (2020). https://doi.org/10.1007/s11277-019-06981-z

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