Research on job burnout evaluation and turnover tendency prediction of knowledge workers based on BP neural network

• Main Authors: Jiang, Mengmeng, Jakaria Dasan, Li, Xiaoxiao
• Format: Article
• Language: English; English
• Published: Hindawi 2022
• Subjects: HF5548.7-5548.85 Industrial psychology
• Online Access: https://eprints.ums.edu.my/id/eprint/33609/2/Research%20on%20job%20burnout%20evaluation%20and%20turnover%20tendency%20prediction%20of%20knowledge%20workers%20based%20on%20BP%20neural%20network.pdf; https://eprints.ums.edu.my/id/eprint/33609/3/Research%20on%20job%20burnout%20evaluation%20and%20turnover%20tendency%20prediction%20of%20knowledge%20workers%20based%20on%20BP%20neural%20network.pdf; https://eprints.ums.edu.my/id/eprint/33609/; https://www.hindawi.com/journals/scn/2022/6370886/; https://doi.org/10.1155/2022/6370886

The traditional dimission prediction method for knowledge workers does not take into account the impact of job burnout on employees’ dimission tendency, resulting in low accuracy of dimission prediction. In view of the above problems, this paper introduces the employee burnout evaluation and researches the knowledge employee burnout evaluation and turnover tendency prediction method based on BP neural network. After analyzing the influencing factors of job burnout of knowledge workers, the evaluation index system of job burnout is established. The weight of the job burnout evaluation index was determined by fuzzy hierarchy, and BP neural network model was established. Boosting method added the fusion layer of the correlation analysis of job burnout and turnover intention to the neural network model to predict the turnover intention of employees. In the method test, the accuracy rate of employee turnover tendency prediction is higher than 90%, the reliability of employee burnout evaluation is higher, and it is more helpful for human resource management.