The Impact of Improving Employee Psychological Empowerment and Job Performance Based on Deep Learning and Artificial Intelligence

Introduction

With the development of Internet technology and globalization, the structure of many enterprises is changing, and the pressure of competition between enterprises is also increasing. Employee satisfaction with work, work-life balance, and occupational stress are related. Work stress has a negative impact on efficiency and performance (Piao et al., 2022). Psychological empowerment can mediate the positive correlation between structures. Job involvement and job empowerment are positively correlated with task performance. Therefore, psychological empowerment is crucial to job performance (Amor et al., 2021). Artificial Intelligence (AI) technology is used to improve workplace flexibility, professional autonomy, independent learning of knowledge, and leadership, which can significantly motivate employees to enter and exit the workplace and improve work performance (H. Zhang et al., 2022). Therefore, to enhance the development of enterprises and promote employees' enthusiasm, it is necessary to explore employees' work performance based on AI technology and psychology.

Coun et al. (2022) pointed out a positive correlation between psychological empowerment and workplace initiative. Yao et al. (2022) pointed out that AI technology could impact future work. The strategic foresight generated by AI promotes the change of career research and practice from passive to active, which can positively promote employees' work safety, mental health, and other aspects (Howard, 2019). AI and employee service quality can explain the significant differences in overall service quality assessment, customer satisfaction, and customer loyalty. This will help enterprises optimize resource allocation and improve employee performance (Prentice et al., 2020a, 2020b). Tong et al. (2021) researched the factors affecting employee performance and, based on deep learning, improved the ability to collect and analyze information, which played a certain role in improving employees' working conditions. Deep learning can be used to diagnose, analyze, and judge employees' work behavior (Liu et al., 2019). Zhang & Qi (2022) collected data through questionnaires to analyze employees' working conditions. They used deep learning to predict employees' pressure to improve the enterprise's human resource management and to prevent and improve employees' work pressure and performance. BPNN is a supervised learning algorithm in deep learning, a multilayer feed forward network trained by the error back propagation algorithm, and one of the most widely used neural network models (Cui & Jing, 2019; Feng & Chen, 2022). Based on BPNN, the human resource management system is constructed to conduct statistical analysis on the relevant data of employees' work. The system can play a role in improving work performance (Bao et al., 2021; Wei & Jin, 2021). AI technology can improve employees' psychological empowerment, health, and satisfaction. Based on the BPNN of deep learning, employees' working state under psychological empowerment was evaluated. To prove that psychological empowerment under the influence of AI technology can improve employees' work performance, adding a deep learning algorithm is essential.