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

IF 3.6 3区 管理学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Journal of Organizational and End User Computing Pub Date : 2023-04-14 DOI:10.4018/joeuc.321639
Xiaoxue Fan, Shulang Zhao, Xuan Zhang, Lingchai Meng
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引用次数: 5

Abstract

In order to improve the management mode of the human resources system and enhance the work efficiency of employees, this paper uses artificial intelligence (AI) technology to enhance the psychological empowerment of employees. It affects employees' work performance from a psychological perspective with the help of psychological empowerment. The questionnaire survey method collects data on the influencing factors of employees' psychological empowerment. The statistical data are analyzed by regression. Back propagation neural network (BPNN) algorithm based on deep learning is used to establish the work condition evaluation model. The job satisfaction, pressure, and performance of employees based on psychological empowerment are analyzed.
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基于深度学习和人工智能的员工心理赋权对工作绩效的影响
为了改进人力资源系统的管理模式,提高员工的工作效率,本文利用人工智能(AI)技术增强员工的心理赋能。它通过心理授权从心理学角度影响员工的工作绩效。问卷调查法收集员工心理授权的影响因素数据。对统计数据进行回归分析。采用基于深度学习的反向传播神经网络(BPNN)算法建立了工况评估模型。分析了基于心理授权的员工工作满意度、压力和绩效。
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来源期刊
Journal of Organizational and End User Computing
Journal of Organizational and End User Computing COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
6.00
自引率
9.20%
发文量
77
期刊介绍: The Journal of Organizational and End User Computing (JOEUC) provides a forum to information technology educators, researchers, and practitioners to advance the practice and understanding of organizational and end user computing. The journal features a major emphasis on how to increase organizational and end user productivity and performance, and how to achieve organizational strategic and competitive advantage. JOEUC publishes full-length research manuscripts, insightful research and practice notes, and case studies from all areas of organizational and end user computing that are selected after a rigorous blind review by experts in the field.
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