背景(考古学)
人工智能
知识管理
工作满意度
应用心理学
情境伦理学
形势意识
人工神经网络
心理学
机器学习
计算机科学
工程类
社会心理学
生物
航空航天工程
古生物学
作者
Shanyu Lin,Esra Sipahi Döngül,Serdar Vural Uygun,Mutlu Başaran Öztürk,Dinh Tran Ngoc Huy,Phạm Văn Tuấn
出处
期刊:Sustainability
[MDPI AG]
日期:2022-02-09
卷期号:14 (4): 1949-1949
被引量:12
摘要
(1) Background: Our study aims to explore the impact of abusive management and self-efficacy on corporate performance in the context of artificial intelligence-based human–machine interaction technology in enterprise performance evaluation. (2) Methods: Surveys were distributed to 578 participants in selected international companies in Turkey, Taiwan, Japan, and China. To reduce uncertainty and errors, the surveys were rigorously evaluated and did not show a normal distribution, as it was determined that 85 participants did not consciously fill out the questionnaires, and the questionnaires from the remaining 493 participants were used. By using the evaluation model of employee satisfaction based on a back propagation (BP) neural network, we explored the manifestation and impact of abusive management and self-efficacy. Using the listed real estate businesses as an example, we proposed a deep learning BP neural network-based employee job satisfaction evaluation model and a human–machine technology-based employee performance evaluation system under situational perception, according to the design requirements of human–machine interaction. (3) Results: The results show that the human–machine interface can log in according to the correct verbal instructions of the employees. In terms of age and education level variables, employees’ perceptions of leaders’ abusive management and self-efficacy are significantly different from their job performances, respectively (p < 0.01). (4) Conclusions: artificial intelligence (AI)-based human–machine interaction technology, malicious management, and self-efficacy directly affect enterprise performance and employee satisfaction.
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