In this study, in order to analyze the stress sources and stress-coping strategies of employees in construction enterprises, to explore the influencing factors of enterprise technical management cost, and to offer suggestions for mental health education of employees, 372 employees of Shandong Construction Engineering Group Co., Ltd. were selected for a questionnaire survey. The influences of stress sources and stress-coping strategies on the mental health of employees were compared, based on different demographic variables. The evaluation model was constructed using the matter-element analysis to rank the factors influencing the enterprise technology management cost. The results showed that the stress value of work characteristics was the highest (4.26 ± 0.511), followed by the organizational structure and atmosphere (4.15 ± 0.382); stress-coping strategies at the individual level (1.84 ± 0.315) scored higher than that at the organizational level (1.67 ± 0.248) (P < 0.05). Notable differences were observed in balance between work and family between males and females (P < 0.05); in work characteristics, role orientation, personal relationship, and balance between work and family between subjects of different ages (P < 0.05); in work characteristics, and balance between work and family between the married and the unmarried (P < 0.05); and in role stress and work characteristics between subjects in different positions (P < 0.05). The evaluation results revealed that the factors influencing the technology management cost of enterprises included price index, development cost, fixed investment proportion, power equipment rate, mechanical artificial intelligence, labor cost, rate of technical equipment, the output value, informatization of technology management, and national policy. In conclusion, the two major sources of stress for employees in Luoyang Construction Engineering Group Co., Ltd. were as follows: (1) work characteristics and (2) organizational structure and atmosphere. Besides, many employees adopted the stress-coping strategies at the individual level, and enterprises needed to strengthen the psychological health education for employees at the organizational level. In practice, the enterprise needed to add importance to the development of mechanical artificial intelligence, informatization of technology management, and national policy.
The purpose is to explore the application potential of HCI (Human-Computer Interaction) technology under AI (Artificial Intelligence) in enterprise performance evaluation and the influence of abusive management and self-efficacy on enterprise performance. Guided by psychological theory, employees from a listed real estate enterprise are selected, and the research themes of abusive management, self-efficacy, and employee performance are assumed. Afterward, the employee job satisfaction and performance evaluation model and system interface based on deep learning BPNN (BackPropagation Neural Network), SVM (Support Vector Machine) regression, and HCI are innovatively proposed. The results show that the HCI interface can be accessed accurately according to the employee's verbal instructions. BPNN model has reached the best performance at the iteration of 70times, and all indexes have reached the expected employee satisfaction.
This study combines the discovery methods and training of innovative talents, China’s requirements for improving talent training capabilities, and analyses the relationship between the number of professional enrollments in colleges and universities and the demand for skills in specific places. The research learns the characteristics and training models of innovative talents, deep learning (DL), neural networks, and related concepts of the seasonal difference Autoregressive Moving Average (ARMA) Model. These concepts are used to propose seasonal autoregressive integrated moving average back propagation (SARIMA-BP). Firstly, the SARIMA-BP artificially sets the weight parameter values and analyzes the model’s convergence speed, superiority, and versatility. Then, particle swarm optimization (PSO) algorithm is used to pre-process the model and test its independence. The accuracy of the model is checked to ensure its proper performance. Secondly, the model analyzes and predicts the relationship between the number of professional enrollments of 10 colleges and universities in a specific place and the talent demand of local related enterprises. Moreover, the established model is optimized and tested by wavelet denoising. Independent testing is done to ensure the best possible performance of the model. Finally, the weight value will not significantly affect the model’s versatility obtained by experiments. The prediction results of professional settings and corporate needs reveal that: there is a moderate correlation between professional locations and corporate needs; colleges and universities should train professional talents for local enterprises and eliminate the practical education concepts.
Consumers in China highly favor the consumer-to-consumer (C2C) e-commerce model, so it is crucial to understand the relationship between consumers' trust in merchants, perceived benefits, and purchase intentions. This article first elaborates on applying trust, perceived benefits, and purchase intention in China's C2C e-commerce model. Then, corresponding hypotheses are proposed, and the questionnaire is designed. Finally, reliability, validity, correlation, and regression analysis are applied to analyze the sample structure and the relationships between various variables. The experimental results show that the reliability and validity detection values are higher than 0.8 and 0.75, respectively, indicating that the reliability and validity of the questionnaire designed are qualified. In the correlation analysis, the hypothesis proposed is validated through correlation coefficients, and the rationality of the hypothesis is further verified through regression analysis.
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