The “New Generation” High-tech Enterprise&#39;s Early Warning Model and Its Empirical Research Based on the Perspective of Organizational Commitment Theory

With the continuous advancement of emerging technologies such as big data, cloud computing, Internet of Things, blockchain, artificial intelligence, and 5G communications, China's “new generation” high-tech companies are developing rapidly, but the loss of core employees restricts their healthy development. How to manage the core employees of “new generation” high-tech enterprises is a grim reality in front of theorists and industrialists. Based on the results of the current theoretical research on organizational commitment, the research group proposed a “new generation” high-tech enterprise core employee resignation early warning model to provide decision-making basis and methodological reference for the “new generation” high-tech enterprise high-quality development.

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Analysis of Financial Risk Early Warning Systems of High-Tech Enterprises under Big Data Framework

Scientific Programming ◽

10.1155/2022/9055294 ◽

2022 ◽

Vol 2022 ◽

pp. 1-9

Author(s):

Maotao Lai

Keyword(s):

Big Data ◽

Financial Crisis ◽

Early Warning ◽

Logistic Model ◽

High Tech ◽

Financial Indicators ◽

Early Warning Model ◽

Early Warning Indicators ◽

Warning Model ◽

Warning Indicators

With the further development of China's market economy, the competition faced by companies in the market has become more intense, and many companies have difficulty facing pressure and risks. Among the many types of enterprises, high-tech enterprises are the riskiest. The emergence of big data technologies and concepts in recent years has provided new opportunities for financial crisis early warning. Through in-depth study of the theoretical feasibility and practical value of big data indicators, the use of big data indicators to develop an early warning system for financial crises has important theoretical value for breaking through the stagnant predicament of financial crisis early warning. As a result of the preceding context, this research focuses on the influence of big data on the financial crisis early warning model, selects and quantifies the big data indicators and financial indicators, designs the financial crisis early warning model, and verifies its accuracy. The specific research design ideas include the following: (1) We make preliminary preparations for model construction. Preliminary determination and screening of training samples and early warning indicators are carried out, the samples needed to build the model and the early warning indicator system are determined, and the principles of the model methods used are briefly described. First, we perform a significant analysis of financial indicators and screen out early warning indicators that can clearly distinguish between financial crisis companies and nonfinancial crisis companies. (2) We analyze the sentiment tendency of the stock bar comment data to obtain big data indicators. Then, we establish a logistic model based on pure financial indicators and a logistic model that introduces big data indicators. Finally, the two models are tested and compared, the changes in the model's early warning effect before and after the introduction of big data indicators are analyzed, and the optimization effect of big data indicators on financial crisis early warning is tested.

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Risk Early-Warning Model of High-Tech Entrepreneurial Enterprise based on BP Neural Network

ISME 2016 - Information Science and Management Engineering IV ◽

10.5220/0006442900130018 ◽

2016 ◽

Author(s):

Wang Tian ◽

Ke Wu ◽

Xiaofeng Li

Keyword(s):

Neural Network ◽

Early Warning ◽

Bp Neural Network ◽

High Tech ◽

Early Warning Model ◽

Warning Model

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Establishment and Validation of Early-warning Model of Perioperative Pain for the Elders

Case Medical Research ◽

10.31525/ct1-nct04228705 ◽

2020 ◽

Author(s):

Keyword(s):

Early Warning ◽

Early Warning Model ◽

Perioperative Pain ◽

Warning Model

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The Level of Public Acceptance to the Development of a Coastal Flooding Early Warning System in Jakarta

Sustainability ◽

10.3390/su13020566 ◽

2021 ◽

Vol 13 (2) ◽

pp. 566

Author(s):

Nelly Florida Riama ◽

Riri Fitri Sari ◽

Henita Rahmayanti ◽

Widada Sulistya ◽

Mohamad Husein Nurrahmat

Keyword(s):

Path Analysis ◽

Early Warning ◽

Early Warning System ◽

Warning System ◽

Coastal Flooding ◽

Coastal Areas ◽

Public Acceptance ◽

Community Attitudes ◽

Early Warning Model ◽

Warning Model

Coastal flooding is a natural disaster that often occurs in coastal areas. Jakarta is an example of a location that is highly vulnerable to coastal flooding. Coastal flooding can result in economic and human life losses. Thus, there is a need for a coastal flooding early warning system in vulnerable locations to reduce the threat to the community and strengthen its resilience to coastal flooding disasters. This study aimed to measure the level of public acceptance toward the development of a coastal flooding early warning system of people who live in a coastal region in Jakarta. This knowledge is essential to ensure that the early warning system can be implemented successfully. A survey was conducted by distributing questionnaires to people in the coastal areas of Jakarta. The questionnaire results were analyzed using cross-tabulation and path analysis based on the variables of knowledge, perceptions, and community attitudes towards the development of a coastal flooding early warning system. The survey result shows that the level of public acceptance is excellent, as proven by the average score of the respondents’ attitude by 4.15 in agreeing with the establishment of an early warning system to manage coastal flooding. Thus, path analysis shows that knowledge and perception have a weak relationship with community attitudes when responding to the coastal flooding early warning model. The results show that only 23% of the community’s responses toward the coastal flooding early warning model can be explained by the community’s knowledge and perceptions. This research is expected to be useful in implementing a coastal flooding early warning system by considering the level of public acceptance.

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Safety Warning Model of Coal Face Based on FCM Fuzzy Clustering and GA-BP Neural Network

Symmetry ◽

10.3390/sym13061082 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1082

Author(s):

Fanqiang Meng

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Early Warning ◽

Back Propagation ◽

Back Propagation Neural Network ◽

Coal Face ◽

Early Warning Model ◽

Working Face ◽

The Neural Network ◽

Warning Model

Risk and security are two symmetric descriptions of the uncertainty of the same system. If the risk early warning is carried out in time, the security capability of the system can be improved. A safety early warning model based on fuzzy c-means clustering (FCM) and back-propagation neural network was established, and a genetic algorithm was introduced to optimize the connection weight and other properties of the neural network, so as to construct the safety early warning system of coal mining face. The system was applied in a coal face in Shandong, China, with 46 groups of data as samples. Firstly, the original data were clustered by FCM, the input space was fuzzy divided, and the samples were clustered into three categories. Then, the clustered data was used as the input of the neural network for training and prediction. The back-propagation neural network and genetic algorithm optimization neural network were trained and verified many times. The results show that the early warning model can realize the prediction and early warning of the safety condition of the working face, and the performance of the neural network model optimized by genetic algorithm is better than the traditional back-propagation artificial neural network model, with higher prediction accuracy and convergence speed. The established early warning model and method can provide reference and basis for the prediction, early warning and risk management of coal mine production safety, so as to discover the hidden danger of working face accident as soon as possible, eliminate the hidden danger in time and reduce the accident probability to the maximum extent.

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