Journal of Information Systems Engineering and Management

Journal of Information Systems Engineering and Management, 2024 - Volume 9 Issue 4, Article No: 25195
https://doi.org/10.55267/iadt.07.15437

Published Online: 14 Oct 2024

Views: 18 | Downloads: 11

This research examines the efficiency of general services in data transfer during the big data era. It analyzes data propagation characteristics and illustrates the attributes of data propagation in general services. A prediction model for general service motivation and performance optimization is proposed based on the general service motivation theory with its accuracy and convergence compared to typical cases. The study explores the influence mechanisms and improvement theories of commuting motivation and performance, integrating these into a unified model. Finally, recommendations are provided to enhance the motivation and work performance of general staff based on data transmission and motivation improvement analysis. In conclusion, the modeling process of the Prophet prediction model is pre-processing, data set partition, selection of seasonal and holiday items in the model, the fine-tuning of model parameters, and setting of model parameters. In the case of limited machine learning training, the Prophet prediction model proposed quickly achieves low exponential error, the Prophet prediction model error is relatively small, and the algorithm has high prediction accuracy. The Prophet prediction model has lower MAE values (average absolute error) on different datasets and smaller errors compared with the other two algorithms, indicating that the algorithm has a certain accuracy. General service motivation, as an internal motivation to motivate individuals to serve and safeguard general interests, can encourage government workers to contribute more to themselves. Therefore, general service motivation positively affects not only task performance but also situational performance.

Artificial Intelligence Digital Information Communication Channel General Service Work Performance Prediction Algorithm

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