Relationship between employees’ career maturity and career planning of edge computing and cloud collaboration from the perspective of organizational behavior

• Published in: PloS one Vol. 16; no. 10; p. e0257582
• Main Authors: Zhang, Rui, Song, Yue
• Format: Journal Article
• Language: English
• Published: San Francisco Public Library of Science 06.10.2021; Public Library of Science (PLoS)
• Subjects: Acuity; Algorithms; Analysis; Bandwidths; Career choice; Career development; Career development planning; Careers; Cloud computing; Collaboration; Computer and Information Sciences; Computer centers; Data processing; Delay; Edge computing; Efficiency; Employees; Engineering and Technology; Factor analysis; Internet of Things; Maturity; Methods; Modelling; Optical character recognition; Organizational aspects; Organizational behavior; Physical Sciences; Planning; Psychology; Research and Analysis Methods; Scheduling; Servers; Social Sciences; Stability analysis; Australia
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0257582

A new IoT (Internet of Things) analysis platform is designed based on edge computing and cloud collaboration from the perspective of organizational behavior, to fundamentally understand the relationship between enterprise career maturity and career planning, and meet the actual needs of enterprises. The performance of the proposed model is further determined according to the characteristic of the edge near data sources, with the help of factor analysis, and through the study and analysis of relevant enterprise data. The model is finally used to analyze the relationship between enterprise career maturity and career planning through simulation experiments. The research results prove that career maturity positively affects career planning, and vocational delay of gratification plays a mediating role in career maturity and career planning. Besides, the content of career choice in career maturity is influenced by mental acuity, result acuity and loyalty. The experimental results indicate that when the load at both ends of the edge and cloud exceeds 80%, the edge delay of the IoT analysis platform based on edge computing and cloud collaboration is 10s faster than that of other models. Meanwhile, the system slowdown is reduced by 36% while the stability is increased when the IoT analysis platform analyzes data. The results of the edge-cloud collaboration scheduling scheme are similar to all scheduling to the edge end, which saves 19% of the time compared with cloud computing to the cloud end. In Optical Character Recognition and Aeneas, compared with the single edge-cloud coordination mode, the model with the Nesterov Accelerated Gradient algorithm achieves the optimal performance. Specifically, the communication delay is reduced by about 25% on average, and the communication time decreased by 61% compared with cloud computing to the edge end. This work has significant reference value for analyzing the relationship between enterprise psychology, behavior, and career planning.