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Automatic modelling of networked innovation outsourcing-oriented talent competency in the era of artificial intelligence

Author

Listed:
  • Yanmin Guan

    (Cangzhou Jiaotong College)

  • Na Wang

    (Cangzhou Jiaotong College)

Abstract

Under economic globalization, networked innovation outsourcing has become a crucial direction of enterprise innovation, and the continuous development of artificial intelligence (AI) technology is bound to quickly replace the standardized, process-oriented, and highly repetitive basic posts. To promote the continuous innovation of enterprises and prevent innovative talents from being replaced by AI, this paper studies the core competencies (CC) of networked innovation outsourcing-oriented talents. First, the user information is input to construct the talent skill structure, and, consequently, the automatically networked innovation outsourcing-oriented talent CC evaluation model is implemented. The simulation experiment selects 100 graduate students of business school and divides them into two groups to complete the input and modeling process of talent skill structure information in different ways. Afterward, the accuracy and integrity of the automatic talent skill structure model are verified through the combination of experiment and expert review. The matching similarity calculation method is used to obtain the matching type between the task demand CC and the talent CC. Regarding talent matching, the proposed talent CC evaluation model can well match relevant tasks, and the highest matching value between the selected talents and tasks is 0.88. The relevant experience of talents is highly related to the task CC. To sum up, the proposed networked innovation outsourcing-oriented talent CC evaluation model can effectively promote the online automatic matching between networked innovation outsourcing-oriented tasks and talents.

Suggested Citation

  • Yanmin Guan & Na Wang, 2023. "Automatic modelling of networked innovation outsourcing-oriented talent competency in the era of artificial intelligence," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(1), pages 408-414, February.
    • Handle: RePEc:spr:ijsaem:v:14:y:2023:i:1:d:10.1007_s13198-022-01810-2
    DOI: 10.1007/s13198-022-01810-2
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    References listed on IDEAS

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