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Human Capital Digital Incentive Mechanism Construction Based on Deep Learning

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  • Jie He
  • Jianhua Zhang
  • Xuefeng Shao

Abstract

The introduction of human capital can be traced back to the ancient Greek period, emphasizing the important role of knowledge and skills in the production and life process. Human capital reward mechanism promotes modern social and economic development and is an important part of social and economic growth. The core management of the enterprise is the management of human capital, and the central work of human capital management is the incentive of human capital. Many enterprises are now facing difficulties in industrial operation, serious brain drain, and lack of core competitiveness in the market. As a result, enterprises cannot adapt to the speed and requirements of today’s social and economic development. One of the important reasons is that the enterprise lacks attention to the value of the human capital incentive system, or the human capital incentive system of the enterprise is unreasonable, which leads to a series of problems such as poor employee enthusiasm and low enterprise performance. How to establish a reasonable and effective incentive mechanism to mobilize the enthusiasm and creativity of employees has become a problem that enterprises must pay attention to. Taking the technical managers and general technicians of a high-tech enterprise as an example, combined with the deep learning method, the article made a detailed analysis of the four major incentive factors of enterprise human capital. It made employees’ satisfaction with corporate cultural incentives reaching 56.1%, which showed that emotional motivation was also one of the key factors for employees to be satisfied with the corporate incentive system.

Suggested Citation

  • Jie He & Jianhua Zhang & Xuefeng Shao, 2022. "Human Capital Digital Incentive Mechanism Construction Based on Deep Learning," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-12, August.
  • Handle: RePEc:hin:jnlmpe:6180883
    DOI: 10.1155/2022/6180883
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