IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/5489369.html
   My bibliography  Save this article

Human Resource Management of Energy Companies Based on Big Data Analysis

Author

Listed:
  • Peng Gui
  • Min Zhang
  • Xiaoshuang Li

Abstract

Human resource management mode refers to a comprehensive summary of management objectives, processes, content, methods, and other elements. The more common two modes are control mode and commitment mode. The enterprise human resource management model has many different types. The generation pair promotes the development of enterprise human resource management from the traditional model to the platform model, processing complex data with the help of data-based technical means, and realizing the integration and sharing of resource data. This paper takes an energy company as an example to carry out a detailed study. The article takes the big data as the background and the company as the research object. From the perspective of human resource management, this paper tries to find out the performance management, compensation and benefits management, and other issues of the company in human resource management under the background of the big data era and puts forward corresponding solutions for the current problems. In particular, the company gave certain opinions on how to build a human resource management system in the context of the current big data era. By conducting field research on the company and issuing questionnaires, this paper finds out the current problems of the company in human resource management and proposes corresponding solutions for these problems.

Suggested Citation

  • Peng Gui & Min Zhang & Xiaoshuang Li, 2022. "Human Resource Management of Energy Companies Based on Big Data Analysis," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-7, May.
  • Handle: RePEc:hin:jnlmpe:5489369
    DOI: 10.1155/2022/5489369
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/mpe/2022/5489369.pdf
    Download Restriction: no

    File URL: http://downloads.hindawi.com/journals/mpe/2022/5489369.xml
    Download Restriction: no

    File URL: https://libkey.io/10.1155/2022/5489369?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hin:jnlmpe:5489369. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.