IDEAS home Printed from https://ideas.repec.org/a/igg/jdsst0/v16y2024i1p1-18.html
   My bibliography  Save this article

MEREC-Based Combined Framework for Multiple-Attribute Group Decision-Making in Probabilistic Linguistic Environment: Applications to Technology Finance Development Efficiency Evaluation

Author

Listed:
  • Xiaotong Liang

    (China National Gold Group Asset Management Corporation, China)

Abstract

The deployment of our country's innovation driven strategy has been implemented, and transforming the economic development mode has become an important content. It is particularly important to use finance to serve technological innovation and promote economic development. The efficiency evaluation of technology finance development is multiple-attribute group decision-making (MAGDM). Presently, the utilization of Logarithmic TODIM (LogTODIM) and CoCoSo approach was implemented to address the MAGDM challenges. To effectively evaluate the development of technology finance, the application of probabilistic linguistic term sets (PLTSs) has been illustrated as a method to characterize uncertain information. This research introduces the probabilistic linguistic LogTODIM-CoCoSo (PL-LogTODIM-CoCoSo) approach to address MAGDM concerns within PLTSs. Additionally, the MEREC approach is illustrated to handle the weight assignment process under PLTSs. Finally, numerical example for efficiency evaluation of technology finance development is put forward to validate the LogTODIM-CoCoSo approach.

Suggested Citation

  • Xiaotong Liang, 2024. "MEREC-Based Combined Framework for Multiple-Attribute Group Decision-Making in Probabilistic Linguistic Environment: Applications to Technology Finance Development Efficiency Evaluation," International Journal of Decision Support System Technology (IJDSST), IGI Global Scientific Publishing, vol. 16(1), pages 1-18, January.
    • Handle: RePEc:igg:jdsst0:v:16:y:2024:i:1:p:1-18
    as

    Download full text from publisher

    File URL: https://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJDSST.361906
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Aichuan Li & Tian Li, 2023. "Fusion of XLNet and BiLSTM-TextCNN for Weibo Sentiment Analysis in Spark Big Data Environment," International Journal of Ambient Computing and Intelligence (IJACI), IGI Global, vol. 14(1), pages 1-18, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.

      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:igg:jdsst0:v:16:y:2024:i:1:p:1-18. 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.

      If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Journal Editor (email available below). General contact details of provider: https://www.igi-global.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.