IDEAS home Printed from https://ideas.repec.org/a/eee/finlet/v86y2025ipds1544612325018641.html

The value of data: Data assetization and enterprises' business credit

Author

Listed:
  • Chen, Lu
  • Tang, Siqi

Abstract

This study examines the essential function of data assetization—a transformative process that converts raw data into valuable, actionable, and marketable assets—in improving organizations' commercial creditworthiness under global economic instability and the decline of traditional trust mechanisms. This study demonstrates a positive causal association between data assetization and commercial lending, utilizing data from 2018 to 2023. Mechanism analysis indicates that this enhancement functions through two interconnected channels: optimizing credit resource allocation (by facilitating precise, advantageous credit terms aligned with operational requirements) and enhancing capital liquidity (by expediting cash conversion and short-term solvency), thereby creating a virtuous cycle that magnifies the overall effect. This study not only validates data assetization as a determinant of creditworthiness but also highlights its significance as a strategic capability rather than a mere technical procedure. It converts digital infrastructure into established financial credibility, addressing a crucial gap in previous research by focusing on management cognition in the conversion of technological access into financial trust. These findings offer practical insights for companies seeking to establish robust credit profiles in a progressively data-driven environment.

Suggested Citation

  • Chen, Lu & Tang, Siqi, 2025. "The value of data: Data assetization and enterprises' business credit," Finance Research Letters, Elsevier, vol. 86(PD).
  • Handle: RePEc:eee:finlet:v:86:y:2025:i:pd:s1544612325018641
    DOI: 10.1016/j.frl.2025.108610
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1544612325018641
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.frl.2025.108610?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
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ni, Yunsong, 2025. "Data assets and corporate ESG performance: Evidence from Chinese listed companies," Finance Research Letters, Elsevier, vol. 86(PF).

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    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:eee:finlet:v:86:y:2025:i:pd:s1544612325018641. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/frl .

    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.