IDEAS home Printed from https://ideas.repec.org/a/wly/mgtdec/v45y2024i4p2023-2041.html
   My bibliography  Save this article

How to achieve the high‐quality development of SRDI enterprises? Evidence from machine learning

Author

Listed:
  • Guimin Qu
  • Jingkun Bai

Abstract

Exploring how SRDI enterprises achieve high‐quality development constitutes a pivotal task for small and medium‐sized enterprises (SMEs) and niche leaders. While prior research primarily concentrated on the influence of innovative policies on SRDI enterprises, it has disregarded the intrinsic propelling forces intrinsic to these enterprises. To bridge this research gap, our study leverages a machine learning model that incorporates 19 feature variables spanning four dimensions—“specialization, refinement, distinctiveness, and innovation”—to anticipate high‐quality development in enterprises. Drawing on a sample of 667 A‐share SRDI‐listed enterprises from 2012 to 2022, and after subjecting the data to preprocessing, the study employs the mean of five machine learning models to predict high‐quality development in enterprises. Moreover, we discern pivotal feature variables and dimensions. Notably, outcomes underscore the paramount significance of market share in achieving high‐quality progress within SRDI enterprises, with refinement emerging as the foremost feature dimension among the four. Moreover, during the three stages delineated by SRDI policies, research and development intensity, equity financing, and market share emerge as the preeminent feature variables within their respective stages.

Suggested Citation

  • Guimin Qu & Jingkun Bai, 2024. "How to achieve the high‐quality development of SRDI enterprises? Evidence from machine learning," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 45(4), pages 2023-2041, June.
  • Handle: RePEc:wly:mgtdec:v:45:y:2024:i:4:p:2023-2041
    DOI: 10.1002/mde.4114
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/mde.4114
    Download Restriction: no

    File URL: https://libkey.io/10.1002/mde.4114?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:wly:mgtdec:v:45:y:2024:i:4:p:2023-2041. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www3.interscience.wiley.com/cgi-bin/jhome/7976 .

    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.