IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v316y2024i3p1179-1191.html
   My bibliography  Save this article

A comparison of chance-constrained data envelopment analysis, stochastic nonparametric envelopment of data and bootstrap method: A case study of cultural regeneration performance of cities

Author

Listed:
  • Lin, Sheng-Wei
  • Lu, Wen-Min

Abstract

This study comprehensively compares three efficiency measurement methods—chance-constrained data envelopment analysis (CCDEA), stochastic nonparametric envelopment of data (StoNED), and the bootstrap method—in the context of the cultural regeneration performance of cities. The research examines these methods’ methodological differences, advantages, and disadvantages with a focus on uncertainty handling, production function assumptions, and computational requirements. The analysis reveals that CCDEA and the bootstrap method yield similar efficiency scores, while StoNED tends to produce lower efficiency scores. Furthermore, regions exhibit higher value-creation efficiency of cultural and creative industry than operational management efficiency, thus highlighting the untapped potential for improving value creation in cultural regeneration projects. This comprehensive comparison enables researchers and practitioners to further understand the nuances among these methods and select the most suitable method for their specific needs and objectives when evaluating the performance of cultural regeneration projects or other applications.

Suggested Citation

  • Lin, Sheng-Wei & Lu, Wen-Min, 2024. "A comparison of chance-constrained data envelopment analysis, stochastic nonparametric envelopment of data and bootstrap method: A case study of cultural regeneration performance of cities," European Journal of Operational Research, Elsevier, vol. 316(3), pages 1179-1191.
    • Handle: RePEc:eee:ejores:v:316:y:2024:i:3:p:1179-1191
    DOI: 10.1016/j.ejor.2024.03.018
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221724002133
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ejor.2024.03.018?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 search for a different version of it.

    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:ejores:v:316:y:2024:i:3:p:1179-1191. 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/eor .

    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.