IDEAS home Printed from https://ideas.repec.org/p/hal/journl/hal-04325623.html
   My bibliography  Save this paper

Information technology and performance : Integrating data envelopment analysis and configurational approach

Author

Listed:
  • Jiawen Liu
  • Yeming Gong

    (EM - EMLyon Business School)

  • Joe Zhu
  • Ryad Titah

Abstract

While several studies claim that information technology (IT) improves business performance, others claim that the impact of IT on performance remains unclear. Based on data envelopment analysis (DEA), this paper empirically examines the relationship between IT factors, intermediate performance metrics, and business outcomes. It also advances a new conceptual perspective to investigate the relationship between IT investment and performance. We propose a theoretical framework based on network DEA models, considering multiple periods, multiple inputs and outputs to study and understand the influence of IT on performance. Using a sample of 86 firms from Asia, Europe, and the US, we measure information technology performance with network DEA models, advance an explanation of the relationship between IT and performance and compare this relationship by regions and industries. By integrating DEA and a configurational analysis, we also develop a set of configurations of IT performance to understand the differences by regions and industries. Our results show that: IT performance shows little regional difference, but significant industrial diversity. We found four configurations to capture industrial differences in IT performance, and found that the efficiency of IT operations rather than IT investments, was the main reason leading to an increase in business performance.

Suggested Citation

  • Jiawen Liu & Yeming Gong & Joe Zhu & Ryad Titah, 2022. "Information technology and performance : Integrating data envelopment analysis and configurational approach," Post-Print hal-04325623, HAL.
  • Handle: RePEc:hal:journl:hal-04325623
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    More about this item

    Keywords

    Data Science; DEA;

    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:hal:journl:hal-04325623. 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: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    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.