IDEAS home Printed from https://ideas.repec.org/a/igg/jismd0/v5y2014i2p1-21.html
   My bibliography  Save this article

Service Innovation Analytics: Leveraging Existing Unstructured Data to Assess Service Innovation Capability

Author

Listed:
  • Marc Kohler

    (Karlsruhe Service Research Institute, Karlsruhe Institute of Technology, Karlsruhe, Germany)

  • Niels Feldmann

    (Karlsruhe Service Research Institute, Karlsruhe Institute of Technology, Karlsruhe, Germany)

  • Steven O. Kimbrough

    (The Wharton School, University of Pennsylvania, Philadelphia, PA, USA)

  • Hansjörg Fromm

    (Karlsruhe Service Research Institute, Karlsruhe Institute of Technology, Karlsruhe, Germany)

Abstract

The importance of innovation for firms for gaining competitive advantage has been widely acknowledged. Innovation in services exhibits some particular challenges. In order to support formal service innovation management, several frameworks of capabilities for service innovation have been published in recent years. However, these frameworks often do not support the use of existing information to apply them to a firm's context and to guide managerial decisions. In this paper the authors aim to show that a firm's service innovation capability can be operationally diagnosed with the help of such a framework in a more concrete way, using existing unstructured data. Building on established methods in text mining, the authors are working towards an approach to realise this. The paper outlines the approach and presents the encouraging results from our exploratory study, as well as avenues for further development of the approach and its implementation in a management information system.

Suggested Citation

  • Marc Kohler & Niels Feldmann & Steven O. Kimbrough & Hansjörg Fromm, 2014. "Service Innovation Analytics: Leveraging Existing Unstructured Data to Assess Service Innovation Capability," International Journal of Information System Modeling and Design (IJISMD), IGI Global, vol. 5(2), pages 1-21, April.
    • Handle: RePEc:igg:jismd0:v:5:y:2014:i:2:p:1-21
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/ijismd.2014040101
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Fotis Kitsios & Maria Kamariotou, 2021. "Artificial Intelligence and Business Strategy towards Digital Transformation: A Research Agenda," Sustainability, MDPI, vol. 13(4), pages 1-14, February.

    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:jismd0:v:5:y:2014:i:2:p:1-21. 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: 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.