IDEAS home Printed from https://ideas.repec.org/a/nms/mamere/10.5771-0935-9915-2017-2-175.html
   My bibliography  Save this article

Expanding the Job Demands-Resources Model to Classify Innovation-Predicting Working Conditions

Author

Listed:
  • Adler, Mareike
  • Koch, Anna K.

Abstract

We applied the job demands-resources (JD-R) model (Demerouti, Bakker, Nachreiner, & Schaufeli, 2001) and a new categorization approach to study the relationship between working conditions and innovation. By applying confirmatory factor analysis and structural equation modeling to a cross-sectional online study (N = 780), we showed that two types of demands, hindrance and challenge, and two types of job resources, task-related and social, represent different types of working conditions with respect to innovation. Task-related and social job resources positively predicted individual innovation. Social job resources and challenge job demands revealed a positive association with perception of organizational innovation, whereas hindrance job demands were negatively related to it. The relevance of the studied types of working conditions for individual and perceived organizational innovation varied.

Suggested Citation

  • Adler, Mareike & Koch, Anna K., 2017. "Expanding the Job Demands-Resources Model to Classify Innovation-Predicting Working Conditions," management revue - Socio-Economic Studies, Nomos Verlagsgesellschaft mbH & Co. KG, vol. 28(2), pages 175-203.
  • Handle: RePEc:nms:mamere:10.5771/0935-9915-2017-2-175
    DOI: 10.5771/0935-9915-2017-2-175
    as

    Download full text from publisher

    File URL: https://www.nomos-elibrary.de/10.5771/0935-9915-2017-2-175
    Download Restriction: no

    File URL: https://libkey.io/10.5771/0935-9915-2017-2-175?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
    ---><---

    Citations

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


    Cited by:

    1. Elisabeth Nöhammer & Stefan Stichlberger, 2019. "Digitalization, innovative work behavior and extended availability," Journal of Business Economics, Springer, vol. 89(8), pages 1191-1214, December.
    2. Florence Nande & Marie-Laure Weber & Stéphanie Bouchet, 2022. "Exploring success conditions for innovative performance through Qualitative Comparative Analysis (QCA): does job autonomy matter?," Public Organization Review, Springer, vol. 22(4), pages 1257-1277, December.

    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:nms:mamere:10.5771/0935-9915-2017-2-175. 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: Nomos Verlagsgesellschaft mbH & Co. KG (email available below). General contact details of provider: http://www.nomos.de/ .

    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.