IDEAS home Printed from https://ideas.repec.org/a/eee/jbrese/v164y2023ics0148296323003582.html
   My bibliography  Save this article

Organizational frontlines in the digital age: The Consumer–Autonomous Technology–Worker (CAW) framework

Author

Listed:
  • van Doorn, Jenny
  • Smailhodzic, Edin
  • Puntoni, Stefano
  • Li, Jia
  • Schumann, Jan Hendrik
  • Holthöwer, Jana

Abstract

While organizational frontlines in the digital age involve complex interactions between consumers, autonomous technology (AT), and frontline workers, research so far largely focuses on the effect of AT on either the consumer or the worker. Bridging the fields of marketing and organizational behavior, we develop the Consumer–Autonomous Technology–Worker (CAW) framework, which reflects the implications of consumer–worker–AT interactions. We consider that AT can be consumer-facing, such as service robots, or worker-facing, such as AT-enabled knowledge-based systems supporting a worker’s decision-making. Drawing on illustrative interviews in hospitality contexts with workers who co-work with robots and the consumers served, we develop research propositions that highlight avenues for future research. We expect consumer–worker relations to strengthen when AT augments instead of replaces the worker. Human leadership is critical for consumers’ and workers’ acceptance of AT, while AT anthropomorphism is less critical in the presence of a human worker.

Suggested Citation

  • van Doorn, Jenny & Smailhodzic, Edin & Puntoni, Stefano & Li, Jia & Schumann, Jan Hendrik & Holthöwer, Jana, 2023. "Organizational frontlines in the digital age: The Consumer–Autonomous Technology–Worker (CAW) framework," Journal of Business Research, Elsevier, vol. 164(C).
    • Handle: RePEc:eee:jbrese:v:164:y:2023:i:c:s0148296323003582
    DOI: 10.1016/j.jbusres.2023.114000
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0148296323003582
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jbusres.2023.114000?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:jbrese:v:164:y:2023:i:c:s0148296323003582. 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/jbusres .

    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.