IDEAS home Printed from https://ideas.repec.org/a/taf/tbitxx/v42y2023i9p1374-1388.html
   My bibliography  Save this article

Development of the Questionnaire on Non-Driving Related Tasks (QNDRT) in automated driving: revealing age and gender differences

Author

Listed:
  • Klemens Weigl
  • Clemens Schartmüller
  • Andreas Riener

Abstract

Automated vehicles (AVs) enable driver-passengers to perform non-driving related tasks (NDRTs). However, it remains unclear if and how they would engage with NDRTs. Therefore, we developed a questionnaire on NDRTs, the QNDRT, for SAE Level 3 (L3) and 5 (L5) driving automation. It comprises 24 items with a general NDRT Work subscale querying general aspects of working while driving in an AV (8 items; L3: 4 items, L5: 4) and a specific NDRT Communication subscale assessing different communication modalities (16 items; L3: 8, L5: 8). Hence, we carried out a cross-sectional questionnaire study and queried 725 participants (351 female, 374 male) from 18 to 96 years. We applied factor analyses and extracted a stable unidimensional factor structure for both NDRT subscales with good psychometric properties (e.g. high and clear factor loadings; satisfactory communalities, item discrimination, and internal consistency). The initial findings revealed that significantly smaller values were assigned to both subscale factors by participants older than 55 years in contrast to younger ones and by women when compared to men. The QNDRT can be used in future (quasi-)experimental L3 and L5 studies and in population surveys to obtain more insight into working and communicating while driving in an AV.

Suggested Citation

  • Klemens Weigl & Clemens Schartmüller & Andreas Riener, 2023. "Development of the Questionnaire on Non-Driving Related Tasks (QNDRT) in automated driving: revealing age and gender differences," Behaviour and Information Technology, Taylor & Francis Journals, vol. 42(9), pages 1374-1388, July.
  • Handle: RePEc:taf:tbitxx:v:42:y:2023:i:9:p:1374-1388
    DOI: 10.1080/0144929X.2022.2073473
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/0144929X.2022.2073473
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/0144929X.2022.2073473?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.

    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:taf:tbitxx:v:42:y:2023:i:9:p:1374-1388. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/tbit .

    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.