IDEAS home Printed from https://ideas.repec.org/a/taf/tbitxx/v42y2023i12p1931-1945.html
   My bibliography  Save this article

Using expectancy theory as a lens for exploring the reasons behind teachers’ lack of motivation for self-development in online teaching

Author

Listed:
  • Gökhan Özaslan
  • Aslı Özaslan

Abstract

Our aim in this multiple-case study was to uncover the reasons for teachers’ lack of motivation for self-development in online teaching and to make recommendations on how to develop that motivation. We used Edward Lawler's model of expectancy theory as a lens to study this phenomenon. Our findings from twelve K-12 teachers revealed the following barriers to motivation: (1) feeling unable to achieve desired outcomes due to reduced instructional time, excessive student absences from online classes and lack of interaction with students, (2) insufficient external rewards, (3) inability to do what is necessary to maximise satisfaction with online teaching, (4) perceived temporariness of online teaching, and (5) lack of formal training necessary to understand the possibilities and limitations of online teaching. In addition, we have seen that Lawler's version of expectancy theory, although in need of improvement in some respects, is still appropriate for research on understanding teachers’ motivation for self-development in online teaching. Based on the findings, we discussed implications for future research and practice. Our study contributes to the literature on online teaching motivation by providing a good example of a multiple-case study in which a well-defined theory is applied to a motivational phenomenon.

Suggested Citation

  • Gökhan Özaslan & Aslı Özaslan, 2023. "Using expectancy theory as a lens for exploring the reasons behind teachers’ lack of motivation for self-development in online teaching," Behaviour and Information Technology, Taylor & Francis Journals, vol. 42(12), pages 1931-1945, September.
    • Handle: RePEc:taf:tbitxx:v:42:y:2023:i:12:p:1931-1945
    DOI: 10.1080/0144929X.2022.2103026
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/0144929X.2022.2103026
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/0144929X.2022.2103026?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:12:p:1931-1945. 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.