IDEAS home Printed from https://ideas.repec.org/a/taf/tbitxx/v43y2024i14p3490-3509.html
   My bibliography  Save this article

Effective reporting system to encourage users’ reporting behavior in social media platforms: an empirical study based on structural empowerment theory

Author

Listed:
  • Hong Zhou
  • Yaobin Lu
  • Ling Zhao
  • Bin Wang
  • Ting Li

Abstract

Reporting systems are gradually gaining attention as a mechanism for users to engage in platform content governance, such as reporting problematic content in terms of misinformation or misbehaviors. However, there is little research on how a reporting system can be effectively designed to facilitate users’ reports. This study presents a model that is grounded in structural empowerment theory and the Stimulus-Organism-Response (S-O-R) framework to bridge this gap and considers the embedded reporting system on social media as a mechanism to empower users to participate in reporting problematic content. Using a two-stage survey and a partial least squares structural equation modelling (PLS-SEM), the authors find that three empowerment structures of the reporting system (i.e. information transparency, tool usability, and platform reporting support) stimulate users’ reporting self-efficacy through unequal pathways and then promote users’ engagement in reporting platform content. The contribution of this paper is that it explores the application of structural empowerment theory in empowering user reporting on social media platforms, explains the relationship between the three empowerment structures, and then provides a reference to content governance practices for the platform owner and other stakeholders.

Suggested Citation

  • Hong Zhou & Yaobin Lu & Ling Zhao & Bin Wang & Ting Li, 2024. "Effective reporting system to encourage users’ reporting behavior in social media platforms: an empirical study based on structural empowerment theory," Behaviour and Information Technology, Taylor & Francis Journals, vol. 43(14), pages 3490-3509, October.
  • Handle: RePEc:taf:tbitxx:v:43:y:2024:i:14:p:3490-3509
    DOI: 10.1080/0144929X.2023.2281491
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/0144929X.2023.2281491
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/0144929X.2023.2281491?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:43:y:2024:i:14:p:3490-3509. 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.