IDEAS home Printed from https://ideas.repec.org/a/taf/amstat/v74y2020i4p380-391.html
   My bibliography  Save this article

Going Viral, Binge-Watching, and Attention Cannibalism

Author

Listed:
  • Scott D. Grimshaw
  • Natalie J. Blades
  • Candace Berrett

Abstract

Binge-watching behavior is modeled for a single season of an original program from a streaming service to understand and make predictions about how individuals watch newly released content. Viewers make two choices in binge watching. First, the onset when individuals begin viewing the program is modeled using a change point between epidemic viewing with a nonconstant hazard rate and endemic viewing with a constant hazard rate. Second, the time it takes for individuals to complete the full season is modeled using an expanded negative binomial hurdle model to account for both binge racers (who watch all episodes in a single day) and other viewers. With the rapid increase in original content for streaming services, network executives are interested in the decision of simultaneously releasing multiple original programs or staggering premiere dates. The two model results are used to investigate competing risks to determine how the amount of time between premieres impacts attention cannibalism, when a viewer takes a long time watching their first choice program and consequently never watches the second program.

Suggested Citation

  • Scott D. Grimshaw & Natalie J. Blades & Candace Berrett, 2020. "Going Viral, Binge-Watching, and Attention Cannibalism," The American Statistician, Taylor & Francis Journals, vol. 74(4), pages 380-391, October.
  • Handle: RePEc:taf:amstat:v:74:y:2020:i:4:p:380-391
    DOI: 10.1080/00031305.2020.1774415
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00031305.2020.1774415
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/00031305.2020.1774415?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:amstat:v:74:y:2020:i:4:p:380-391. 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/UTAS20 .

    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.