IDEAS home Printed from https://ideas.repec.org/a/inm/ormksc/v3y1984i2p113-124.html
   My bibliography  Save this article

An Audience Flow Model of Television Viewing Choice

Author

Listed:
  • Roland T. Rust

    (University of Texas)

  • Mark I. Alpert

    (University of Texas)

Abstract

A model for the prediction and explanation of individual television viewing choice is presented, incorporating considerations of utility, audience flow, and audience segmentation. The proposed model provides a quantifiably explicit theoretical explanation of television viewing choice, and its validation on large-sample network viewing data provides a baseline degree of accuracy against which the performance of future television viewing models may be compared. Of direct relevance to advertising agencies and the television networks is the suitability of the model for estimating the comparative impact of alternative programs on the audience size and composition of competing programs in the immediate and subsequent time slots.

Suggested Citation

  • Roland T. Rust & Mark I. Alpert, 1984. "An Audience Flow Model of Television Viewing Choice," Marketing Science, INFORMS, vol. 3(2), pages 113-124.
  • Handle: RePEc:inm:ormksc:v:3:y:1984:i:2:p:113-124
    DOI: 10.1287/mksc.3.2.113
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mksc.3.2.113
    Download Restriction: no

    File URL: https://libkey.io/10.1287/mksc.3.2.113?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
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chen Lin & Sriram Venkataraman & Sandy D. Jap, 2013. "Media Multiplexing Behavior: Implications for Targeting and Media Planning," Marketing Science, INFORMS, vol. 32(2), pages 310-324, March.
    2. Nickolay V. Moshkin & Ron Shachar, 2002. "The Asymmetric Information Model of State Dependence," Marketing Science, INFORMS, vol. 21(4), pages 435-454, August.
    3. Sha Yang & Vishal Narayan & Henry Assael, 2006. "Estimating the Interdependence of Television Program Viewership Between Spouses: A Bayesian Simultaneous Equation Model," Marketing Science, INFORMS, vol. 25(4), pages 336-349, July.
    4. Song, Lianlian & Shi, Yang & Tso, Geoffrey Kwok Fai & Lo, Hing Po, 2021. "Forecasting week-to-week television ratings using reduced-form and structural dynamic models," International Journal of Forecasting, Elsevier, vol. 37(1), pages 302-321.
    5. Jungwon Yeo, 2017. "The Weekend Effect in Television Viewership and Prime-Time Scheduling," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 51(3), pages 315-341, November.
    6. Gaurav Sabnis & Rajdeep Grewal, 2015. "Cable News Wars on the Internet: Competition and User-Generated Content," Information Systems Research, INFORMS, vol. 26(2), pages 301-319, June.
    7. Danaher, Peter & Dagger, Tracey, 2012. "Using a nested logit model to forecast television ratings," International Journal of Forecasting, Elsevier, vol. 28(3), pages 607-622.
    8. Jo, Jee Hyung & Lee, Jong Hee & Cho, Shin, 2020. "The characteristics of videos on demand for television programs and the determinants of their viewing patterns: Evidence from the Korean IPTV market," Telecommunications Policy, Elsevier, vol. 44(8).
    9. Srinivas K. Reddy & Jay E. Aronson & Antonie Stam, 1998. "SPOT: Scheduling Programs Optimally for Television," Management Science, INFORMS, vol. 44(1), pages 83-102, January.
    10. Givon, Moshe & Grosfeld-Nir, Abraham, 2008. "Using partially observed Markov processes to select optimal termination time of TV shows," Omega, Elsevier, vol. 36(3), pages 477-485, June.
    11. Kenneth C. Wilbur, 2008. "A Two-Sided, Empirical Model of Television Advertising and Viewing Markets," Marketing Science, INFORMS, vol. 27(3), pages 356-378, 05-06.
    12. Danaher, Peter J. & Dagger, Tracey S. & Smith, Michael S., 2011. "Forecasting television ratings," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1215-1240, October.
    13. Ronald Goettler & Ron Shachar, 2000. "Estimating Product Characteristics and Spatial Competition in the Network Television Industry," Econometric Society World Congress 2000 Contributed Papers 1691, Econometric Society.
    14. Kenneth C. Wilbur & Linli Xu & David Kempe, 2013. "Correcting Audience Externalities in Television Advertising," Marketing Science, INFORMS, vol. 32(6), pages 892-912, November.
    15. Kelton, Christina M. L. & Schneider Stone, Linda G., 1998. "Optimal television schedules in alternative competitive environments," European Journal of Operational Research, Elsevier, vol. 104(3), pages 451-473, February.

    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:inm:ormksc:v:3:y:1984:i:2:p:113-124. 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 Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    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.