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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
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    Citations

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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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).
    6. Kenneth C. Wilbur & Linli Xu & David Kempe, 2013. "Correcting Audience Externalities in Television Advertising," Marketing Science, INFORMS, vol. 32(6), pages 892-912, November.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. Nickolay V. Moshkin & Ron Shachar, 2002. "The Asymmetric Information Model of State Dependence," Marketing Science, INFORMS, vol. 21(4), pages 435-454, August.

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