IDEAS home Printed from https://ideas.repec.org/a/inm/ormksc/v25y2006i4p336-349.html
   My bibliography  Save this article

Estimating the Interdependence of Television Program Viewership Between Spouses: A Bayesian Simultaneous Equation Model

Author

Listed:
  • Sha Yang

    (Marketing Department, Stern School of Business, New York University, New York, New York 10012)

  • Vishal Narayan

    (Marketing Department, Stern School of Business, New York University, New York, New York 10012)

  • Henry Assael

    (Marketing Department, Stern School of Business, New York University, New York, New York 10012)

Abstract

When making product choices, consumers are influenced by the preferences of other consumers, such as family members, friends, neighbors, and colleagues. Preference interdependence among family members is likely to be significant because of cohabitation and strong emotional ties. To estimate the preference interdependence, we specify a simultaneous equation model and propose a Bayesian estimation approach. Unlike existing models that use a spatial autoregressive structure to capture the interdependence of consumer preferences, we are able to estimate the potential asymmetry in the preference interdependence among family members in a more flexible way. In a simulation study, we show that models that ignore interdependence of preferences yield biased estimates of consumers' sensitivity to observed attribute preferences. In an empirical application, we estimate the interdependence of the viewership of television programs between husbands and wives in 481 households. We find that wives' viewing behavior depends more strongly on their husbands' viewing behavior than husbands' viewing behavior depends on their wives' viewing behavior. There exist significant differences in parameter estimates of dependence across categories of television programs. Differences in levels of spousal interdependence across households are partially explained by the age and the education level of the spouses.

Suggested Citation

  • 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.
  • Handle: RePEc:inm:ormksc:v:25:y:2006:i:4:p:336-349
    DOI: 10.1287/mksc.1060.0195
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mksc.1060.0195
    Download Restriction: no

    File URL: https://libkey.io/10.1287/mksc.1060.0195?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
    ---><---

    References listed on IDEAS

    as
    1. Kapteyn, Arie, et al, 1997. "Interdependent Preferences: An Econometric Analysis," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(6), pages 665-686, Nov.-Dec..
    2. Jeffrey H. Horen, 1980. "Scheduling of Network Television Programs," Management Science, INFORMS, vol. 26(4), pages 354-370, April.
    3. Nickolay V. Moshkin & Ron Shachar, 2002. "The Asymmetric Information Model of State Dependence," Marketing Science, INFORMS, vol. 21(4), pages 435-454, August.
    4. Andrew Ainslie & Xavier Drèze & Fred Zufryden, 2005. "Modeling Movie Life Cycles and Market Share," Marketing Science, INFORMS, vol. 24(3), pages 508-517, November.
    5. Case, Anne C, 1991. "Spatial Patterns in Household Demand," Econometrica, Econometric Society, vol. 59(4), pages 953-965, July.
    6. Robert E. Krider & Tieshan Li & Yong Liu & Charles B. Weinberg, 2005. "The Lead-Lag Puzzle of Demand and Distribution: A Graphical Method Applied to Movies," Marketing Science, INFORMS, vol. 24(4), pages 635-645, April.
    7. Goettler, Ronald L & Shachar, Ron, 2001. "Spatial Competition in the Network Television Industry," RAND Journal of Economics, The RAND Corporation, vol. 32(4), pages 624-656, Winter.
    8. Yong Liu & Daniel S. Putler & Charles B. Weinberg, 2004. "Is Having More Channels Really Better? A Model of Competition Among Commercial Television Broadcasters," Marketing Science, INFORMS, vol. 23(1), pages 120-133, July.
    9. Keane, Michael P, 1997. "Modeling Heterogeneity and State Dependence in Consumer Choice Behavior," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(3), pages 310-327, July.
    10. David Godes & Dina Mayzlin, 2004. "Using Online Conversations to Study Word-of-Mouth Communication," Marketing Science, INFORMS, vol. 23(4), pages 545-560, June.
    11. H. Leibenstein, 1950. "Bandwagon, Snob, and Veblen Effects in the Theory of Consumers' Demand," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 64(2), pages 183-207.
    12. Roland T. Rust & Mark I. Alpert, 1984. "An Audience Flow Model of Television Viewing Choice," Marketing Science, INFORMS, vol. 3(2), pages 113-124.
    13. Kai, Li, 1998. "Bayesian inference in a simultaneous equation model with limited dependent variables," Journal of Econometrics, Elsevier, vol. 85(2), pages 387-400, August.
    14. Allenby, Greg M. & Rossi, Peter E., 1998. "Marketing models of consumer heterogeneity," Journal of Econometrics, Elsevier, vol. 89(1-2), pages 57-78, November.
    15. Chib, Siddhartha, 1992. "Bayes inference in the Tobit censored regression model," Journal of Econometrics, Elsevier, vol. 51(1-2), pages 79-99.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Sridhar Narayanan, 2013. "Bayesian estimation of discrete games of complete information," Quantitative Marketing and Economics (QME), Springer, vol. 11(1), pages 39-81, March.
    2. Jonathan Z. Zhang, 2019. "Dynamic customer interdependence," Journal of the Academy of Marketing Science, Springer, vol. 47(4), pages 723-746, July.
    3. 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.
    4. Yan Chen & Youran Qi & Qing Liu & Peter Chien, 2018. "Sequential sampling enhanced composite likelihood approach to estimation of social intercorrelations in large-scale networks," Quantitative Marketing and Economics (QME), Springer, vol. 16(4), pages 409-440, December.
    5. Irina R. KANCHEVA, 2017. "Gender Role Distribution In Residential Real Estate Family Decision Making," Network Intelligence Studies, Romanian Foundation for Business Intelligence, Editorial Department, issue 10, pages 123-130, December.
    6. Vishal Narayan & Vithala R. Rao & Carolyne Saunders, 2011. "How Peer Influence Affects Attribute Preferences: A Bayesian Updating Mechanism," Marketing Science, INFORMS, vol. 30(2), pages 368-384, 03-04.
    7. Wesley Hartmann & Puneet Manchanda & Harikesh Nair & Matthew Bothner & Peter Dodds & David Godes & Kartik Hosanagar & Catherine Tucker, 2008. "Modeling social interactions: Identification, empirical methods and policy implications," Marketing Letters, Springer, vol. 19(3), pages 287-304, December.
    8. Martijn G. de Jong & Jan-Benedict E. M. Steenkamp & Bernard P. Veldkamp, 2009. "A Model for the Construction of Country-Specific Yet Internationally Comparable Short-Form Marketing Scales," Marketing Science, INFORMS, vol. 28(4), pages 674-689, 07-08.
    9. Jessica Clark & Jean-François Paiement & Foster Provost, 2023. "Who’s Watching TV?," Information Systems Research, INFORMS, vol. 34(4), pages 1622-1640, December.
    10. Singh, Sonika & Ratchford, Brian T. & Prasad, Ashutosh, 2014. "Offline and Online Search in Used Durables Markets," Journal of Retailing, Elsevier, vol. 90(3), pages 301-320.
    11. Foutz, Natasha Zhang, 2017. "Entertainment Marketing," Foundations and Trends(R) in Marketing, now publishers, vol. 10(4), pages 215-333, October.
    12. Jorge Silva-Risso & Irina Ionova, 2008. "—A Nested Logit Model of Product and Transaction-Type Choice for Planning Automakers' Pricing and Promotions," Marketing Science, INFORMS, vol. 27(4), pages 545-566, 07-08.
    13. Sridhar Narayanan, 2013. "Bayesian estimation of discrete games of complete information," Quantitative Marketing and Economics (QME), Springer, vol. 11(1), pages 39-81, March.
    14. Hee Mok Park & Puneet Manchanda, 2015. "When Harry Bet with Sally: An Empirical Analysis of Multiple Peer Effects in Casino Gambling Behavior," Marketing Science, INFORMS, vol. 34(2), pages 179-194, March.
    15. Belén Pérez-Sánchez & Martín González & Carmen Perea & Jose J. López-Espín, 2021. "A New Computational Method for Estimating Simultaneous Equations Models Using Entropy as a Parameter Criteria," Mathematics, MDPI, vol. 9(7), pages 1-9, March.
    16. Peter Lenk, 2014. "Bayesian estimation of random utility models," Chapters, in: Stephane Hess & Andrew Daly (ed.), Handbook of Choice Modelling, chapter 20, pages 457-497, Edward Elgar Publishing.
    17. Keita Kinjo & Shinya Sugawara, 2014. "An Empirical Analysis for a Case-based Decision to Watch Japanese TV dramas," CIRJE F-Series CIRJE-F-940, CIRJE, Faculty of Economics, University of Tokyo.
    18. Moon, Sangkil & Azizi, Kathryn, 2013. "Finding Donors by Relationship Fundraising," Journal of Interactive Marketing, Elsevier, vol. 27(2), pages 112-129.
    19. Jang, Sungha & Prasad, Ashutosh & Ratchford, Brian T., 2016. "Consumer spending patterns across firms and categories: Application to the size- and share-of-wallet," International Journal of Research in Marketing, Elsevier, vol. 33(1), pages 123-139.
    20. Wesley R. Hartmann, 2010. "Demand Estimation with Social Interactions and the Implications for Targeted Marketing," Marketing Science, INFORMS, vol. 29(4), pages 585-601, 07-08.
    21. Kosuke Uetake & Nathan Yang, 2020. "Inspiration from the “Biggest Loser”: Social Interactions in a Weight Loss Program," Marketing Science, INFORMS, vol. 39(3), pages 487-499, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. 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.
    3. Jean‐Pierre Dubé & Günter J. Hitsch & Peter E. Rossi, 2010. "State dependence and alternative explanations for consumer inertia," RAND Journal of Economics, RAND Corporation, vol. 41(3), pages 417-445, September.
    4. 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.
    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. Yong Liu & Daniel S. Putler & Charles B. Weinberg, 2006. "A Reply to “A Comment on ‘Is Having More Channels Really Better? A Model of Competition Among Commercial Television Broadcasters' ”," Marketing Science, INFORMS, vol. 25(5), pages 543-546, September.
    7. Nickolay V. Moshkin & Ron Shachar, 2002. "The Asymmetric Information Model of State Dependence," Marketing Science, INFORMS, vol. 21(4), pages 435-454, August.
    8. Steven M. Shugan, 2006. "—Antibusiness Movies and Folk Marketing," Marketing Science, INFORMS, vol. 25(6), pages 681-685, 11-12.
    9. 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.
    10. Jorge Araña & Carmelo León, 2007. "Repeated Dichotomous Choice Formats for Elicitation of Willingness to Pay: Simultaneous Estimation and Anchoring Effect," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 36(4), pages 475-497, April.
    11. Shijie Lu & Xin (Shane) Wang & Neil Bendle, 2020. "Does Piracy Create Online Word of Mouth? An Empirical Analysis in the Movie Industry," Management Science, INFORMS, vol. 66(5), pages 2140-2162, May.
    12. Sumit Raut & Sanjeev Swami & Eunkyu Lee & Charles B. Weinberg, 2008. "How Complex Do Movie Channel Contracts Need to Be?," Marketing Science, INFORMS, vol. 27(4), pages 627-641, 07-08.
    13. 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).
    14. Gaenssle Sophia & Budzinski Oliver & Astakhova Daria, 2018. "Conquering the Box Office: Factors Influencing Success of International Movies in Russia," Review of Network Economics, De Gruyter, vol. 17(4), pages 245-266, December.
    15. Peter Kooreman, 2007. "Time, money, peers, and parents; some data and theories on teenage behavior," Journal of Population Economics, Springer;European Society for Population Economics, vol. 20(1), pages 9-33, February.
    16. Jorge E. Araña & Carmelo J. León, 2012. "Scale-perception bias in the valuation of environmental risks," Applied Economics, Taylor & Francis Journals, vol. 44(20), pages 2607-2617, July.
    17. Hazel Bateman & Christine Eckert & Fedor Iskhakov & Jordan Louviere & Stephen Satchell & Susan Thorp, 2017. "Default and naive diversification heuristics in annuity choice," Australian Journal of Management, Australian School of Business, vol. 42(1), pages 32-57, February.
    18. Delre, Sebastiano A. & Panico, Claudio & Wierenga, Berend, 2017. "Competitive strategies in the motion picture industry: An ABM to study investment decisions," International Journal of Research in Marketing, Elsevier, vol. 34(1), pages 69-99.
    19. Singh, Sonika & Ratchford, Brian T. & Prasad, Ashutosh, 2014. "Offline and Online Search in Used Durables Markets," Journal of Retailing, Elsevier, vol. 90(3), pages 301-320.
    20. Curatola, Giuliano, 2017. "Portfolio choice and asset prices when preferences are interdependent," Journal of Economic Behavior & Organization, Elsevier, vol. 140(C), pages 197-223.

    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:25:y:2006:i:4:p:336-349. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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.