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Cable News Wars on the Internet: Competition and User-Generated Content

Author

Listed:
  • Gaurav Sabnis

    (School of Business, Stevens Institute of Technology, Hoboken, New Jersey 07030)

  • Rajdeep Grewal

    (Kenan-Flagler Business School, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599)

Abstract

Academics and practitioners alike recognize that user-generated content (UGC), such as blog posts, help not only predict but also boost performance (e.g., sales). However, the role of competition in the UGC domain is not well understood. Building on extant research pertaining to the UGC-performance relationship, the authors document empirical evidence for a relationship between competitor UGC and focal firm performance. Data from a 30-week period describe the viewership of competing cable news shows on Fox News, CNN, and MSNBC during the 7:00 p.m. –9:00 p.m. time slots. They find evidence of a statistically significant relationship between competitor UGC and viewership and of heterogeneity in the direction of these competitive relationships, positive in some time slots and negative in others. The predictive power of UGC for viewership is enhanced by 3% to 5% simply by incorporating competitors’ UGC, in addition to a show’s own UGC. Thus, the study, as well as formulation of UGC-related marketing strategies, should incorporate competitive relationships.

Suggested Citation

  • 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.
    • Handle: RePEc:inm:orisre:v:26:y:2015:i:2:p:301-319
    DOI: 10.1287/isre.2015.0579
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    References listed on IDEAS

    as
    1. Roland T. Rust & Mark I. Alpert, 1984. "An Audience Flow Model of Television Viewing Choice," Marketing Science, INFORMS, vol. 3(2), pages 113-124.
    2. Charles F. Manski, 1993. "Identification of Endogenous Social Effects: The Reflection Problem," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 60(3), pages 531-542.
    3. Stock, James H & Wright, Jonathan H & Yogo, Motohiro, 2002. "A Survey of Weak Instruments and Weak Identification in Generalized Method of Moments," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(4), pages 518-529, October.
    4. Yifan Dou & Marius F. Niculescu & D. J. Wu, 2013. "Engineering Optimal Network Effects via Social Media Features and Seeding in Markets for Digital Goods and Services," Information Systems Research, INFORMS, vol. 24(1), pages 164-185, March.
    5. Chib, Siddhartha & Winkelmann, Rainer, 2001. "Markov Chain Monte Carlo Analysis of Correlated Count Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(4), pages 428-435, October.
    6. Raji Srinivasan & Pamela Haunschild & Rajdeep Grewal, 2007. "Vicarious Learning in New Product Introductions in the Early Years of a Converging Market," Management Science, INFORMS, vol. 53(1), pages 16-28, January.
    7. Peter Ebbes & Michel Wedel & Ulf Böckenholt & Ton Steerneman, 2005. "Solving and Testing for Regressor-Error (in)Dependence When no Instrumental Variables are Available: With New Evidence for the Effect of Education on Income," Quantitative Marketing and Economics (QME), Springer, vol. 3(4), pages 365-392, December.
    8. Hausman, Jerry A & Taylor, William E, 1983. "Identification in Linear Simultaneous Equations Models with Covariance Restrictions: An Instrumental Variables Interpretation," Econometrica, Econometric Society, vol. 51(5), pages 1527-1549, September.
    9. 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.
    10. Oded Netzer & Ronen Feldman & Jacob Goldenberg & Moshe Fresko, 2012. "Mine Your Own Business: Market-Structure Surveillance Through Text Mining," Marketing Science, INFORMS, vol. 31(3), pages 521-543, May.
    11. Ramya Neelamegham & Pradeep Chintagunta, 1999. "A Bayesian Model to Forecast New Product Performance in Domestic and International Markets," Marketing Science, INFORMS, vol. 18(2), pages 115-136.
    12. Oliver J. Rutz & Michael Trusov, 2011. "Zooming In on Paid Search Ads--A Consumer-Level Model Calibrated on Aggregated Data," Marketing Science, INFORMS, vol. 30(5), pages 789-800, September.
    13. Peter Ebbes & Michel Wedel & Ulf Böckenholt, 2009. "Frugal IV alternatives to identify the parameter for an endogenous regressor," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(3), pages 446-468, April.
    14. Pechmann, Cornelia & Stewart, David W, 1990. "The Effects of Comparative Advertising on Attention, Memory, and Purchase Intentions," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 17(2), pages 180-191, September.
    15. Dina Mayzlin & Hema Yoganarasimhan, 2012. "Link to Success: How Blogs Build an Audience by Promoting Rivals," Management Science, INFORMS, vol. 58(9), pages 1651-1668, September.
    16. Prasad A. Naik & Kalyan Raman & Russell S. Winer, 2005. "Planning Marketing-Mix Strategies in the Presence of Interaction Effects," Marketing Science, INFORMS, vol. 24(1), pages 25-34, June.
    17. Peter E. Rossi & Greg M. Allenby, 2003. "Bayesian Statistics and Marketing," Marketing Science, INFORMS, vol. 22(3), pages 304-328, July.
    18. David Godes & Dina Mayzlin, 2004. "Using Online Conversations to Study Word-of-Mouth Communication," Marketing Science, INFORMS, vol. 23(4), pages 545-560, June.
    19. 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.
    20. David Godes & Dina Mayzlin, 2009. "Firm-Created Word-of-Mouth Communication: Evidence from a Field Test," Marketing Science, INFORMS, vol. 28(4), pages 721-739, 07-08.
    21. Robert C. Blattberg & Kenneth J. Wisniewski, 1989. "Price-Induced Patterns of Competition," Marketing Science, INFORMS, vol. 8(4), pages 291-309.
    22. 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.
    23. Wedel, M, et al, 1993. "A Latent Class Poisson Regression Model for Heterogeneous Count Data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 8(4), pages 397-411, Oct.-Dec..
    24. Garrett P. Sonnier & Leigh McAlister & Oliver J. Rutz, 2011. "A Dynamic Model of the Effect of Online Communications on Firm Sales," Marketing Science, INFORMS, vol. 30(4), pages 702-716, July.
    25. David Godes & José C. Silva, 2012. "Sequential and Temporal Dynamics of Online Opinion," Marketing Science, INFORMS, vol. 31(3), pages 448-473, May.
    26. Jean-Pierre Dubé & Puneet Manchanda, 2005. "Differences in Dynamic Brand Competition Across Markets: An Empirical Analysis," Marketing Science, INFORMS, vol. 24(1), pages 81-95, September.
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