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Cross-Platform Spillover Effects in Consumption of Viral Content: A Quasi-Experimental Analysis Using Synthetic Controls

Author

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  • Haris Krijestorac

    (McCombs School of Business, The University of Texas at Austin, Austin, Texas 78705)

  • Rajiv Garg

    (McCombs School of Business, The University of Texas at Austin, Austin, Texas 78705)

  • Vijay Mahajan

    (McCombs School of Business, The University of Texas at Austin, Austin, Texas 78705)

Abstract

To inform product release and distribution strategies, research has analyzed cross-market spillovers in new product adoption. However, models that examine these effects for digital and viral media are still evolving. Given resistance to advertising, firms often seek to promote their own viral content to boost brand awareness. However, a key shortcoming of virality is its ephemeral nature. To gain insight into sustaining virality, we develop a quasi-experimental approach that estimates the backward spillover onto a focal platform by introducing a piece of content onto a new platform. We posit that introducing content to the audience of a new platform can generate word of mouth, which may affect its consumption within an earlier platform. We estimate these spillovers using data on 381 viral videos on 26 platforms (e.g., YouTube, Vimeo) and observe how consumption of videos on an initial “lead” platform is affected by their subsequent introduction onto “lag” platforms. This spillover is estimated as follows: for each multiplatform video, we compare its view growth after being introduced onto a new platform to that of a synthetic control based on similar single-platform videos. Analysis of 275 such spillover scenarios reveals that introducing a video onto a lag platform roughly doubles its subsequent view growth in the lead platform. This positive cross-platform spillover is persistent, bursty, and strongest in the first 42 days. We find that spillover is boosted when the video is consumed more in the lag platform, when the consumption rate peaks earlier in the lag platform, and when the lag platform targets a foreign market. Delaying a video’s introduction onto a lag platform affects spillover concavely, whereas its introduction onto additional platforms shows diminishing returns. We find further support for positive spillover through a small-scale randomized field experiment. Implications are discussed for platforms, content creators, and policy makers.

Suggested Citation

  • Haris Krijestorac & Rajiv Garg & Vijay Mahajan, 2020. "Cross-Platform Spillover Effects in Consumption of Viral Content: A Quasi-Experimental Analysis Using Synthetic Controls," Information Systems Research, INFORMS, vol. 31(2), pages 449-472, June.
  • Handle: RePEc:inm:orisre:v:31:y:2020:i:2:p:449-472
    DOI: 10.1287/isre.2019.0897
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    as
    1. Wright, Peter, 1975. "Factors Affecting Cognitive Resistance to Advertising," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 2(1), pages 1-9, June.
    2. Coffman, Makena & Noy, Ilan, 2012. "Hurricane Iniki: measuring the long-term economic impact of a natural disaster using synthetic control," Environment and Development Economics, Cambridge University Press, vol. 17(2), pages 187-205, April.
    3. Alberto Abadie & Javier Gardeazabal, 2003. "The Economic Costs of Conflict: A Case Study of the Basque Country," American Economic Review, American Economic Association, vol. 93(1), pages 113-132, March.
    4. Seshadri Tirunillai & Gerard J. Tellis, 2017. "Does Offline TV Advertising Affect Online Chatter? Quasi-Experimental Analysis Using Synthetic Control," Marketing Science, INFORMS, vol. 36(6), pages 862-878, November.
    5. Peter J. Lenk & Ambar G. Rao, 1990. "New Models from Old: Forecasting Product Adoption by Hierarchical Bayes Procedures," Marketing Science, INFORMS, vol. 9(1), pages 42-53.
    6. V. Srinivasan & Charlotte H. Mason, 1986. "Technical Note—Nonlinear Least Squares Estimation of New Product Diffusion Models," Marketing Science, INFORMS, vol. 5(2), pages 169-178.
    7. Alejandro Zentner & Michael Smith & Cuneyd Kaya, 2013. "How Video Rental Patterns Change as Consumers Move Online," Management Science, INFORMS, vol. 59(11), pages 2622-2634, November.
    8. van der Lans, R.J.A. & van Bruggen, G.H. & Eliashberg, J. & Wierenga, B., 2009. "A Viral Branching Model for Predicting the Spread of Electronic Word-of-Mouth," ERIM Report Series Research in Management ERS-2009-029-MKT, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    9. Susan Athey & Guido W. Imbens, 2017. "The State of Applied Econometrics: Causality and Policy Evaluation," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 3-32, Spring.
    10. Christopher J. Easingwood & Vijay Mahajan & Eitan Muller, 1983. "A Nonuniform Influence Innovation Diffusion Model of New Product Acceptance," Marketing Science, INFORMS, vol. 2(3), pages 273-295.
    11. Ravi Bapna & Akhmed Umyarov, 2015. "Do Your Online Friends Make You Pay? A Randomized Field Experiment on Peer Influence in Online Social Networks," Management Science, INFORMS, vol. 61(8), pages 1902-1920, August.
    12. Sinan Aral & Dylan Walker, 2011. "Creating Social Contagion Through Viral Product Design: A Randomized Trial of Peer Influence in Networks," Management Science, INFORMS, vol. 57(9), pages 1623-1639, February.
    13. W. Wayne Fu & Clarice C. Sim, 2011. "Aggregate bandwagon effect on online videos' viewership: Value uncertainty, popularity cues, and heuristics," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 62(12), pages 2382-2395, December.
    14. Abadie, Alberto & Diamond, Alexis & Hainmueller, Jens, 2010. "Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program," Journal of the American Statistical Association, American Statistical Association, vol. 105(490), pages 493-505.
    15. W. Wayne Fu & Clarice C. Sim, 2011. "Aggregate bandwagon effect on online videos' viewership: Value uncertainty, popularity cues, and heuristics," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 62(12), pages 2382-2395, December.
    16. Erik Brynjolfsson & Yu (Jeffrey) Hu & Michael D. Smith, 2003. "Consumer Surplus in the Digital Economy: Estimating the Value of Increased Product Variety at Online Booksellers," Management Science, INFORMS, vol. 49(11), pages 1580-1596, November.
    17. Ken Hendricks & Alan Sorensen, 2009. "Information and the Skewness of Music Sales," Journal of Political Economy, University of Chicago Press, vol. 117(2), pages 324-369, April.
    18. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, April.
    19. Eocman Lee & Jeho Lee & Jongseok Lee, 2006. "Reconsideration of the Winner-Take-All Hypothesis: Complex Networks and Local Bias," Management Science, INFORMS, vol. 52(12), pages 1838-1848, December.
    20. Anita Elberse & Jehoshua Eliashberg, 2003. "Demand and Supply Dynamics for Sequentially Released Products in International Markets: The Case of Motion Pictures," Marketing Science, INFORMS, vol. 22(3), pages 329-354.
    21. Gong, Jing & Smith, Michael D. & Telang, Rahul, 2015. "Substitution or Promotion? The Impact of Price Discounts on Cross-Channel Sales of Digital Movies," Journal of Retailing, Elsevier, vol. 91(2), pages 343-357.
    22. Dobele, Angela & Toleman, David & Beverland, Michael, 2005. "Controlled infection! Spreading the brand message through viral marketing," Business Horizons, Elsevier, vol. 48(2), pages 143-149.
    23. 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.
    24. Zentner, Alejandro, 2006. "Measuring the Effect of File Sharing on Music Purchases," Journal of Law and Economics, University of Chicago Press, vol. 49(1), pages 63-90, April.
    25. Duan, Wenjing & Gu, Bin & Whinston, Andrew B., 2008. "The dynamics of online word-of-mouth and product sales—An empirical investigation of the movie industry," Journal of Retailing, Elsevier, vol. 84(2), pages 233-242.
    26. Ozkan Eren & Serkan Ozbeklik, 2016. "What Do Right‐to‐Work Laws Do? Evidence from a Synthetic Control Method Analysis," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 35(1), pages 173-194, January.
    27. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    28. Brett Danaher & Samita Dhanasobhon & Michael D. Smith & Rahul Telang, 2010. "Converting Pirates Without Cannibalizing Purchasers: The Impact of Digital Distribution on Physical Sales and Internet Piracy," Marketing Science, INFORMS, vol. 29(6), pages 1138-1151, 11-12.
    29. Hubert Gatignon & Jehoshua Eliashberg & Thomas S. Robertson, 1989. "Modeling Multinational Diffusion Patterns: An Efficient Methodology," Marketing Science, INFORMS, vol. 8(3), pages 231-247.
    30. Khim-Yong Goh & Cheng-Suang Heng & Zhijie Lin, 2013. "Social Media Brand Community and Consumer Behavior: Quantifying the Relative Impact of User- and Marketer-Generated Content," Information Systems Research, INFORMS, vol. 24(1), pages 88-107, March.
    31. repec:wly:soecon:v:81:2:y:2014:p:387-408 is not listed on IDEAS
    32. Hema Yoganarasimhan, 2012. "Impact of social network structure on content propagation: A study using YouTube data," Quantitative Marketing and Economics (QME), Springer, vol. 10(1), pages 111-150, March.
    33. Mauro Bampo & Michael T. Ewing & Dineli R. Mather & David Stewart & Mark Wallace, 2008. "The Effects of the Social Structure of Digital Networks on Viral Marketing Performance," Information Systems Research, INFORMS, vol. 19(3), pages 273-290, September.
    34. William P. Putsis, Jr. & Sridhar Balasubramanian & Edward W. Kaplan & Subrata K. Sen, 1997. "Mixing Behavior in Cross-Country Diffusion," Marketing Science, INFORMS, vol. 16(4), pages 354-369.
    35. P. K. Kannan & Barbara Kline Pope & Sanjay Jain, 2009. "—Pricing Digital Content Product Lines: A Model and Application for the National Academies Press," Marketing Science, INFORMS, vol. 28(4), pages 620-636, 07-08.
    36. Chong (Alex) Wang & Xiaoquan (Michael) Zhang & Il-Horn Hann, 2018. "Socially Nudged: A Quasi-Experimental Study of Friends’ Social Influence in Online Product Ratings," Information Systems Research, INFORMS, vol. 29(3), pages 641-655, September.
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