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The Effect of Signal Quality and Contiguous Word of Mouth on Customer Acquisition for a Video-on-Demand Service

Author

Listed:
  • Sungjoon Nam

    (Rutgers Business School, Rutgers, The State University of New Jersey, Newark, New Jersey 07102)

  • Puneet Manchanda

    (Ross School of Business, University of Michigan, Ann Arbor, Michigan 48109)

  • Pradeep K. Chintagunta

    (Booth School of Business, University of Chicago, Chicago, Illinois 60637)

Abstract

This paper documents the existence and magnitude of contiguous word-of-mouth effects of signal quality of a video-on-demand (VOD) service on customer acquisition. We operationalize contiguous word-of-mouth effect based on geographic proximity and use behavioral data to quantify the effect. The signal quality for this VOD service is exogenously determined, objectively measured, and spatially uncorrelated. Furthermore, it is unobserved to the potential subscriber and is revealed postadoption. For a subscriber, the signal quality translates directly into the number of movies available for viewing, thus representing a part of the overall service quality. The combination of signal quality along with location and neighborhood information for each subscriber and potential subscriber allows us to resolve the typical challenges in measuring causal social network effects. We find that contiguous word of mouth affects about 8% of the subscribers with respect to their adoption behavior. However, this effect acts as a double-edged sword because it is asymmetric. We find that the effect of negative word of mouth arising from poor signal quality is more than twice as large as the effect of positive word of mouth arising from excellent signal quality. Besides contiguous word of mouth, we find that advertising and the retail environment also play a role in adoption.

Suggested Citation

  • Sungjoon Nam & Puneet Manchanda & Pradeep K. Chintagunta, 2010. "The Effect of Signal Quality and Contiguous Word of Mouth on Customer Acquisition for a Video-on-Demand Service," Marketing Science, INFORMS, vol. 29(4), pages 690-700, 07-08.
    • Handle: RePEc:inm:ormksc:v:29:y:2010:i:4:p:690-700
    DOI: 10.1287/mksc.1090.0550
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    References listed on IDEAS

    as
    1. Conley, Timothy G. & Topa, Giorgio, 2007. "Estimating dynamic local interactions models," Journal of Econometrics, Elsevier, vol. 140(1), pages 282-303, September.
    2. Herr, Paul M & Kardes, Frank R & Kim, John, 1991. "Effects of Word-of-Mouth and Product-Attribute Information on Persuasion: An Accessibility-Diagnosticity Perspective," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 17(4), pages 454-462, March.
    3. Puneet Manchanda & Ying Xie & Nara Youn, 2008. "The Role of Targeted Communication and Contagion in Product Adoption," Marketing Science, INFORMS, vol. 27(6), pages 961-976, 11-12.
    4. Daniel Kahneman & Amos Tversky, 2013. "Prospect Theory: An Analysis of Decision Under Risk," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 6, pages 99-127, World Scientific Publishing Co. Pte. Ltd..
    5. David Bell & Sangyoung Song, 2007. "Neighborhood effects and trial on the internet: Evidence from online grocery retailing," Quantitative Marketing and Economics (QME), Springer, vol. 5(4), pages 361-400, December.
    6. Heckman, James & Singer, Burton, 1984. "A Method for Minimizing the Impact of Distributional Assumptions in Econometric Models for Duration Data," Econometrica, Econometric Society, vol. 52(2), pages 271-320, March.
    7. Seetharaman, P B & Chintagunta, Pradeep K, 2003. "The Proportional Hazard Model for Purchase Timing: A Comparison of Alternative Specifications," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(3), pages 368-382, July.
    8. 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.
    Full references (including those not matched with items on IDEAS)

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