IDEAS home Printed from https://ideas.repec.org/a/inm/ormksc/v29y2010i5p944-957.html
   My bibliography  Save this article

The Effects of Online User Reviews on Movie Box Office Performance: Accounting for Sequential Rollout and Aggregation Across Local Markets

Author

Listed:
  • Pradeep K. Chintagunta

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

  • Shyam Gopinath

    (Kellogg School of Management, Northwestern University, Evanston, Illinois 60208)

  • Sriram Venkataraman

    (Goizueta Business School, Emory University, Atlanta, Georgia 30322)

Abstract

Our objective in this paper is to measure the impact (valence, volume, and variance) of national online user reviews on designated market area (DMA)-level local geographic box office performance of movies. We account for three complications with analyses that use national-level aggregate box office data: (i) aggregation across heterogeneous markets (spatial aggregation), (ii) serial correlation as a result of sequential release of movies (endogenous rollout), and (iii) serial correlation as a result of other unobserved components that could affect inferences regarding the impact of user reviews. We use daily box office ticket sales data for 148 movies released in the United States during a 16-month period (out of the 874 movies released) along with user review data from the Yahoo! Movies website. The analysis also controls for other possible box office drivers. Our identification strategy rests on our ability to identify plausible instruments for user ratings by exploiting the sequential release of movies across markets--because user reviews can only come from markets where the movie has previously been released, exogenous variables from previous markets would be appropriate instruments in subsequent markets. In contrast with previous studies that have found that the main driver of box office performance is the volume of reviews, we find that it is the valence that seems to matter and not the volume. Furthermore, ignoring the endogenous rollout decision does not seem to have a big impact on the results from our DMA-level analysis. When we carry out our analysis with aggregated national data, we obtain the same results as those from previous studies, i.e., that volume matters but not the valence. Using various market-level controls in the national data model, we attempt to identify the source of this difference. By conducting our empirical analysis at the DMA level and accounting for prerelease advertising, we can classify DMAs based on their responsiveness to firm-initiated marketing effort (advertising) and consumer-generated marketing (online word of mouth). A unique feature of our study is that it allows marketing managers to assess a DMA's responsiveness along these two dimensions. The substantive insights can help studios and distributors evaluate their future product rollout strategies. Although our empirical analysis is conducted using motion picture industry data, our approach to addressing the endogeneity of reviews is generalizable to other industry settings where products are sequentially rolled out.

Suggested Citation

  • Pradeep K. Chintagunta & Shyam Gopinath & Sriram Venkataraman, 2010. "The Effects of Online User Reviews on Movie Box Office Performance: Accounting for Sequential Rollout and Aggregation Across Local Markets," Marketing Science, INFORMS, vol. 29(5), pages 944-957, 09-10.
  • Handle: RePEc:inm:ormksc:v:29:y:2010:i:5:p:944-957
    DOI: 10.1287/mksc.1100.0572
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mksc.1100.0572
    Download Restriction: no

    File URL: https://libkey.io/10.1287/mksc.1100.0572?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. 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.
    2. Andrew Ainslie & Xavier Drèze & Fred Zufryden, 2005. "Modeling Movie Life Cycles and Market Share," Marketing Science, INFORMS, vol. 24(3), pages 508-517, November.
    3. Elberse, Anita & Anand, Bharat, 2007. "The effectiveness of pre-release advertising for motion pictures: An empirical investigation using a simulated market," Information Economics and Policy, Elsevier, vol. 19(3-4), pages 319-343, October.
    4. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-838, May.
    5. Bart J. Bronnenberg & Carl F. Mela, 2004. "Market Roll-Out and Retailer Adoption for New Brands," Marketing Science, INFORMS, vol. 23(4), pages 500-518, September.
    6. 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.
    7. 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.
    8. Andrews, Donald W K, 1991. "Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation," Econometrica, Econometric Society, vol. 59(3), pages 817-858, May.
    9. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
    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. Charles C. Moul, 2007. "Measuring Word of Mouth's Impact on Theatrical Movie Admissions," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 16(4), pages 859-892, December.
    12. 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.
    Full references (including those not matched with items on IDEAS)

    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. Hailin Zhang & Xina Yuan & Tae Ho Song, 2020. "Examining the role of the marketing activity and eWOM in the movie diffusion: the decomposition perspective," Electronic Commerce Research, Springer, vol. 20(3), pages 589-608, September.
    2. Marchand, André & Hennig-Thurau, Thorsten & Wiertz, Caroline, 2017. "Not all digital word of mouth is created equal: Understanding the respective impact of consumer reviews and microblogs on new product success," International Journal of Research in Marketing, Elsevier, vol. 34(2), pages 336-354.
    3. Karniouchina, Ekaterina V., 2011. "Impact of star and movie buzz on motion picture distribution and box office revenue," International Journal of Research in Marketing, Elsevier, vol. 28(1), pages 62-74.
    4. Gazley, Aaron & Clark, Gemma & Sinha, Ashish, 2011. "Understanding preferences for motion pictures," Journal of Business Research, Elsevier, vol. 64(8), pages 854-861, August.
    5. Delre, Sebastiano A. & Luffarelli, Jonathan, 2023. "Consumer reviews and product life cycle: On the temporal dynamics of electronic word of mouth on movie box office," Journal of Business Research, Elsevier, vol. 156(C).
    6. 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.
    7. Fernanda Gutierrez-Navratil & Victor Fernandez-Blanco & Luis Orea & Juan Prieto-Rodriguez, 2014. "How do your rivals’ releasing dates affect your box office?," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 38(1), pages 71-84, February.
    8. Kim, Taegu & Hong, Jungsik & Kang, Pilsung, 2015. "Box office forecasting using machine learning algorithms based on SNS data," International Journal of Forecasting, Elsevier, vol. 31(2), pages 364-390.
    9. Suman Basuroy & S. Abraham Ravid & Richard T. Gretz & B. J. Allen, 2020. "Is everybody an expert? An investigation into the impact of professional versus user reviews on movie revenues," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 44(1), pages 57-96, March.
    10. Jason M. T. Roos & Ron Shachar, 2014. "When Kerry Met Sally: Politics and Perceptions in the Demand for Movies," Management Science, INFORMS, vol. 60(7), pages 1617-1631, July.
    11. Kim, Ho & Hanssens, Dominique M., 2017. "Advertising and Word-of-Mouth Effects on Pre-launch Consumer Interest and Initial Sales of Experience Products," Journal of Interactive Marketing, Elsevier, vol. 37(C), pages 57-74.
    12. Onishi, Hiroshi & Manchanda, Puneet, 2012. "Marketing activity, blogging and sales," International Journal of Research in Marketing, Elsevier, vol. 29(3), pages 221-234.
    13. Amit M. Joshi & Dominique M. Hanssens, 2009. "Movie Advertising and the Stock Market Valuation of Studios: A Case of “Great Expectations?”," Marketing Science, INFORMS, vol. 28(2), pages 239-250, 03-04.
    14. Huang Dongling & Strijnev Andrei & Ratchford Brian, 2015. "Role of Advertising and Consumer Interest in the Motion Picture Industry," Review of Marketing Science, De Gruyter, vol. 13(1), pages 1-40, November.
    15. Stefan Stremersch & Jorge Gonzalez & Albert Valenti & Julian Villanueva, 2023. "The value of context-specific studies for marketing," Journal of the Academy of Marketing Science, Springer, vol. 51(1), pages 50-65, January.
    16. Daekook Kang, 2021. "Box-office forecasting in Korea using search trend data: a modified generalized Bass diffusion model," Electronic Commerce Research, Springer, vol. 21(1), pages 41-72, March.
    17. Shyam Gopinath & Pradeep K. Chintagunta & Sriram Venkataraman, 2013. "Blogs, Advertising, and Local-Market Movie Box Office Performance," Management Science, INFORMS, vol. 59(12), pages 2635-2654, December.
    18. Bae, Giwoong & Kim, Hye-jin, 2019. "The impact of movie titles on box office success," Journal of Business Research, Elsevier, vol. 103(C), pages 100-109.
    19. 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.
    20. Frédérique Bec & Mélika Ben Salem & Marine Carrasco, 2010. "Detecting Mean Reversion in Real Exchange Rates from a Multiple Regime star Model," Annals of Economics and Statistics, GENES, issue 99-100, pages 395-427.

    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:29:y:2010:i:5:p:944-957. 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.