IDEAS home Printed from https://ideas.repec.org/a/taf/jmedec/v29y2016i3p139-152.html
   My bibliography  Save this article

Bidirectional Causality for Word of Mouth and the Movie Box Office: An Empirical Investigation of Panel Data

Author

Listed:
  • Yuan-Lin Hsu
  • Wen-Jhan Jane

Abstract

Word-of-mouth (WOM) is informal communication between consumers about products and services. By using text mining techniques, WOM measured for volume and valence at the movie box office in Taiwan. A simultaneous regression of a panel Granger causality test for WOM and corresponding film performance was performed. The empirical results show that dynamic causality only runs from box office to WOM volume in the short run analysis, and the causality between WOM volume and box office is bidirectional in the long run analysis. Potential customers attend movies because of WOM in the short run, but in the long run, box office forms a signal and creates WOM. In WOM valence analysis, causality runs from positive critics to the box office in the short run, and it runs from negative critics to box office in the long run. WOM providers may need to develop different strategies for encouraging WOM behavior among their users. The implication for film managers and marketers is that a reliable way to affect box office is to stimulate positive critics in the short run and negative critics in the long run.

Suggested Citation

  • Yuan-Lin Hsu & Wen-Jhan Jane, 2016. "Bidirectional Causality for Word of Mouth and the Movie Box Office: An Empirical Investigation of Panel Data," Journal of Media Economics, Taylor & Francis Journals, vol. 29(3), pages 139-152, July.
  • Handle: RePEc:taf:jmedec:v:29:y:2016:i:3:p:139-152
    DOI: 10.1080/08997764.2016.1208206
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/08997764.2016.1208206
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/08997764.2016.1208206?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Im, Kyung So & Pesaran, M. Hashem & Shin, Yongcheol, 2003. "Testing for unit roots in heterogeneous panels," Journal of Econometrics, Elsevier, vol. 115(1), pages 53-74, July.
    2. Vijay Mahajan & Eitan Muller & Roger A. Kerin, 1984. "Introduction Strategy for New Products with Positive and Negative Word-of-Mouth," Management Science, INFORMS, vol. 30(12), pages 1389-1404, December.
    3. Erkan Erdil & I. Hakan Yetkiner, 2009. "The Granger-causality between health care expenditure and output: a panel data approach," Applied Economics, Taylor & Francis Journals, vol. 41(4), pages 511-518.
    4. David Godes & Dina Mayzlin, 2004. "Using Online Conversations to Study Word-of-Mouth Communication," Marketing Science, INFORMS, vol. 23(4), pages 545-560, June.
    5. Dellarocas, Chrysanthos, 2003. "The Digitization of Word-of-mouth: Promise and Challenges of Online Feedback Mechanisms," Working papers 4296-03, Massachusetts Institute of Technology (MIT), Sloan School of Management.
    6. Sang Ho Kim & Namkee Park & Seung Hyun Park, 2013. "Exploring the Effects of Online Word of Mouth and Expert Reviews on Theatrical Movies' Box Office Success," Journal of Media Economics, Taylor & Francis Journals, vol. 26(2), pages 98-114, June.
    7. 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.
    8. 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.
    9. Chrysanthos Dellarocas, 2003. "The Digitization of Word of Mouth: Promise and Challenges of Online Feedback Mechanisms," Management Science, INFORMS, vol. 49(10), pages 1407-1424, October.
    10. Jane, Wen-Jhan, 2010. "Raising salary or redistributing it: A panel analysis of Major League Baseball," Economics Letters, Elsevier, vol. 107(2), pages 297-299, May.
    11. Brown, Jacqueline Johnson & Reingen, Peter H, 1987. "Social Ties and Word-of-Mouth Referral Behavior," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 14(3), pages 350-362, December.
    12. Abhijit V. Banerjee, 1992. "A Simple Model of Herd Behavior," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 107(3), pages 797-817.
    13. repec:dau:papers:123456789/6159 is not listed on IDEAS
    14. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(2), pages 277-297.
    15. Mohanbir S. Sawhney & Jehoshua Eliashberg, 1996. "A Parsimonious Model for Forecasting Gross Box-Office Revenues of Motion Pictures," Marketing Science, INFORMS, vol. 15(2), pages 113-131.
    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. Jane, Wen-Jhan, 2021. "Cultural distance in international films: An empirical investigation of a sample selection model," Journal of Economics and Business, Elsevier, vol. 113(C).
    2. Jordi McKenzie, 2023. "The economics of movies (revisited): A survey of recent literature," Journal of Economic Surveys, Wiley Blackwell, vol. 37(2), pages 480-525, April.

    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. Jehoshua Eliashberg & Anita Elberse & Mark A.A.M. Leenders, 2006. "The Motion Picture Industry: Critical Issues in Practice, Current Research, and New Research Directions," Marketing Science, INFORMS, vol. 25(6), pages 638-661, 11-12.
    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. Michel Clement & Anke Hille & Bernd Lucke & Christina Schmidt-Stölting & Frank Sambeth, 2008. "Der Einfluss von Rankings auf den Absatz — Eine empirische Analyse der Wirkung von Bestsellerlisten und Rangpositionen auf den Erfolg von Büchern," Schmalenbach Journal of Business Research, Springer, vol. 60(8), pages 746-777, December.
    4. 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.
    5. 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.
    6. Young-Jin Lee & Kartik Hosanagar & Yong Tan, 2015. "Do I Follow My Friends or the Crowd? Information Cascades in Online Movie Ratings," Management Science, INFORMS, vol. 61(9), pages 2241-2258, September.
    7. 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.
    8. Williams, Martin & Buttle, Francis, 2011. "The Eight Pillars of WOM management: Lessons from a multiple case study," Australasian marketing journal, Elsevier, vol. 19(2), pages 85-92.
    9. Yubo Chen & Jinhong Xie, 2008. "Online Consumer Review: Word-of-Mouth as a New Element of Marketing Communication Mix," Management Science, INFORMS, vol. 54(3), pages 477-491, March.
    10. Xinxin Li & Lorin M. Hitt, 2008. "Self-Selection and Information Role of Online Product Reviews," Information Systems Research, INFORMS, vol. 19(4), pages 456-474, December.
    11. Babutsidze, Zakaria, 2018. "The rise of electronic social networks and implications for advertisers," Technological Forecasting and Social Change, Elsevier, vol. 137(C), pages 27-39.
    12. Floyd, Kristopher & Freling, Ryan & Alhoqail, Saad & Cho, Hyun Young & Freling, Traci, 2014. "How Online Product Reviews Affect Retail Sales: A Meta-analysis," Journal of Retailing, Elsevier, vol. 90(2), pages 217-232.
    13. repec:hal:spmain:info:hdl:2441/7eeckjdtj29ncak518t23a2j25 is not listed on IDEAS
    14. Błoński Krzysztof, 2023. "Analysis of Citations and Co-Citations of the Term ‘Word of Mouth’ Based on Publications in the Field of Social Sciences," Marketing of Scientific and Research Organizations, Sciendo, vol. 48(2), pages 111-133, June.
    15. Juan Feng & Xin Li & Xiaoquan (Michael) Zhang, 2019. "Online Product Reviews-Triggered Dynamic Pricing: Theory and Evidence," Information Systems Research, INFORMS, vol. 30(4), pages 1107-1123, December.
    16. 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).
    17. Sanjeev Dewan & Jui Ramaprasad, 2012. "Research Note ---Music Blogging, Online Sampling, and the Long Tail," Information Systems Research, INFORMS, vol. 23(3-part-2), pages 1056-1067, September.
    18. Daniel Kaimann & Joe Cox, 2014. "The Interaction of Signals: A Fuzzy set Analysis of the Video Game Industry," Working Papers Dissertations 13, Paderborn University, Faculty of Business Administration and Economics.
    19. Bartschat, Maria & Cziehso, Gerrit & Hennig-Thurau, Thorsten, 2022. "Searching for word of mouth in the digital age: Determinants of consumers’ uses of face-to-face information, internet opinion sites, and social media," Journal of Business Research, Elsevier, vol. 141(C), pages 393-409.
    20. 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.
    21. Wang, Zhan & Kim, Hyun Gon, 2017. "Can Social Media Marketing Improve Customer Relationship Capabilities and Firm Performance? Dynamic Capability Perspective," Journal of Interactive Marketing, Elsevier, vol. 39(C), pages 15-26.

    More about this item

    Statistics

    Access and download statistics

    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:taf:jmedec:v:29:y:2016:i:3:p:139-152. 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 Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/HMEC20 .

    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.