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An Exploration of the Dynamics Between Social Media and Box Office Performance

Author

Listed:
  • Nan Feng

    (Tianjin University
    Tianjin University (Qingdao) Ocean Engineering Research Institute Co. LTD)

  • Yanan Shi

    (Tianjin University)

  • Yadi Li

    (Tianjin University)

  • Dahui Li

    (University of Minnesota Duluth)

  • Jie Zhang

    (University of Texas at Arlington)

  • Minqiang Li

    (Tianjin University)

Abstract

Prior studies have reported the positive impact of social media on box office performance. There is limited evidence of the dynamics of the positive impact and how box office performance influences social media. This study examines how owned social media (OSM) and earned social media (ESM) impact box office performance in a dynamic manner and vice versa in the opening week of movies. We also examine how these impacts vary with firm size of a movie’s marketing company and platform specificity of where the movie is advertised. We applied the panel vector auto-regression method to analyze a merged dataset of 289 movies distributed in the Chinese movie market. We found that OSM and ESM had different impacts on box office performance. Box office performance reacted faster to ESM than to OSM and the reaction lasted longer to OSM than to ESM. In addition, the impact of OSM was more significant for large firms while ESM’s impact was more significant for small firms. Finally, social media on specialized platforms did not have a faster impact on box office performance than that on general platforms. The impact on general platforms did not last longer than that on specialized platform. Overall, we recommend movie marketing firms develop tailored social media strategies by means of customizing their choices of social media based on firm size and platform specificity.

Suggested Citation

  • Nan Feng & Yanan Shi & Yadi Li & Dahui Li & Jie Zhang & Minqiang Li, 2024. "An Exploration of the Dynamics Between Social Media and Box Office Performance," Information Systems Frontiers, Springer, vol. 26(2), pages 591-608, April.
    • Handle: RePEc:spr:infosf:v:26:y:2024:i:2:d:10.1007_s10796-023-10389-3
    DOI: 10.1007/s10796-023-10389-3
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    1. Shuba Srinivasan & Oliver J. Rutz & Koen Pauwels, 2016. "Paths to and off purchase: quantifying the impact of traditional marketing and online consumer activity," Journal of the Academy of Marketing Science, Springer, vol. 44(4), pages 440-453, July.
    2. 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.
    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. Braojos-Gomez, Jessica & Benitez-Amado, Jose & Javier Llorens-Montes, F., 2015. "How do small firms learn to develop a social media competence?," International Journal of Information Management, Elsevier, vol. 35(4), pages 443-458.
    5. Galariotis, Emilios C. & Makrichoriti, Panagiota & Spyrou, Spyros, 2016. "Sovereign CDS spread determinants and spill-over effects during financial crisis: A panel VAR approach," Journal of Financial Stability, Elsevier, vol. 26(C), pages 62-77.
    6. Peukert, Christian & Claussen, Jörg & Kretschmer, Tobias, 2017. "Piracy and box office movie revenues: Evidence from Megaupload," International Journal of Industrial Organization, Elsevier, vol. 52(C), pages 188-215.
    7. Xueming Luo & Jie Zhang & Wenjing Duan, 2013. "Social Media and Firm Equity Value," Information Systems Research, INFORMS, vol. 24(1), pages 146-163, March.
    8. James H. Stock & Mark W. Watson, 2001. "Vector Autoregressions," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 101-115, Fall.
    9. 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.
    10. Douglas Holtz-Eakin & Whitney Newey & Harvey S. Rosen, 1987. "Wages and Hours: Estimating Vector Autoregressions with Panel Data," Working Papers 602, Princeton University, Department of Economics, Industrial Relations Section..
    11. Thorsten Hennig-Thurau & Mark Houston & Shrihari Sridhar, 2006. "Can good marketing carry a bad product? Evidence from the motion picture industry," Marketing Letters, Springer, vol. 17(3), pages 205-219, July.
    12. 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.
    13. Tingting Song & Jinghua Huang & Yong Tan & Yifan Yu, 2019. "Using User- and Marketer-Generated Content for Box Office Revenue Prediction: Differences Between Microblogging and Third-Party Platforms," Service Science, INFORMS, vol. 30(1), pages 191-203, March.
    14. Mitchell J. Lovett & Richard Staelin, 2016. "The Role of Paid, Earned, and Owned Media in Building Entertainment Brands: Reminding, Informing, and Enhancing Enjoyment," Marketing Science, INFORMS, vol. 35(1), pages 142-157, January.
    15. 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.
    16. V. Kumar & JeeWon Brianna Choi & Mallik Greene, 2017. "Synergistic effects of social media and traditional marketing on brand sales: capturing the time-varying effects," Journal of the Academy of Marketing Science, Springer, vol. 45(2), pages 268-288, March.
    17. Andrew Ainslie & Xavier Drèze & Fred Zufryden, 2005. "Modeling Movie Life Cycles and Market Share," Marketing Science, INFORMS, vol. 24(3), pages 508-517, November.
    18. Douglas Holtz-Eakin & Whitney Newey & Harvey Rosen, 1987. "Wages and Hours: Estimating Vector Autoregressions with Panel Data," Working Papers 602, Princeton University, Department of Economics, Industrial Relations Section..
    19. Legoux, Renaud & Larocque, Denis & Laporte, Sandra & Belmati, Soraya & Boquet, Thomas, 2016. "The effect of critical reviews on exhibitors' decisions: Do reviews affect the survival of a movie on screen?," International Journal of Research in Marketing, Elsevier, vol. 33(2), pages 357-374.
    20. 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.
    21. 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.
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