IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i16p6602-d399262.html
   My bibliography  Save this article

Movie Production Efficiency Moderating between Online Word-of-Mouth and Subsequent Box Office Revenue

Author

Listed:
  • Sangjae Lee

    (College of Business Administration, Sejong University, Seoul 05006, Korea)

  • Joon Yeon Choeh

    (Department of Software, Sejong University, Seoul 05006, Korea)

Abstract

The studies are almost nonexistent regarding production efficiency of movies which is determined based on the relationship between movie resources powers (powers of actors, directors, distributors, and production companies) and box office. Our study attempts to examine how efficiency moderates the relationship between eWOM (online word-of-mouth) and revenue, and to show the difference in prediction performance between efficient and inefficient movies. Using data envelopment analysis to suggest efficiency of movies, movie efficiency negatively moderates the effects of review depth and volume on subsequent box office revenue compensating negative effects of smaller box office in previous period while efficiency exert a positive moderating effect on the influences of review rating and the number of positive reviews on revenue. This shows that review depth and volume are affected by the slack of movie resources powers for inefficient movies, and high rating and positive response for efficient movies to affect revenue. The results of decision trees, k-nearest-neighbors, and linear regression analysis based on ensemble methods using eWOM or movie variables indicate that the movies with the inefficient movie resources powers are providing greater prediction performance than movies with efficient movie resources powers. This show that diverse variation in the efficiency of movie resources powers contributes to prediction performance.

Suggested Citation

  • Sangjae Lee & Joon Yeon Choeh, 2020. "Movie Production Efficiency Moderating between Online Word-of-Mouth and Subsequent Box Office Revenue," Sustainability, MDPI, vol. 12(16), pages 1-18, August.
  • Handle: RePEc:gam:jsusta:v:12:y:2020:i:16:p:6602-:d:399262
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/16/6602/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/12/16/6602/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    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. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    4. 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.
    5. Moez Hababou & Nawel Amrouche & Kamel Jedidi, 2016. "Measuring Economic Efficiency in the Motion Picture Industry: a Data Envelopment Analysis Approach," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 3(3), pages 144-158, December.
    6. 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.
    7. Julianne Treme, 2010. "Effects of Celebrity Media Exposure on Box-Office Performance," Journal of Media Economics, Taylor & Francis Journals, vol. 23(1), pages 5-16.
    8. Julianne Treme & Lee A. Craig, 2013. "Celebrity star power: Do age and gender effects influence box office performance?," Applied Economics Letters, Taylor & Francis Journals, vol. 20(5), pages 440-445, March.
    9. Yong-bae Ji & Choonjoo Lee, 2010. "Data envelopment analysis," Stata Journal, StataCorp LP, vol. 10(2), pages 267-280, June.
    10. Jordi Mckenzie, 2008. "Bayesian Information Transmission and Stable Distributions: Motion Picture Revenues at the Australian Box Office," The Economic Record, The Economic Society of Australia, vol. 84(266), pages 338-353, September.
    11. 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.
    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. Ronny Behrens & Natasha Zhang Foutz & Michael Franklin & Jannis Funk & Fernanda Gutierrez-Navratil & Julian Hofmann & Ulrike Leibfried, 2021. "Leveraging analytics to produce compelling and profitable film content," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 45(2), pages 171-211, June.
    2. Sangjae Lee & Joon Yeon Choeh, 2020. "The impact of online review helpfulness and word of mouth communication on box office performance predictions," Palgrave Communications, Palgrave Macmillan, vol. 7(1), pages 1-12, December.
    3. 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.
    4. Thaís L. D. Souza & Marislei Nishijima & Ana C. P. Fava, 2019. "Do consumer and expert reviews affect the length of time a film is kept on screens in the USA?," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 43(1), pages 145-171, March.
    5. Lee, Youseok & Kim, Sang-Hoon & Cha, Kyoung Cheon, 2021. "Impact of online information on the diffusion of movies: Focusing on cultural differences," Journal of Business Research, Elsevier, vol. 130(C), pages 603-609.
    6. 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.
    7. Moez Hababou & Nawel Amrouche & Kamel Jedidi, 2016. "Measuring Economic Efficiency in the Motion Picture Industry: a Data Envelopment Analysis Approach," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 3(3), pages 144-158, December.
    8. 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).
    9. 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.
    10. Julianne Treme & Zoe VanDerPloeg, 2014. "The Twitter Effect: Social Media Usage as a Contributor to Movie Success," Economics Bulletin, AccessEcon, vol. 34(2), pages 793-809.
    11. 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.
    12. 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.
    13. Yucheng Zhang & Zhiling Wang & Lin Xiao & Lijun Wang & Pei Huang, 2023. "Discovering the evolution of online reviews: A bibliometric review," Electronic Markets, Springer;IIM University of St. Gallen, vol. 33(1), pages 1-22, December.
    14. 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.
    15. Alessandra Cepparulo & Gilles Mourre, 2020. "How and How Much? The Growth-Friendliness of Public Spending through the Lens," European Economy - Discussion Papers 132, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
    16. Kaushik, Kapil & Mishra, Rajhans & Rana, Nripendra P. & Dwivedi, Yogesh K., 2018. "Exploring reviews and review sequences on e-commerce platform: A study of helpful reviews on Amazon.in," Journal of Retailing and Consumer Services, Elsevier, vol. 45(C), pages 21-32.
    17. Oleg Badunenko & Harald Tauchmann, 2019. "Simar and Wilson two-stage efficiency analysis for Stata," Stata Journal, StataCorp LP, vol. 19(4), pages 950-988, December.
    18. Matheus Koengkan & José Alberto Fuinhas & Emad Kazemzadeh & Fariba Osmani & Nooshin Karimi Alavijeh, 2022. "Measuring the economic efficiency performance in Latin American and Caribbean countries: An empirical evidence from stochastic production frontier and data envelopment analysis," International Economics, CEPII research center, issue 169, pages 43-54.
    19. Li, Yimeng & Xiong, Yu & Mariuzzo, Franco & Xia, Senmao, 2021. "The underexplored impacts of online consumer reviews: Pricing and new product design strategies in the O2O supply chain," International Journal of Production Economics, Elsevier, vol. 237(C).
    20. Qihua Liu & Xiaoyu Zhang & Liyi Zhang & Yang Zhao, 2019. "The interaction effects of information cascades, word of mouth and recommendation systems on online reading behavior: an empirical investigation," Electronic Commerce Research, Springer, vol. 19(3), pages 521-547, September.

    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:gam:jsusta:v:12:y:2020:i:16:p:6602-:d:399262. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.