Skip to main content
SpringerLink
Log in

Box-office forecasting in Korea using search trend data: a modified generalized Bass diffusion model

  • Published:
Electronic Commerce Research Aims and scope Submit manuscript

Abstract

This study aimed to develop a new diffusion model for box-office forecasting by modifying the generalized Bass diffusion model with incorporation of search trend data and historical movie-audience data. To that end, first, movie-audience data (i.e., the number of moviegoers) and NAVER search trend data for each of the top 30 movies released in Korea in 2018 were collected by day. Then, the modified generalized Bass diffusion model, newly proposed in this paper, was applied in order to estimate the diffusion parameters. The results of our empirical case study on the Korean film market show that NAVER search trend data plays an important role in box-office forecasting after a movie is released. This study contributes to the extant literature by proposing a new diffusion model, which is a novel online big-data-driven methodology of box-office forecasting. In addition, comparison analysis with two other representative diffusion models was conducted, and the proposed model showed superior prediction power.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Abel, F., Diaz-Aviles, E., Henze, N., Krause, D., & Siehndel, P. (2010). Analyzing the blogosphere for predicting the success of music and movie products. In 2010 international conference on advances in social networks analysis and mining, IEEE (pp. 276–280).

  2. Ainslie, A., Drèze, X., & Zufryden, F. (2005). Modeling movie life cycles and market share. Marketing Science, 24(3), 508–517.

    Article  Google Scholar 

  3. Akoglu, H. (2018). User’s guide to correlation coefficients. Turkish Journal of Emergency Medicine, 18(3), 91–93. https://doi.org/10.1016/j.tjem.2018.08.001.

    Article  Google Scholar 

  4. Baek, H., Oh, S., Yang, H.-D., & Ahn, J. (2017). Electronic word-of-mouth, box office revenue and social media. Electronic Commerce Research and Applications, 22, 13–23.

    Article  Google Scholar 

  5. Bass, F. M. (1969). A new product growth for model consumer durables. Management Science, 15(5), 215–227.

    Article  Google Scholar 

  6. Bass, F. M., Krishnan, T. V., & Jain, D. C. (1994). Why the Bass model fits without decision variables. Marketing Science, 13(3), 203–223.

    Article  Google Scholar 

  7. Beck, J. (2007). The sales effect of word of mouth: a model for creative goods and estimates for novels. Journal of Cultural Economics, 31(1), 5–23.

    Article  Google Scholar 

  8. Boswell, P. (2011). Google analytics: Measuring content use and engagement. Society for Technical Communication Summit, 41, 135–138.

    Google Scholar 

  9. Brewer, S. M., Kelley, J. M., & Jozefowicz, J. J. (2009). A blueprint for success in the US film industry. Applied Economics, 41(5), 589–606.

    Article  Google Scholar 

  10. Calantone, R. J., Yeniyurt, S., Townsend, J. D., & Schmidt, J. B. (2010). The effects of competition in short product life-cycle markets: The case of Motion Pictures. Journal of Product Innovation Management, 27(3), 349–361.

    Article  Google Scholar 

  11. Chai, Y., Luo, H., Zhang, Q., Cheng, Q., Lui, C. S. M., & Yip, P. S. F. (2019). Developing an early warning system of suicide using Google Trends and media reporting. Journal of Affective Disorders, 255, 41–49. https://doi.org/10.1016/j.jad.2019.05.030.

    Article  Google Scholar 

  12. Chen, K., & Yin, J. (2017). Information competition in product launch: Evidence from the movie industry. Electronic Commerce Research and Applications, 26, 81–88. https://doi.org/10.1016/j.elerap.2017.10.003.

    Article  Google Scholar 

  13. Chintagunta, P. K., Gopinath, S., & Venkataraman, S. (2010). The effects of online user reviews on movie box office performance: Accounting for sequential rollout and aggregation across local markets. Marketing Science, 29(5), 944–957.

    Article  Google Scholar 

  14. Chumnumpan, P., & Shi, X. (2019). Understanding new products’ market performance using Google Trends. Australasian Marketing Journal (AMJ), 27(2), 91–103.

    Article  Google Scholar 

  15. Clark, M., Wilkins, E. J., Dagan, D. T., Powell, R., Sharp, R. L., & Hillis, V. (2019). Bringing forecasting into the future: Using Google to predict visitation in U.S. national parks. Journal of Environmental Management, 243, 88–94. https://doi.org/10.1016/j.jenvman.2019.05.006.

    Article  Google Scholar 

  16. Delen, D., Sharda, R., & Kumar, P. (2007). Movie forecast Guru: A Web-based DSS for Hollywood managers. Decision Support Systems, 43(4), 1151–1170.

    Article  Google Scholar 

  17. Dellarocas, C., Zhang, X. M., & Awad, N. F. (2007). Exploring the value of online product reviews in forecasting sales: The case of motion pictures. Journal of Interactive Marketing, 21(4), 23–45.

    Article  Google Scholar 

  18. Dhar, T., & Weinberg, C. B. (2016). Measurement of interactions in non-linear marketing models: The effect of critics’ ratings and consumer sentiment on movie demand. International Journal of Research in Marketing, 33(2), 392–408. https://doi.org/10.1016/j.ijresmar.2015.10.003.

    Article  Google Scholar 

  19. Ding, C., Cheng, H. K., Duan, Y., & Jin, Y. (2017). The power of the “like” button: The impact of social media on box office. Decision Support Systems, 94, 77–84.

    Article  Google Scholar 

  20. Duan, W., Gu, B., & Whinston, A. B. (2008). The dynamics of online word-of-mouth and product sales—An empirical investigation of the movie industry. Journal of Retailing, 84(2), 233–242.

    Article  Google Scholar 

  21. Elberse, A., & Eliashberg, J. (2003). Demand and supply dynamics for sequentially released products in international markets: The case of motion pictures. Marketing Science, 22(3), 329–354.

    Article  Google Scholar 

  22. Eliashberg, J., Jonker, J.-J., Sawhney, M. S., & Wierenga, B. (2000). MOVIEMOD: An implementable decision-support system for prerelease market evaluation of motion pictures. Marketing Science, 19(3), 226–243.

    Article  Google Scholar 

  23. Eliashberg, J., & Shugan, S. M. (1997). Film critics: Influencers or predictors? Journal of Marketing, 61(2), 68–78.

    Article  Google Scholar 

  24. Esmaeili Aliabadi, D., Avşar, B., Yousefnezhad, R., & Esmaeili Aliabadi, E. (2019). Investigating global language networks using Google search queries. Expert Systems with Applications, 121, 66–77. https://doi.org/10.1016/j.eswa.2018.12.016.

    Article  Google Scholar 

  25. Fantazzini, D., & Fomichev, N. (2014). Forecasting the real price of oil using online search data. International Journal of Computational Economics and Econometrics, 4(1–2), 4–31.

    Article  Google Scholar 

  26. Feng, Y., Li, G., Sun, X., & Li, J. (2019). Forecasting the number of inbound tourists with Google Trends. Procedia Computer Science, 162, 628–633. https://doi.org/10.1016/j.procs.2019.12.032.

    Article  Google Scholar 

  27. Fruchter, G. E., & Van den Bulte, C. (2011). Why the Generalized Bass Model leads to odd optimal advertising policies. International Journal of Research in Marketing, 28(3), 218–230. https://doi.org/10.1016/j.ijresmar.2011.03.005.

    Article  Google Scholar 

  28. Geng, X., & Hou, P. (2015). Pre-release prediction of crowd opinion on movies by label distribution learning. In Twenty-fourth international joint conference on artificial intelligence.

  29. Ghiassi, M., Lio, D., & Moon, B. (2015). Pre-production forecasting of movie revenues with a dynamic artificial neural network. Expert Systems with Applications, 42(6), 3176–3193.

    Article  Google Scholar 

  30. Ginsberg, J., Mohebbi, M. H., Patel, R. S., Brammer, L., Smolinski, M. S., & Brilliant, L. (2009). Detecting influenza epidemics using search engine query data. Nature, 457(7232), 1012–1014.

    Article  Google Scholar 

  31. Gong, J. J., Young, S. M., & Van der Stede, W. A. (2011). Real options in the motion picture industry: Evidence from film marketing and sequels. Contemporary Accounting Research, 28(5), 1438–1466.

    Article  Google Scholar 

  32. Guo, J.-F., & Ji, Q. (2013). How does market concern derived from the Internet affect oil prices? Applied Energy, 112, 1536–1543.

    Article  Google Scholar 

  33. Hao, Z., Liu, M., & Ge, X. (2019). Evaluating the impact of health awareness events on Google search frequency. Preventive Medicine Reports, 15, 100887.

    Article  Google Scholar 

  34. Hariharan, V., Kwon, C., & Talukdar, D. (2010) Dynamic optimal marketing mix strategies for new products with influential and imitator consumer segments. In Presentation at the 2010 INFORMS marketing science conference, June 17–20, Cologne, Germany.

  35. Hur, M., Kang, P., & Cho, S. (2016). Box-office forecasting based on sentiments of movie reviews and Independent subspace method. Information Sciences, 372, 608–624.

    Article  Google Scholar 

  36. Jun, S.-P., Sung, T.-E., & Park, H.-W. (2017). Forecasting by analogy using the web search traffic. Technological Forecasting and Social Change, 115, 37–51.

    Article  Google Scholar 

  37. Kamiński, M., Skonieczna-Żydecka, K., Nowak, J. K., & Stachowska, E. (2020). Global and local diet popularity rankings, their secular trends and seasonal variation in Google Trends data. Nutrition, 79–80, 110759.

    Article  Google Scholar 

  38. Kim, N., Lučivjanská, K., Molnár, P., & Villa, R. (2019). Google searches and stock market activity: Evidence from Norway. Finance Research Letters, 28, 208–220.

    Article  Google Scholar 

  39. Kim, T., Hong, J., & Kang, P. (2015). Box office forecasting using machine learning algorithms based on SNS data. International Journal of Forecasting, 31(2), 364–390.

    Article  Google Scholar 

  40. Krishnan, T. V., & Jain, D. C. (2006). Optimal dynamic advertising policy for new products. Management Science, 52(12), 1957–1969.

    Article  Google Scholar 

  41. Lazer, D., Kennedy, R., King, G., & Vespignani, A. (2014). The parable of Google Flu: Traps in big data analysis. Science, 343(6176), 1203–1205.

    Article  Google Scholar 

  42. Lee, J. H., Jung, S. H., & Park, J. (2017). The role of entropy of review text sentiments on online WOM and movie box office sales. Electronic Commerce Research and Applications, 22, 42–52. https://doi.org/10.1016/j.elerap.2017.03.001.

    Article  Google Scholar 

  43. Lee, K. J., & Chang, W. (2009). Bayesian belief network for box-office performance: A case study on Korean movies. Expert Systems with Applications, 36(1), 280–291.

    Article  Google Scholar 

  44. Lee, Y., Kim, S.-H., & Cha, K. C. (2012). A generalized Bass model for predicting the sales patterns of motion pictures having seasonality and herd behavior. Journal of Global Scholars of Marketing Science, 22(4), 310–326.

    Article  Google Scholar 

  45. Li, X., Ma, J., Wang, S., & Zhang, X. (2015). How does Google search affect trader positions and crude oil prices? Economic Modelling, 49, 162–171.

    Article  Google Scholar 

  46. Litman, B. R. (1983). Predicting success of theatrical movies: An empirical study. The Journal of Popular Culture, 16(4), 159–175.

    Article  Google Scholar 

  47. Liu, Y. (2006). Word of mouth for movies: Its dynamics and impact on box office revenue. Journal of Marketing, 70(3), 74–89.

    Article  Google Scholar 

  48. Lovallo, D., Clarke, C., & Camerer, C. (2012). Robust analogizing and the outside view: two empirical tests of case-based decision making. Strategic Management Journal, 33(5), 496–512.

    Article  Google Scholar 

  49. Ma, H., Kim, J. M., & Lee, E. (2019). Analyzing dynamic review manipulation and its impact on movie box office revenue. Electronic Commerce Research and Applications, 35, 100840.

    Article  Google Scholar 

  50. Makridakis, S. (1993). Accuracy measures: theoretical and practical concerns. International Journal of Forecasting, 9(4), 527–529.

    Article  Google Scholar 

  51. Marshall, P., Dockendorff, M., & Ibáñez, S. (2013). A forecasting system for movie attendance. Journal of Business Research, 66(10), 1800–1806.

    Article  Google Scholar 

  52. Nagamma, P., Pruthvi, H., Nisha, K., & Shwetha, N. (2015) An improved sentiment analysis of online movie reviews based on clustering for box-office prediction. In International conference on computing, communication & automation, IEEE (pp. 933–937).

  53. Nagao, S., Takeda, F., & Tanaka, R. (2019). Nowcasting of the U.S. unemployment rate using Google Trends. Finance Research Letters, 30, 103–109. https://doi.org/10.1016/j.frl.2019.04.005.

    Article  Google Scholar 

  54. Neelamegham, R., & Chintagunta, P. (1999). A Bayesian model to forecast new product performance in domestic and international markets. Marketing Science, 18(2), 115–136.

    Article  Google Scholar 

  55. Nguyen, C. P., Schinckus, C., & Hong Nguyen, T. V. (2019). Google search and stock returns in emerging markets. Borsa Istanbul Review. https://doi.org/10.1016/j.bir.2019.07.001.

    Article  Google Scholar 

  56. Niesert, R. F., Oorschot, J. A., Veldhuisen, C. P., Brons, K., & Lange, R.-J. (2019). Can Google search data help predict macroeconomic series? International Journal of Forecasting. https://doi.org/10.1016/j.ijforecast.2018.12.006.

    Article  Google Scholar 

  57. Oh, C., Roumani, Y., Nwankpa, J. K., & Hu, H.-F. (2017). Beyond likes and tweets: Consumer engagement behavior and movie box office in social media. Information & Management, 54(1), 25–37.

    Article  Google Scholar 

  58. Padhi, S. S., & Pati, R. K. (2017). Quantifying potential tourist behavior in choice of destination using Google Trends. Tourism Management Perspectives, 24, 34–47. https://doi.org/10.1016/j.tmp.2017.07.001.

    Article  Google Scholar 

  59. Pakkala, H., Presser, K., & Christensen, T. (2012). Using Google Analytics to measure visitor statistics: The case of food composition websites. International Journal of Information Management, 32(6), 504–512.

    Article  Google Scholar 

  60. Philippas, D., Philippas, N., Tziogkidis, P., & Rjiba, H. (2020). Signal-herding in cryptocurrencies. Journal of International Financial Markets, Institutions and Money. https://doi.org/10.1016/j.intfin.2020.101191.

    Article  Google Scholar 

  61. Preis, T., Moat, H. S., & Stanley, H. E. (2013). Quantifying trading behavior in financial markets using Google Trends. Scientific Reports, 3, 1684.

    Article  Google Scholar 

  62. Qin, L. (2011). Word-of-blog for movies: A predictor and an outcome of box office revenue? Journal of Electronic Commerce Research, 12(3), 187.

    Google Scholar 

  63. Ramírez-Hassan, A., & Montoya-Blandón, S. (2020). Forecasting from others’ experience: Bayesian estimation of the generalized Bass model. International Journal of Forecasting, 36(2), 442–465. https://doi.org/10.1016/j.ijforecast.2019.05.016.

    Article  Google Scholar 

  64. Ru, Y., Li, B., Liu, J., & Chai, J. (2018). An effective daily box office prediction model based on deep neural networks. Cognitive Systems Research, 52, 182–191. https://doi.org/10.1016/j.cogsys.2018.06.018.

    Article  Google Scholar 

  65. Sawhney, M. S., & Eliashberg, J. (1996). A parsimonious model for forecasting gross box-office revenues of motion pictures. Marketing Science, 15(2), 113–131.

    Article  Google Scholar 

  66. She, Z.-Y., Cao, R., Xie, B.-C., Ma, J.-J., & Lan, S. (2019). An analysis of the wind power development factors by Generalized Bass Model: A case study of China’s eight bases. Journal of Cleaner Production, 231, 1503–1514. https://doi.org/10.1016/j.jclepro.2019.05.255.

    Article  Google Scholar 

  67. Sneddon, J., Soutar, G., & Mazzarol, T. (2011). Modelling the faddish, fashionable and efficient diffusion of agricultural technologies: A case study of the diffusion of wool testing technology in Australia. Technological Forecasting and Social Change, 78(3), 468–480.

    Article  Google Scholar 

  68. Tofallis, C. (2015). A better measure of relative prediction accuracy for model selection and model estimation. Journal of the Operational Research Society, 66(8), 1352–1362.

    Article  Google Scholar 

  69. VdM Smit, E., & Pangarker, N. (2013). The determinants of box office performance in the film industry revisited. South African Journal of Business Management, 44(3), 47–58.

    Article  Google Scholar 

  70. Wang, F., Zhang, Y., Li, X., & Zhu, H. (2010). Why do moviegoers go to the theater? The role of prerelease media publicity and online word of mouth in driving moviegoing behavior. Journal of Interactive Advertising, 11(1), 50–62.

    Article  Google Scholar 

  71. Wang, X., White, L., Chen, X., Gaikar, D. D., Marakarkandy, B., & Dasgupta, C. (2015). Using Twitter data to predict the performance of Bollywood movies. Industrial Management & Data Systems, 115(9), 1604.

    Article  Google Scholar 

  72. Wang, Z., Zhang, J., Ji, S., Meng, C., Li, T., & Zheng, Y. (2020). Predicting and ranking box office revenue of movies based on big data. Information Fusion, 60, 25–40. https://doi.org/10.1016/j.inffus.2020.02.002.

    Article  Google Scholar 

  73. Wen, K.-H., & Yang, C.-Q. (2011). Determinants of the box office performance of motion picture in China-indication for Chinese motion picture market by adapting determinants of the box office (Part II). Journal of Science and Innovation, 1(4), 17–26.

    Google Scholar 

  74. Wright, M., Upritchard, C., & Lewis, T. (1997). A validation of the Bass new product diffusion model in New Zealand. Marketing Bulletin-Department of Marketing Massey University, 8, 15–29.

    Google Scholar 

  75. Yea, S.-J., Jang, Y., Seong, B., & Kim, C. (2015). Comparative analysis of web search trends between experts and public for medicinal herbs in Korea. Journal of Ethnopharmacology, 176, 463–468.

    Article  Google Scholar 

  76. Yeh, F.-C., & Yeh, C.-H. (2019). Developing mortality surveillance systems using Google trend: A pilot study. Physica A: Statistical Mechanics and its Applications, 527, 121125.

    Article  Google Scholar 

  77. Yu, L., Zhao, Y., Tang, L., & Yang, Z. (2019). Online big data-driven oil consumption forecasting with Google trends. International Journal of Forecasting, 35(1), 213–223.

    Article  Google Scholar 

  78. Zhang, L., Luo, J., & Yang, S. (2009). Forecasting box office revenue of movies with BP neural network. Expert Systems with Applications, 36(3), 6580–6587.

    Article  Google Scholar 

  79. Zhang, W., & Skiena, S. (2009) Improving movie gross prediction through news analysis. In 2009 IEEE/WIC/ACM international joint conference on web intelligence and intelligent agent technology, IEEE (pp. 301–304).

  80. Zhang, Z., Li, B., Deng, Z., Chai, J., Wang, Y., & An, M. (2015). Research on movie box office forecasting based on Internet data. In: 2015 8th International Symposium on Computational Intelligence and Design (ISCID), IEEE (pp. 83–86).

Download references

Acknowledgements

This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (2019R1G1A1006073).

Author information

Authors and Affiliations

  1. Department of Industrial and Management Engineering, Institute of Digital Anti-aging Health care, Inje University, 197, Inje-ro, Gimhae-si, Gyeongsangnam-do, Republic of Korea

    Daekook Kang

Authors
  1. Daekook Kang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Daekook Kang.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kang, D. Box-office forecasting in Korea using search trend data: a modified generalized Bass diffusion model. Electron Commer Res 21, 41–72 (2021). https://doi.org/10.1007/s10660-020-09456-7

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10660-020-09456-7

Keywords

Search

Navigation