Forecasting Cinema Attendance at the Movie Show Level: Evidence from Poland

1. Ainslie, A., Drèze, X., Zufryden, F. (2005),” Modeling movie life cycles and market share“, Marketing Science, Vol. 24, No. 3, pp. 508-517.10.1287/mksc.1040.0106Search in Google Scholar

2. Baranowski, P., Komor, M., Wójcik, S. (2018),” Whose feedback matters? Empirical evidence from online auctions “, Applied Economics Letters, Vol. 25, No. 17, pp. 1226–1229.10.1080/13504851.2017.1412070Search in Google Scholar

3. Bloom, N. (2014),” Fluctuations in uncertainty”, Journal of Economic Perspectives, Vol. 28, No. 2, pp. 153-76.10.1257/jep.28.2.153Search in Google Scholar

4. Bose, I., Mahapatra, R.K. (2001),” Business data mining—A machine learning perspective “, Information & Management, Vol. 39, No. 3, pp. 211–225.10.1016/S0378-7206(01)00091-XSearch in Google Scholar

5. Bukovina, J. (2016), “Social media big data and capital markets—An overview”, Journal of Behavioral and Experimental Finance, Vol. 11, pp. 18-26.10.1016/j.jbef.2016.06.002Search in Google Scholar

6. Cameron, S. (1988),” The Impact of Video Recorders on Cinema Attendance“, Journal of Cultural Economics, Vol. 12, No. 1, pp. 73–80.10.1007/BF00220047Search in Google Scholar

7. Cameron, S. (1999),” Rational addiction and the demand for cinema“, Applied Economics Letters, Vol. 6, No. 9, pp. 617-620.10.1080/135048599352736Search in Google Scholar

8. Cameron, A.C., Trivedi, P.K. (2005),” Microeconometrics: Methods and applications “, Cambridge University Press.10.1017/CBO9780511811241Search in Google Scholar

9. Casson, M. (2006),” Culture and economic performance“, in Ginsburg, V.A., Throsby, D. (Eds.), Handbook of the Economics of Art and Culture, 1, pp. 359–397.10.1016/S1574-0676(06)01012-XSearch in Google Scholar

10. Collins A., Hand, C. (2005),” Analyzing Moviegoing Demand: An Individual-level Cross-sectional Approach“, Managerial and Decision Economics, Vol. 26, No. 5, pp. 319–330.10.1002/mde.1231Search in Google Scholar

11. Collins, A., Scorcu, A.E., Zanola, R. (2009),” Distribution Conventionality in the Movie Sector: An Econometric Analysis of Cinema Supply“, Managerial and Decision Economics, Vol. 30, No. 8, pp. 517–527.10.1002/mde.1469Search in Google Scholar

12. Craig, C. S., Greene, W. H., Versaci, A. (2015), “E-word of mouth: Early predictor of audience engagement: How pre-release “e-WOM” drives box-office outcomes of movies“, Journal of Advertising Research, Vol. 55, No. 1, pp. 62-72.Search in Google Scholar

13. Cuffe, H.E. (2018),” Rain and museum attendance: Are daily data fine enough?“, Journal of Cultural Economics, Vol. 42, No. 2, pp. 213–241.10.1007/s10824-017-9298-9Search in Google Scholar

14. De Vany, A. (2003),” Hollywood economics: How extreme uncertainty shapes the film industry“, Routledge.10.4324/9780203489970Search in Google Scholar

15. De Vany, A.S., Walls, W.D. (1999),” Uncertainty in the Movie Industry: Does Star Power Reduce the Terror of the Box Office? “, Journal of Cultural Economics, Vol. 23, No. 4, pp. 285–318.10.1023/A:1007608125988Search in Google Scholar

16. Dellarocas, C., Zhang, X., Awad, N.F. (2007),” Exploring the value of online product reviews in forecasting sales: The case of motion pictures “, Journal of Interactive Marketing, Vol. 21, No. 4, pp. 23–45.10.1002/dir.20087Search in Google Scholar

17. Dewenter, R., Westermann, M. (2005),” Cinema demand in Germany “, Journal of Cultural Economics, Vol. 29, No. 3, pp. 213–231.10.1007/s10824-005-6421-0Search in Google Scholar

18. 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, Vol. 94, pp. 77-84.10.1016/j.dss.2016.11.002Search in Google Scholar

19. Doury, N. (2001),” Successfully integrating cinemas into retail and leisure complexes: An operator’s perspective “, Journal of Retail & Leisure Property, Vol. 1, No. 2, pp. 119–126.10.1057/palgrave.rlp.5090113Search in Google Scholar

20. Duan, W., Gu, B., Whinston, A.B. (2008),” Do online reviews matter? — An empirical investigation of panel data “, Decision Support Systems, Vol. 45, No. 4, pp. 1007–1016.10.1016/j.dss.2008.04.001Search in Google Scholar

21. Feng, G. C. (2017), “The dynamics of the Chinese film industry: factors affecting Chinese audiences’ intentions to see movies“, Asia Pacific Business Review, Vol. 23, No. 5, pp. 658-676.10.1080/13602381.2017.1294353Search in Google Scholar

22. Fu, W.W., Govindaraju, A. (2010), “Explaining global box-office tastes in Hollywood films: Homogenization of national audiences’ movie selections“, Communication Research, Vol. 37, No. 2, pp. 215–238.10.1177/0093650209356396Search in Google Scholar

23. Goczek, Ł., Witkowski, B. (2016), “Determinants of card payments”, Applied Economics, Vol. 48, No. 16, pp. 1530-1543.10.1080/00036846.2015.1102846Search in Google Scholar

24. Gmerek, N. (2015), “The determinants of Polish movies’ box office performance in Poland“, Journal of Marketing and Consumer Behaviour in Emerging Markets, Vol. 1, No. 1, pp. 15–35.10.7172/2449-6634.jmcbem.2015.1.2Search in Google Scholar

25. Hand, C. (2002), “The Distribution and Predictability of Cinema Admissions“, Journal of Cultural Economics, Vol. 26, No. 1, pp. 53–64.10.1023/A:1013389211323Search in Google Scholar

26. Hand, C., Judge, G. (2012), “Searching for the picture: Forecasting UK cinema admissions using Google Trends data“, Applied Economics Letters, Vol. 19, No. 11, pp. 1051–1055.10.1080/13504851.2011.613744Search in Google Scholar

27. Hofmann-Stölting, C., Clement, M., Wu, S., Albers, S. (2017), “Sales forecasting of new entertainment media products”, Journal of Media Economics, Vol. 30, No. 3, pp. 143-171.10.1080/08997764.2018.1452746Search in Google Scholar

28. Jansen, C. (2005), “The performance of German motion pictures, profits and subsidies: Some empirical evidence“, Journal of Cultural Economics, Vol. 29, No. 3, pp. 191-212.10.1007/s10824-005-1157-4Search in Google Scholar

29. Jeffrey, D., Barden, R. R. (2001), “Multivariate models of hotel occupancy performance and their implications for hotel marketing”, International Journal of Tourism Research, Vol. 3, No. 1, pp. 33-44.10.1002/1522-1970(200101/02)3:1<33::AID-JTR291>3.0.CO;2-ISearch in Google Scholar

30. Jones, S.G. (1986),” Trends in the Leisure Industry since the Second World War “, The Service Industries Journal, Vol. 6, No. 3, pp. 330-348.10.1080/02642068600000042Search in Google Scholar

31. Koçaş, C., Akkan, C. (2016), “A system for pricing the sales distribution from blockbusters to the long tail“, Decision Support Systems, Vol. 89, pp. 56-65.10.1016/j.dss.2016.06.008Search in Google Scholar

32. Klinger, T., Lanzendorf, M. (2016), “Moving between mobility cultures: what affects the travel behavior of new residents?”, Transportation, Vol. 43, No. 2, pp. 243-271.10.1007/s11116-014-9574-xSearch in Google Scholar

33. Li, J. (2012), “From “D-Buffs” to the “D-Generation”: Piracy, Cinema, and An Alternative Public Sphere in Urban China“, International Journal of Communication, Vol. 6, pp. 542–563.Search in Google Scholar

34. Litman, B.R. (1983), “Predicting the Success of Theatrical Movies: An Empirical Study“, Journal of Popular Culture, Vol. 16, pp. 159–175.10.1111/j.0022-3840.1983.1604_159.xSearch in Google Scholar

35. Luňáček, J., Feldbabel, V. (2014), “Elasticity of demand of the Czech consumer“, Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Vol. 59, No. 7, pp. 225–236.10.11118/actaun201159070225Search in Google Scholar

36. Machowska, D. (2018), “Investigating the role of customer churn in the optimal allocation of offensive and defensive advertising: The case of the competitive growing market“, Economics and Business Review, Vol. 4, No. 2, pp. 3–23.10.18559/ebr.2018.2.1Search in Google Scholar

37. MacMillan, P., Smith, I. (2001), “Explaining post-war cinema attendance in Great Britain“, Journal of Cultural Economics, Vol. 25, No. 2, pp. 91-108.10.1023/A:1007630400082Search in Google Scholar

38. Makridakis, S., Hogarth, R. M., Gaba, A. (2009), “Forecasting and uncertainty in the economic and business world“, International Journal of Forecasting, Vol. 25, No. 4, pp. 794-812.10.1016/j.ijforecast.2009.05.012Search in Google Scholar

39. Marshall, P., Dockendorff, M., Ibáñez, S. (2013), “A forecasting system for movie attendance“, Journal of Business Research, Vol. 66, pp. 1800–1806.10.1016/j.jbusres.2013.01.013Search in Google Scholar

40. Moore, A. (2017), “Measuring economic uncertainty and its effects“, Economic Record, Vol. 93, No. 303, pp. 550-575.10.1111/1475-4932.12356Search in Google Scholar

41. Nelson, R. A., Donihue, M. R., Waldman, D. M., Wheaton C. (2001), “What’s an Oscar worth?“, Economic Inquiry, Vol. 39, No. 1, pp. 1-16.10.1093/ei/39.1.1Search in Google Scholar

42. Pautz, M. C. (2002), “The decline in average weekly cinema attendance, 1930-2000“, Issues in political economy, Vol. 11, pp. 1-19.Search in Google Scholar

43. Sharda, R., Delen, D. (2006), “Predicting box-office success of motion pictures with neural networks“, Expert Systems with Applications, Vol. 30, No. 2, pp. 243–254.10.1016/j.eswa.2005.07.018Search in Google Scholar

44. Sisto, A., Zanola, R. (2007), “Cinema and TV: An Empirical Investigation of Italian Consumers“, In Bianchi, M. (Ed.), The Evolution of Consumption: Theories and Practices (Advances in Austrian Economics, Volume 10), Emerald Group Publishing Limited, pp.139 – 154.10.1016/S1529-2134(07)10006-5Search in Google Scholar

45. Sztaudynger, M. (2018), “Macroeconomic Factors and Consumer Loan Repayment”, Gospodarka Narodowa, Vol. 296, No. 4, pp. 155-177.10.33119/GN/102228Search in Google Scholar

46. Treme, J., VanDerPloeg, Z. (2014), “The twitter effect: Social media usage as a contributor to movie success”, Economics Bulletin, Vol. 34, No. 2, pp. 793-809.Search in Google Scholar

47. Treme, J., Craig, L.A., Copland, A. (2018), “Gender and box office performance“, Applied Economics Letters, Vol. 34, No. 4, pp. 1–5.Search in Google Scholar

48. Walls, W.D. (2005), “Modeling Movie Success when ‘Nobody Knows Anything’: Conditional Stable-Distribution Analysis of Film Returns“, Journal of Cultural Economics, Vol. 29, pp. 177–190.10.1007/s10824-005-1156-5Search in Google Scholar

49. Wayne, M.L. (2018), “Netflix, Amazon, and branded television content in subscription video on-demand portals“, Media, Culture & Society, Vol. 40, No. 5, pp. 725-741.10.1177/0163443717736118Search in Google Scholar

50. Weziak-Bialowolska, D., Białowolski, P., Sacco, P. (2018), “Involvement With the Arts and Participation in Cultural Events-Does Personality Moderate Impact on Well-Being? Evidence from the U.K. Household Panel Survey“, Psychology of Aesthetics, Creativity, and the Arts, Vol. 13, No. 3, pp. 348-358.10.1037/aca0000180Search in Google Scholar

51. Wu, Y., Huang, W., Lu, Y., Liu, J. (2018), “Box office forecasting for a cinema with movie and cinema attributes”, in IEEE 3rd International Conference on Cloud Computing and Big Data Analysis (ICCCBDA), IEEE, pp. 385-389.10.1109/ICCCBDA.2018.8386547Search in Google Scholar

52. Yang, Z., Cai, J. (2016), “Do regional factors matter? Determinants of hotel industry performance in China“, Tourism Management, Vol. 52, pp. 242-253.10.1016/j.tourman.2015.06.024Search in Google Scholar

53. Yu, X., Liu, Y., Huang, X., An, A. (2012), “Mining online reviews for predicting sales performance: A case study in the movie domain“, IEEE Transactions on Knowledge and Data engineering, Vol. 24, No. 4, pp. 720-734.10.1109/TKDE.2010.269Search in Google Scholar

54. Yuan, H., Xu, W., Li, Q., Lau, R. (2018). “Topic sentiment mining for sales performance prediction in e-commerce”, Annals of Operations Research, Vol. 270, Issue 1-2, pp. 553-576.Search in Google Scholar