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Do consumer and expert reviews affect the length of time a film is kept on screens in the USA?

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Abstract

We evaluate the effect of critical reviews by consumers and experts on a film’s running time at movie theaters in the USA using survival regression analysis. In addition to the usual expert critics’ reviews, we employ the consumer reviews rating and their affectivity about films as proxies for the consumer influence effect. To provide measures for consumer affectivity, we perform affective computing using mining techniques of sentiment and emotion on consumer reviews. We build a very rich film dataset by collecting information from the Box Office Mojo and the Rotten Tomatoes sites, including all matched films released between 2004 and 2015 that are available on these sites. We find evidences of consumer ratings matter in keeping a film running longer at the theaters, but experts’ ratings have a larger influence on the movie market as a whole. Estimates by genre indicate that the influence of expert reviews on the length of run of widely opening film releases, which include blockbusters, is null, but that their influence on narrowly released films is large. Also, film running times of genres like foreign, drama and action films are greatly influenced by sentiments and emotions spread by consumers through their reviews.

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Fig. 1

Source Authors

Fig. 2

Source authors using data from the Rotten Tomatoes site

Fig. 3

Source Authors using data from the Box Office Mojo and Rotten Tomatoes sites

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Notes

  1. There is no full documentation of Watson tools available, but they employ deep learning techniques and became famous after winning the game of Jeopardy against human beings. https://www.ibm.com/watson/.

  2. We perform an exploratory review in economics, business and computer areas in the main research machine learning, results available with authors.

  3. https://alchemy-language-demo.mybluemix.net/ and https://console.ng.bluemix.net.

  4. We follow Boatwright et al. (2007) in using only the top critics’ reviews instead of all professional reviewers.

  5. We use this year as an average for “the date of broadband release in the U.S.” based on Lee and Chan-Olmsted (2004), who indicate that Internet broadband in USA with DSL technology was developed by the early 1990s, but was not marketed until 1999.

  6. https://www.forbes.com/actors/#5825be4040fe.

  7. Shrum (1991) studying the effects of critical reviews on shows in Edinburgh concludes for different effects in popular and highbrow genres.

  8. To compare the size of potential bias of expert reviews when not controlled for our consumer reviews, check Table 8.

  9. See Table 9 in “Appendix” for examples of consumer critical reviews.

  10. Note that the same results could be interpreted simply as people caring more about the average ratings rather than the actual (written) reviews, and the genres results could be due to spurious correlations since negative sentiments and negative emotions (anger and disgust) being significant and positive signed could support the results as well. However, since for the wide opening genres, where consumption is less connected with expert influence, the consumer rating coefficients became bigger after including sentiments and emotions variables in the models, and for narrow openings we got the opposite, we are sure this is not a spurious correlation. In addition, our sentiment positive and negative are regarding the neutral sentiment omitted in the models, and since the emotions are measured by the presence of a sentiment in a consumer review, in some sense they also can be measured as compared with the absence of emotion.

  11. Even Legoux et al. (2016) control for potential endogeneity of the expert reviews using auxiliary regressions and average consumer critic reviews, but they do not control for consumer review endogeneity.

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Acknowledgements

We thank Ricardo Pires, Patrícia R. Oliveira, Adriana Schor, Marcelo Fantinato, Alexandre Brincalepe Campo and Bruno Faria Freitas for their helpful comments and guidance. All errors are our own.

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Authors and Affiliations

  1. Federal Institute of Paraná, Rua João XXIII, 600, Jd. Dom Bosco, Londrina, PR, 86060-370, Brazil

    Thaís L. D. Souza

  2. University of São Paulo, Av. Prof. Lúcio Martins Rodrigues, s/n, travessas 4 e 5, Cidade Universitária, São Paulo, 05508-020, Brazil

    Marislei Nishijima

  3. Federal University of ABC, Alameda da Universidade, s/n, Bairro Anchieta, São Bernardo do Campo, SP, 09606-045, Brazil

    Ana C. P. Fava

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  1. Thaís L. D. Souza
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Correspondence to Thaís L. D. Souza.

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Appendix

Appendix

See Figs. 4, 5 and 6 and Tables 7, 8 and 9.

Fig. 4
figure 4

Source Plutchik (2001)

Wheel of feelings.

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Fig. 5
figure 5

Distribution of the number of top critic reviews by film

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Fig. 6
figure 6

Source Authors using building dataset

Emotions average over film genre.

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Table 7 Variables used on the mains estimates including controls
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Table 8 Estimates of top critics reviews with and without consumer reviews variables as controls for endogeneity biases
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Table 9 Examples of consumer critical reviews
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Souza, T.L.D., Nishijima, M. & Fava, A.C.P. Do consumer and expert reviews affect the length of time a film is kept on screens in the USA?. J Cult Econ 43, 145–171 (2019). https://doi.org/10.1007/s10824-018-9332-6

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  • DOI: https://doi.org/10.1007/s10824-018-9332-6

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