Debates and assumptions about motion picture performance: a meta-analysis

Abstract

Across the many studies of motion picture box office success, unresolved debates and untested assumptions about the contributing factors persist. Using an accessibility–diagnosticity framework and a meta-analysis of 634 effect sizes from 150 studies, the current article seeks to clarify the relationships of star brand equity and product reviews (from consumers and critics) with box office success. The popularity of stars (market and media appeals) exerts a stronger impact on box office success than their artistic recognition (as per award nominations and wins) at the moment of a movie’s release but not over its extended theatrical run. Whereas the impact of popular stars on box office success decreases over time, the influence of artistically recognized stars remains steady. The findings also identify a dual role for critics, who influence consumers’ movie choice and predict box office performance by merely reflecting moviegoers’ tastes. Finally, this study refutes the assumption that the impact of users’ reviews strengthens over time, relative to critics’ reviews.

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Notes

  1. 1.

    Reviews are often referred to as third-party evaluations (e.g., Basuroy et al. 2006; Chang and Ki 2005; Chen et al. 2012), but we opt for the more direct “product reviews” terminology for simplicity.

  2. 2.

    Although Hofmann et al. (2017) consider this assumption, they obtain different results than we do, as we detail subsequently.

  3. 3.

    According to the between-study movie overlap analysis, few years were oversampled by the primary studies, so not all movies released were covered. Generalizing our results remains an important objective, despite the relatively smaller population compared with other meta-analysis projects.

  4. 4.

    The sampling error for the users’ valence–short-term BO effect size was greater than 50%, but the random effects model still was adequate, because the null hypothesis of distribution homogeneity was rejected (Q-statistic [13]) = 44.2, p < .001).

  5. 5.

    The total number of effect sizes that we could include in this latter model is limited (k = 13; artistic = 6 and media = 7), and only the effects of the continuous parameters and the artistic dimension could be estimated. The full model results are available on request.

  6. 6.

    To do so, we use meta-regressions to estimate which moderators from Model 2 are significant for the market and media dimensions. Then, with Johnson and Huedo-Medina’s (2011) procedure, we “move” the model’s intercept across the values of the significant moderator(s) to compute a series of confidence intervals. Dummy variables for categorical moderators are orthogonally coded (−1; 1).

  7. 7.

    In 1948, federal antitrust law prevented studios from engaging in two previously common practices: signing low-wage creative personnel to long-term contracts and vertically integrating film distribution (Balio 1985).

  8. 8.

    We excluded 2000–2013 to compare the pre- and post-1948 eras without contamination by competing cues, such as users’ reviews, which became far more widespread after 2000. Including this period yields similar results (available on request).

  9. 9.

    We thank an anonymous reviewer for suggesting this analysis.

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Acknowledgements

The authors are thankful to Jan Heide and Ashish Sinha for their helpful comments on a previous version of this article as well as to Raphael Heffron for his assistance with data collection. This research benefited from the financial support of the Research and Knowledge Transfer Office of HEC Montréal, from the Fonds de Recherche du Québec - Société et Culture (FRQSC; grant # 146958), and from a University of Cambridge Judge Business School research grant.

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Appendix

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Table 7 Correlation matrix of variables in the star brand equity analyses
Table 8 Correlation matrix of variables in the product reviews analyses

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Carrillat, F.A., Legoux, R. & Hadida, A.L. Debates and assumptions about motion picture performance: a meta-analysis. J. of the Acad. Mark. Sci. 46, 273–299 (2018). https://doi.org/10.1007/s11747-017-0561-6

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Keywords

  • Cinema
  • Meta-analysis
  • Star brand equity
  • Product reviews
  • Critics’ reviews
  • Users’ reviews
  • Cue diagnosticity