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

Journal of the Academy of Marketing Science volume 46, pages 273–299 (2018)Cite this article

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

  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. 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. 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. 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. 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. 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. We thank an anonymous reviewer for suggesting this analysis.

References

  • Adler, M. (1985). Stardom and talent. The American Economic Review, 75(1), 208–212.

    Google Scholar 

  • Akdeniz, M. B., & Talay, M. B. (2013). Cultural variations in the use of marketing signals: A multilevel analysis of the motion picture industry. Journal of the Academy of Marketing Science, 41(5), 601–624.

    Article  Google Scholar 

  • Akdeniz, M. B., Calantone, R. J., & Voorhees, C. M. (2013). Effectiveness of marketing cues on consumer perceptions of quality: The moderating roles of brand reputation and third-party information. Psychology and Marketing, 30(1), 76–89.

    Article  Google Scholar 

  • Akerlof, G. A. (1970). The market for 'lemons': Quality uncertainty and the market mechanism. Quarterly Journal of Economics, 84(3), 488–500.

    Article  Google Scholar 

  • Albert, S. (1998). Movie stars and the distribution of financially successful films in the motion picture industry. Journal of Cultural Economics, 22(4), 249–270.

    Article  Google Scholar 

  • Anand, N., & Watson, M. R. (2004). Tournament rituals in the evolution of fields: The case of the Grammy awards. Academy of Management Journal, 47(1), 59–80.

    Google Scholar 

  • Babić Rosario, A., Sotgiu, F., Valck, K. D., & Bijmolt, T. H. (2016). The effect of electronic word of mouth on sales: A meta-analytic review of platform, product, and metric factors. Journal of Marketing Research, 53(3), 297–318.

    Article  Google Scholar 

  • Bagella, M., & Becchetti, L. (1999). The determinants of motion picture box office performance: Evidence from movies produced in Italy. Journal of Cultural Economics, 23(4), 237–256.

    Article  Google Scholar 

  • Balio, T. (1985). The American film industry. Madison: University of Wisconsin Press.

    Google Scholar 

  • Basuroy, S., Chatterjee, S., & Ravid, S. A. (2003). How critical are critical reviews? The box office effects of film critics, star power, and budgets. Journal of Marketing, 67(4), 103–117.

    Article  Google Scholar 

  • Basuroy, S., Desai, K. K., & Talukdar, D. (2006). An empirical investigation of signaling in the motion picture industry. Journal of Marketing Research, 43(2), 287–295.

    Article  Google Scholar 

  • Bijmolt, T. H. A., & Pieters, R. G. M. (2001). Meta-analysis in marketing when studies contain multiple measurements. Marketing Letters, 12(2), 157–169.

    Article  Google Scholar 

  • Boatwright, P., Basuroy, S., & Kamakura, W. (2007). Reviewing the reviewers: The impact of individual film critics on box-office performance. Quantitative Marketing and Economics, 5(4), 401–425.

    Article  Google Scholar 

  • Borenstein, M. (1994). Effect size for continuous data. In H. Cooper, L. V. Hedges, & J. Valentine (Eds.), The handbook of research synthesis and meta-analysis. New York: Russel Edge Foundation.

    Google Scholar 

  • Borenstein, M., Hedges, L. V., Higgins, J., & Rothstein, H. R. (2009). Introduction to meta-analysis. Chichester: John Wiley & Sons.

    Book  Google Scholar 

  • Brightlocal. (2016). Local consumer review survey. Retrieved from https://www.brightlocal.com/learn/local-consumer-review-survey. Accessed 19 June 2017.

  • Brunet, J., Legoux, R., & Najar, S. (2008). Controlled and uncontrolled buzz as a marketing strategy in the film industry: The effectiveness of the junkets. Paper presented at the meeting Cultural Production in a Global Context: The Worldwide Film Industries, Copenhagen.

  • Bughin, J., Doogan, J., & Vetvik, O. J. (2010). A new way to measure word-of-mouth marketing. The McKinsey Quarterly, 2, 113–116.

    Google Scholar 

  • Chakravarty, A., Liu, Y., & Mazumdar, T. (2010). The differential effects of online word-of-mouth and critics reviews on pre-release movie evaluation. Journal of Interactive Marketing, 24(3), 185–197.

    Article  Google Scholar 

  • Chang, B.-H., & Ki, E.-J. (2005). Devising a practical model for predicting theatrical movie success: Focusing on the experience good property. Journal of Medical Economics, 18(4), 247–269.

    Article  Google Scholar 

  • Chen, Y., Liu, Y., & Zhang, J. (2012). When do third-party product reviews affect firm value and what can firms do? The case of media critics and professional movie reviews. Journal of Marketing, 76(2), 116–134.

    Article  Google Scholar 

  • Chisholm, D. C., Fernandez-Blanco, V., Ravid, S. A., & Walls, W. D. (2015). Economics of motion pictures: The state of the art. Journal of Cultural Economics, 39(1), 1–13.

    Article  Google Scholar 

  • d'Astous, A., & Touil, N. (1999). Consumer evaluations of movies on the basis of critics judgments. Psychology and Marketing, 16(8), 677–694.

    Article  Google Scholar 

  • De Matos, C. A., & Rossi, C. A. V. (2008). Word-of-mouth communications in marketing: A meta-analytic review of the antecedents and moderators. Journal of the Academy of Marketing Science, 36(4), 578–596.

    Article  Google Scholar 

  • De Vany, A. (2004). Hollywood economics: How extreme uncertainty shapes the film industry. New York: Routledge.

    Book  Google Scholar 

  • Desai, K. K., & Basuroy, S. (2005). Interactive influence of genre familiarity, star power, and critics reviews in the cultural goods industry: The case of motion pictures. Psychology and Marketing, 22(3), 203–223.

    Article  Google Scholar 

  • Deuchert, E., Adjamah, K., & Florian, P. (2005). For Oscar glory or Oscar money? Journal of Cultural Economics, 29(3), 159–176.

    Article  Google Scholar 

  • 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 

  • Duval, S., & Tweedie, R. (2000). A nonparametric “trim and fill” method of accounting for publication bias in meta-analysis. Journal of the American Statistical Association, 95(449), 89–98.

    Google Scholar 

  • Eisend, M., & Tarrahi, F. (2014). Meta-analysis selection bias in marketing research. International Journal of Research in Marketing, 31(3), 317–326.

    Article  Google Scholar 

  • Elberse, A. (2007). The power of stars: Do star actors drive the success of movies? Journal of Marketing, 71(4), 102–120.

    Article  Google Scholar 

  • Eliashberg, J., & Sawhney, M. S. (1994). Modeling goes to Hollywood: Predicting individual differences in movie enjoyment. Management Science, 40(9), 1151–1173.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Fátharta, C. Ó. (2016). Kevin Thornton's restaurant to close after loss of Michelin star. Irish examiner. Retrieved from http://www.irishexaminer.com/ireland/kevin-thorntons-restaurant-to-close-after-loss-of-michelin-star-418956.html. Accessed 10 April 2017.

  • Feldman, J. M., & Lynch, J. G. (1988). Self-generated validity and other effects of measurement on belief, attitude, intention, and behavior. Journal of Applied Psychology, 73(3), 421–435.

    Article  Google Scholar 

  • Ferguson, C. J., & Brannick, M. T. (2012). Publication bias in psychological science: Prevalence, methods for identifying and controlling, and implications for the use of meta-analyses. Psychological Methods, 17(1), 120.

    Article  Google Scholar 

  • Flanagin, A. J., & Metzger, M. J. (2013). Trusting expert-versus user-generated ratings online: The role of information volume, valence, and consumer characteristics. Computers in Human Behavior, 29(4), 1626–1634.

    Article  Google Scholar 

  • Floyd, K., Freling, R., Alhoqail, S., Cho, H. Y., & Freling, T. (2014). How online product reviews affect retail sales: A meta-analysis. Journal of Retailing, 90(2), 217–232.

    Article  Google Scholar 

  • Gazley, A., Clark, G., & Sinha, A. (2011). Understanding preferences for motion pictures. Journal of Business Research, 64(8), 854–861.

    Article  Google Scholar 

  • Hadida, A. L. (2009). Motion picture performance: A review and research agenda. International Journal of Management Reviews, 11(3), 297–335.

    Article  Google Scholar 

  • Hadida, A. L. (2010). Commercial success and artistic recognition of motion picture projects. Journal of Cultural Economics, 34(1), 45–80.

    Article  Google Scholar 

  • Hennig-Thurau, T., Walsh, G., & Wruck, O. (2001). An investigation of the factors determining the success of service innovations: The case of motion pictures. Academy of Marketing Science Review, 2001(6), 1–23.

    Google Scholar 

  • Hennig-Thurau, T., Wiertz, C., & Feldhaus, F. (2015). Does twitter matter? The impact of microblogging word of mouth on consumers adoption of new movies. Journal of the Academy of Marketing Science, 43(3), 375–394.

    Article  Google Scholar 

  • Hofmann, J., Clement, M., Völckner, F., & Hennig-Thurau, T. (2017). Empirical generalizations on the impact of stars on the economic success of movies. International Journal of Research in Marketing, 34(2), 442–461.

    Article  Google Scholar 

  • Holbrook, M. B., & Addis, M. (2007). Taste versus the market: An extension of research on the consumption of popular culture. Journal of Consumer Research, 34, 415–424.

    Article  Google Scholar 

  • Hunter, J. E., & Schmidt, F. L. (2004). Methods of meta-analysis: Correcting error and bias in research findings. Thousand Oaks: Sage Publications.

    Book  Google Scholar 

  • Internet Movie Database (2016). Advanced title search. Available at http://www.imdb.com/search/title.

  • Johnson, B. T., & Huedo-Medina, T. B. (2011). Depicting estimates using the intercept in meta-regression models: The moving constant technique. Research Synthesis Methods, 2(3), 204–220.

    Article  Google Scholar 

  • Johnston, N. (2009). IMDb movie ratings over the years. Retrieved from http://www.njohnston.ca/2009/10/imdb-movie-ratings-over-the-years/. Accessed 11 March 2017.

  • Joshi, A. (2015). Movie stars and the volatility of movie revenues. Journal of Medical Economics, 28(4), 246–267.

    Article  Google Scholar 

  • Joshi, A. M., & Hanssens, D. M. (2009). Movie advertising and the stock market valuation of studios: A case of great expectations? Marketing Science, 28(2), 239–250.

    Article  Google Scholar 

  • Joshi, A., & Mao, H. (2012). Adapting to succeed? Leveraging the brand equity of best sellers to succeed at the box office. Journal of the Academy of Marketing Science, 40(4), 558–571.

    Article  Google Scholar 

  • Kepes, S., Banks, G. C., McDaniel, M., & Whetzel, D. L. (2012). Publication bias in the organizational sciences. Organizational Research Methods, 15(4), 624–662.

    Article  Google Scholar 

  • Kermode, M. (2014). Hatchet job: Love movies, hate critics. London: Pan MacMillan.

    Google Scholar 

  • Knapp, A. K., Hennig-Thurau, T., & Mathys, J. (2014). The importance of reciprocal spillover effects for the valuation of bestseller brands: Introducing and testing a contingency model. Journal of the Academy of Marketing Science, 42(2), 205–221.

    Article  Google Scholar 

  • Knoll, J., & Matthes, J. (2017). The effectiveness of celebrity endorsements: A meta-analysis. Journal of the Academy of Marketing Science, 45(1), 55–75.

    Article  Google Scholar 

  • Konstantopoulos, S. (2011). Fixed effects and variance components estimation in three-level meta-analysis. Research Synthesis Methods, 2(1), 61–76.

    Article  Google Scholar 

  • Langan, R., Besharat, A., & Varki, S. (2017). The effect of review valence and variance on product evaluations: An examination of intrinsic and extrinsic cues. International Journal of Research in Marketing, 34(2), 414–429.

    Article  Google Scholar 

  • Legoux, R., Larocque, D., Laporte, S., Belmati, S., & Boquet, T. (2016). The effect of critical reviews on exhibitors' decisions: Do reviews affect the survival of a movie on screen? International Journal of Research in Marketing, 33(2), 357–374.

    Article  Google Scholar 

  • Levin, A. M., Levin, I. P., & Heath, C. E. (1997). Movie stars and authors as brand names: Measuring brand equity in experiential products. Advances in Consumer Research, 24(1), 175–181.

    Google Scholar 

  • Lin, D.-Y., & Sullivan, P. F. (2009). Meta-analysis of genome-wide association studies with overlapping subjects. American Journal of Human Genetics, 85, 862–872.

    Article  Google Scholar 

  • Lipsey, M. W., & Wilson, D. (2001). Practical meta-analysis. Thousand Oaks: Sage Publications.

    Google Scholar 

  • Litman, B. R. (1983). Predicting success of theatrical movies: An empirical study. The Journal of Popular Culture, 16(4), 156–175.

    Article  Google Scholar 

  • 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 

  • Liu, A., Liu, Y., & Mazumdar, T. (2014). Star power in the eye of the beholder: A study of the influence of stars in the movie industry. Marketing Letters, 25(4), 385–396.

    Article  Google Scholar 

  • Luo, L., Chen, X., Han, J., & Whan Park, C. (2010). Dilution and enhancement of celebrity brands through sequential movie releases. Journal of Marketing Research, 47(6), 1114–1128.

    Article  Google Scholar 

  • Lynch, J. G., Marmorstein, H., & Weigold, M. F. (1988). Choices from sets including remembered brands: Use of recalled attributes and prior overall evaluations. Journal of Consumer Research, 15(2), 169–184.

    Article  Google Scholar 

  • Marich, R. (2014). Movie star power dims as actors become hucksters. Huffington post. Retrieved from http://www.huffingtonpost.com/robert-marich/movie-star-power-dims-as-_b_5556322.html. Accessed 27 March 2017.

  • McClintock, P. & Galloway, S. (2017). Matt Damon’s ‘the Great Wall’ to lose $75 million; future U.S.-China productions in doubt. Hollywood Reporter. Retrieved from http://www.hollywoodreporter.com/news/what-great-walls-box-office-flop-will-cost-studios-981602. Accessed 2 April 2017.

  • Merry, S. (2015). Are A-list actors a waste of money? Sydney morning herald. Retrieved from http://www.smh.com.au/entertainment/movies/are-alist-actors-a-waste-of-money-20151125-gl7f0i.html. Accessed 7 Aug 2016.

  • Mukhopadhyay, P. (2008). Theory and methods of survey sampling. New Delhi: PHI Learning Pvt. Ltd..

    Google Scholar 

  • Munder, T., Brütsch, O., Leonhart, R., Gerger, H., & Barth, J. (2013). Researcher allegiance in psychotherapy outcome research: An overview of reviews. Clinical Psychology Review, 33(4), 501–511.

    Article  Google Scholar 

  • Nelson, P. (1970). Information and consumer behavior. Journal of Political Economy, 78(2), 311–329.

    Article  Google Scholar 

  • Nielsen (2015). Global trust in advertising. Retrieved from http://www.nielsen.com/us/en/insights/reports/2015/global-trust-in-advertising-2015.html]. Accessed 7 Aug 2016.

  • Patten, D. (2017). Oscar ratings down double digits in demo; 32.9m Viewership hits 9-year low. Deadline Hollywood. Retrieved from http://deadline.com/2017/02/oscars-ratings-2017-down-best-picture-mistake-jimmy-kimmel-abc-1202009034/. Accessed 31 March 2017.

  • Perry, K., & Hyde, D. (2014). Celebrity chefs' restaurants among most overpriced and worst food. The telegraph. Retrieved from http://www.telegraph.co.uk/foodanddrink/11272075/Celebrity-chefs-restaurants-among-most-overpriced-and-worst-food.html. Accessed 10 Apr 2017.

  • Politis, D. N., Romano, J. P., & Wolf, M. (2001). On the asymptotic theory of subsampling. Statistica Sinica, 11(4), 1105–1124.

    Google Scholar 

  • Proserpio, D., & Zervas, G. (2017). Online reputation management: Estimating the impact of management responses on consumer reviews. Marketing Science, 1–21. https://doi.org/10.1287/mksc.2017.1043.

  • Purohit, D., & Srivastava, J. (2001). Effect of manufacturer reputation, retailer reputation, and product warranty on consumer judgments of product quality: A cue diagnosticity framework. Journal of Consumer Psychology, 10(3), 123–134.

    Article  Google Scholar 

  • PwC (2015). Box office resilience underscores the continuing popularity of the cinematic experience. Retrieved from: http://www.pwc.com/gx/en/global-entertainment-media-outlook/segment-insights/filmed-entertainment.jhtml]. Accessed 7 Aug 2016.

  • Ravid, S. A. (1999). Information, blockbusters, and stars: A study of the film industry. Journal of Business Research, 72(4), 463–492.

    Google Scholar 

  • Reddy, S. K., Swaminathan, V., & Motley, C. M. (1998). Exploring the determinants of Broadway show success. Journal of Marketing Research, 35(3), 370–383.

    Article  Google Scholar 

  • Reinstein, D. A., & Snyder, C. M. (2005). The influence of expert reviews on consumer demand for experience goods: A case study of movie critics. Journal of Industrial Economics, 53(1), 27–51.

    Article  Google Scholar 

  • Rosen, S. (1981). The economics of superstars. American Economic Review, 71(5), 845–858.

    Google Scholar 

  • Rosenberg, M. S. (2005). The file-drawer problem revisited: A general weighted method for calculating fail-safe numbers in meta-analysis. Evolution, 59(2), 464–468.

    Article  Google Scholar 

  • Rosenthal, R. (1979). The “file drawer problem” and tolerance for null results. Psychological Bulletin, 86(3), 638–641.

    Article  Google Scholar 

  • Sedgwick, J. (1999). The comparative popularity of stars in mid-1930s Britain. Journal of Popular British Cinema, 2, 121–128.

    Google Scholar 

  • Sifferlin, A. (2016). Food endorsed by celebrities are almost all terrible for you. Retrived from: http://time.com/4358156/celebrity-endorsements-food-soda-drink/. Accessed 8 June 2017.

  • Song, R., Moon, S., Chen, H. A., & Houston, M. B. (2017). When marketing strategy meets culture: The role of culture in product evaluations. Journal of the Academy of Marketing Science. https://doi.org/10.1007/s11747-017-0525-x1-19.

  • Sood, S., & Drèze, X. (2006). Brand extensions of experiential goods: Movie sequel evaluations. Journal of Consumer Research, 33(3), 352–360.

    Article  Google Scholar 

  • Stanley, T. D., & Doucouliagos, H. (2012). Meta-regression analysis in economics and business (Vol. 5). New York: Routledge.

    Book  Google Scholar 

  • Sterne, J. A., & Egger, M. (2005). Regression methods to detect publication and other bias in meta-analysis. In A. R. Rothstein, A. J. Sutton, & M. Borenstein (Eds.), Publication bias in meta-analysis: Prevention, assessment, and adjustments. New York: Wiley.

    Google Scholar 

  • Stigler, G. J., & Becker, G. S. (1977). De gustibus non est disputandum. American Economic Review, 67(2), 76–90.

    Google Scholar 

  • Stockdale, D. (2015). 10 worst products endorsed by celebrities. Retrieved from http://www.twcc.com/articles/2015/12/28/1/10-worst-products-endorsed-by-celebrities.3. Accessed 8 June 2017.

  • Suárez-Vázquez, A. (2011). Critic power or star power? The influence of hallmarks of quality of motion pictures: An experimental approach. Journal of Cultural Economics, 35(2), 119–135.

    Article  Google Scholar 

  • The Economist (2016). Fading stars: Hollywood studios can no longer bank on the pulling-power of famous actors. Retrieved from http://www.economist.com/news/business/21693591-hollywood-studios-can-no-longer-bank-pulling-power-famous-actors-fading-stars]. Accessed 8 Aug 2016.

  • Van den Bulte, C., & Lilien, G. L. (2001). Medical innovation revisited: Social contagion versus marketing effort. American Journal of Sociology, 106(5), 1409–1435.

    Article  Google Scholar 

  • Van den Noortgate, W., López-López, J. A., Marín-Martínez, F., & Sánchez-Meca, J. (2015). Meta-analysis of multiple outcomes: A multilevel approach. Behavior Research Methods, 47(4), 1274–1294.

    Article  Google Scholar 

  • Viechtbauer, W. (2010). Conducting meta-analyses in R with the Metafor package. Journal of Statistical Software, 63(3), 1–48.

    Google Scholar 

  • Watson IV, G. F., Beck, J. T., Henderson, C. M., & Palmatier, R. W. (2015). Building, measuring, and profiting from customer loyalty. Journal of the Academy of Marketing Science, 43(6), 790–825.

    Article  Google Scholar 

  • You, Y., Vadakkepatt, G. G., & Joshi, A. (2015). A meta-analysis of electronic word-of-mouth elasticity. Journal of Marketing, 79(2), 19–39.

    Article  Google Scholar 

  • Zhang, X. & Dellarocas, C. (2006). The lord of the ratings: Is a movie's fate is influenced by reviews? Proceedings of the International Conference on Information Systems, Milwaukee.

  • Zuckerman, E. W., & Kim, T. Y. (2003). The critical trade-off: Identity assignment and box-office success in the feature film industry. Industrial and Corporate Change, 12(1), 27–67.

    Article  Google Scholar 

  • Zuckerman, E. W., Kim, T. Y., Ukanwa, K., & Rittmann, J. V. (2003). Robust identities or nonentities? Typecasting in the feature-film labor market. American Journal of Sociology, 108(5), 1018–1073.

    Article  Google Scholar 

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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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  1. University of Technology Sydney, Business School, Ultimo NSW, 2007, Australia

    François A. Carrillat

  2. HEC Montréal, 3000, Chemin de la Cote-Sainte-Catherine, Montréal, QC, H3T 2A7, Canada

    Renaud Legoux

  3. University of Cambridge, Trumpington Street, Cambridge, CB2 1AG, UK

    Allègre L. Hadida

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  1. François A. Carrillat
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Appendix

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