Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Journal of the Academy of Marketing Science
Published in: Journal of the Academy of Marketing Science 2/2018

02-10-2017 | Original Empirical Research

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

Authors: François A. Carrillat, Renaud Legoux, Allègre L. Hadida

Published in: Journal of the Academy of Marketing Science | Issue 2/2018

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

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.
previous article Understanding the long-term implications of retailer returns in business-to-business relationships
next article Building a multi-category brand: when should distant brand extensions be introduced?

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
Appendix
Available only for authorised users
Footnotes
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.
 
Literature
Adler, M. (1985). Stardom and talent. The American Economic Review, 75(1), 208–212.
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.CrossRef
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.CrossRef
Akerlof, G. A. (1970). The market for 'lemons': Quality uncertainty and the market mechanism. Quarterly Journal of Economics, 84(3), 488–500.CrossRef
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.CrossRef
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.
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.CrossRef
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.CrossRef
Balio, T. (1985). The American film industry. Madison: University of Wisconsin Press.
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.CrossRef
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.CrossRef
Bijmolt, T. H. A., & Pieters, R. G. M. (2001). Meta-analysis in marketing when studies contain multiple measurements. Marketing Letters, 12(2), 157–169.CrossRef
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.CrossRef
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.
Borenstein, M., Hedges, L. V., Higgins, J., & Rothstein, H. R. (2009). Introduction to meta-analysis. Chichester: John Wiley & Sons.CrossRef
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.
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.CrossRef
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.CrossRef
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.CrossRef
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.CrossRef
d'Astous, A., & Touil, N. (1999). Consumer evaluations of movies on the basis of critics judgments. Psychology and Marketing, 16(8), 677–694.CrossRef
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.CrossRef
De Vany, A. (2004). Hollywood economics: How extreme uncertainty shapes the film industry. New York: Routledge.CrossRef
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.CrossRef
Deuchert, E., Adjamah, K., & Florian, P. (2005). For Oscar glory or Oscar money? Journal of Cultural Economics, 29(3), 159–176.CrossRef
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.CrossRef
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.
Eisend, M., & Tarrahi, F. (2014). Meta-analysis selection bias in marketing research. International Journal of Research in Marketing, 31(3), 317–326.CrossRef
Elberse, A. (2007). The power of stars: Do star actors drive the success of movies? Journal of Marketing, 71(4), 102–120.CrossRef
Eliashberg, J., & Sawhney, M. S. (1994). Modeling goes to Hollywood: Predicting individual differences in movie enjoyment. Management Science, 40(9), 1151–1173.CrossRef
Eliashberg, J., & Shugan, S. M. (1997). Film critics: Influencers or predictors? Journal of Marketing, 61(April), 68–78.CrossRef
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.CrossRef
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.CrossRef
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.CrossRef
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.CrossRef
Gazley, A., Clark, G., & Sinha, A. (2011). Understanding preferences for motion pictures. Journal of Business Research, 64(8), 854–861.CrossRef
Hadida, A. L. (2009). Motion picture performance: A review and research agenda. International Journal of Management Reviews, 11(3), 297–335.CrossRef
Hadida, A. L. (2010). Commercial success and artistic recognition of motion picture projects. Journal of Cultural Economics, 34(1), 45–80.CrossRef
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.
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.CrossRef
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.CrossRef
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.CrossRef
Hunter, J. E., & Schmidt, F. L. (2004). Methods of meta-analysis: Correcting error and bias in research findings. Thousand Oaks: Sage Publications.CrossRef
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.CrossRef
Joshi, A. (2015). Movie stars and the volatility of movie revenues. Journal of Medical Economics, 28(4), 246–267.CrossRef
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.CrossRef
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.CrossRef
Kepes, S., Banks, G. C., McDaniel, M., & Whetzel, D. L. (2012). Publication bias in the organizational sciences. Organizational Research Methods, 15(4), 624–662.CrossRef
Kermode, M. (2014). Hatchet job: Love movies, hate critics. London: Pan MacMillan.
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.CrossRef
Knoll, J., & Matthes, J. (2017). The effectiveness of celebrity endorsements: A meta-analysis. Journal of the Academy of Marketing Science, 45(1), 55–75.CrossRef
Konstantopoulos, S. (2011). Fixed effects and variance components estimation in three-level meta-analysis. Research Synthesis Methods, 2(1), 61–76.CrossRef
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.CrossRef
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.CrossRef
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.
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.CrossRef
Lipsey, M. W., & Wilson, D. (2001). Practical meta-analysis. Thousand Oaks: Sage Publications.
Litman, B. R. (1983). Predicting success of theatrical movies: An empirical study. The Journal of Popular Culture, 16(4), 156–175.CrossRef
Liu, Y. (2006). Word of mouth for movies: Its dynamics and impact on box office revenue. Journal of Marketing, 70(3), 74–89.CrossRef
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.CrossRef
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.CrossRef
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.CrossRef
Mukhopadhyay, P. (2008). Theory and methods of survey sampling. New Delhi: PHI Learning Pvt. Ltd..
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.CrossRef
Nelson, P. (1970). Information and consumer behavior. Journal of Political Economy, 78(2), 311–329.CrossRef
Politis, D. N., Romano, J. P., & Wolf, M. (2001). On the asymptotic theory of subsampling. Statistica Sinica, 11(4), 1105–1124.
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.CrossRef
Ravid, S. A. (1999). Information, blockbusters, and stars: A study of the film industry. Journal of Business Research, 72(4), 463–492.
Reddy, S. K., Swaminathan, V., & Motley, C. M. (1998). Exploring the determinants of Broadway show success. Journal of Marketing Research, 35(3), 370–383.CrossRef
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.CrossRef
Rosen, S. (1981). The economics of superstars. American Economic Review, 71(5), 845–858.
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.CrossRef
Rosenthal, R. (1979). The “file drawer problem” and tolerance for null results. Psychological Bulletin, 86(3), 638–641.CrossRef
Sedgwick, J. (1999). The comparative popularity of stars in mid-1930s Britain. Journal of Popular British Cinema, 2, 121–128.
Sood, S., & Drèze, X. (2006). Brand extensions of experiential goods: Movie sequel evaluations. Journal of Consumer Research, 33(3), 352–360.CrossRef
Stanley, T. D., & Doucouliagos, H. (2012). Meta-regression analysis in economics and business (Vol. 5). New York: Routledge.CrossRef
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.
Stigler, G. J., & Becker, G. S. (1977). De gustibus non est disputandum. American Economic Review, 67(2), 76–90.
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.CrossRef
Van den Bulte, C., & Lilien, G. L. (2001). Medical innovation revisited: Social contagion versus marketing effort. American Journal of Sociology, 106(5), 1409–1435.CrossRef
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.CrossRef
Viechtbauer, W. (2010). Conducting meta-analyses in R with the Metafor package. Journal of Statistical Software, 63(3), 1–48.
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.CrossRef
You, Y., Vadakkepatt, G. G., & Joshi, A. (2015). A meta-analysis of electronic word-of-mouth elasticity. Journal of Marketing, 79(2), 19–39.CrossRef
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.CrossRef
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.CrossRef
Metadata
Title
Debates and assumptions about motion picture performance: a meta-analysis
Authors
François A. Carrillat
Renaud Legoux
Allègre L. Hadida
Publication date
02-10-2017
Publisher
Springer US
Published in
Journal of the Academy of Marketing Science / Issue 2/2018
Print ISSN: 0092-0703
Electronic ISSN: 1552-7824
DOI
https://doi.org/10.1007/s11747-017-0561-6

Other articles of this Issue 2/2018

Journal of the Academy of Marketing Science 2/2018 Go to the issue

Original Empirical Research

Building a multi-category brand: when should distant brand extensions be introduced?

Original Empirical Research

An exploratory study of business-to-business online customer reviews: external online professional communities and internal vendor scorecards

Original Empirical Research

The impact of a sales team’s perceived entitativity on customer satisfaction

Original Empirical Research

Cause marketing and customer profitability

Conceptual/Theoretical Paper

Pre-release consumer buzz

Original Empirical Research

Hey big spender! A golden (color) atmospheric effect on tipping behavior