Effects of Celebrity Media Exposure on Box-Office Performance

Treme, Julianne

doi:10.1080/08997761003590457

Cited by 33 publications

(14 citation statements)

References 26 publications

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“…Zufryden (1996) analyzed the effects of advertising and the intensities of the theatre distribution on the success of a film. Treme (2010) analyzed the effects of media exposure of a celebrity in the magazine, "People" (2020). They found that in most cases the media exposure of the celebrity involved in the movie plays a vital role in determining the fate of the film rather than promotional advertisements before the release of the film.…”

Section: Previous Workmentioning

confidence: 99%

A Machine Learning Approach to Predict Movie Revenue Based on Pre-Released Movie Metadata

Mahmud¹,

Shuchi²,

Tawsif³

et al. 2020

Journal of Computer Science

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With the growth of the movie industry, it is becoming increasingly important for the stakeholders to get an idea about the probable profit made by the movie in the box office. In fact, among movies produced between 2000 and 2010 in the United States, only 36% had box office revenues higher than their production budgets, which further highlights the importance of making the right investment decisions. To address this issue, different machine learning algorithms like Logistic Regression, Support Vector Machine (SVM) and Multi Layer Perceptron (MLP) are used in this study to predict the box office return of a movie based on the data available before the release of the movie. The models use 35 movie parameters from 3200 movies as inputs to predict the profit made by a movie and classify the success of a movie from "flop" to "blockbuster" based on the generated revenue. An analysis of different machine learning architectures is also presented in this research. Finally, a system is proposed that produces comparable results with existing researches in this field and it can predict the profit generated by a movie with a "one class away" accuracy of 85.31% without using any sales information.

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Section: Previous Workmentioning

confidence: 99%

A Machine Learning Approach to Predict Movie Revenue Based on Pre-Released Movie Metadata

Mahmud¹,

Shuchi²,

Tawsif³

et al. 2020

Journal of Computer Science

View full text Add to dashboard Cite

show abstract

“…Faulkner and Anderson 1987;Litman and Kohl 1989;Wallace, Seigerman, and Holbrook 1993;Prag and Casavant 1994;Sochay 1994;Sawhney and Eliashberg 1996;Neelamegham and Chintagunta 1999;Basuroy, Chatterjee, and Ravid 2003;Elberse and Eliashberg 2003;Ainslie, Drèze, and Zufryden 2005;Elberse 2007;Hadida 2010;Treme 2010;Karniouchina 2011;Nelson and Glotfelty 2012).…”

Section: Current State Of Researchmentioning

confidence: 99%

“…In the case of MPAs, consumer knowledge is generated through media coverage. Treme (2010) stresses the importance of media coverage of MPAs that is useful to both actors and movie studios.…”

Section: Actor's Media Presencementioning

confidence: 99%

Competencies and success of motion picture actors: a resource-based and competence-based empirical analysis

Wirtz

Mermann

Daiser

2016

Journal of Media Practice

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Motion picture actors (MPA) have to compete in a highly competitive, complex and dynamic environmental setting. However, only little knowledge concerning their success factors is available. The present study addresses this lack of research by using a structural equation model-driven approach, testing and using data from a survey with 566 MPAs. The aim of this approach is to provide insights concerning specific resources and competencies that increase the possibility of successful casting for MPAs. Therefore, we tested six hypotheses and identified significant positive effects on MPAs' success from the following resources and competencies: personal network of contacts, talent agency's competence, actor's social competence, actor's media presence and actor's pricing competence. The empirical findings further illustrate the essential role of the personal network of contacts as a source of MPAs' success and offer advice for MPAs to enable them to actively and strategically develop their careers. Since this is one of the first studies that analyses success factors of MPAs based on a large sample of primary data using a multivariate method, the empirical findings constitute an important theoretical contribution. Furthermore, several practical implications are provided to MPAs. ARTICLE HISTORY

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“…As Couldry (1999, p. 59) observed, cultural studies have given only marginal attention to the practices of people rich in economic and cultural capital, compared with the abundance of material on the cultural and media-related practices of the working class. Thus, instead of studying celebrities as agents and respondents, most research efforts have examined fans' consumption of celebrity (e.g., Gray et al 2007) or the effects of celebrity (e.g., Elliott 2011, Treme 2010). …”

Section: Interviewing Celebritiesmentioning

confidence: 99%

Expanding celebrity studies’ research agenda: theoretical opportunities and methodological challenges in interviewing celebrities

Driessens

2014

Celebrity Studies

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Effects of Celebrity Media Exposure on Box-Office Performance

Cited by 33 publications

References 26 publications

A Machine Learning Approach to Predict Movie Revenue Based on Pre-Released Movie Metadata

A Machine Learning Approach to Predict Movie Revenue Based on Pre-Released Movie Metadata

Competencies and success of motion picture actors: a resource-based and competence-based empirical analysis

Expanding celebrity studies’ research agenda: theoretical opportunities and methodological challenges in interviewing celebrities

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