Star power in the eye of the beholder: A studyof the influence of stars in the movie industry

Angela Liu & Yong Liu & Tridib Mazumdar

# Springer Science+Business Media New York 2013

Abstract Organizations employ various risk-mitigation strategies to cope with theuncertainty in marketing new products. In the motion picture industry, an importantstrategy is to cast star actors and actresses in movies. The ultimate box-office success,however, depends on multiple stakeholders involved with financing, making, distribut-ing, and watching the movie. In pursuing different goals and interests, the stakeholdersmay look for different aspects of star power to mitigate their own risk. This paperexamines how the influence of stars varies across key stakeholders in the movie market.The results show that, in general, stars have a greater impact on the stakeholdersinvolved in the earlier stages of movie development and exhibition (where the risksare greater) than on those in later stages. Movie project financiers and exhibitors arestrongly and directly influenced by star power, but news media and movie audiences areinfluenced less and only indirectly. Situated at the early stage of the movie “valuechain,” the financiers are most concerned with stars’ past box-office performance.Exhibitors, however, are influenced by the “match” between a star’s genre participationhistory and the genre of a specific movie. By contrast, news media and movie audiencesare influenced indirectly through the stars’ impact on earlier stakeholders and theirdecisions. The findings shed light on the value of employing star elements in newproducts, the marketing of stars, and movie promotion strategies.

Keywords Entertainment marketing . Movies . Movie financing . New productstrategies . Risk mitigation . Stars . Star power

Mark LettDOI 10.1007/s11002-013-9258-x

A. LiuSchool of Economics and Management, Tsinghua University, Beijing, Chinae-mail: liux@sem.tsinghua.edu.cn

Y. Liu (*)Eller College of Management, University of Arizona, Tucson, AZ 85721, USAe-mail: yoliu@eller.arizona.edu

T. MazumdarWhitman School of Management, Syracuse University, Syracuse, NY 13244, USAe-mail: tmazumdar@syr.edu

1 Introduction

The phenomenon of stars and star power is ubiquitous across industries (Adler 1985).In the movie business, the roles and effects of star actors and actresses have beencontroversial. Stars are thought to be a critical element of movie success. Studiosconstantly seek the involvement of stars in movie production and promotion. How-ever, the stars’ astronomical earnings often raise the concern whether they trulydeserve the income and to what extent they contribute to a movie’s success.1

In studying the effect of movie stars, past research has focused on their direct impacton box office revenue (and related financial metrics). Mixed results were reported. DeVany and Walls (1999) show stars do not influence box office rankings. Wallace et al.(1993) find certain but not all stars are capable of affecting the revenue that goes to thestudio. Ravid (1999) finds that stars do not contribute to return-on-investments of movieprojects. Elberse (2007) shows the announcements of stars joining or leaving the castinfluence expected box office revenue. Karniouchina (2011) finds that stars boost boxoffice revenue during the opening week.

The approach of assessing stars’ direct impact on sales does not accommodate theimportant reality that multiple stakeholders are involved in the movie business, and starscould exert different influences on them. Taking a movie from script to production, andcontinuing to release, is a complex value chain involving multiple participants that playdifferent roles. For instance, movie financiers directly influence the production budgetand whether a movie can be made in the first place (Vogel 2007). Exhibitors make thestrategic decision about whichmovie to screen in theaters (Elberse and Eliashberg 2003;Krider et al. 2005). News media is a critical element of the movie market and reports onstars and movies for their readers (Moul and Shugan 2005). Finally, movie audiencesdecide whether to see a movie. They may also influence others through word of mouth(Chakravarty et al. 2010; Liu 2006).

There is anecdotal evidence that stars possess different attributes and the stakeholdersmay view them from different perspectives. Some stars are recruited mainly for theirability to secure financing. In industry terms, they help the movie project get “green-lighted” (Vogel 2007). Meanwhile, some stars are sought after because of their boxoffice drawing power (Vogel 2007) or ability to help obtain wide opening (De Vany andWalls 1999).

In this paper, we extend the literature on movies and star power to investigate theinfluence of stars on different stakeholders. The financers, exhibitors, news media, andaudiences all play important but distinct roles in movie development and exhibition.Together their actions ultimately determine movie success. Therefore, the roles of starscan be better understood only when their influences on these stakeholders are assessed.To our knowledge, this is the first paper that analyzes the differential effects on majorstakeholders when studying the star phenomenon. Departing from previous studies, weutilize a riskmanagement framework to capture the critical element of uncertainty and toconceptualize how distinct uncertainty situations may lead the stakeholders to focus ondifferent aspects of stars.

Although our study is situated in movies, the star phenomenon is general. Thestructure of our study can be applied beyond creative industries. The findings shed lights

1 For illustration, see the series of reports in Forbes (8/6/2007, 6/12/2002, 6/19/2003, 5/17/2004).

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on the broad issue of how the quality of labor inputs affect multiple decision makersinvolved at different stages of marketing a product.

2 Conceptual background

The development and distribution of new products often involve significant risks. Thetask of risk control becomes particularly complex when multiple stakeholders areinvolved. Figure 1 illustrates the four key stakeholders in movies, each looking to starsas a way to ensure positive outcomes. Considering that these stakeholders are involvedat different stages of movie development and distribution, the risks facing each of themare different. Therefore, they likely will focus on different attributes of a star. In otherwords, rather than simply looking for a “star” versus a non-star, they will benefit fromcomparing the strengths and weaknesses of different stars and look for the right star.This is the key premise of our study.

In measuring what is the “right” star, we need to distinguish the key components ofstar power. We follow Elberse (2007) to separate out commercial success versus artisticsuccess. In addition, we examine another star-related variable that is well recognized inthe industry but remains largely unstudied—the genre “match” between a star’s movieparticipation history and the particular movie. A long-held Hollywood belief is that, tobe successful, a star must find the right vehicle, i.e., starring in the most familiar rolesthat the audiences accept (Wyatt 1994). Numerous examples show how things can gowrong when an actor/actress steps out of his/her genre. For instance, Bill Murray attractsaudience in comedies likeGhostbusters but not in a somber drama like The Razor’s Edge(Wyatt 1994).

To obtain investment, a movie project is usually outlined in terms of cast, story lines,director, producer, and an estimated budget (Vogel 2007). At the financing stage,demand signals such as critic reviews are unavailable, and the participation of starsprovides a useful quality signal to potential investors. The financiers should payattention to all star-related signals for sound investment decisions and, because of the

Star’s Impacts on Different Stakeholders

Financiers: investment decision

(Production Budget)

Exhibitors: screening decision

(Opening Theaters) Movie audiences: movie watching decision

(Box Office Revenues)News media: news coverage decision

(New Reports)

Time Line

StarDifferences among stars: box office success, artistic

success, genre fit with the focal movie

Fig. 1 Impacts of star on stakeholders in the movie industry: an integrated framework

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need of reducing financial risk, star attributes that provide the greatest assurance offinancial return would be the most relevant. Therefore, box office history, a directpredictor of future financial success, should be the most influential component of starson financiers.

Movie exhibitors, like the financiers, have financial stake in the movie because signingup a “wrong”movie misses the opportunity of screening a better one.When the exhibitorsenter into screening contracts with distributors, a star cast is an important signal of moviequality and demand. However, in the sequence of decisions from financing and produc-tion to exhibition, a downstream stakeholder could benefit from the decisions made bytheir predecessors. The exhibitors will have more ex post information such as productionbudget to rely on. This implies the impact of stars is likely to be less on exhibitors than onfinanciers. Moreover, the sequential nature of the decisions indicates that there will be anindirect impact of stars on a later stakeholder (e.g., the theaters) through their impact onearlier ones (e.g., financiers). In the empirical analysis, we capture the sequential decisionsand discuss both direct and indirect effects of stars on stakeholders that appear later in themovie value chain.

Stars generate free promotion for a movie as the newsmedia often reports on particularmovies and stars around movie release time. As Moul and Shugan (2005) indicate, suchpublicity, or “free ink,” is a cost-effective tool that generates public awareness andinterests for the movie. The media operates in a typical two-side market where itgenerates advertising (and subscription) revenue by attracting readers (Schmalenseeand Evans 2005). As stars generate buzz and gossips, the media is more likely to covermovies participated by stars than those without stars (Karniouchina 2011). However, formovies with stars, the media will be more interested if the star has greater box officesuccess and stronger artistic recognition such as winning the Oscar, because thesenewsworthy records are more likely to attract readers. Genre match between a star andthe movie may or may not generate media buzz as both a match and a mismatch could benewsworthy. Finally, as a downstream stakeholder, the impact of stars on news mediacould exhibit indirectly through the stars’ impact on earlier stakeholders, namely finan-ciers and exhibitors.

The audiences eventually decide whether to watch the movie. As noted earlier,previous studies have examined the direct impact of stars on movie audiences butproduced mixed findings. Thus, we leave the direct impact of stars on the audiencesas an empirical question. However, it is possible to make an unambiguous prediction—given that audiences are at the end of the value chain, there will be indirect effects of starpower through its impact on earlier stakeholders and their decisions. For instance, thenumber of theaters has strong influence on the audiences (Elberse and Eliashberg 2003;Krider et al. 2005). To the extent that the exhibitors are influenced by star power, thatinfluence will indirectly affect the audiences through screening.

3 Data and model

Our sample includesmovies released between 2000 and 2005 in the USA. It is importantto note that our objective is not to investigate the first-order effects of having a starversus not having one, which has been investigated by previous studies. Our focusinstead is on how different dimensions of a “star” influence different stakeholders.

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Therefore, the sample needs to include movies participated by stars who are measurablydifferent from each other in these dimensions. An additional benefit of examiningmovies with stars is to eliminate the potential endogeneity in movie project selection:stars, in comparison with non-stars, could have privileged access to better movie scriptsand projects simply because of their status as “star.”

We follow the literature to identify a sample of stars from the Power 100 List ofPremiere (De Vany andWalls 1999; Liu 2006). To avoid ambiguity inmeasuring the starpower dimensions for each star and eachmovie, we focus on those movies featuring onestar. A total of 203 movies are obtained after excluding 12 that have missing box officedata. Movie-specific information is gathered from IMDB.com, the-numbers.com, andboxofficemojo.com. For each star, we collected the movie participation and awardhistory.

3.1 Measures of star impact on stakeholders

The impact of stars needs to be measured for movie project financiers, exhibitors, newsmedia, and the audiences. Measuring a star’s ability to attract financing is relativelystraightforward. We use the movie’s final production budget (Budget) to measure theoutcomes of financing. Everything else equal, stars with greater financing power shouldenable the movie to receive a larger budget.

We use the number of theaters in the opening week (Theater) to measure the impactof stars on movie exhibition. This is consistent with the notion that an important role ofstars is the “opening power” (De Vany and Walls 1999).

For the star impact on news media, we use the number of news reports (News) thatwere published for a specific movie. The data were collected from Newslibrary.com,which pools news articles from 2,169 major newspapers and media across the USA. Wegathered the news reports during the 1-month before movie release.

Lastly, we use the opening week box-office sales to examine star impact on the movieaudiences. Opening week sales are an important proportion and key driver of total sales(Krider et al. 2005). Moreover, restricting the audience and exhibitor measures to theopening week mitigates the potential audience-affecting-screens endogeneity. We adjustfor inflation by using the number of audiences instead of revenue, and take logtransformation to deal with the well-known skewness.

3.2 Measures of star power components

Following the literature (Elberse 2007), we use the average box office revenue of a star’smost recent five movies to measure commercial success (Star_Box), and the totalnumber of Oscar nominations and awards that the star had received before a particularmovie to measure artistic success (Star_Art). Star-genre match (Star_Genre) is bydefinition star-movie specific. It is computed as the percentage of times a star acted inthe particular genre in his/her most recent five movies.

Figure 2 presents the stars in a BOXHIST–OSCAR plot. There is no significantcorrelation between box office and Oscar history (p>0.27), supporting the use of them asseparate star power components. The 15 stars who received no Oscar nominations orAwards differ significantly in box office history, with Jim Carey, Tobey Maguire, andMike Myers at the top and Jennifer Lopes, Collin Farrell, and Steve Martin near the

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bottom. Conversely, TomHanks is high on both BOXHISTand OSCARwith five Oscarnominations/Awards, while Denzel Washington, also with five nominations/Awards,has less than half of the box office success as TomHanks. Star_Genre does not correlatewith either Star_Box or Star_Art.

3.3 Model specification

We control for several variables that could affect the dependent variables. These includethe source of movie script (Book, RealLife, TV, Comic, and Remake), movie genre(Adventure, Drama, Comedy, Thriller, versus Action), MPAA ratings, seasonality (Sum-mer andHoliday), and whether the movie is a sequel (SEQUEL). Following the literature(Basuroy et al. 2006), we distinguish R-rated movies (R) from the rest. To control for theinfluence of directors, we again consult the Power 100 List and use dummy variableDirector to indicate whether a powerful director is involved. We measure critic ratingusing metascore (Metarating) obtained from metacritic.com. Finally, we follow Elberseand Eliashberg (2003) to measure competition from newly released and on-going movies(Comp_Scr_New, Comp_Scr_Ong and Comp_Rev). All variables are summarized inTable 1.

As Fig. 1 indicates, endogeneity exists in the system of Budget, Theater, News, andViewers. Thus, we employ simultaneous equations for estimation (Basuroy et al. 2006;Elberse and Eliashberg 2003):

Fig. 2 Differences among the stars

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Budgeti ¼ α0 þ α1XCi þ α2ZBi þ eBi ð1Þ

Theateri ¼ β0 þ β1Budgeti þ β2XCi þ β3ZSi þ eSi ð2Þ

Newsi ¼ γ0 þ γ1Budgeti þ γ2Theatersi þ γ3XCi þ γ4ZNi þ eNi ð3Þ

log viewersið Þ ¼ κ0 þ κ1Budgeti þ κ2Theatersi þ κ3Newsi þ κ4XCi þ κ4ZVi þ eVi ð4Þ

Table 1 Variable definition

Variables Descriptions

Star-related variables

Star_Box The star’s financial power measured with past box office success

Star_Art The star’s artistic power measured with Oscar nominations and awards

Star_Genre The match between the star’s past genre types and the genre of the movie

Response and mediating variables

Budget Production budget of the movie (in millions)

Theater Number of theaters in the opening week (in thousands)

News Number of news reports during the 1-month period before movie release(in thousands)

log(viewers) Log-transformed number of viewers in the opening week

Movie-specific control variables

Adventure Whether movie genre is adventure

Drama Musical

Comedy Romantic comedy or black comedy

Thriller Horror

TV Whether the source of the script is from TV

Comic From a comic book

Real life Based on real-life events

Book From a book

Remake Whether the movie is a remake

R Whether the MPAA rating is R

Sequel Whether the movie is a sequel

Summer Whether the movie is released during summer

Holiday Whether the movie is released on a holiday weekend

Majorstudio Whether the movie is release by a major studio

Metarating Summary measure of critic ratings from metacritic.com

Director Whether the director is on the Power 100 List

Ads Movie advertising spending

Comp_Rev Sum of the reciprocal of the age of the (other) top 25 movies of same genreas the focal movie)

Comp_Scr_New Sum of the production budgets of new releases, except the focal movie

Comp_Scr_Ong The average age, in weeks, of the top 25 movies in the previous week

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Xci includes the three star power components for star C in movie i. Z are thecovariates, with subscripts B, S, N, and V denoting the budget, theater, news and viewerequations. Our approach is ex ante in that we try to specify the sequence of decisionsconsistently with the actual time frame of movie production and exhibition. Forexample, opening theaters and news media should not affect production budget. Sim-ilarly, movie exhibition decisions are usually made long before movie release, precedingthe news coverage stage (Vogel 2007). On average, studios announce the targetedrelease dates 40 weeks prior to actual movie release (Foutz and Kadiyali 2006; Einav2010). This happens for two important reasons. First, the studios always want to securetheater locations where the characteristics of audiences match those that are likely to bemost responsive to the movie’s theme and genre (Vogel 2007). Second, the exhibitorsare usually not vertically integrated with the studios. Thus, it is necessary to startnegotiations early (Einav 2007). Furthermore, the opening exhibition intensity (wideversus platform releases) is often publically known before movie release (e.g., “openingeverywhere” versus “in selected cities” in advertising messages), thus influencing thenews media. As a result, we allow Theaters to influence News but not vice versa.

The control variables are specified following the literature (e.g., De Vany and Walls1999; Elberse and Eliashberg 2003; Liu 2006; Ravid 1999; Wallace et al. 1993) andinclude those that could have significant impact on each stakeholder. The budgetequation includes director, script source, movie genre, sequel, and MPAA ratings. Inthe theater equation, we include director, a dummy variable for major studio as distrib-utor (Majorstudio), and competition for screens. The news equation controls for directorand script source. In the viewer equation, we control for director, movie genre, sequel,MPAA ratings, competition for audiences, seasonality, and critical reviews.

4 Findings and discussion

We applied three-stage least squares (3SLS) for estimation (Elberse and Eliashberg2003). Table 2 presents the results. As Model (1) shows, production budget, whichdepends on the success of movie project financing, is influenced by several movie- andstar-related factors such as Sequel, MPAA-R, and Director. Regarding star power, thestar’s box office history exerts significantly positive impact on movie project financing.However, the star’s artistic history and the star-movie genre match do not. This suggeststhat movie project financiers rely on box office history as the most important signal forfuture financial success.

Model (2) provides the estimation results for opening theater. Distribution by majorstudios and a larger production budget both increases the number of opening theaters.Among the star power variables, star-genre match has a significantly positive effect: amovie enlisting stars whose genre history matches with the genre of the movie itself ismore likely to receive screening support. The effects of box office and artistic history arenot significant. However, as production budget has a positive effect on opening theatersand the star’s box office history influences the budget, there is positive and indirectimpact of the star’s box office history on movie exhibition.

Model (3) shows that the star variables have minimal influence on media coverage.Neither box office history nor the star-genre match has a significant impact, and theeffect of artistic record is marginal (10 % sig.). As production budget has a positive

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impact on news coverage, star power (box office record in particular) influences thenews media indirectly through budget.

Finally, similar to the case of news coverage, the star variables do not have significantdirect impact on audiences (model 4). Consistent with previous research (Elberse andEliashberg 2003; Karniouchina 2011), opening theaters significantly influences theopening week audiences. As opening theaters are directly affected by star-genre match,and indirectly affected by the star’s box office history, these variables indirectly influ-ence movie audiences.2

2 The direct impact of box office history on opening week audiences is marginally negative (10 %). However, thestrong indirect impact through opening theaters makes the total effects of box office history positive. Furthermore,the marginal negative effect could turn to non-significance in alternative estimations (see Robustness Checks).

Table 2 3SLS estimation of star impact

Variables Budget Theater News Viewer

Star_Box 0.202*** (0.060) −0.0021 (0.0017) 4.24e−05 (0.0002) −0.0039* (0.0023)

Star_Art 1.197 (1.837) −0.0660 (0.0482) 0.0115* (0.0064) 0.0418 (0.0613)

Star_Genre 4.718 (7.821) 0.654*** (0.190) 0.0218 (0.0273) −0.455 (0.280)

Budget 0.0192*** (0.0028) 0.0018*** (0.0006) −0.0094 (0.0167)

Theater 0.0199 (0.0166) 1.803*** (0.214)

News 3.334 (3.348)

Director 11.78** (5.794) −0.129 (0.167) −0.0142 (0.0222) 0.154 (0.296)

TV −18.24 (13.00) −0.0012 (0.0449)

Comic 5.244 (12.38) 0.0675 (0.0422)

Reallife 21.96*** (8.429) 0.102*** (0.0297)

Remake 14.48* (7.685) 0.0631** (0.0271)

Book 11.55** (5.137) 0.0470*** (0.0182)

Adventure 1.585 (8.544) −0.308 (0.500)

Drama −46.96*** (6.739) 0.428 (0.306)

Comedy −42.20*** (6.669) 0.449 (0.314)

Thriller −31.48*** (8.363) −0.0242 (0.382)

Sequel 22.00*** (7.758) 0.131 (0.356)

R −17.62*** (4.264) 0.0867 (0.169)

Majorstudio 0.905*** (0.168)

Comp_Scr_New −0.0036*** (0.0007)

Comp_Scr_Ong −0.0040 (0.0027)

Comp_Rev −0.0012 (0.0015)

Holiday −0.0529 (0.181)

Summer 0.212 (0.232)

Metarating 0.0034 (0.0084)

Constant 69.08*** (7.333) 0.890*** (0.228) 0.0970*** (0.0247) −4.354*** (0.594)

Obs 203 203 203 203

R2 0.475 0.549 0.483 0.810

Standard errors are enclosed in parentheses

*p<0.10; **p<0.05; ***p<0.01

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4.1 Robustness checks: roles of advertising

One may wonder if movie advertising should be included in the estimation. As mostadvertising spending occurs in the few weeks prior to release (Elberse and Anand 2007),advertising could influence news media and the audiences. In industry practice, how-ever, advertising spending is often a fixed percentage of production budget (Einav 2007;Vogel 2007). Thus, production budget is the driving force and its effects are the mostfundamental. Moreover, from the econometric perspective, production budget andadvertising spending are highly correlated; including both might cause multicollinearityconcerns. As a result, similar to existing studies (e.g., Basuroy et al. 2006; De Vany andWalls 1999; Hennig-Thurau et al. 2009), our analysis has focused on production budgetin the estimation.

Nevertheless, in an effort to demonstrate the robustness of the main results, weconsider the roles of advertising in a series of tests. We were able to obtain advertisingspending for 100 movies in the sample from the-Numbers.com and TNS MediaIntelligence. In Table 3, Model (1) includes both production budget and advertisingspending (ads), and models (2) and (3) include them separately. Although the samplesize is reduced, the results are consistent with the key findings reported earlier. That is,movie financing is most influenced by a star’s box office history, movie exhibition isinfluenced directly by star-genre match and there is an indirect impact through produc-tion budget, and neither news media nor the audiences are directly impacted by starpower.

5 Discussions

Overall, these results show that the financiers and exhibitors are directly influencedby star power, but news media and movie audiences are influenced less and onlyindirectly. From the perspective of new product development, financiers and exhib-itors face greater risk in judging the demand potential. This is not only because theyare at the early stages of product development when little demand signal exists, theyalso have higher financial stake in the movie than either news media or audiences.

Although financiers and exhibitors are both influenced by stars, they pay attention todifferent aspects of star power. The explanation relates to the notion that stars and theircredentials help manage the risk that particular stakeholders face. Both financiers andexhibitors take financial risks but the nature of their decisions is different. In decidingwhether and how much to invest, the financiers are exposed to the greatest level offinancial risk. Thus, a star’s financially relevant characteristic should be highly salientand those with greater box office success are judged to be more attractive and “bank-able.” Such bankability helps the movie project attract investment, get “green lighted,”and be produced with higher budget (Vogel 2007). Note that as the financiers seek starswith greater box office success to reduce financial risk, these stars typically come in withhigher demand for compensation. This further contributes to the higher productionbudget of the movie.

Financial record, however, does not directly influence exhibitors. They benefit fromthe financiers’ earlier decision of investing and producing a movie. In other words, they

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can “free ride” on the financiers’ decision and focus on additional dimensions of starpower (i.e., the genre match) to gauge the sales potential. News media and the audiencesface less risk; positioned at the later stage of the movie value chain, they are affected bythe decisions of earlier stakeholders, and stars influence them indirectly.

The multidimensional view of star power suggests that studios should use targetedstrategies when promoting star(s) and the movie. For instance, in promoting a movieproject to potential investors, stars’ past box office success should be emphasized.However, when promoting to exhibitors, the studio should highlight how the star hasbeen successful in the particular genre.

These results provide a potential way to reconcile the mixed findings in the literatureon how stars affect box office revenue. That is, the particular preference and risk-takingattitude of producers, investors, and exhibitors will result in different stars being hired

Table 3 Robustness checks: rolesof advertising

Constant and the same controlvariables as the main analysisare included

Variables Budget and Ads Budget only Ads only

Budget equation

Star_Box 0.187** 0.181** 0.190**

Star_Art −0.417 −0.612 −0.131Star_Genre −7.506 −6.81 −8.661Director 15.295** 15.177* 14.984*

Theater equation

Star_Box −0.004* −0.003 −0.004*Star_Art −0.095 −0.089 −0.095Star_Genre 0.830*** 0.766*** 0.831***

Budget 0.021*** 0.018*** 0.021***

Director −0.215 −0.137 −0.216Constant 0.789** 0.919*** 0.810**

News equation

Star_Box 0.000 0.000 0.000

Star_Art −0.002 −0.001 −0.002Star_Genre −0.016 0.01 −0.031Budget 0.000 0.001*

Theater 0.022 0.021 0.029*

Director −0.026 −0.019 −0.021Ads 0.004* 0.004***

Viewer equation

Star_Box −0.003 −0.003 −0.004*Star_Art 0.048 0.047 0.04

Star_Genre −0.635 −0.503 −0.540**Budget −0.008 −0.005Theater 1.465*** 1.431*** 1.496***

News 0.664 0.969 −0.656Director 0.179 0.193 0.045

Ads 0.012 0.01

Obs 100 100 100

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for movies, leading to different financial outcomes. The impact of stars on audiences canbe internalized by earlier decision makers. Thus, stars still matter for audiences eventhough the direct impact could be weak.

Finally, as we focus on movies with one star to more clearly measure the star powerdimensions, our movie sample does not necessarily represent the majority of movies.Future research could consider the issue of multiple stars in a movie. For example, it willbe interesting to examine whether the highest, lowest, or the weighted average of certainstar attributes among all the stars involved matters the most for each stakeholder.

Acknowledgments Angela (Xia) Liu’s research is supported in part by National Natural Science Foundationof China with grant no. 71102007. Yong Liu acknowledges the support from the Gary M. Munsinger Chair inEntrepreneurship & Innovation and summer research awards from the Eller College of Management at theUniversity of Arizona. The authors are listed alphabetically and contributed equally to this research.

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Mark Lett