Thorsten Hennig-Thurau, Victor Henning, Henrik Sattler, Felix Eggers, & Mark B. Houston
The Last Picture Show? Timing andOrder of Movie Distribution ChannelsMovies and other media goods are traditionally distributed across distinct sequential channels (e.g., theaters, homevideo, video on demand). The optimality of the currently employed timing and order of channel openings hasbecome a matter of contentious debate among both industry experts and marketing scholars. In this article, theauthors present a model of revenue generation across four sequential distribution channels, combining choice-based conjoint data with other information. Drawing on stratified random samples for three major markets—namely,the United States, Japan, and Germany—and a total of 1770 consumers, the empirical results suggest that thestudios that produce motion pictures can increase their revenues by up to 16.2% through sequential distributionchain timing and order changes when applying a common distribution model for all movies in a country and thatrevenue-optimizing structures differ strongly among countries. Under the conditions of the study, the authors findthat the simultaneous release of movies in theaters and on rental home video generates maximum revenues formovie studios in the United States but has devastating effects on other players, such as theater chains. The authorsdiscuss different scenarios and their implications for movie studios and other industry players, and barriers for theimplementation of the revenue-maximizing distribution models are critically reflected.
Thorsten Hennig-Thurau is Professor of Marketing and Media, Depart-ment of Marketing and Media Research, Bauhaus-University of Weimar,and Research Professor of Marketing, Cass Business School, London(e-mail: [email protected]). Victor Henning is a doctoral studentof Marketing, Department of Marketing and Media Research, Bauhaus-University of Weimar (e-mail: [email protected]).Henrik Sattler is Professor of Marketing (e-mail: [email protected]), and Felix Eggers is a doctoral student (e-mail:[email protected]), Institute of Marketing and Media, Univer-sity of Hamburg. Mark B. Houston is Eunice and James L. West Chair ofAmerican Enterprise and Associate Professor of Marketing, Texas Christ-ian University (e-mail: [email protected]). The authors thankFranziska Völckner, Michel Clement, and the four anonymous JM review-ers for their helpful and constructive comments on previous versions ofthis article and Nielsen Entertainment and an anonymous Hollywood stu-dio executive for providing confidential information.
To read and contribute to reader and author dialogue on JM, visithttp://www.marketingpower.com/jmblog.
“Ten years from now, we’ll release a film, and you’ll beable to consume it however you want.”
—Yair Landau, Vice Chairman of Sony Pictures(Smith 2005, p. 52)
Sequential distribution describes a marketing strategythat is designed to maximize producer income bymaking a product available to consumers in different
markets in succession (Hennig-Thurau, Houston, and Walsh2006; Vogel 2004). Sequential distribution is used mainly tomarket entertainment products, including electronic gamesand books (Lehmann and Weinberg 2000). A primary chal-lenge facing practitioners and marketing scholars regardingsequential distribution strategy is when and in which orderto enter sequential channels to maximize producer revenue.
This article addresses this challenge empirically bystudying the motion picture industry, which relies heavilyon sequential distribution (Eliashberg, Elberse, and Leen-ders 2006; Lehmann and Weinberg 2000). Traditional dis-
tribution for a film begins with a theater premiere, followedby a release to retail markets (rental or sale of DVDs), dis-play on premium satellite or cable channels, and, eventu-ally, television. Because revenues generated by nontheatri-cal markets exceed theatrical box office grosses (e.g., U.S.box office of $9 billion in 2005 compared with revenues of$24.9 billion through DVD/VHS sales and rentals; Enter-tainment Merchants Association 2006; Motion PictureAssociation of America [MPAA] 2006) and because newchannels, such as video on demand (VOD), have entered themarket, this traditional sequencing of channels has comeunder siege by film studios (Stanley 2005), which are artic-ulating interest in opening nontheatrical channels earlierand are even changing the established order of channels.For example, Warner Bros. Entertainment chairman BarryMeyer publicly envisions major movies debuting “on DVDsimultaneously with their theatrical release,” proposing thatfuture premieres “will be in Wal-Mart” (Bond 2005) andthat theater revenues will be mere “added value.” As aresult, the window between the theatrical and home videorelease of a motion picture is shrinking (Saccone 2005), andconsumers are able to (pre)order the DVD of a movie evenbefore it has opened theatrically in some major export mar-kets. Such fundamental shifts in sequencing strategieswould almost certainly affect players, such as theater own-ers (Eliashberg, Elberse, and Leenders 2006; Vogel 2004).John Fithian, president of the National Association ofTheater Owners, considers timing and order changes “thebiggest threat to the viability of the cinema industry today”(Canadian Broadcasting Corporation 2006, p. 1). With thegrowth of alternative ways to watch films, will movie thea-ters soon see their “last picture show?”
The potential impact of timing and order changes onmovie studio revenues and profits is unclear. The currentindustry discussion is clearly dominated by speculationbased on proprietary consultancy reports for which theunderlying data, assumptions, and analyses are not open for
64 / Journal of Marketing, October 2007
verification. For example, a JPMorgan report suggests that asimultaneous release of a film in theaters and on DVDwould lead to an overall 36% increase in studio revenues(Snyder 2005). In terms of scholarly research, a limitednumber of researchers have studied the effect of changes insequential distribution timing on studios (e.g., Lehmann andWeinberg 2000), but extant studies present either theoreticalmodels of specific aspects of the sequential distributionprocess (Prasad, Bronnenberg, and Mahajan 2004) orempirical models that are based on aggregated prior marketdata (Frank 1994; Lehmann and Weinberg 2000). Noresearch has yet modeled the multistage sequential chainsthat reflect normal marketplace conditions (i.e., involvingthree or more channels and two or more release windowsthat must be optimized simultaneously), and none has mod-eled the potential effects of order changes on studio reve-nues. Furthermore, previous research has not examinedregional differences, despite the influence of culturalvariables on the consumption of entertainment products(Hennig-Thurau, Walsh, and Bode 2004) and the impor-tance of export markets for U.S. entertainment industries(about half of motion picture revenues come from non-U.S.markets; Omsyc 2002).
The goal of this article is to identify sequential distribu-tion configurations that maximize movie studio revenues.The approach we employ here extends the extant literaturein three ways. We (1) consider multiple channels that con-sumers face in reality; (2) use individual-level discretechoice consumer data that enable us to model potential mar-ket configurations, such as simultaneous releases in theatersand other channels (e.g., home video) whose economicappeal cannot be assessed by prior market data; and (3)account for country differences. Drawing from the extantliterature on sequential distribution, we develop an integra-tive framework of sequential distribution’s impact on studiorevenues and use this framework to present a sequential dis-tribution net present value (NPV) model. Combining adiscrete-choice conjoint design with self-reported customerdata, we apply our model to three leading motion-picturemarkets—the United States, Japan, and Germany—bydrawing on random samples for each of these markets and1770 consumers to allow for market-specific effects. Weuse the model to test systematically the effects of changesin the timing and order of the windows of the sequentialdistribution chain on consumer choices and, subsequently,movie studio revenues in the different countries. We isolateconfigurations of the sequential distribution chain that,under the given assumptions, provide optimal payoffs to themovie studio and differentiate our findings for differentmovie genres. We discuss these results and highlight poten-tial obstacles that studios might face when changing theexisting distribution structure.
Sequential Distribution of MotionPictures: Literature and Conceptual
FrameworkOverview of Channel Timing and Order ResearchExtant literature on sequential distribution that examinesthe optimal timing and order of channels is rare. The few
existing studies on this topic have identified several sequen-tial distribution chain characteristics, which we use as cen-tral elements of our conceptual model of sequential distrib-ution (Frank 1994; Lehmann and Weinberg 2000; Luan2005; Prasad, Bronnenberg, and Mahajan 2004). Althoughmost authors recommend the current theater-to-home-videowindow to be shortened, no study accounts for today’s mul-tichannel nature of movie distribution in modeling the effectof window length changes.
Moreover, no academic research has yet addressed thepotential impact of order changes in the sequential chain onstudio revenues. Most studies of sequential distribution treatthe order of motion picture channels as fixed, and someargue that to open a movie in any channel other than thea-ters is “suicidal” (Frank 1994, p. 125). Essentially, twoarguments are used in the extant literature to support thecurrent sequence of motion picture channels. First, it isargued that products should be distributed first throughchannels that generate the “highest revenues over the leastamount of time” and then cascaded down to markets thatreturn less revenue per unit time (Eliashberg, Elberse, andLeenders 2006, p. 27). Second, the power to attract public“buzz” is viewed as exclusive to the theatrical channel(Lippman 2000). However, these arguments are being chal-lenged by current market conditions. Beyond the overallhigher revenues earned by films in ancillary markets, studiochannel margins now are higher for DVD sales than fortheater “sales” (Blume 2004; Cohen 2003; Vogel 2004). Inaddition, because other cultural products, such as music andbooks, are well known for their ability to stimulate mediabuzz for openings in retail stores, “[i]t isn’t that radical aproposition that movies could follow that same path” (Gen-tile 2005). Consistent with these arguments, Eliashberg,Elberse, and Leenders (2006, p. 27) conjecture “that newmovies on [pay-per-view] or VOD prior to the theatricalrelease could be sold to millions of viewers.” Overall, thesecontrasting views suggest that an empirical examination ofsequential channel order changes is merited.
A Conceptual Framework for Studio RevenueOptimization
Drawing on extant research on sequential distribution, wepresent a conceptual framework for sequential distributionoptimization. As Figure 1 illustrates, the framework postu-lates that maximum studio revenues depend on three opti-mization variables: the timing of distribution channels, theorder in which these channels open, and the price for whichthe product is made available in each channel. Furthermore,it proposes that these optimization variables are influencedby several microlevel and macrolevel factors.
Microlevel factors. We argue that the revenue-maximizing channel configuration essentially depends onsix microlevel characteristics of sequential distributionchains. These factors include four that are suggested by theextant literature—interchannel cannibalization, perishabil-ity, customer expectations, and success-breeds-success(SBS) effects—and two specific financial factors—theindustry-specific discount rate and the channel-specificrevenue allocation.
The Last Picture Show? / 65
With regard to interchannel cannibalization, we assumethat the release of a movie in a second channel has thepotential to cannibalize revenues from an existing channelbecause of consumers’ willingness to switch between chan-nels. Interchannel cannibalization was first discussed byFrank (1994), who models the interrelationships betweentheater visits and home video rental revenues and finds thatcannibalization occurs if a film is released on video “tooearly.” Lehmann and Weinberg (2000) also consider chan-nel cannibalization between theater and video releases andsuggest that the size of each market should determine thedelay period. In addition, cannibalization is reflected byindustry thinking that “[a] good movie is a good movie,regardless of where it’s shown” (Bregman, qtd. in Arnold2005). As Prasad, Bronnenberg, and Mahajan (2004) argue,cannibalization effects can be either complete or partial,depending on consumers’ perceptions of substitutabilitybetween movie channels.
Regarding perishability, we draw on the work of Frank(1994), Lehmann and Weinberg (2000), and Prasad, Bron-nenberg, and Mahajan (2004), who propose a “wear-out”
effect, which exists if a film is “too old” when it is releasedin secondary channels. Adapting their argument, we assumethat the revenues generated by movies in subsequent chan-nels should be affected by the time elapsed since the moviewas first available, with demand declining over time. Thisassumption is shared by industry executives, such as BobChapek, president of Buena Vista Home Entertainment,who compared a movie “to a melting ice cube. The longer itsits, the smaller it becomes” (Dutka 2005).
Regarding customer expectations, Prasad, Bronnenberg,and Mahajan (2004) argue that as studios shorten the timebetween a film’s theatrical run and its rental availability,consumers will strategically defer their consumption of themovie in the first channel because they expect the movie tobe available soon in another channel that they prefer for cer-tain reasons (e.g., lower price, multiple viewings). Buildingon this, we assume that consumers have expectationsregarding the release of a motion picture in subsequentchannels and that these expectations will influence channelchoice, such as passing up a theater visit in lieu of a laterrental or purchase (Prasad, Bronnenberg, and Mahajan
FIGURE 1A Conceptual Framework of Sequential Distribution Revenue Maximization
66 / Journal of Marketing, October 2007
2004). These expectations can be based on experience or oninformation from retailers and media (e.g., movie-relatedWeb sites). For example, Star Wars: Episode III was thebest-selling DVD on Amazon.com in Germany the weekbefore the movie was released to German theaters; cus-tomers received e-mails from the online retailer invitingthem to preorder the DVD for the new movie.
Regarding SBS effects, Prasad, Bronnenberg, andMahajan (2004) demonstrate the existence of complemen-tary effects between channels by linking the success of amovie in theaters to video revenues. We distinguishbetween multiple-purchase SBS and information-cascadingSBS. In multiple-purchase SBS, an individual consumerpays to see a movie more than once, with the first viewingcausing a desire for subsequent viewings (Luan 2005). Mul-tiple purchasing will affect subsequent channel revenuesuntil the movie can be purchased by the consumer (i.e.,when the consumer can then view the film repeatedly with-out additional cost). In contrast, information-cascading SBSrefers to the impact of a movie’s success in one channel onother consumers’ behavior in subsequent channels.Information-cascading SBS can be based on either personalexperiences that are shared (i.e., word of mouth [Liu 2006]or informed cascades [DeVany and Walls 2002]) or boxoffice results that are made public (i.e., uninformed cas-cades [DeVany and Lee 2001]). Although information-cascading SBS effects have so far been stirred by movies’theatrical releases, we argue that they could be similarlycreated in other channels, such as DVD sales or VOD, ifmovies were released there first. Empirical evidence forSBS in a movie context has been reported by Elberse andEliashberg (2003) and Hennig-Thurau, Houston, and Walsh(2006).
Finally, distribution chain decisions are also influencedby specific financial factors. The industry-specific discountrate must be considered because future revenues need to bediscounted because of risk and opportunity costs, whichreduce the attractiveness of delayed channel openings.When the movie Bubble was the first to receive a simultane-ous release in theaters, on DVD, and on pay-per-view inJanuary 2006, its producer highlighted “the acceleratedtimetable for getting our money back” as an anticipatedbenefit (Bowles, qtd. in Box Office Mojo 2006). In addition,the revenue share the studio received in each channel con-stitutes a key criterion for the optimal sequential channelstructure because revenues are divided among differentplayers (e.g., theater chains, studios) in each channel, andthe percentage that accrues to the studio differs acrosschannels.
Macrolevel factors. The microlevel characteristics andthe revenue-maximizing channel structure that can bederived from them are influenced by the macrolevel factorsof channel preference and country. Specifically, microlevelcharacteristics are influenced by consumers’ preferencestoward distribution channels, such as movie theaters, DVDpurchases, DVD rentals, and online downloading (Vogel2004), all of which must be considered simultaneously.Channel preferences differ; whereas some consumers prefergoing to the movies (“I love the mythos of the darkenedtheater”; customer statement in Puig 2005), others argue
1We construe VOD as an umbrella concept that summarizes dif-ferent media services under a common label. In this study, wefocus on download-to-rent VOD, the dominant model when theempirical study was conducted, which allows consumers to watcha movie that has been downloaded from the Internet for a limitedtime (usually 24 hours). To increase readability, we use the termsVOD and download-to-rent VOD interchangeably.
that “there’s no place like home” (Clark 2005). This chan-nel preference determines, among other things, the extent ofinterchannel cannibalization and perishability becausestrong preferences for a certain channel limit the degree ofcannibalization among channels and reduce the impact ofperishability on channel revenues. Another macrolevel fac-tor we consider is country characteristics. A wealth ofresearch suggests that consumers across countries differ intheir decision-making processes. In a film context, culturalfactors (e.g., Hennig-Thurau, Walsh, and Bode 2004) andinformational factors (e.g., Elberse and Eliashberg 2003)might explain these differences. Such country characteris-tics affect consumers’ expectations about the opening ofsecondary channels, as well as the extent of multiple pur-chasing and the role of word of mouth and charts for movieconsumption. They might also affect the financial parame-ters of our framework.
An NPV Model of Movie Studios’Sequential Distribution Revenues
General ConsiderationsUsing the sequential distribution framework described pre-viously, we now develop an NPV model of movie studiorevenues. In contrast to studies that focus on overall indus-try revenues and other shared outcomes (Frank 1994; Luan2005), we view the revenues that each channel returns to the movie studio as decisive for determining the optimalsequential channel structure. This consideration is based onthe notion that an individual firm’s channel decisions arenot made to maximize overall industry revenues but ratherto generate maximum revenues for that individual player.
In our model, we argue that the revenues a movie gener-ates are the result of consumers’ choices among differentchannels when the movie becomes available. To cover themultichannel nature of motion picture distribution ade-quately, we include four channels among which the con-sumer can choose: movie theater consumption, DVD pur-chases, DVD rentals, and download-to-rent VOD.1 Inaddition to consumer expectations regarding release datesthat are modeled as known, the model accounts for theeffects of interchannel cannibalization and perishability onconsumer decision making because consumers can chooseconsciously among different channels, and it accounts forthe respective opening dates in each channel in relation tothe consumers’ willingness to accept a consumption delay.By modeling channel preference at the individual consumerlevel as part of the customer’s choice decision, the modelalso considers varying degrees of interchannel substitution.Moreover, the model accounts for interchannel effects;channel revenues are influenced by multiple-purchase SBSand both informed-cascading SBS (e.g., through word of
The Last Picture Show? / 67
2For DVD sales, theaters, and DVD rental, we were providedwith aggregate-level information on the weekly revenue patternsof all 2005 studio releases. Because industrywide information wasnot available for the VOD channel, we relied on aggregate-level
mouth) and uninformed-cascading SBS (e.g., through boxoffice data).
Because channel revenues do not flow back instantlyafter the channel’s opening, we model the weekly percent-age of the channel-specific revenue return as a function f(w)of the number of weeks w after opening. To estimate f(w),we used weekly revenue data for the studio movies releasedin 2005 provided by IMDbPro (for theaters), Video Businessmagazine (DVD rental), Nielsen VideoScan (DVD sales),and an anonymous Hollywood studio (VOD).2 We fit differ-ent regression models to mirror these data, assuming thatthe weekly percentage of revenue return becomes zero after78 weeks (i.e., 1.5 years), which was implied by the actualrevenue distribution patterns. For theatrical revenues andDVD rental returns, log-linear functions fit best with theindustry data, whereas for DVD sales, a multiplicative func-tion had the best fit, and for VOD returns, a quadratic func-tion had the best fit. In all cases, the fit was excellent, withR-squares ranging from .96 to .99. The functions appear inFigure 2.
information on the VOD performance for one studio’s 2005movies. The VOD revenue data were monthly, and thus we inter-polated them to weekly revenues. To predict the percentage ofrevenue return from the dollar revenues, we modeled and fore-
Formal Model Description
We formally describe the model as follows:
( )
( )
( )
( )1
1
1NPV
f w R
r
rTH
TH THw
w
MtTH
= ×
×+
+
+
∑β
βDVVD-S
DVD-S DVD-S
DVD-S×
×+
+
∑f w R
r
r
ww
Mt
( )
( )
( )
1
1
( )
( )
( )+ ×
×+
+
∑βDVD R
DVD R DVD Rw
w
Mt
f w R
r
r DV-
- -
1
1 DD R
VODVOD
VOD VODw
w
Mt
f w R
r
r
-
( )
( )
( ),+ ×
×+
+
∑β
1
1
FIGURE 2Revenue Distribution Over Time per Channel
Notes: The subscripts name the respective channel. TH = theater, DVD-S = DVD sales, DVD-R = DVD rental, and VOD = download-to-rentVOD.
f ww
THw
( )exp( . . )
exp[ln(= − ×
=∑17 56 35
178 weeklyy revenue)]
f ww
DVD Rw
-we
( )exp( . . )
exp[ln(= − ×
=∑16 53 23
178 eekly revenue)]
f ww w
VODw
( )exp( . . . )
exp[l= + × − ×
=∑5 39 75 33 2
178 nn( )]weekly revenue
f w wDVD S- ( ) exp[ . . ln( )]= − − ×1 36 1 24
68 / Journal of Marketing, October 2007
where NPV is the studio’s net present value of a movie andR represents the revenues of a movie generated through aspecific channel, which are discounted with f(w) for therespective channel’s rate of flow (see Figure 2). We use aweekly discount rate of .183%, which equals an annualindustry-specific discount rate of r = 10%.3 The monthlyequivalent to r was represented by rM, which is used to dis-count channel revenues to the opening of the first channel; tis the time difference in months between the opening of thefirst channel and the opening of the channel under consider-ation (i.e., window length); and β is the percentage of reve-nues allocated to the studio for each channel.
Revenues are generated through consumers’ choicesamong the different channels, with choices x being a func-tion of the channel attributes x = f(p, t, m, π), where p is theprice consumers pay to see a movie; m is the medium (orchannel); and π is a vector that reflects other factors, suchas the language in which the movie is shown and the pres-ence of bonus material. We model a consumer’s individualchoice given a set of channel alternatives with the multi-nomial logit model, as follows:
where x(i|J) is a consumer’s choice share for channel i in aspecific scenario with J movie consumption alternatives(including an option not to see the movie in one of the givenchannel alternatives; i.e., to wait for the movie to be madeavailable on television for free) and θ is a parameter vectorthat reflects the consumer’s preference structure for thechannel attributes.
Individual-level choice shares are complemented withindividual-level SBS information. It is important to modelmultiple-purchase SBS and information-cascading SBS onan individual level because consumers might not be equallylikely to react to these effects. Accordingly, we get comple-mented individual-level choice quantities x′, as follows:
( ) ( ) ,
( )
3 1
4
′ = × + + + ×
′
x x x
x
TH THWOM
THC
TH TH FC
DV
γ γ δ
DD S DVD SWOM
DVD SC
DVD S
DVD S F
x
x
- - - -
-
= × + +
+ ×
( )1 γ γ
δ CC
DVD R DVD RWOM
DVD RC
DVD Rx x
,
( ) ( )5 1′ = × + +
+
- - - -γ γ
δδDVD R FCx- , and×
( ) ( )exp( )
exp(
2 x i Jp t m
p
p i t i m i i
p j
| =+ + +
+
θ θ θ θ π
θ
π
θθ θ θ ππt j m j jj
J
t m+ +=
∑ )
,
1
where x is the choice share for the channel indicated by thesubscript according to the multinomial logit model; γ repre-sents channel-specific information-cascading SBS effects,where γWOM is the parameter for informed cascades (e.g.,the percentage of movies seen in the respective channelexclusively due to word of mouth generated by previouschannels) and γ C is the parameter for uninformed cascades(e.g., the percentage of movies seen in the respective chan-nel exclusively due to chart information from previouschannels); δTH, δDVD-R, δVOD, and δDVD-S are multiple-purchase parameters for theaters, DVD rental, VOD, andDVD sales, respectively; and xFC represents the proportionof choice to see the movie in the channels that open first(equal to 0 if the movie is first made available through thechannel under consideration).
Both information-cascading SBS parameters, γWOM andγ C, become zero if the movie is first made available throughthe channel under consideration. With regard to themultiple-purchase parameters, δTH, δDVD-R, and δVOD arezero if the movie is first made available through the respec-tive channel or opens exclusively through DVD sales. Inaddition, we model the consumer’s desire to rewatch themovie in a theater, on a rental DVD, or through VOD to bezero immediately after he or she consumes it for the firsttime in a different channel and then to rise gradually overtime, following an exponential saturation function. Specifi-cally, we set δ = a × [1 – exp(–.5 × t)], where a is thechannel-specific repeat consumption probability of the indi-vidual consumer—that is, the percentage of movieswatched in the channel indicated by the subscript δ thatwere previously seen in other channels (xFC). In addition,δDVD-S is zero if the movie is first made available throughthis specific channel (i.e., DVD sales). However, becausethe consumer’s desire to own a movie is formed immedi-ately after viewing it in a different channel and remainsconstant thereafter in our model until fulfilled, the multiple-purchase parameter for the DVD sales channel is timeinvariant at δDVD-S = aDVD-S.
We calculate the overall channel-specific revenues bytaking the arithmetic mean of each channel’s complementedchoice quantity across all consumers (X′) and multiplying itby the respective channel price. For example, theater reve-nues can be calculated by RTH = pTH × X′TH. This informa-tion enables us to calculate the weekly return and the NPVof studio revenues. Appendix A contains an illustrativeapplication of the model.
Model Assumptions
It is important to note that the model we described is basedon several assumptions. In line with our studio perspective,we focus on studio-produced motion pictures and the condi-tions under which such movies are distributed. Specifically,we assume that motion pictures are released widely intheaters (the dominant distribution model) and do not dis-tinguish between producers and distributors of motion pic-tures with regard to revenue maximization, because most
γ γVOD VODWOM
VODCx x′ = × + +( ) (6 1 VVOD
VOD FCx
)
,+ ×δ
casted the revenues of the respective channel for up to 78 weeksand related each predicted weekly revenue to the total amount ofrevenues to obtain the required percentage for these channels. Inthe case of DVD sales, we were provided with weeklypercentages.
3Although information on suitable discount rates for the valua-tion of movie studios is scarce, available sources cite annual dis-count rates of 9.0% for Sony Pictures (Sony 1997), 9.1% for Dis-ney, 11.0% for MGM, and 11.8% for Pixar (Chalmers 2002).Thus, a discount rate of 10% seemed reasonable.
The Last Picture Show? / 69
movies produced by a major Hollywood studio are distrib-uted by sister companies over which the studio has com-plete control (e.g., Warner Bros. Pictures, Warner Bros. Pic-tures Domestic Distribution, and Warner Home Video areall subsidiaries of Time Warner Inc.). We also assume thatconsumers who want to see a movie in a channel that isalready open are able to do so; that is, there are no shortagesof screens at the theater or of DVD copies in rental storesand at retailers to limit consumption, and all movies areavailable through any channel. This is in line with the mar-ket efficiency hypothesis, which matches the reality ofmovie distribution well for wide studio releases (Hennig-Thurau, Houston, and Walsh 2006). Moreover, we assumethat studio advertising is effective, with consumers beingaware of new studio releases and making their channelchoices deliberately, and that its effectiveness is the samefor all channels. Consistent with the early announcementpolicy of new movies by studios and retailers, we assumethat consumers have homogeneous expectations (i.e.,knowledge) about the timing of new studio movies’ releasesin different channels, with these expectations matching theactual release dates. Furthermore, we assume that cus-tomers watch a movie in theaters only once, which corre-sponds with norms reported in industry information (Hindes1998). We also assume that SBS is not exclusive to theatri-cal releases but exists for any channel in which a new movieis made available for the first time, and we assume that theallocation of revenues between studios and other players isconstant over the course of a movie’s release (i.e., the stu-dio’s share is identical in Week 1 and the weeks that fol-low). Moreover, because our focus is on customer prefer-ences, we do not consider potential market barriers causedby other players, such as movie theaters, that might hinderstudios’ implementation of certain distribution models (butwe discuss their impact subsequently). Finally, we excludepiracy from our model because the effect of such illegalconsumption options on traditional distribution channels ofmotion pictures remains an unanswered question.
Research DesignTo account for the existence of country factors and becauseof the enormous relevance of export markets for U.S.motion pictures (in 2005, cumulative foreign box officeexceeded domestic theatrical revenues by 60%; MPAA2006), we applied our model not only to the U.S. market butalso to those of Japan and Germany, two film markets thatare important and culturally diverse. These three countriesconstitute 56.4% of the worldwide theatrical market(MPAA 2003), and Japan and Germany are the world’sthird- and fourth-largest theatrical export markets, respec-tively. Furthermore, Japan is the second-largest home videomarket, with annual revenues of $5.5 billion, and Germanyis the fifth-largest home video market, with annual revenuesof $1.7 billion (International Video Federation 2004).
Stratified random samples of the U.S., Japanese, andGerman populations were drawn in cooperation with aglobal marketing research company. With age and gender asinterlocked strata, 5094 consumers (United States = 1701,Japan = 1802, and Germany = 1591) were randomly
4The nine studio-produced movies, which cover a wide range ofgenres, were Harry Potter and the Goblet of Fire, Jarhead, KingKong, Perfume: The Story of a Murderer, Pink Panther, TheChronicles of Narnia, The DaVinci Code, Wallace & Gromit: TheCurse of the Were-Rabbit, and X-Men 3. None had been released atthe time of the data collection.
selected from the research company’s database, which mir-rors each country’s overall population, and they wereinvited by e-mail to fill out an Internet questionnaire andwere offered $1 for participation. A total of 1859 consumersresponded. For quality reasons, we eliminated respondentswho completed the questionnaire in less than five minutes,leaving a sample of 1770 (n = 588 in the United States, fora response rate of 34.6%; n = 593 in Japan, for a responserate of 32.9%; and n = 589 in Germany, for a response rateof 37.0%). (Demographic characteristics of the subsamplesare available on request.)
The questionnaire required respondents to participate inseveral discrete-choice tasks and to answer rating-scaledquestions. To increase the realism of the choice tasks,respondents were first presented with nine upcomingmotion pictures and were asked to choose the movie theywere most interested in seeing.4 Short descriptions of thenine movies’ plots, directors, and stars were provided, aswere posters and trailers. An additional option for respon-dents was to wait until all nine movies were shown on tele-vision and could be watched free of charge; consumers whovoted for this option were excluded from the remainder ofthe questionnaire (Gilbride and Allenby 2004).
For the movie selected, seven choice sets embedded in achoice-based conjoint design were presented to the respon-dents (Louviere and Woodworth 1983; for conjoint work inchannels contexts, see Wuyts et al. 2004). Each choice setcontained four hypothetical channel options for watchingthe movie (i.e., conjoint stimuli) and a “no-consumption”option (Figure 3). Regarding conjoint attributes, each con-joint stimulus was described by four (U.S.) or five (Japanand Germany) attributes, and attribute levels varied system-atically (Table 1). Specifically, the attributes used to gener-ate conjoint stimuli in the U.S. questionnaire were (1) thechannel through which the movie was consumed, (2) thetiming of availability, (3) the price a consumer must pay towatch the movie, and (4) any additional content (e.g.,deleted scenes, commentaries) made accessible to the con-sumer. As a result of pretesting and depth interviews withindustry experts, we included the fourth attribute to increaserealism. In Japan and Germany, we used identical attributesand levels (with price levels transformed into yen and euros,respectively). Because motion pictures are often presentedin “dubbed” versions in theaters in these countries (i.e.,movies are translated into Japanese or German), weincluded language in both cases as an additional attribute.We modeled attribute-level combinations, which might haveresulted in improbable alternatives and respondent confu-sion, as prohibited pairs. We created stimuli and conjointchoice sets according to a computer-generated randomizeddesign that accounted for the design principles (1) minimaloverlap, (2) level balance, and (3) orthogonality (Huber andZwerina 1996).
70 / Journal of Marketing, October 2007
FIGURE 3Example of Conjoint Task
5Because movies have always been released in theaters first, wecould not ask respondents for an interchannel multiple consump-tion effect from home entertainment channels on theaters.Acknowledging that this is a limitation of the present study, weused the consumers’ DVD rental behavior as a proxy for sharesimulations in which theaters are not the first channel, and wemodeled consumers as being only half as likely to watch a moviein theaters after having watched it on DVD than vice versa, whichcan be considered a conservative assumption. With regard totheater-related SBS effects, we also adopted the respective DVDSBS parameters as a proxy for both theater-related SBS parame-
Finally, respondents were asked to provide movieconsumption-related responses, which we used as proxiesfor the SBS parameters. To calculate the multiple consump-tion parameters δ, respondents were asked what percentageof movies they had seen in theaters and had later bought orrented on DVD/home video or downloaded from the Inter-net for a fee (for full items, see Appendix B).5 We modeled
the exponential saturation function for the multiple con-sumption parameter for DVD rental and VOD to convergetoward the multiple consumption value stated by the indi-vidual consumer for the respective channel. For the DVDsales channel, we set the multiple consumption parameter tobe equal to the percentage of the individual consumer’sDVDs that had been purchased after having watched amovie in theaters. With regard to information-cascadingparameters γWOM and γ C, respondents were asked what per-centage of their DVD purchases, DVD rentals, and legalInternet downloads of movies they had not seen before intheaters was primarily triggered by information about thesuccess of the movie in theaters (i.e., based on charts) or bypersonal information (i.e., based on word of mouth). Studio
ters in such scenarios. As we report subsequently, sensitivityanalyses show that the results are reasonably robust to variationsin the levels of these parameters.
The Last Picture Show? / 71
TABLE 1Attributes and Levels Included in Conjoint Study
Levels
Attribute Description United States Japan Germany
Channel The channel (or medium)through which the movie
is consumed
Movie theater, DVD purchase,DVD rental, legal Internet
download
As in the U.S. design As in the U.S.design
Timing Time since the moviewas first available forconsumers through a
Movie only, movie with a limitedamount of bonus material (i.e.,making-of feature), movie withextensive bonus material (i.e.,
several making-of features,deleted scenes, multiple audio
commentaries)
As in the U.S. design As in the U.S.design
Languageoptions
The language optionsbetween which the
consumer can choose
Not included Choice betweenJapanese and
English audio track,Japanese audio track
only
Choice betweenGerman andEnglish audiotrack, German
audio track only
6Specifically, we used the following shares: 50% of theaterrevenues (the remaining 50% go to the theater owner; Blume2004; Vogel 2004), 60% of DVD sales (40% for the DVD retailer;Blume 2004; Cohen 2003; Manly 2005), 40% of DVD rental reve-nues (60% for the DVD rental company; Rentrak 2005), and 50%of VOD revenues (50% for the download company; Manly 2005;Sweeting 2005).
revenue shares were set according to industry information.6To minimize any impact of language on the results, we useda translation–back translation procedure for the Japaneseand German questionnaires.
Results
Estimation and Validation of Conjoint DataTo compute the preference data variables xTH, xDVD-S,xDVD-R, and xVOD, we estimated individual-level partworthsfrom the conjoint results through a Hierarchical Bayes rou-tine (Arora and Huber 2001). We used 10,000 burn-in itera-tions and another subsequent 10,000 iterations to generateparameter estimates; we saved every 10th iteration. Eachrespondent’s utility was represented by the mean utilityacross these 1000 draws.
We randomly generated five of the seven choice tasksand used them for partworth estimation, and we used theremaining two tasks for reliability and validity testing. Withregard to test–retest reliability, we referred to the agreementbetween respondents’ choices in the first and seventh choice
tasks; the latter was a replication of the first task (Ghiselli,Campbell, and Zedeck 1981). With identical choices by73.6% for the U.S. sample (four attributes per stimuli),72.2% for the German sample, and 68.1% for the Japanesesample (both sets of five attributes), reliability is satisfac-tory for all three subsamples. To measure predictivevalidity, we draw on the aggregate choice shares of a hold-out task and test the extent to which a model based on thepartworths estimated through the Choice Tasks 1–5 is ableto predict correctly the observed choice behavior withinChoice Task 6 (the holdout task) (Huber et al. 1993). Toobtain share predictions, we transformed the partworthsinto choice shares for the respective profiles using a logittransformation (Equation 2). Table 2 shows that the overallfit is good in all three countries; predicted shares are closeto actual shares in terms of mean absolute error, root meansquare error, and chi-square (Moore, Gray-Lee, and Lou-viere 1998) and clearly outperform the chance model,which assumes that each profile is equally likely to be cho-sen. The holdout scenario was identical to the predictedchoice in 66.0% of the U.S. sample cases and in 73.0% and64.4% of the Japanese and German cases, respectively.
Comparing our results with real-world market dataenables us to examine the external validity of our model.We applied our model and U.S. data to a situation thatreflects actual market conditions observed at the time weconducted our analysis (U.S. benchmark model: tTH = 0,tDVD-R = 6, tDVD-S = 6, tVOD = 12, and pDVD-S = $17.25;Epstein 2005). We found that the studio revenues in thisbenchmark model match actual studio revenues per channel
72 / Journal of Marketing, October 2007
TABLE 2Choice-Based Conjoint Prediction Accuracy for the Three Samples
Predicted Shares
Actual Shares (Holdout) Chance Estimated Shares (Logit)
Movie theater 32.4832.0451.61
20.0020.0020.00
25.6129.9650.75
DVD purchase 16.845.739.85
20.0020.0020.00
17.135.918.49
DVD rental 36.0547.7222.75
20.0020.0020.00
40.7051.2425.09
Legal online 4.594.897.81
20.0020.0020.00
3.371.584.90
None 10.039.617.98
20.0020.0020.00
13.2011.3110.76
Chance Model Logit ModelAverage Attribute
Importance
MAE 11.414015.906013.7440
3.23992.15742.0475
Channel 36.9625.9830.70
RMSE 2.75723.81223.7455
.8972
.5528
.4922
Timing 12.9613.9016.62
Chi-square 38.010472.662970.1427
3.58397.55242.8917
Fee 42.4444.3641.96
Bonus material 7.657.508.14
Language options N.A.8.262.58
Notes: Values in the top row belong to the U.S. sample, values in the middle row to the Japanese sample, and values in the bottom row to theGerman sample. MAE = mean absolute error, and RMSE = root mean square error. N.A. = not applicable.
closely. Specifically, 23.7% of studio revenues are gener-ated by theaters in our simulated benchmark model,whereas the studio shares of the actual theatrical revenuesaccounted for 25.3% (or $4.5 billion) of the studios’ reve-nues in the United States in 2005. In addition, 19.2% of ourbenchmark model studio revenues stem from DVD rentals,mirrored in real-world DVD rental studio revenues of19.2% ($3.4 billion), and 57.1% of the benchmark modelstudio revenues are generated by DVD sales, whereas actualDVD sales revenues constitute 55.5% ($9.8 billion) of themajor studios’ combined theatrical and home-viewing reve-nues (Entertainment Merchants Association 2006; MPAA2006). This ability to reproduce current revenue patterns
suggests reasonable external validity of the model and theapplied conjoint procedure.
Sequential Distribution Chain Optimization:A Stepwise Approach
This research is the first to consider the timing of sequentialdistribution systems as a multiple-window problem thatrequires simultaneous optimization. Because several chan-nel participants are involved, each of whom impose restric-tions on the implementation of distribution chain changes,we decided to use a stepwise approach when applying ourmodel to the data. Specifically, we test three differentgroups of scenarios, which differ in terms of restrictedness.
The Last Picture Show? / 73
7We held prices constant because the focus of our analysis is onstudio revenues, and all channels except DVD sales follow arevenue-sharing model in which the pricing decision lies with therespective final distributor and not with the studio (Van der Veenand Venugopal 2005).
Scenario Group 1 retains the traditional order of moviedistribution (i.e., tTH < tDVD-R, tDVD-S, and tVOD; tDVD-R ≤tDVD-S; and tVOD > tDVD-R and tDVD-S), paralleling previouswork on sequential distribution in the film industry (e.g.,Lehmann and Weinberg 2000). We carry out this first sce-nario group with prices for all channels held constant, andwe allow DVD prices to vary.7 Scenario Group 2 then letsmovie studios freely decide when and in which order toopen channels and how to price DVDs, with the exceptionthat movies are not allowed to open elsewhere before beingshown in theaters (i.e., tTH ≤ tDVD-R, tDVD-S, and tVOD). Thisremaining restriction is then lifted in Scenario Group 3, inwhich any possible channel order is considered regardlessof the potential obstacles that might hinder practicalimplementation.
Within each of the three scenario groups, we appliedour model to all scenarios that met the respective con-straints and calculated the studio’s maximum NPV for eachscenario. To avoid biases, we refrain from using interpola-tions between the attribute levels used in our conjointdesign, but we use a complete enumeration approachinstead. Given all constraints, Scenario Group 1 consists offour scenarios per country when DVD sales prices are fixedand 20 scenarios with flexible DVD sales prices. ScenarioGroup 2 contains 320 possible scenarios per country, andScenario Group 3, the most flexible, contains 875 scenariosper country. We begin our analyses with the U.S. data andthen replicate our approach with Japanese and German sam-ples. Table 3 summarizes the three best configurations interms of studio NPV for each scenario group and country,and Figure 4 compares the NPV of each group’s top sce-nario with the respective benchmark model.
Scenario Group 1 results (United States). With fixedchannel sequence and fixed prices, we find that the NPV ofthe current distribution configuration is optimal and cannotbe increased by changes in the timing of distribution win-dows. Even when the pricing constraint is lifted for DVDsales (i.e., when DVD prices are allowed to fluctuate), thecurrent theater-to-DVD window of six months remainssuperior for the studio. However, the results suggest that ifthe retail DVD price is set at $22 (versus $17.25), studiorevenues increase by 2.1% compared with the benchmarkconfiguration. Because consumer expectations now incor-porate the higher DVD retail price, choice shares shiftslightly away from retail DVDs toward theaters, rentalDVDs, and VOD.
Scenario Group 2 results (United States). Removing allorder constraints for home entertainment channels, exceptfor not opening earlier than theaters, we observe majorchanges in terms of the channel structure that maximizesstudio revenues. Under these conditions, studio revenuesare maximized when movies are released simultaneously inmovie theaters, on rental DVD, and in VOD, with DVDs
being released for sale after a three-month window for aprice of $22. In this scenario, studio revenues increase by16.2% compared with the benchmark constellation. How-ever, these studio revenue gains impose a heavy cost onmovie theaters, which lose 40.1% of their revenues as aresult of cannibalization. In addition to movie studios, thebeneficiaries of this scenario are DVD retailers whose reve-nues increase by 49.6%.
When we examine the next-best scenarios under thisconstraint set, common patterns exist. The four revenue-maximizing configurations for studios all involve a simulta-neous release in theaters and on rental DVD, with a DVDsales channel window of three months. Finally, the retailDVD price of $22 is common to the nine best scenarios,suggesting that DVDs are currently priced too low to maxi-mize studio revenues. This result is consistent with thenotion that “Wal-Mart, Best Buy, and other mass marketersare happily using DVDs and CDs as loss leaders and slash-ing prices to a level where even [rental chain] Blockbusteracknowledges it can’t compete” (Amdur 2004).
Scenario Group 3 results (United States). Allowingtheatrical releases to occur after other channels have beenopened, we find that the most economically attractive sce-narios remain unchanged from Scenario Group 2. Conse-quently, the results suggest that a delayed theater release isnot optimal for studios, because the loss of shared revenuesdue to severe losses by movie theaters is not offset byincreases in shared revenues from gains in the other chan-nels. Considering the devastation such configurations wouldcause to movie theater chains without delivering additionalrevenues to the studios, channel order changes that shifttheaters from the start of the distribution sequence do notappear to be a desirable strategy in the U.S. market.
Scenario analyses for foreign markets. In the restrictiveScenario Group 1 (with flexible DVD prices), strategyimplications for Japan and Germany resemble those for theUnited States. In Japan, the optimal scenario employs a six-month DVD window, albeit with a slightly lower DVDretail price, and generates 1.4% more in studio revenuesthan the benchmark configuration. In Germany, a six-monthDVD window also generates the highest revenues. By rais-ing the retail DVD price to $22 in this scenario, studios canincrease their revenues by 4.0%, while retaining the estab-lished channel order. However, when home entertainmenttiming constraints are removed in Scenario Group 2, thesimilarities between the U.S. and Japanese market simula-tions end. Although the settings now allow for simultaneousreleases, the most attractive scenarios for studios retaintheaters as the sole first channel. At the same time, theresults for Japan suggest that narrowing the theater-to-DVD-sales window would increase studio revenues.Specifically, the five best scenarios in this group share thedistinct pattern of releasing a movie in theaters first, open-ing the DVD sales channel after three months, and delayingthe rental DVD release by another nine months, a configu-ration that, according to our results, would improve studiorevenues by up to 11.6%. Contrary to the U.S. market,lower DVD retail prices increase studio revenues in Japan.
In Germany, the three revenue-maximizing configura-tions are essentially the same as in Japan, except that DVD
74 / Journal of Marketing, October 2007
TAB
LE
3To
p S
cen
ario
s fr
om
Sce
nar
io G
rou
ps
and
Co
un
trie
s
Tim
ing
Res
tric
tio
ns
Pri
cin
g
Res
tric
tio
ns
Un
ited
Sta
tes
Jap
anG
erm
any
Sce
nario
Gro
up 1
(with
all
pric
esfix
ed)
Mov
ie t
heat
ers
<D
VD
ren
tal ≤
DV
Dsa
les
< V
OD
Mov
ie t
heat
er=
$7.
75,
DV
D r
enta
l= $
3.00
,D
VD
pur
chas
e=
$17
.25
(Jap
an:$
22.0
0), V
OD
=$3
.00
(1)a
t TH
= 0
, t D
VD
-R=
6,
t DV
D-S
= 6
, t V
OD
= 1
2,
NP
VS
= +
.0%
(1)a
t TH
= 0
, t D
VD
-R=
6,
t DV
D-S
= 6
, t V
OD
= 1
2,
NP
VS
= +
.0%
(1)
at T
H=
0,
t DV
D-R
= 6
, t D
VD
-S=
6,
t VO
D=
12,
N
PV
S=
+.0
%
(2)
t TH
= 0
, t D
VD
-R=
3,
t DV
D-S
= 3
, t V
OD
= 6
, N
PV
S=
–1.
4%
(2)
t TH
= 0
, t D
VD
-R=
3,
t DV
D-S
= 3
, t V
OD
= 1
2,
NP
VS
= –
2.1%
(2)
t TH
= 0
, t D
VD
-R=
3,
t DV
D-S
= 3
, t V
OD
= 1
2,
NP
VS
= –
2.5%
(3)
t TH
= 0
, t D
VD
-R=
3,
t DV
D-S
= 3
, t V
OD
= 1
2,
NP
VS
= –
1.5%
(3)
t TH
= 0
, t D
VD
-R=
3,
t DV
D-S
= 3
, t V
OD
= 6
, N
PV
S=
–2.
1%
(3)
t TH
= 0
, t D
VD
-R=
3,
t DV
D-S
= 3
, t V
OD
= 6
, N
PV
S=
–2.
8%
Sce
nario
Gro
up 1
(with
fle
xibl
e D
VD
purc
hase
pric
es)
Mov
ie t
heat
ers
<D
VD
ren
tal ≤
DV
Dsa
les
< V
OD
Mov
ie t
heat
er=
$7.
75,
DV
D r
enta
l= $
3.00
,D
VD
pur
chas
e ≥
$3.0
0an
d ≤
$22.
00, V
OD
=$3
.00
(1)
t TH
= 0
, t D
VD
-R=
6,
t DV
D-S
= 6
, t V
OD
= 1
2,
p DV
D-S
= $
22,
NP
VS
= +
2.1%
(1)
t TH
= 0
, t D
VD
-R=
6,
t DV
D-S
= 6
, t V
OD
= 1
2,
p DV
D-S
= $
17.2
5,
NP
VS
= +
1.4%
(1)
t TH
= 0
, t D
VD
-R=
6,
t DV
D-S
= 6
, t V
OD
= 1
2,
p DV
D-S
= $
22,
NP
VS
= +
4.0%
(2)
t TH
= 0
, t D
VD
-R=
6,
t DV
D-S
= 6
, t V
OD
= 1
2,
p DV
D-S
= $
17.2
5,
NP
VS
= +
.0%
(2)
t TH
= 0
, t D
VD
-R=
3,
t DV
D-S
= 3
, t V
OD
= 1
2,
p DV
D-S
= $
17.2
5,
NP
VS
= +
.9%
(2)
t TH
= 0
, t D
VD
-R=
3,
t DV
D-S
= 3
, t V
OD
= 1
2,
p DV
D-S
= $
22,
NP
VS
= +
.2%
(3)
t TH
= 0
, t D
VD
-R=
3,
t DV
D-S
= 3
, t V
OD
= 6
, p D
VD
-S=
$22
, N
PV
S=
–.9
%
(3)
t TH
= 0
, t D
VD
-R=
3,
t DV
D-S
= 3
, t V
OD
= 6
, p D
VD
-S=
$17
.25,
N
PV
S=
+.5
%
(3)
t TH
= 0
, t D
VD
-R=
6,
t DV
D-S
= 6
, t V
OD
= 1
2,
p DV
D-S
= $
17.2
5,
NP
VS
= +
.0%
Sce
nario
Gro
up 2
Mov
ie t
heat
ers
=0,
DV
D r
enta
l ≥0,
DV
D s
ales
≥0,
VO
D ≥
0
Mov
ie t
heat
er=
$7.
75,
DV
D r
enta
l= $
3.00
,D
VD
pur
chas
e≥
$3.0
0an
d≤
$22.
00, V
OD
=$3
.00
(1)
t TH
= 0
, t D
VD
-R=
0,
t DV
D-S
= 3
, t V
OD
= 0
, p D
VD
-S=
$22
, N
PV
S=
+16
.2%
(1)
t TH
= 0
, t D
VD
-R=
12,
t D
VD
-S=
3,
t VO
D=
12,
p D
VD
-S=
$17
.25,
NP
VS
= +
11.6
%
(1)
t TH
= 0
, t D
VD
-R=
12,
t D
VD
-S=
3,
t VO
D=
12,
p D
VD
-S=
$22
, N
PV
S=
+14
.2%
(2)
t TH
= 0
, t D
VD
-R=
0,
t DV
D-S
= 3
, t V
OD
= 6
, p D
VD
-S=
$22
, N
PV
S=
+15
.7%
(2)
t TH
= 0
, t D
VD
-R=
12,
t D
VD
-S=
3,
t VO
D=
6,
p DV
D-S
= $
17.2
5,
NP
VS
= +
10.1
%
(2)
t TH
= 0
, t D
VD
-R=
12,
t D
VD
-S=
3,
t VO
D=
0,
p DV
D-S
= $
22,
NP
VS
= +
12.7
%
(3)
t TH
= 0
, t D
VD
-R=
0,
t DV
D-S
= 3
, t V
OD
= 1
2,
p DV
D-S
= $
22,
NP
VS
= +
15.6
%
(3)
t TH
= 0
, t D
VD
-R=
12,
t D
VD
-S=
3,
t VO
D=
0,
p DV
D-S
= $
17.2
5,
NP
VS
= +
8.1%
(3)
t TH
= 0
, t D
VD
-R=
12,
t D
VD
-S=
3,
t VO
D=
6,
p DV
D-S
= $
17.2
5,
NP
VS
= +
12.7
%
The Last Picture Show? / 75
TAB
LE
3C
on
tin
ued
Tim
ing
Res
tric
tio
ns
Pri
cin
g
Res
tric
tio
ns
Un
ited
Sta
tes
Jap
anG
erm
any
Sce
nario
Gro
up 3
Mov
ie t
heat
ers
≥0,
DV
D r
enta
l ≥0,
DV
D s
ales
≥0,
VO
D ≥
0
Mov
ie t
heat
er=
$7.
75,
DV
D r
enta
l= $
3.00
,D
VD
pur
chas
e ≥
$3.0
0an
d ≤
$22.
00, V
OD
=$3
.00
(1)
t TH
= 0
, t D
VD
-R=
0,
t DV
D-S
= 3
, t V
OD
= 0
, p D
VD
-S=
$22
, N
PV
S=
+16
.2%
(1)
t TH
= 0
, t D
VD
-R=
12,
t D
VD
-S=
3,
t VO
D=
12,
p D
VD
-S=
$17
.25,
N
PV
S=
+11
.6%
(1)
t TH
= 0
, t D
VD
-R=
12,
t D
VD
-S=
3,
t VO
D=
12,
p D
VD
-S=
$22
, N
PV
S=
+14
.2%
(2)
t TH
= 0
, t D
VD
-R=
0,
t DV
D-S
= 3
, t V
OD
= 6
, p D
VD
-S=
$22
, N
PV
S=
+15
.7%
(2)
t TH
= 3
, t D
VD
-R=
12,
t D
VD
-S=
3,
t VO
D=
0,
p DV
D-S
= $
17.2
5,
NP
VS
= +
11.1
%
(2)
t TH
= 0
, t D
VD
-R=
12,
t D
VD
-S=
3,
t VO
D=
0,
p DV
D-S
= $
22,
NP
VS
= +
12.7
%
(3)
t TH
= 0
, t D
VD
-R=
0,
t DV
D-S
= 3
, t V
OD
= 1
2,
p DV
D-S
= $
22,
NP
VS
= +
15.6
%
(3)
t TH
= 0
, t D
VD
-R=
12,
t D
VD
-S=
3,
t VO
D=
6,
p DV
D-S
= $
17.2
5,
NP
VS
= +
10.1
%
(3)
t TH
= 0
, t D
VD
-R=
12,
t D
VD
-S=
3,
t VO
D=
6,
p DV
D-S
= $
17.2
5,
NP
VS
= +
12.7
%
a Ben
chm
ark
scen
ario
for
the
resp
ectiv
e co
untr
y;al
l NP
VS
perc
enta
ge in
crea
ses/
decr
ease
s ar
e ag
ains
t th
is s
cena
rio.
Not
es:t
= t
ime
of r
elea
se (
mon
ths)
, p
= p
rice
(dol
lars
), T
H=
the
ater
cha
nnel
, D
VD
-S=
DV
D s
ales
cha
nnel
, an
d D
VD
-R=
DV
D r
enta
l cha
nnel
.
76 / Journal of Marketing, October 2007
FIGURE 4Revenue Changes for Top Scenario per Group (in Percentage Compared with the Benchmark Model)
The Last Picture Show? / 77
prices are higher and the timing of the VOD channel differs.Here, we find that the theaters and DVD retailers wouldalso profit greatly from a three-month window for DVDsales and a 12-month window for DVD rental and VOD; themost attractive scenario would promise studios a revenueincrease of 14.2%. DVD retailer revenues and theater reve-nues jump by 28.3% and 14.6%, respectively, while therental chains’ earnings plummet by 30.9%. Notably, thetiming of the VOD release varies across the differentrevenue-maximizing scenarios, ranging from an immediateopening to a 12-month delay. Although the VOD channelperforms better with a shorter release window, it does notexert much influence on the studios’ revenues because oflimited cannibalization. As with the U.S. market, lifting thefinal constraint in Scenario Group 3 does not change theresults in Japan and Germany. The best scenarios remainthose found in Scenario Group 2; the only exception is thatthe new second-best scenario in Japan suggests an exclusiveVOD premiere, followed by a three-month window fortheaters and DVD retail and a 12-month window for DVDrentals.
Sensitivity Analysis
Because respondents self-reported some of the informationused for model estimation regarding their behavior underthe current channel structure, we conducted a set of sensi-tivity analyses to determine how robust our results are withregard to these measures. Specifically, we systematicallyvaried the individual responses for all self-reported behav-iors (multiple consumption SBS, word-of-mouth-basedSBS, and charts-based SBS) for each channel by +/–20%.Table 4 provides the results of these analyses, showing howvariations in the measures affect the respective group-bestscenario’s NPV change in relation to the benchmark sce-nario. For example, under Scenario Group 2 conditions, a20% increase of the multiple consumption parameter forDVD purchases in Germany would result in a studio NPVincrease of 15.2% compared with the benchmark model(instead of 14.2% when the multiple consumption parame-ter for DVD purchases is not manipulated), whereas areduction of the same parameter by 20% would result in anincrease of 13% in studio NPV.
In general, the pattern and magnitude of the results aresubstantively robust to the parameter variations. Overall, theNPV growth in the group-best scenarios in which parame-ters are varied differs by less than 1% from NPV growth forthe original parameters. A notable exception is the variationof the individual multiple consumption parameter for DVDpurchases in U.S. Scenario Groups 2 and 3, in which a+/–20% variation leads to an NPV increase of 20.7% and10.6%, respectively, compared with an increase of 16.2%under nonvaried conditions. Further support for the robust-ness of our results comes from the finding that the group-best scenario remains the best in 230 of 234 variations; wefound changes in only 4 configurations, all of which involvea 20% decrease of the multiple consumption DVD purchaseparameter. Specifically, in U.S. Scenario Groups 2 and 3,the DVD rental window is moved back to 12 months in thenew revenue-maximizing scenario, and in the Japanese Sce-
8We conducted additional sensitivity analyses for the effects ofpotential changes in channel revenue functions and conjointattribute utilities. Regarding channel revenue functions, modelinglog-linear functions for all four channels does not change any win-ner scenario or NPV growth number. With regard to conjointattribute utilities, we varied the different utilities on the individualconsumer level by +/–20%, finding that the pattern and magnitudeof the results are substantively robust to the variations. Specifi-cally, the maximal reduction of NPV growth of any group-bestscenario is only 1.8% compared with the respective benchmarkscenario, and in 64 of the 72 varied conditions, the effect on NPVgrowth is less than 1%. The group-best scenarios remain the sameas in the nonvaried condition in 62 of 72 variations. In addition toreflecting the high reliability and internal/external validity of theconjoint results already demonstrated through established conjointvalidation methods, these further analyses show that within a rea-sonable range, potential changes in the consumers’ perceivedimportance of channel characteristics (i.e., channel, timing, andprice) should have only a limited effect on optimal distributionstructures.
nario Groups 2 and 3, the new top scenario features atheatrical and DVD retail opening 3 months after the VODpremiere, and the DVD rental is delayed to a 12-monthwindow.8
Accounting for Heterogeneity: The Impact ofMovie Genres
The results reported so far assume that one distributionmodel is ideal for all movies. To account for potentialheterogeneity that would undermine this assumption, weexamined whether genre-specific distribution models mightgenerate additional revenues for studios. We tested the reve-nue potential of such a genre-specific approach by applyinga two-step procedure. First, we assigned the movies in oursample to genres by drawing on genre classifications byIMDbPro. This resulted in five genres (action, comedy,drama, fantasy, and thriller) with two movies in each genre(one movie was assigned to two genres). Second, werepeated the optimization process used to identify generalrevenue-maximizing distribution models for each of the fivegenres, considering only the respective subsample (e.g.,only respondents who selected fantasy movies).
There appear to be differences in consumer preferences.In the United States, preferences toward rental channels aresomewhat higher for comedies, and preferences towardtheaters and DVD purchases are higher for action and fan-tasy movies, which implies moving forward rental channelsfor comedies and moving back the DVD rental channelbehind the DVD purchase channel for action and fantasymovies. However, as a whole, genre effects on NPV out-comes are moderate, surpassing the general distributionmodel revenues by only .8% (United States), 1.6% (Japan),and 2.1% (Germany). Of (3 countries × 4 scenario groups ×5 genres =) 60 constellations, we found only one in which agenre-specific model outperforms the general model bymore than 5% (Scenario Group 3 in Japan for action moviesoutperformed the general model by 5.5%). Given these rela-tively small revenues gains and considering that the imple-mentation of genre-specific distribution models wouldlikely cause consumer confusion (e.g., when new movies
78 / Journal of Marketing, October 2007
TABLE 4Results from Sensitivity Analyses
aUnder these conditions, the former group-best scenario became the second-best channel configuration by a small margin.Notes: Numbers before the parentheses are the group-best scenario NPV in relation to the benchmark scenario NPV when the respective
parameter is increased by 20%. Numbers in parentheses are the group-best scenario NPV in relation to the benchmark scenario NPVwhen the respective parameter is decreased by 20%.
Parameter Varied by +/–20% United States Japan Germany
Scenario Group 1 (with all prices fixed) Baseline: .0% Baseline: .0% Baseline: .0%
combine elements of two or more genres that have differentdistribution patterns; e.g., Evan Almighty, the $250 millionsequel to Bruce Almighty, is described by its studio as “a
spectacle fantasy and also a comedy”; Muñoz 2006), wefocus on the general distribution approach when we discusspotential implications for the movie industry.
The Last Picture Show? / 79
Discussion and ImplicationsThis study uses a multi-indicator approach that featureshierarchical Bayes choice-based conjoint information forthe intertemporal prediction of market shares. We apply anNPV model of movie studio revenues across complex andmultiwindow sequential distribution chains and find that byadjusting the configuration of distribution channels and theprice of DVDs, motion picture studios could, all else beingequal, boost their revenues by 16.2% (or $3.5 billion) in theUnited States alone. Moreover, we demonstrate that con-sumers’ channel preferences and movie consumption deci-sions differ among three major markets (the United States,Japan, and Germany), thus offering insights into how stu-dios might fine-tune distribution strategy by country.
Implications for Research and the Motion PictureIndustry
Our results suggest that the movie industry’s current distrib-ution model is not optimal in terms of revenue generation.Our key implication is that studio revenues can be increasedby changing both the timing and the order of distributionwindows. The channel configuration that performs best inthe United States includes making a film simultaneouslyavailable in theaters, as a DVD rental, and through VOD,followed three months later in the DVD sale channel at aprice of $22. According to our findings, if this configurationwere to be used to distribute motion pictures in the UnitedStates, studios would receive only 12.2% of their total reve-nues from theaters (versus 25.3% in 2005) and only 14.1%from DVD rentals (versus 19.2% in 2005), but contributionsfrom DVD sales would soar to 73.6% (from 55.5% in2004).
Our results suggest that recent industry speculationabout simultaneous channel releases, called a “death threat”by theater owners (Stanley 2005), would indeed be devas-tating for movie theaters. However, such a change might befinancially attractive to movie studios and DVD retailers ifexecuted in the U.S. market, though externalities must beconsidered if the theater channel were to be irreparablydamaged; we discuss this in more detail subsequently. Thistype of simultaneous-release approach is not equallypromising for studios in the major export markets of Ger-many and Japan, in which the interchannel cannibalizationof theater revenues would not be offset by DVD salesgrowth to the same extent as in the United States. In thesemarkets, our results indicate that the optimal U.S. configu-ration would lead to a studio revenue gain over the bench-mark of only 1.8% in Germany and even a revenue loss of5.8% in Japan.
The results also imply that an exclusive “Wal-Mart pre-miere” is not the most promising option for studios. In noneof the three countries examined in our study do the empiri-cal results suggest that theaters should be shifted away fromthe start of the distribution chain. An examination of thechannel market shares and revenues suggests that an exclu-sive movie opening in DVD retail stores would not take fulladvantage of multiple-purchasing behavior, because manyof the consumers who would buy the DVD in such a retail-premiere scenario would also have bought it after having
first consumed the movie in theaters (or other rentalchannels).
The results also suggest that the timing of the VODchannel has little influence on studio revenues. Thereappears to be a distinct consumer segment for VOD, but thesize of the market is not strongly affected by moving theVOD release forward. For example, whereas the marketshare for VOD is 4.4% in the benchmark scenario, it growsonly to 5.3% when a movie is initially released on VODalongside theaters and DVD rentals in the studio revenue-maximizing U.S. scenario. Note that this is the case eventhough our model assumes that all movies are availablethrough all four channels (which is not the case in realityfor VOD), which signals a somewhat limited growth poten-tial for the channel. Still, Apple Chief Executive OfficerSteve Jobs’s vision of offering movies through onlinedownloads at the same time they hit retail shelves has beenlikened to “walking into a lion’s den” (CinemaNow ChiefExecutive Officer Curt Marvis; Grove 2005).
Our findings underscore why potential changes to tradi-tional channel sequences are currently at the center of Hol-lywood’s attention and the subject of rancorous debate. Tomaximize studio revenues, radical changes to the extantmovie distribution model are proposed, and substantialshares of business are shifted among the various players.Most glaringly, U.S. theaters stand to lose 40% of theirrevenues, whereas DVD retailers’ revenues could increaseby approximately 50%. Similarly, in the configurations thatmaximize studio payoff, Japanese and German DVD rentalchains would face revenue losses of 21% and 31%, respec-tively, and their retailing counterparts’ respective revenuescould jump by 66% and 28%. These results raise the ques-tion whether U.S. theater chains or Japanese and Germanvideo rental chains would be able to scale down their opera-tions, or whether such scenarios would be fatal. If novel dis-tribution strategies were to trigger the disintegration ofentire industry branches, such as theatrical exhibition inrural areas, this outcome not only would be a financial set-back for studios but also would have widespread conse-quences, such as a disastrous loss of cultural heritage andjobs.
How could theaters adapt to such changes? One reactionmight be for theaters to diversify into multichannel opera-tions, transforming themselves into “one-stop shops” inwhich audiences can watch a theatrical exhibition and rentor buy the DVD afterward (perhaps receiving discounts formultiple channel consumption). Another reaction tochanges to the traditional distribution model seems lessspeculative. Changes will be met with fierce resistance bythe respective industry players that perceive a threat to theirstakes. North American “[t]heater owners have already lam-basted Disney [Chief Executive Officer] Bob Iger for evenmentioning that he might reconsider the windowsapproach,” and “Wal-Mart,… the country’s largest DVDretailer, will go bat-crazy” over attempts to change the DVDbusiness model in favor of VOD (Grove 2005). Studiosexperienced a hint of what might happen when the simulta-neous release of the film Bubble in multiple channels waswidely met with boycotts by theaters (Canadian Broadcast-ing Corporation 2006). Therefore, it is important to stress
80 / Journal of Marketing, October 2007
that our results do not include the costs that might arisefrom distribution model transformations, such as lost reve-nues caused by the boycotting of movies by theater chainsor image deterioration as a result of media debates. Couldsuch resistance be broken by the studios? One approachwould be to offer theaters compensation for acceptingshorter distribution windows (e.g., a higher revenue share)(Grove 2006). We ran additional sensitivity analyses todetermine how changes in revenue allocation would affectthe attractiveness of our optimal distribution model in theUnited States. We found that an allocation of 60% of boxoffice grosses to theater owners (versus 50%) would have alimited effect, with the revenue-maximizing structureremaining the same and studio revenues still being 13.4%higher than in the benchmark scenario.
A potential alternative would be to search, post hoc, forconfigurations in which every market participant gains reve-nues (or at least does not lose any). Our simulations suggestthat such scenarios exist in the United States and Germany,but we do not identify such a “win–win” configuration forthe Japanese market. In the United States, a three-monththeatrical-to-DVD-retail window with a higher DVD retailprice, followed by the DVD rental and VOD releasesanother three months later, lifts studio revenues by 7.3%over the benchmark. This growth goes hand in hand withincreases in revenues of 11.1% for DVD retailers as a resultof the shorter window. DVD rentals and VOD gain 4.5%and 7.5%, respectively, because of the higher DVD price,which provides them with marginal gains in choice shares,while theater revenues are not cannibalized. The Germanwin–win scenario appears similar, with the exception of theVOD window being 12 months. The outcome here wouldbe a 7.6% revenue increase for studios, revenue growth of19.1% for DVD retailers, marginal benefits for rentalchains, and no changes for theaters and VOD. Althoughthese scenarios promise no negative effects for all partiesinvolved, implementation would likely be met with resis-tance because it requires breaking with the industry tradi-tion of opening the rental channel before (or simultaneouslywith) the retail channel. Rental chains would likely resist achange that promises no gains for them but moves them fur-ther down the distribution chain. However, because DVDretailers are the cobeneficiaries in every studio revenue-maximizing configuration identified in our analyses, thestudios should have powerful allies in retailing giants, suchas Wal-Mart (United States) and the Metro Group (Europe).
Altogether, this study integrates the sparse research oninterchannel effects relevant to the optimization of sequen-tial distribution chains into a coherent model. Our modelbuilds on characteristics of sequential distribution systemsthat prior research has identified. Industries that rely on dis-tribution windowing could tailor our framework and empiri-cal approach to their context. For example, the major recordlabel Sony BMG recently introduced sequential distributionto the music industry, a strategy that Booz Allen Hamiltonconsultants recommended (Bhatia, Gay, and Honey 2001).Other entertainment goods producers that already employwindowing, such as book publishers and computer game
developers, may benefit financially from examining thegeneral characteristics we derived herein to gain insightsinto how to refine their distribution models and increaserevenues.
Limitations, Future Research Opportunities, andConclusion
In addition to our modeling assumptions, this study hassome limitations. We do not consider the impact of distribu-tion chain changes on piracy. Next to sequential distribu-tion, piracy is the movie industry’s most important concern(Hennig-Thurau, Henning, and Sattler 2007) and has beendescribed by the MPAA (2004) as “the greatest threat to theeconomic basis of moviemaking in its 110-year history.”Industry executives have expressed concern that advancedreleases on DVD or VOD might increase piracy becausehigh-quality digital versions of movies would be accessibleto potential pirates earlier in the distribution chain (TheEconomist 2002). However, this effect might be limited insize because illegal copies of nearly all new movies arealready available in file-sharing networks before or duringtheir theatrical run (Byers et al. 2004). Effective copy pro-tection measures would certainly reduce the studios’ riskassociated with closing the window between theaters andhome channels. Future studies should examine the impactof channel configuration on piracy.
Although our model optimizes studio revenues, itignores the costs of producing, marketing, and distributingmotion pictures. Although production costs will be largelyunaffected by distribution chain changes, an increase in thenumber of DVDs sold might create economies of scale thatwould lower costs per DVD and increase studio profit mar-gins. However, considering the first-copy-cost character ofmotion pictures with limited variable costs, revenue opti-mization should be a good proxy for profits. Still, furtherresearch could integrate cost and margin information.
It is important to stress that our empirical model doesnot explicitly consider implementation barriers to channelrestructuring. Although we identify problems that would beassociated with the modification of channel configurations,uncertainty remains, including the costs that might beincurred through negative responses by channel partnersthat have been alienated. Our win–win constellations wouldprobably cause less resistance from other industry playersand might be considered an acceptable compromise for allinvolved.
Although this article is the first to model more than twochannels, our findings are limited insofar as we includeonly download-to-rent VOD, not download-to-own VOD.However, we assume that the results would remain fairlystable, given the limited role of VOD for movie revenuesand the small preferences of the respondents in our studytoward VOD. The same could be said for other channels wedo not consider (e.g., mobile devices). In addition, we pre-ferred a multinomial choice scenario, asking consumers fortheir “first choice” in terms of watching a new movie, overa multivariate conjoint approach, because the latter wouldhave required that consumers anticipate their choice behav-ior over time, but we do not test our model against a multi-variate alternative.
The Last Picture Show? / 81
We model revenue allocation between different partiesas constant over time, which is true for most channels con-sidered, but studios’ share of box office grosses oftendecreases with the time a movie is available in theaters.Given that the intrachannel flow of revenues remains con-stant across distribution models, this should not affect ourresults. However, theater owners should be aware that ifmovies were shown in their venues for a shorter period in anew distribution model, the theatrical share of revenueswould decrease because the percentage of weeks that gener-ate less-than-average revenues would increase. However,this is based on neither theory nor data. Similarly, althoughthe assumption that consumers have “perfect expectations”on release times is logical, this will not be the case for everyconsumer or any movie release. Information asymmetriesmight allow studios to issue films earlier in secondary chan-nels than consumers expect; however, consumers will learnand adapt their expectations accordingly, anticipating futuremovies to be released earlier than announced by studios.
Although we account for key market variables, we donot control for all factors that might affect the results. Forexample, we do not consider movie quality, which canstimulate word of mouth (Liu 2006). That said, we believethat the potential for studios to differentiate distribution onthe basis of quality is limited, because a later release of“good” movies and an earlier release of “bad” movies willaffect customers’ expectations. Audiences might even actstrategically, staying away from theaters to prompt studiosto open secondary channels earlier. Furthermore, our resultsdo not consider seasonality, movie competition at release,or cross-country influences (e.g., the impact of U.S. resultson Germany results; see Elberse and Eliashberg 2003).Optimal structures might differ as a result of these factors,and thus we suggest that the role of these factors needs to betested in future work.
The choice-based conjoint design reveals consumerpreferences for currently nonexisting, but possible scenar-ios. However, the SBS parameters are based on self-reportsof previous consumer behavior in traditional sequential dis-tribution sequences. Although sensitivity analyses show thatthe self-reported data affect the results only to a limiteddegree, we acknowledge that no objective data are availableon how SBS might evolve in different channel structures,leaving this as a challenge for further research. Althoughour samples contain movies from major genres and thoughwe found only limited genre-specific differences in terms ofrevenue-maximizing distribution models, it would be laud-able to replicate our findings with a different (and larger)set of movies.
In conclusion, our results suggest that the currentsequential distribution configuration in the motion pictureindustry does not maximize revenues for the studios thatproduce movies. Channel configurations play an importantrole in motion picture success. Although theaters will notsee their “last picture show” immediately, theater ownersand movie audiences are almost certain to face significantchanges in the near future.
9Note that for readability reasons, this example does not containfurther channel attributes π.
Appendix AIllustrative Model Calculation9
Consider a scenario with J = 5 channel alternatives:
1. Theater visit (= mTH) at the movie’s release date (i.e., tTH =0 months, at price pTH = $12.50).
2. DVD rental (= mDVD-R) at the movie’s release date (i.e.,tDVD-R = 0 months, at price pDVD-R = $7.75).
3. DVD sales (= mDVD-S) 3 months after the movie’s releasedate (i.e., tDVD-S = 3 months, at price pDVD-S = $22).
4. VOD (= mVOD) 12 months after the movie’s release date(i.e., tVOD = 12 months, at price pDVD-S = $3).
5. Waiting for the movie to be released on television (theno-consumption option).
Given this set of alternatives and a consumer’s preferencestructure, Option 1 might obtain choice shares of x(TH|J) =.25. Thus, of 100 movie consumption occasions, this con-sumer would visit the theater 25 times. Likewise, choiceshares for the remaining channels might be x(DVD-R|J) =.15, x(DVD-S|J) = .45, and x(VOD|J) = .05. Consequently,10% of choice shares would be allocated to the no-consumption option. In this case, xFC will be represented bythe choice shares for theaters and DVD rental because bothchannels open simultaneously at the movie’s release date(tTH = tDVD-R = 0 months); that is, xFC = x(TH|J) + x(DVD-R|J) = .4. Thus, if a consumer typically buys aDVD of a movie he or she has seen before in other channelsin 10% of the cases (δDVD-S = aDVD-S = .1), the multiple-purchase effect will increase the choice shares for DVDsales by 4%. Likewise, if the consumer buys a DVD in 5%of the cases exclusively because he or she heard from otherpeople that it was a success (γWOM
DVD-S = .05) and in 15%of the cases exclusively because of favorable chart informa-tion (γ C
DVD-S = .15), the information-cascading SBS effectwould result in an increase in choice shares of xDVD-S ×(γWOM
DVD-S + γ CDVD-S) = .45 × (.05 + .125) = 9%. The total
updated choice share would then be x′DVD-S = .45 × (1 +.05 + .15) + .1 × .4 = .58.
With the price for a theater visit being pTH = $12.50 andif we assume that the mean choice share for theaters acrossall consumers is .2, the expected revenue of theaters wouldbe RTH = $12.50 × .2 = $2.5. Multiplying by 100 gives abetter interpretation of this result (i.e., the expected theaterrevenue from 100 movie consumption occasions, given thespecific scenario of available channel alternatives). Accord-ing to the over-time revenue distribution function f(w) weestimated for theaters (see Figure 2), after the first week,29.37% of the $2.5 would flow back to theaters. This pro-portion then needs to be discounted with the weekly dis-count rate of .183%—that is, ($2.5 × .2937)/1.00183. Thesecond week would produce another 20.74% of the totalrevenue that needs to be discounted for two weeks—that is,($2.5 × .2074)/(1.00183)2. We simulate the revenue returnfor up to 78 weeks in this manner. Adding up these dis-counted values gives the present value of the theater-
82 / Journal of Marketing, October 2007
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APPENDIX BSBS Questions
Multiple Consumption SBS In general: What proportion of all movies you have seen in a movie theater …
DVD purchase Did you later also purchase on DVD? ___%
DVD rental Did you later also rent on DVD in a video store? ___%
Download-to-rent VOD Did you later also download from a legal internet service (e.g., MovieLink, CinemaNow)for a fee? ___%
Word-of-Mouth-Based SBS
DVD purchase Of all DVDs you have purchased so far, what proportion of those did you purchasebecause you missed the movie in theaters, but heard from friends or acquaintances it was
good? ___%
DVD rental Of all DVDs you have rented from a video store so far, what proportion of those did yourent because you missed the movie in theaters, but heard from friends or acquaintances it
was good? ___%
Download-to-rent VOD Of all movies you have downloaded from legal online services so far, what proportion ofthose did you download because you missed the movie in theaters, but heard from
friends or acquaintances it was good? ___%
Charts-Based SBS
DVD purchase Of all DVDs you have purchased so far, what proportion of those did you purchasebecause you missed the movie in theaters, but it was a huge box office success? ___%
DVD rental Of all DVDs you have rented from a video store so far, what proportion of those did yourent because you missed the movie in theaters, but it was a huge box office success?
___%
Download-to-rent VOD Of all movies you have downloaded from legal online services so far, what proportion ofthose did you download because you missed the movie in theaters, but it was a huge box
office success? ___%
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