Brand Extension Effects and Core Attributes of ExperienceProduct Franchises: A Bayesian ApproachGoksel Yalcinkaya and Tevfik Aktekin
An experience product’s quality is difficult to assess prior to purchase, largely due to the limited availability ofinformation before consumption. In the absence of perfect information, firms routinely use certain market signals toprovide product quality information to consumers. Accordingly, drawing from signaling theory, this research aims toidentify a collection of product core attributes in the form of signals and brand extension features to successfullymanage experience product franchises. In doing so, we make use of Bayesian models with both deterministic effects viathe use of predictor variables and probabilistic effects via the use of brand extension properties. Such models allow usto explore specifically the relative performance effects of the parent product of a franchise and of its extensions giventhe same level of product core attributes. The results of this study, based on the motion picture franchise data, indicatethat there are critical product core attributes such as continuity, timing, and prior perception that collectively lead tosuccessful successive generations. Furthermore, our study shows that brand features measured by the relationshipsbetween the parent product and its subsequent extensions at the infancy of the franchise are essential for thecontinuation of experience products. Similarly, our results indicate that the parent product’s success on later exten-sions’ performance starts to diminish, implying that the established “brand name” is what carries the franchiseforward.
Introduction
C onsumers often have incomplete informationabout product attributes, quality, and benefits.Such imperfect information steers consumers to
rely more on brand names as quality signals to reduceuncertainty and to increase perceived quality (Erdem andKeane, 1996) because brands represent valuable sourcesof information for consumers to make decisions (Aaker,1991; Keller, 1993). Accordingly, understanding the wayconsumers perceive quality and lessen uncertainty hasbeen an important topic in both economics and marketingliteratures at least since Akerlof (1970) argued that poorquality would prevail over high quality products if therewere no signaling mediums in the marketplace. Firmstherefore often use various signals to convey crediblemessages to the market regarding the future prospects ofthe products prior to launch. In the absence of suchsignals, consumers faced with greater uncertainty mightdelay their decision to adopt a new product. Thus, exam-ining how and/or what type of potential signals can be
used by managers to communicate product quality toconsumers, consequently reducing uncertainty andenforcing product adoption, may provide importantmanagerial insights for the successful introduction ofnew products to markets.
Despite this strongly supported managerial impor-tance, little is known about the signals that firms use toconvey directly unobservable product quality informationto consumers. Signaling theory (Spence, 1973) highlightsthe importance of credible signals and explains why firmsare likely to focus their attention on cues that mitigateconsumers’ perceived uncertainties regarding the qualityof products. Accordingly, by using signaling theory asour theoretical base, the purpose of this paper is to inves-tigate two main streams of research questions in thecontext of experience products and their brand exten-sions. First, the study attempts to identify the brandextension signals or core attributes that affect an experi-ence product’s (e.g., a motion picture) performance.Second, the paper will shed light on the performancerelationship between the parent product (e.g., first movieof a franchise) and its several extensions (i.e., multiplesequels) with special emphasis on the length of the fran-chise, product continuity, timing, prior perception, andhow they are linked together. In doing so, the study pro-poses to explore specifically the relative performance
Address correspondence to: Goksel Yalcinkaya, Department of Market-ing, Peter T. Paul College of Business and Economics, University of NewHampshire, Durham, New Hampshire 03824. E-mail: [email protected]. Tel: 603-862-3376.
effect of a first extension as opposed to a second, third, orfourth of a franchise given the same level of signals orcore attributes.
In answering the previously raised questions, thepaper proposes a novel Bayesian hierarchical model withtwo core components: one to account for the determinis-tic effects of product attributes or signals and one toaccount for multiple extension specific random effects.As a result, once the product attributes in the form ofsignals are identified or controlled for, our proposedmodel will be able to account for many other unobservedfactors (i.e., unobserved heterogeneity) one would natu-rally encounter in various extensions of a parent productunlike traditional methods such as the least squaresregression. For instance, it is possible that the uncertainty(measured by variance) of a first extension is higher thanthat of a parent given their individual core attributes orsignals. In addition, the study aims to provide findingsthat are easily interpretable by practitioners and thusadopts a Bayesian inference point of view which gives usresults interpretable in terms of probabilities. Forinstance, as a by-product of our proposed model, onewould be able to exactly compute the probability that asecond extension will perform better than a first extensiongiven their respective core attributes which is very valu-able from a practical perspective. The use of Bayesianmethods in the experience products literature is scarcewith the exception of Neelamegham and Chintagunta(1999) where a Bayesian Poisson model is considered to
model/forecast the attendance counts at different stagesof the new product launch process in the context ofmotion pictures.
The current study, unlike the rest of the literature, isable to capture the effects of several brand extensions ofexperience products instead of just one due to the intro-duction of the random extension effects structure of ourmodel that could give us further insights about a fran-chise, thus making it more appealing and interesting froma brand extension point of view. In fact, correlationsbetween the performance of a parent and its extensionsgiven similar core attributes can be calculated. To the bestof our knowledge, such a feature had not been consideredpreviously in the literature. As a result, the findings canshow roughly when the performance of a parent in afranchise starts to lose its importance after a certainnumber of extensions. In addition, the findings showdecisive support in favor of using the extension effects inassessing market success by using fit/predictive perfor-mance measures commonly used in Bayesian analysis.Another point is on the common belief in the literaturethat first extensions do worse than their parents (Basuroyand Chatterjee, 2008; Moon, Bergey, and Iacobucci,2010; Ravid, 1999) for experience products such asmotion picture franchises. Our findings show that firstextensions do worse than their parents consistent withwhat is suggested in the literature but also found thatsecond extensions do better than first ones on averagegiven core attributes, thus making the second extension acritical turning point in the life of a brand.
To empirically support the research questions, thestudy makes use of motion picture franchise data as anexample of an experience product. A number of uniquecharacteristics make the motion picture industry an idealcase for this study. First, motion pictures have a relativelyshort product life cycle which would be of interest tomanagers as these products are characterized by unpre-dictable demand, frequent market entries, and rapidmarket exits. Second, the motion picture industry isincreasingly relying on sequels, namely brand extensionsof a parent product. Movie sequels comprise a growingportion of box office revenues. According to Box OfficeMojo, all top ten-grossing movies in 2012, nine of the topten-grossing movies in 2011, and eight of the top ten-grossing movies in 2010 were sequels (Anderton, 2010;Evers, 2013; Kurtzleben, 2012), providing an idealsetting to test our model. Furthermore, a large academicliterature exists on motion pictures, serving as a usefulbenchmark for assessing and comparing the importanceof sequels as brand extensions. The proposed model withthe random effects structure of brand extensions is also
BIOGRAPHICAL SKETCHES
Dr. Goksel Yalcinkaya is an associate professor of the MarketingDepartment at the Paul College of Business and Economics at theUniversity of New Hampshire. His current research interests includenew product launch, diffusion of innovations, and international market-ing. Within these broad areas, his major research areas are the emer-gence of aggregate level diffusion patterns from individual leveladoption decisions, the relative impact of exploitation and explorationcapabilities on product innovation and market performance, and the newproduct launch strategies. His work has previously been published invarious academic journals including Journal of the Academy of Market-ing Science, Journal of International Marketing, Journal of World Busi-ness, International Marketing Review, and Journal of ProductInnovation Management. Currently, he serves on the Editorial Boards ofJournal of International Marketing and Journal of Global Academy ofMarketing Science.
Dr. Tevfik Aktekin is an assistant professor of decision sciences at thePeter T. Paul College of Business and Economics in the University ofNew Hampshire. Dr. Aktekin received his PhD in decision sciences witha minor in statistics from the George Washington University. Hisresearch interests are in the areas of Bayesian inference, state space-timeseries, and new product innovation.
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applicable to other experiential products such as videogames and books that are franchises where similar rela-tionships between the parent and its extensions exist. Forinstance, in the video game industry, several developersprefer building sequels to games which did well in salesrather than pursing a new franchise due to the risksinvolved in development and to the lack of potentialfan base. In fact, even sequels of video games from pre-vious generations of consoles dominate the video gamemarket place. In addition, core attributes considered formotion picture franchises such as product continuity,franchise length, and timing would also be relevant andinfluential attributes in other experiential products thatare franchises.
The remainder of the paper is organized as follows.The next section introduces theoretical background andsupport. Next is a description of the data followed by theproposed models. We then present and discuss the results,followed by managerial implications. Finally, our paperconcludes with a quick summary, limitations, and futureresearch directions.
Conceptual Framework
Two main bodies of literature indicate the importance ofproduct quality signaling to consumers and provide theo-retical foundations linking it to business performance.First, the new product literature posits that a highproduct-to-product attribute fit positively relates to con-sumer evaluations of a newly introduced product(Bouten, Snelders, and Hultink, 2011; Simonin and Ruth,1998). As the fit between two products increases, con-sumers perceive the products to be comparable and trans-fer their positive attitudes more easily from the existingproduct to the subsequent product. Consistent with thisreasoning, greater similarity between the parent and theextended product creates easier associations for consum-ers (Aaker and Keller, 1990). One common marketingpractice is to introduce brand extensions as a function ofparent brand characteristics to capitalize on brand equity(Aaker and Keller, 1990; Park, Milberg, and Lawson,1991). For firms developing new products, failing to takecore brand attributes into consideration may lead to poorperformance (Beverland, Napoli, and Farrelly, 2010). Ascategorization theorists noted, consumers categorize abrand extension as a function of the established brand toform their evaluations (Boush et al., 1987; Park, Jun, andShocker, 1996). Once consumers more closely relate abrand extension to the parent brand, the two will becategorized more closely in the minds of consumers and
the associations will be more readily transferred (Aakerand Keller, 1990; Bouten et al., 2011; Kane, 1987; Keller,1993). The continuity of core product attributes serves asa quality cue and likely enhances brand recognition,thereby reducing risk and uncertainty associated with thenew product (Klink and Athaide, 2010). The need forquality cues becomes even more pronounced with anexperience product (e.g., entertainment goods) since thequality of experience products (e.g., entertainmentgoods) are difficult to assess prior to purchase and arereadily apparent only after consumption (Nelson, 1970).The limitations of experience products are accentuated bythe life of a product as these typically have short lifecycles and uncertain demands (Calantone, Yeniyurt,Townsend, and Schmidt, 2010; Luan and Sudhir, 2010).Therefore, marketing managers in the entertainmentindustry often only get one chance to make their offeringsappealing to consumers. This requirement consequentlynecessitates an important need to brand new productofferings accurately right from the start.
Second, drawing on the consumer behavior applica-tions of the signaling perspective (Kirmani and Rao,2000; Rao, Qu, and Ruekert, 1999), signaling theoryfocuses on how potential cues can be used to signalquality when key product attributes are not readily iden-tified. Signaling theory has been used as a frameworkfor understanding how two parties (e.g., buyer andseller) cope with unobservable information in a pre-consumption context (Spence, 1973). A signal is a cuethat a seller can utilize “to convey information crediblyabout unobservable product quality to the buyer” (Raoet al., 1999, p. 259). A key concept in signaling theory isasymmetric information. Asymmetric information refersto “limited access of information for at least one of theentities involved in the decision process” since someinformation is private. In the context of marketingsignals, although firms know their own true productquality, customers do not, so information asymmetry andimperfect information are present. This informationasymmetry is resolved if firms signal quality to consum-ers through various mechanisms, such as continuity ofcore product attributes. The signaling perspective of abrand argues that when faced with uncertainty due toimperfect and asymmetric information about a product,consumers utilize what they already know about thebrand in terms of brand credibility and consistency todecrease perceptions of risk and to boost quality percep-tions (Erdem and Swait, 1998; Wernerfelt, 1988). As aresult, signaling theory indicates that firms that betteridentify and understand the signals consumers use toevaluate quality when faced with incomplete information
about product attributes should have superior customerknowledge and be able to develop new offerings thatbetter fit with customer needs (Kirmani and Rao, 2000).Theoretically, the signaling perspective contributes to thenew product launch literature by providing a lens and amethodology to assess the success of managerial strate-gies, such as “sequels” as brand extensions, in influenc-ing the way consumers perceive a product quality. Theresearch stream has examined a variety of marketing cuesas product quality and competitive signals, includingbrand (Erdem and Swait, 1998), product innovation repu-tation (Henard and Dacin, 2010), price (Dawar andParker, 1994), promotional incentives (Song and Parry,2009), warranties (Boulding and Kirmani, 1993), productpre-announcement (Su and Rao, 2010), and advertisingintensity (Hultink and Langerak, 2002). Our studyextends this marketing cue stream by applying signalingtheory in the context of motion picture sequels as brandextensions of experience goods.
Although some researchers have examined the fitbetween parent and extension in the context of an enter-tainment product (Basuroy and Chatterjee, 2008;Hennig-Thurau, Houston, and Heitjans, 2009; Sood andDrèze, 2006), decisions about what contributes to asequel’s success and/or how much attention should begiven to various potential signaling factors has beenlimited. Similarly, a few marketing researchers haveexamined sequels as brand extensions of an experienceproduct. Based on the survey data collected from univer-sity students, Sood and Drèze (2006) find that dissimilarextensions are preferred over similar ones when it comesto movies because consumers tend to prefer differentthemes and/or new story lines. The authors primarilyfocus on consumer reactions to the title and suggest thatsequels with descriptive titles are rated higher than thosewith numbered titles. Basuroy and Chatterjee (2008)demonstrate that sequels tend to perform worse at the boxoffice compared to their parents. The authors also findthat sequels that follow their parents quickly are morelikely to do better than those with longer time lags. Thesequel effects found in these studies are related to a shorttime period or a small sample size. Furthermore, amongvarious potential signals the firm can use, these studiesfocus only on selective ones, thus hampering a holisticunderstanding of the interconnected relationships amongvarious signals. In contrast, our focus is on much broadermarket signals based on a longer and larger data set. Morerecently, Hennig-Thurau et al. (2009) measured the mon-etary value of movie sequels. Although they examinedsequel effects over a longer time period by incorporatinga larger body of extension product attributes, their focus
was only on initial sequels and cannot extend beyond theparent and its sequel. Our study overcomes this importantlimitation by modeling the relationships among multiplesequels.
Data and Modeling of SuccessiveExperience Product Releases
Our data set covers all movie sequels released in theUnited States between November 21, 1976 (i.e.,“Rocky”) to August 19, 2011 (i.e., “Spy Kids: All theTime in the World”). The data are primarily obtainedfrom the popular movie online database site, TheNumbers, and are complemented and cross-validated byother popular online movie data sources such as BoxOffice Mojo, IMDb, and the Movieinsider. Our data setconsists of 677 movie titles from 263 unique franchiseswith 263 sequels, 85 2nd sequels, 34 3rd sequels, 14 4thsequels, 7 5th sequels, 5 6th sequels, 3 7th sequels, 2 8thsequels, and 1 9-10-11th sequels (an average of 1.77sequels). For each movie, the data set includes the fol-lowing box office variables: number of screens, totaldomestic (i.e., U.S.) box office revenues, time lagbetween each sequel, official release date, an estimate ofproduction budget, title continuity, star continuity, direc-tor continuity, and critical reviews. Because of the longsample period, all monetary data (e.g., revenues andbudgets) were deflated (inflated prior to 1983) using theCPI (Consumer Price Index, All Urban Consumers, Allitems, 1982–1984 = 100) data from the Bureau of LaborStatistics to ensure comparability across years.
Our dependent variable is the total amount of grossthat the movie generated in the United States. The pro-duction budget data are drawn from The Numbers. Timelag is calculated as the time between a parent and a sequelor two subsequent sequels in years. Consistent with pre-vious studies, the paper incorporates the distributionintensity as the number of screens the movie was releasedon (Elberse and Eliashberg, 2003; Sawhney andEliashberg, 1996). To capture the effects of core productcontinuity, the study uses three continuity measures oftitle, director, and star that are coded as dummy variables.Title continuity takes a value of 1 when sequels use anumbering title strategy as in “Hangover 2,” and 0 whensequels use a naming title strategy as in “Transformers:Dark of the Moon.” Star continuity takes a value of 1 ifthe main actor(s) from a parent movie appears in thefollowing sequel and 0 when he/she (they) does (do) not.Similarly, director continuity takes the value of 1 if thesame director(s) appears in the sequel and 0 otherwise.
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Each continuity dummy variable is coded by an expertjudge and cross-checked by the other. To account forprior perception, the study uses the average ratingsassigned by critics. Critics’ ratings can play an importantrole in consumer decision-making and are included inthis study (Eliashberg and Shugan, 1997). The critics’rating information was gathered from Rotten Tomatoes(missing values were added from IMDb), which providescritical review information as a percentage of favorablereviews prior to the release of the motion picture. Genrefor each movie is obtained from The Numbers. For somerare cases where movies are not listed on The Numbers,genre is obtained from IMDb. Following previousresearch, movies are categorized into six genres as action,horror, comedy, drama, adventure, and animation. Addi-tionally, MPAA-ratings were obtained for the motionpicture sequels from The Numbers. It should also benoted here that the genre and MPAA-rating variableswere used as control variables as they were found to berelevant in previous studies and no attention will be payedto their inference in this study.
In modeling the U.S. gross of a motion picture withsequels, the study considers a Bayesian hierarchicalstructure. Subsequently, let Yi represent the U.S. gross ofthe ith movie (adjusted accordingly as discussed in theprevious section) and si represent the sequel index withina franchise for the ith movie. Also it is assumed that Yisare realizations from a probability distribution. As shownin the histogram and the smoothed empirical density esti-mate in Figure 1, the sample values of Yis are defined inthe positive real line (scaled by 1,000,000) and are real-izations from a right skewed distribution.
The first candidate for the distribution of Yis is theexponential that can be considered due to its straightfor-ward estimation and whose density can be written asfollows
p Y ei i iYi iλ λ λ( ) = − , (1)
where λi > 0 and is the rate parameter with expectedvalue (mean) 1/λi, namely the average U.S. gross ofthe ith movie. In addition, it is assumed that the para-meter λi is a multiplicative function of deterministicpredictor variables and random sequel effects and isgiven by
λ θi si e= ′b zi, (2)
where i = 1, . . ., I and si = 1, . . ., S, with I and S repre-senting the maximum number of motion pictures and themaximum number of sequels, respectively. In Equation 2,the deterministic effect comes from β, which is the vectorof coefficients, and zi, which is the vector of predictorvariables for the ith movie. The random effects of sequelscan be captured via θsis, representing the overall contri-bution of the sith sequel on Yi. Being able to obtain theposterior joint distribution of θsis can provide a myriad ofmanagerial insights to motion picture practitioners. Forinstance, one can quantify the probability that the effectof the first sequel of a franchise will be higher (or lower)than that of the second sequel on the U.S. gross given thedata. Furthermore, another attractive feature of such astructure is the availability of posterior correlationsbetween θsis which is investigated in our analysis section.Such an analysis would not have been possible usingclassical estimation methods.
The second candidate for the distribution of Yis is theWeibull density, which is more flexible in terms of thedensity shapes it can exhibit as opposed to the exponen-tial model. The Weibull density function given its twoparameters, λi and ν, can be written as
p Y Y ei i i iYi iλ ν νλ ν λ ν
, ,( ) = − −1 (3)
8060
Fre
quen
cy
4020
0
0.00
80.
006
Den
sity
0.00
420
.002
00.
000
0 100 200 300Gross
400 500 0 100 200 300Gross
400 600500
Figure 1. Histogram Plot (Left) and Smoothed Empirical Density Plot (Right) for the U.S. Gross
for λi > 0 and ν > 0. It is noted here that when ν = 1,Equation 3 reduces to the exponential model as in Equa-tion 1. For the scale parameter, λi, a similar multiplicativestructure as in Equation 2 with θsis can be considered. Inestimating the model parameters for both cases, theBayesian inference point of view is adopted and itsdetails/estimation using Markov chain Monte Carlo(MCMC) methods are discussed in the Appendix.
The final step is to assess the fit performance of theproposed models. In order to do so, three sets of mea-sures are considered that are used with sampling-basedmethods. The first fit measure is the Bayes factorapproximation of models with MCMC steps; thismeasure is referred to as the Bayes factor-harmonicmean estimator (BF-HME), which has been discussed byGelfand, Dey, and Chang (1992) and Kass and Raftery(1995) and is computed in the log-scale as log(p(Y)). Analternative method to compare models with sampling-based methods is the calculation of the pseudo Bayesfactor using the conditional predictive ordinate in thelog-scale, log(CPO). Following Gelfand (1996), thecomparison criteria makes use of a cross-validation esti-mate of the marginal likelihood by leaving the observa-tions one by one from the analysis. The main advantageof this approach is that it also assesses the predictiveperformance of proposed models by following a leave-one-out type of approach. The final measure consideredin this study is due to Spiegelhalter, Best, Carlin, andLinde (2002) and is referred to as the deviance informa-tion criteria (DIC). The advantage of the DIC is that itconsists of both a measure of fit and of complexity. Pre-viously discussed measures do not penalize for complex-ity thus do not account for the possibility of over-fittingthe data. A smaller DIC value indicates a more adequatemodel.
Analysis of Motion Picture Data
In this section, the proposed models are estimated usingthe data and the inference methods are described in theAppendix. While doing so, the following abbreviationsare used to preserve space in the narrative.
• M1-Exp: a model where the likelihood is exponentialas in Equation 1 with random sequel effects and deter-ministic predictor variables (budget, genre, MPAA-rating, distributor intensity, and critics’ rating).
• M1-Wei: a model where the likelihood is Weibull as inEquation 3 with random sequel effects and determinis-tic predictor variables (same as above).
• M1-Exp-NoSeq: a model where the likelihood is expo-nential as in Equation 1 with NO random sequel effectsand deterministic predictor variables (same as above).
• M1-Wei-NoSeq: a model where the likelihood is expo-nential as in Equation 1 with random sequel effects anddeterministic predictor variables (same as above).
• M2-Exp: a model where the likelihood is exponentialas in Equation 1 with random sequel effects and deter-ministic predictor variables (budget, genre, title conti-nuity, director continuity, star continuity, time lag,distributor intensity, and critics’ rating).
MCMC Implementation and Convergence
In order to obtain the posterior samples of model param-eters, a combination of MCMC methods were used (seethe Appendix). To generate the samples, the WinBUGSsoftware was used, and the code is available via e-mailupon request from the authors. In addition, flat but properpriors were assumed for model parameters whenrequired. Specifically for M1-Exp, M1-Exp-NoSeq andM2-Exp, θj ∼ G(.001, .001)∀j were used and βk ∼ N(0,.001)∀k, for M1-Wei and M1-Wei-NoSeq, log(ν) ∼ N(0,.001) was used in addition. Note also that the inference ofmodel parameters was not sensitive to the choices ofpriors as long as they were flat but proper. To assess theconvergence of the algorithms, three parallel chains wererun with different initial points. The chains were run for50,000 iterations as the burn-in period and 15,000samples were collected with a thinning interval of 3. Forthe sake of preserving space, a detailed summary of con-vergence for all model parameters will be omitted. Onlyresults for some of the parameters for M1-Exp are pre-sented and similar results were obtained for the rest.
In obtaining the posterior samples, convergence prob-lems are not encountered. This can informally beobserved from the trace plots in Figure 2.
A more formal way of assessing convergence is due tothe Brooks and Gelman plots and the shrink factor; seeBrooks and Gelman (1998). If the shrink factor is around1, then convergence is said to have been attained. TheBrooks and Gelman plots are shown in Figure 3 wherethe shrink factor approaches 1 as the number of iterationsincreases. The estimated shrink factors were between 1and 1.01 for all parameters. Thus, there were no conver-gence issues.
Model Fit and Comparison
In assessing and comparing the adequacy/fit of our pro-posed models, three sets of measures are considered:
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BF-HME, PBF-CPO, and DIC. Table 1 shows the mar-ginal likelihood contributions and cross validations in thelog-scale and the DICs for our proposed models.M1-Exp, which is the exponential model with the sequeleffects, has the highest log-likelihood value and log-crossvalidation (> 20 in the log-scale to its closest competitor)and the smallest DIC value (> 60 to its closest competi-tor), which shows evidence of adequacy/fit in its favor.Another interesting point is that the models which do nottake into account the random sequel effects, M1-Exp-NoSeq and M1-Wei-NoSeq, are significantly worse thantheir counterparts with random sequel effects. Whencomparing the DICs which penalize for model complex-
ity, the models without the random sequel effects havehigher values, which is encouraging since it further jus-tifies the use of the gamma structure on the θsis; namely,the sequel effects. Such a result shows decisive support infavor of accounting for brand extension effects (sequelsin our study) of an experience product’s performance inaddition to core attributes when trying to assess marketperformance (measured by the box office performance inour study). Such a finding further justifies the claim thatsequels influence the consumer perception of productquality and subsequently attitude towards the purchase.
Posterior Inference and Analysis
Since M1-Exp was determined to be the best fit model,the posterior analysis is conducted based on its output.Figure 4 shows the boxplot of the random sequel effectsposterior distributions given the predictor variables,where a higher value implies lower box office perfor-mance. In other words, given the same level of coreattributes or signals (e.g., same level of prior perception,
budget, etc.), several conclusions can be drawn regardingthe relationship between the parent product (first movie)and its extensions (sequels). The findings can concludethat the first sequels generally do worse than the parents;however, the second sequels (i.e., the third movie in aseries) do better than the first sequels given similar coreattributes. After the second sequel, the overall effect ofthe parent product diminishes until the sixth sequel. Oneof the advantages of the Bayesian paradigm is that itallows us to quantify uncertainty via probability distribu-tions, making it easier for practitioners and managers tointerpret and to make use of the results. For instance,based on the posterior results of Figure 4, the probabilityof the effect of the parent being higher on the perfor-mance as opposed to that of the first sequel can be cal-culated via P(θ1 < θ2|D) (which in our case was
calculated approximately to be equal to one). In addition,as the number of sequels increase, the uncertainty abouttheir effect on the performance increases as can beobserved from the size of the boxplots. This is quiteintuitive since there are not that many franchises withmore than five-six movies, that is, when the uncertaintysignificantly starts increasing and is one of the mainadvantages of using Bayesian analysis, which does notrequire large samples for each unit to carry out statisticalinference unlike classical methods (Rossi and Allenby,2003).
Another interesting finding about the random sequeleffects is their posterior correlation structure as shown inTable 2, which shows that the parents are highly corre-lated with the firsts, seconds, and thirds after which itseffect significantly diminishes. It can fairly be argued that
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for franchises running longer than three sequels, theparent product’s success starts losing its importance,which could indicate that the established “brand name” ofa franchise is what keeps the franchise moving forward.On the other hand, however, the parent product’s successcan be argued to be more relevant at the infancy of thefranchise for the sake of the “brand name” to continuegrowing given similar signals. Even if franchises that arerunning longer than six movies are ignored due to theirsmall sample sizes, the correlation structure for the firstsix still indicates that the relationship between the parentand its extensions exhibits a decay as the number ofextensions increase (with respective estimates of .94, .89,.80, .65, .58 that are consistently decreasing).
Unlike classical methods, there are two ways of rep-resenting inference results in Bayesian analysis: as asummary of statistics in the form of tables or graphicallyin the form of posterior density plots. Table 3 shows theposterior summary statistics for the coefficients of therespective predictor variables. In assessing the effects ofthe predictor variables on performance, keep in mind thata negative (positive) coefficient estimate implies a posi-tive (negative) effect due to the mean parametrization ofthe exponential distribution. Since all inference is proba-bilistic in Bayesian inference, the 95% credibility inter-vals can be interpreted as probabilities as intuition wouldsuggest. For instance, it is possible to say that there is a95% probability that the effect of the budget on box officeperformance given all the other predictor variables willbe positive due to the interval (−.015, −.005), which indi-cates a very strong effect. Using the same line of thought,it can be summarized that variables such as the budget,distribution intensity, and critics ratings strongly affectthe domestic gross of a motion picture franchise. Genres
such as comedy and adventure also have strong effects onthe gross, whereas the action genre has a milder effect.The MPAA-rating category does not have a strong effecton domestic gross for motion pictures that are franchisesgiven all other predictor variables. It is noted here onceagain that variables of genre and MPAA-rating were usedas control variables and are of no consequence to ourmain discussion.
Figure 5 shows the posterior density plots of the coef-ficients of the predictor variables that strongly influencethe domestic performance. Such plots are graphical rep-resentations of inference in Bayesian analysis and can beinterpreted as probabilities using the same logic used forthe posterior summary of statistics. For instance, thewhole support for the coefficient of the critics’ ratingvariable used to account for prior perception is in thenegative real line, implying a positive effect on perfor-mance given all the other predictors with probability one.In other words, it can be concluded that there is a 100%chance that a higher prior perception will lead to a higherbox office performance given the same level of all otherpredictor variables (given the same level of budget, dis-tribution intensity, genre, and MPAA-rating). The samearguments can be made regarding the effects of thebudget and the distribution intensity variables based ontheir posterior density plots.
In order to investigate the effects of brand-extension–specific attributes such as product continuity and exten-sion timing in addition to predictor variables of budget,critics’ rating, and distributor intensity, a model referredto as M2-Exp is estimated. Consequently, the parentmovies were excluded so that brand-extension–specificeffects of title, director, star continuity, and extensiontiming can be captured since they can only be definedrelative to a previous observation. A parent product willnot have such attributes since all four are defined withrespect to a previous reference which the parent does nothave. However, for subsequent extensions (i.e., first,second, third sequels, etc.), they can be defined withrespect to the previous installment. In M2-Exp, the like-lihood is exponential with random sequel effects (same asM1-Exp in terms of the model structure). Table 4 showsposterior summary statistics for coefficient parametersand Figure 6 their posterior density plots. Based on the95% credibility intervals, all three product continuityattributes show strong positive effects on the domesticperformance given the other predictor variables. Thefindings imply that when extending experience productbrands, a key signal is product continuity measured bytitle, director, and actor continuity variables in our study.The results about director and actor continuity are not
Table 3. Posterior Statistics Summary for Coefficients,βks, in M1-Exp
surprising; however, title continuity implies that fran-chises with the same title (defined as a numbered title asin Highlander, Highlander 2, and Highlander 3) performbetter contrary to what has been said in the literature(Sood and Drèze, 2006). It is possible that when the titleof a franchise changes, the perception of the customer
towards the brand name shifts since it suggests a differentstory line that could be perceived not to be a continuationof what the original fan base expects. Another brandextension attribute whose effect was found to be strongbut negative given the other predictor variables is theextension timing measured by the time lag (in months)between two installments. The results suggest that thereis approximately a 98% chance (can be roughly seenbased on the posterior density plot in Figure 6 of the timelag variable) that launching the next extension soonerthan later will contribute positively to the performance ofthe installment. On the other hand, the findings indicatethat given the same core attributes and signals, there isstill a 2% chance that a late launch will improve the boxoffice performance. This is probably due to the existenceof special well-known franchises that launched latesequels, such as Indiana Jones and Star Wars, for whicheven after decades there were vast fan bases.
Discussion and Managerial Implications
There is a growing trend for firms to take advantage ofbrand name recognition. This trend is particularly notice-
−0.020 −0.015 −0.010 −0.005 0.000
050
100
150
βBudget
Den
sity
−5 −4 −3 −20.
00.
20.
40.
6
βDistDensity
Den
sity
−2.4 −2.2 −2.0 −1.8 −1.6 −1.4 −1.2 −1.0
0.0
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βCriticRating
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sity
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βGenreAction
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sity
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βGenreComedy
Den
sity
−1.5 −1.0 −0.5 0.0 0.50.
00.
51.
01.
5
βGenreAdventure
Den
sity
Figure 5. Posterior Density Plots for Most Significant Coefficients in M1-Exp
Table 4. Posterior Statistics Summary for Coefficients,βks, in M2-Exp
10 J PROD INNOV MANAG G. YALCINKAYA AND T. AKTEKIN2014;••(••):••–••
able when firms face an experience product that generallypossesses a high degree of pre-purchase informationshortage for consumers to judge its quality (Erdem andKeane, 1996). To cope with information scarcity, con-sumers are increasingly relying on brand names asquality signals. Consequently, many firms tend to pursuea strategy of generating revenue through proven brandnames. The recent emergence of brand extensions bestcharacterizes this trend. From the perspective of consum-ers, the high quality of a parent product signals the poten-
tial quality of a newly extended product due to theassociation between similar attributes (Aaker and Keller,1990). Although the quality signals firms use to conveyunobservable product information before consumptionare an essential part of a product’s survival in the newproduct literature, little is known about the core productattributes that provide the finest quality signals for theconsumers. The goal of this paper is therefore to contrib-ute to the new product literature by identifying brandextension signals that impact an experience product’s
−0.020 −0.015 −0.010 −0.005 0.000 0.005
020
4060
8010
012
0
βBudget
Den
sity
−0.6 −0.4 −0.2 0.0 0.2
01
23
βTitle_ContinuityD
ensi
ty−0.8 −0.6 −0.4 −0.2 0.0 0.2
0.0
0.5
1.0
1.5
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βStar_Continuity
Den
sity
−2.0 −1.5 −1.0 −0.5 0.0 0.5
0.0
0.2
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βDirector_Continuity
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sity
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02
46
8
βTime_Lag
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sity
−7 −6 −5 −4 −3
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βDistributor_IntensityD
ensi
ty
−2.5 −2.0 −1.5 −1.0 −0.5
0.0
0.5
1.0
1.5
βCritics_Rating
Den
sity
Figure 6. Posterior Density Plots for Most Significant Coefficients in M2-Exp
performance as well as exploring the performance rela-tionship between the parent product and its subsequentextensions.
First, the study offers a collection of core attributestheorized as signals that can lead to successful experienceproduct franchises. A key signal is identified as theproduct continuity, measured by the collection of title,director, and actor continuity variables in the case ofmotion picture sequels, all of which are determined tohave positive effects on performance with probability 1given the effects of all other predictor variables. As sig-naling theory argues, such a strong continuity signalincreases the perception of a product’s credibility andconsistency and, in turn, decreases perception of riskinherently attached to the new product. The model indi-cates that the newly introduced product should demon-strate strong continuity of key attributes from the parentto be accepted by the consumer. This is an importantinsight for experience product managers of franchisesthat are in the process of extending their parent product,as the continuity of core product attributes is essential.One of the most influential continuity attributes is the titlecontinuity. Contrary to recent suggestions in the con-sumer marketing literature that dissimilar brand exten-sions are rated higher than similar ones, morespecifically, sequels with descriptive titles are ratedhigher than those with numbered titles (Sood and Drèze,2006), our findings suggest that franchises that use anumbering title strategy perform better than those ofusing a naming title strategy. Given all other predictorvariables, the model implies that, keeping the same titleof an extension by simply numbering it has a very strongpositive effect on the extension launch performance. Thisresult has provided some of the strongest evidence to datelinking a naming title strategy to signal the continuity ofcore product attributes. Consequently, it can be inferredthat when the title of a franchise moves from numberingto descriptive naming, a different story line is implied andis therefore not perceived by customers as a continuationof the parent product.
Other core attributes in the form of signals that aredetermined to strongly influence the performance of anextension are the prior perception measured by thecritics’ rating and distribution intensity. Critics’ ratingsprior to the release signal customers about which movieswill be worth their investment, whereas the distributionintensity acts in the form of a word of mouth type effect.Because studios usually screen motion pictures inadvance for the critics to assess their initial reactions,critics’ rating as a signal is available to customers a priori.The favorable enhanced buzz from ratings contributes to
enhanced talks among consumers and reinforces theirpositive view about the product. Given that the diminish-ing fan base for subsequent sequels is indicated by theprior literature (Basuroy and Chatterjee, 2008), comple-menting the favorable critics’ rating with strong market-ing efforts provides much-needed support for the newproduct’s success and, more importantly, a long series.Similarly, the effect of distribution intensity will be avail-able to customers as a key signal because greater distri-bution intensity expands awareness of the brand in themarket and may signal strength and quality of theproduct. As such, managers should target higher initialdistribution intensity, particularly when working with theshort life cycle of experiential products due to the limitedavailability of a short window of opportunity.
Our findings also offer important new insights regard-ing the extension timing between two subsequent prod-ucts being a strong signal. An interesting question in theanalysis of successive product releases is whether there isan optimal amount of time that should elapse betweenproduct launches to maximize performance. The findingsof our study, to a certain degree, are consistent with thoseof Basuroy and Chatterjee (2008), who found that alonger time lag between the parent and the sequel nega-tively influences the box office performance. This findingsuggests that if the consumer favors a product, its effect isfresher when the consumer experiences an extensionsooner. Firms seeking to enhance their new product per-formance should give considerable attention to the timelag between subsequent product releases in addition tothe previously discussed signals of continuity.
After controlling for the effects of the core productattributes (or signals), there is still unexplained uncer-tainty left in what makes an extension a success. Ourproposed model can also provide insights for experienceproduct brand extension managers by capturing therandom effects of the relationships between the parentand its several extensions. Given all the other predictorvariables, our model confirms previous findings that thefirst sequels generally do worse in the box office thantheir parents (Basuroy and Chatterjee, 2008; Moon et al.,2010). However, our results also point out that the secondsequels do better than the first sequels given all otherpredictor variables. A possible explanation for this mightbe that the success of the parent movie sets a very highexpectation for the sequel, often leading to dissatisfaction(Moon et al., 2010; Oliver, 2010). As pointed out byBudra and Schellenberg (1998), even for cases when thesequel is as good as the parent, it may still be perceived asa disappointment for viewers whose first experience ofthe franchise was unmatched. Thus, for the next time
12 J PROD INNOV MANAG G. YALCINKAYA AND T. AKTEKIN2014;••(••):••–••
around, viewers do not generally have high expectations,mainly because the franchise name as a quality signalmight have lost its appeal. Another possible explanationis that the studios make necessary adjustments for thethird installment after the sequel receives less favorableviews. For example, Men in Black III was released inMay 2012 with the promise to be fresh and novel whilekeeping the core values of its parent (Fritz and Zeitchik,2012). The movie has received generally positive reviewsfrom critics and moviegoers. The consensus states thatMen in Black III is better than its predecessor andmanages to exceed expectations, largely due to its abilityto recapture the spirit of the parent without sacrificingnovelty. Therefore, the findings suggest that the key tofranchise continuation is to ensure the success of a thirdmovie in the series. Another interesting finding from ourstudy is related to the length of a given franchise (i.e.,number of movies in the series). Our findings indicatethat the importance of support needed from a parentproduct diminishes after three or more sequels. Specifi-cally, our model suggests that parent products’ perfor-mance is highly correlated with the first, second, and thirdsequels after which its effect significantly diminishes. Itis apparent from the results that for franchises runninglonger than three sequels, the established “brand name”of a franchise is one of the determining factors for per-formance in addition to the signals previously discussed(or core attributes). On the other hand, the findings indi-cated that at the infancy of the franchise (the first twoextensions), the success of the parent plays an importantrole in the performance of the next extensions. From amarketing communication perspective, our results indi-cate that managers should give greater emphasis forforming their communication effort around the parentproduct’s brand awareness, image, and core attributes forthe first three extensions. The emphasis should graduallyshift to a novel story line with a recollection to coreattributes in the following extensions. Similar argumentscan be made for other experience products such as videogames and books.
Concluding Remarks
This study investigated the effects of multiple brand exten-sions and core attributes via signals on an experienceproduct’s market performance and discussed generalmanagerial insights. In doing so, Bayesian models wereintroduced with both deterministic (via predictor vari-ables) and random components (via successive productextensions). The study considered exponential andWeibull candidates that are capable of capturing the right-
skewed behavior exhibited by our data. MCMC methodsto estimate the model parameters were used. The proposedmodels are able to capture the behavior of successiveexperience product releases unlike most of the literature,by taking into account the random effects of several suc-cessive product extensions that were found to be corre-lated a posteriori in our data. The random effects structureconsidered in the study allows for unobservable heteroge-neity to be accounted for, and its inclusion in explainingproduct performance in brand extensions of experienceproducts has been supported decisively by commonly usedfit/predictive performance measures. To the best of ourknowledge, such a structure and its managerial implica-tions were not previously considered in the context ofexperience products within the brand extension literatureand are major novelties of our study. In addition to thebrand extension effects, our findings indicated the com-bined importance of signals such as product continuity,short timing between extensions, and positive prior per-ception for successful launches of extensions of a parentexperience product. Even though some of these attributeswere considered individually in the context of motionpictures, their combined effects on multiple sequels hadnot been considered previously in the literature.
A number of limitations need to be noted regarding thepresent study. Previous research in the motion pictureliterature has argued that advertising expendituresincrease consumer attention (Basuroy, Desai, andTalukdar, 2006; Joshi and Hanssens, 2009). Advertisingexpenditure as a quality signal in addition to the budgetmight be a factor to consider if the data were available.Although advertising expenditures in the motion pictureindustry are generally available, our sequel data datesback to 1976, which makes it very hard to incorporate inour current work. However, our study includes the totalbudget of a given motion picture that already contains theadverting expenditures. In fact, considering both the totalbudget and the advertising expenditures, which are onaverage a fixed percentage of the total budget, could havecreated multicollinearity problems. Thus, it is possiblethat the budget figures could at least be used as proxies inlieu of the advertising expenditures. Furthermore, toinvestigate if similar conclusions can be drawn regardingthe core products’ attributes and brand extension effectsfor other experience products, the proposed model can beapplied to video game and book franchise data. Suchstudies can further justify the robustness of the currentfindings and open up potential directions of research forexperience product researchers. Another area that ismissing from our study is the effect of sequels as qualitysignals for the international launch of sequels. It would be
interesting to assess the value of sequels as quality signalsin markets other than the United States. This would be apotential area of research that can be addressed in a futurestudy. In addition, if sequel effects are believed to bedependent (correlated) a priori, another potential exten-sion of our model is to introduce a dependent priorprocess such as the equivalence of an autoregressiveprocess on the gamma structure of the θjs. By doing so,one can take into account the effects of previous moviesin the franchise when investigating the effects of thequality signals. Considering such a structure will compli-cate the estimation procedure of the current model and itsusefulness may be investigated in a future study. Anotherextension possibility is to consider the optimal schedul-ing of movies in a theater complex by taking into accountthe sequel effects and the respective signals. Finally,while the current study focused on core product attributesin the form of signals and brand extensions, futureresearch could extend the study by framing it aroundincremental (sequels as brand extensions) and radical(nonsequel new movies) innovations.
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Appendix. Bayesian Inference
Bayesian analysis of any kind follows the likelihood prin-ciple, which implies that the likelihood function has allthe information one needs to carry out inference. Assumethat the domestic gross, Yi are either exponential orWeibull distributed. One of the major differences betweenclassical and Bayesian estimation methods is the specifi-cation of the prior distributions for all model parameters.All Bayesian inference is conditional on the prior distri-butions and the likelihood function. For the exponentialmodel, assume that the sequel effects are independent
gamma distributed as θs s s ii i iGamma a b s S∼ …, , , ,( ) =for 1 witha. and b. being the shape and rate parameters. In addition,the coefficients are assumed to be independent and nor-mally distributed as βk ∼ N(μk, τk), for k = 1, . . . , K, whereμ. and τ. are mean and precision (1/variance) parameters.K represents the total number of predictor variables. Forthe Weibull model, assume that ν is lognormal asν ∼ LN(μν, τν). In general, the values of a a., b., μ., τ., μν,and τν are either elicited from expert knowledge (seeSándor and Wedel, 2011, for a marketing application) orare chosen such that our uncertainty about the modelparameters is vague. In our analysis, the latter case isadopted.
The final step is to obtain/estimate the posterior dis-tributions of all model parameters. Closed form posteriorresults can rarely be found in modern applications. Thus,the use of Markov chain Monte Carlo (MCMC) methodsare key in Bayesian inference. To estimate the modelparameters, the Metropolis–Hastings (MH) algorithmwithin the Gibbs sampler is employed (Chib andGreenberg, 1995; Smith and Gelman, 1992). One of thekey requirements of the Gibbs sampler is to obtain thefull conditional distributions of all model parameters(either in closed form or as a function up to a certainproportionality). For convenience, the notation p(.| . . . ,D) is used to represent the full conditional distribution fora given parameter given all others. For the Weibull model,they can be summarized as
p D Gamma
I s x a I s x e Y b
s x
ii
I
s i ii
I
s
i
i i
θν
=
=
′
=
( )=( ) + =( ) +∑ ∑
… ∼,
,1 1
b zi⎛⎛⎝⎜
⎞⎠⎟
=, , , ,for s Si 1 … (4)
where I(.) represents the indicator function. It is noted herethat the full conditional (Equation 4) for the exponentialmodel can be obtained by replacing Yi
ν with Yi. In addition,
p D exp z e Y p
k
k k ik
i
I
sz
i k k kik i
kβ β θ β μ τβ ν… , , ,( ) ∝ −⎧
⎨⎩
⎫⎬⎭
× ( )
==∑
1
for 11, , ,… K
(5)
where p(βk|μk, τk) denotes the normal prior density func-tion for βk. It is noted here that the full conditional (Equa-tion 5) for the exponential model can be obtained byreplacing Yi
ν with Yi. The full conditional distribution forν would be as follows:
p D Y e Y pIi s i
i
I
iν ν θ ν μ τν νν ν… , exp , ,( ) ∝ −{ } × ( )− ′
=∏ 1
1
b zi (6)
where p(ν|μν, τν) denotes the lognormal prior densityfunction for ν. Since most of the above full conditional
distributions, with the exception of θsis, are not well-known probability distributions, the MH algorithm isused to generate the necessary samples.
A Gibbs sampler coupled with the MH sampling algo-rithm is developed to generate samples from the jointposterior distribution of model parameters, p(θ1, . . . , θJ,β, ν|D) for the Weibull model with random sequel effects.Denoting l as the sample counter, the steps can be sum-marized as follows.
1. Initialize ν(0) and β(0).2. Generate samples of θs
li
( ) for si = 1, . . . , S fromp D Gamma
I s x a I s x e Y
s x
ii
I
s i i
l
i
i
θν
=
=′ −(
( )=( ) + =( )∑ −( )
… ∼,
,1
1b liz1 ))
=∑ +( )i
I
sb i1
as given
by Equation 4.3. Generate samples of βk
l( ) for k = 1, . . . , K fromexp z e Y pk
lik
i
I
sl z
i kl
kikl
ik lβ θ β μβ ν( )
=( ) ( )∑ −{ } × ( )( ) −( )
1
1 as given byEquation 5.
4. Generate samples of ν(l) fromν θ ν μ τν ν
ν νl I
i sl
il
i
IY exp e Y p
i
l( ) − ( ) ′ ( )=( ) −{ } × ( )( ) ( )∏ 11
b liz , as given by
Equation 6.
If the above is repeated for l = 1, . . . , L where L islarge, then samples from p(θ1, . . . , θJ, β, ν|D) are
obtained. In addition, to generate samples from steps 3and 4, a random walk MH algorithm is used whose pro-posal density is multivariate normal. Following Chib andGreenberg (1995), the steps in the MH algorithm for anyparameter ψ (for step 3, ψ = β and for step 4, ψ = ν) canbe summarized as follows:
1. Generate ψ* from q(ψ*|ψ(l−1)) and u from U(0, 1).2. If u ≤ α(ψ(l−1), ψ*) then accept ψ(l) = ψ*; else repeat
the previous step,
where
απ
πy y
y y yy y y
ll
l l−( )
−( )
−( ) −( )( ) =( ) ( )
( ) ( )⎧⎨⎩
⎫11
1 1, ,*
* *
*min
q
q1 ⎬⎬
⎭⎪,
where π(.) is the density that samples are generated fromand q(.|.) is the multivariate normal proposal densitywhose variance-covariance matrix is determined via(− H)−1 with H representing the approximate Hessian ofπ(.) evaluated at its mode (Gelman, Carlin, Stern, andRubin, 2003). The algorithm for the exponential withrandom sequel effects will be similar to the above withthe exclusion of the full conditional of ν in step 4.
16 J PROD INNOV MANAG G. YALCINKAYA AND T. AKTEKIN2014;••(••):••–••
