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The Role of Network Embeddedness in Film Success

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In the early stage of film development when producers assemble a development team, it is important to understand the means by which different team members may contribute to the film's box office. Building upon theories from marketing and sociology, we propose that these contributions arise from team members' positions, or embeddedness, in a social network weaved through past film collaborations. These collaborations provide team members with opportunities to draw knowledge and skills from the network for new film projects. Our conceptual framework accentuates two aspects of network embeddedness: positional embeddedness (PE)—how well a person is tied to well-connected others, and junctional embeddedness (JE)—the extent to which a person bridges sub-communities in the industry. We examine how the importance of PE and JE varies by functional role (cast versus crew), and is moderated by the film's studio affiliation.
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The role of network embeddedness in lm success
Grant Packard
a,1
, Anocha Aribarg
b,2
, Jehoshua Eliashberg
c,3
, Natasha Z. Foutz
d,
a
Laurier School of Business & Economics, Wilfrid Laurier University, Canada
b
Ross School of Business, University of Michigan, United States
c
Wharton School of Business, University of Pennsylvania, United States
d
McIntire School of Commerce, University of Virginia, United States
First received on June 3, 2014 and was under review for 6½ months
Available online xxxx
Area Editor: Oded Netzer
Guest Editor: Eitan Muller
Abstract
In the early stage of lm development when producers assemble a development team, it is important to understand the means by which different
team members may contribute to the lm's box ofce. Building upon theories from marketing and sociology, we propose that these contributions
arise from team members' positions, or embeddedness, in a social network weaved through past lm collaborations. These collaborations provide
team members with opportunities to draw knowledge and skills from the network for new lm projects. Our conceptual framework accentuates two
aspects of network embeddedness: positional embeddedness (PE)how well a person is tied to well-connected others, and junctional
embeddedness (JE)the extent to which a person bridges sub-communities in the industry. We examine how the importance of PE and JE varies
by functional role (cast versus crew), and is moderated by the lm's studio afliation.
Analyzing more than 15,000 industry professionals over nearly two decades of lm collaborations, this research reveals crucial and divergent
relationships: while high PE is more valuable for the cast, high JE is critical for the crew. This role distinction also depends on a lm's studio
afliation. Managerially, these ndings provide guidance to lm executives and producers in revenue maximization through strategic team
assembly, and to talents in career management.
© 2015 Elsevier B.V. All rights reserved.
Keywords: Entertainment marketing; Motion pictures; New product development; Collaboration networks; Network embeddedness; Functional roles
1. Introduction
The movie industry is a prime example of Risky Business.
U.S. film studios are estimated to have spent an average of over
$40 million to produce and market a single film in 2014, yet
these films averaged only $15 million in North American box
office. With budgets approaching $200 million to market a film
internationally, global box office similarly fails to deliver
positive returns for the average global release (McClintock,
2014; Motion Picture Association of America, 2014; Nash
Information Services, 2015). To improve returns on investment,
The authors contributed equally and are listed in random order. The authors
would like to thank Nicole Coviello and participants at the 2013 Empirical and
Theoretical Symposium at Western UniversityIvey for their valuable
feedback; and the McIntire School of Commerce and Batten Institute at the
University of Virginia for financial support of this research.
Corresponding author. Tel.: +1 434 924 0873.
E-mail addresses: gpackard@wlu.ca (G. Packard), aaribarg@umich.edu
(A. Aribarg), eliashberg@wharton.upenn.edu (J. Eliashberg),
nfoutz@virginia.edu (N.Z. Foutz).
1
Tel.: +1 519 884 0710x4030.
2
Tel.: +1 734 763 0599.
3
Tel.: +1 215 898 5246.
www.elsevier.com/locate/ijresmar
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0167-8116© 2015 Elsevier B.V. All rights reserved.
Please cite this article as: Packard, G., et al., The role of network embeddedness in lm success, Intern. J. of Research in Marketing (2015), http://dx.doi.org/10.1016/
j.ijresmar.2015.06.007
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IJRM-01102; No of Pages 14
film executives and producers are keenly interested in
understanding and managing key factors in the early stages of
film development before making such enormous investments.
Given the cost associated with, and the critical contribution of, a
film's core teamthe principal on-camera cast (e.g. lead actors
and actresses) and off-camera crew (e.g. director, cinematogra-
pher, and production designer)to a film's success, it is vital to
identify and assemble a high potential core team of collabora-
tors. Past research has focused on box office success as driven
by product features, such as genre, and post-development
factors such as consumer responses to storyline, advertising,
distribution, critics, and word-of-mouth (Eliashberg, Elberse, &
Leenders, 2006). We extend this literature by emphasizing the
crucial value of the core development team to box office
success.
Movie development is characterized by fluid construction and
dissolution of development teams on a project-by-project basis
(Guimera, Uzzi, Spiro, & Amaral, 2005; Uzzi & Spiro, 2005). For
example, when Leonardo DeCaprio and Tom Hanks collaborated
in Catch Me If You Can, a link between them is established. As
they also work with other people on different film projects, more
links are generated to form an elaborate collaboration networka
structure consisting of connections among individuals through
their prior collaborations in the industry. In light of this networked
structure and guided by prior research examining industrial social
networks (e.g. Ahuja, Galletta, & Carley, 2003; Cattani &
Ferriani, 2008), we take a perspective of interconnected, as
opposed to isolated, individuals in the film industry. In particular,
we examine two key properties of each person's embeddedness in
the collaboration network: positional embeddedness (PE)the
extent to which the person has collaborated with well-connected
others in the network; and junctional embeddedness (JE)the
degree to which the person's prior collaborations bridge different
network sub-communities (Zukin & DiMaggio, 1990). Intuitively,
relations with well-connected others (PE) may increase one's
reputation and image, while connections across sub-communities
in the network (JE) may represent enhanced access to unique or
diverse technical and artistic skills that can benefit future projects
(Cattani & Ferriani, 2008; Grewal, Lilien, & Mallapragada, 2006).
Taking the perspective of film producers who are in direct
charge of team assembly, we theorize that PE and JE hold
differential importance across functional roles in a team, which
we classify as the core front-of-scene cast and behind-the-scene
crew. For example, a cast member with high PE may have a
strong reputation in the industry, helping a movie signal its
quality and generate publicity. This network position should be
less critical to the crew, whose value arises more from their
unique and diverse technical experience. Considering the
different responsibilities and skills required across these different
functional roles, PE is potentially more valuable to the cast and JE
more crucial to the crew.
Furthermore, films affiliated with a major (e.g. Universal), as
opposed to an independent (i.e. indie, e.g. Yari Film Group)
studio may take advantage of their superior brand recognition in
influencing the films' distribution and publicity (Eliashberg et al.,
2006). Hence, we propose a film's studio affiliation as a potential
moderator of the relationship between box office and team
members' network embeddedness. Specifically, given indie
studios' typically low marketing budgets and lack of brand
recognition among exhibitors, promoters, and consumers, it is
likely that high PE among all members will add extra benefits to
indie films.
In summary, we construct a conceptual framework to address
a number of important unanswered questions of theoretical and
managerial significance. Do cast's and crew's positions in the film
industry's network impact their contribution to box office? Does
thenatureofthiscontributiondependonfunctionalroles?
Should a major versus indie studio assemble its team differently?
These inquiries will not only identify key driving forces
underlying the relationship between box office and team
members' network embeddedness, but also offer potential
answers to one of the most challenging questions facing the
film industryHow does a studio assemble a multi-functional
team that maximizes a film's box office potential?
To address these questions, we analyze the box office
revenues of 2110 movies released over a six-year period,
leveraging nearly two decades of collaborative histories involv-
ing more than 15,000 film industry professionals. Building upon
the marketing, management, and sociology literatures, we derive
role-level metrics of network embeddedness (PE and JE) for core
team members. We then link these metrics to box office while
controlling for variations in film quality, talent popularity, and
studio resources. The results show that while PE is more valuable
for the cast, JE is more critical for the crew. Although past
research has focused on the cast's contribution to box office
(e.g. Elberse, 2007; Luo, Chen, & Park, 2010), our research
highlights the importance and distinct value of the crew. Hence
producers may wish to consider assembling a more balanced
team involving a crew with diverse experiences rather than a team
driven solely by a star cast. Finally, we find that indie, but
not major, studios can accrue additional benefits by engaging a
crew that is well-connected to prominent (high PE) industry
collaborators.
The remainder of the paper is organized as follows. We first
construct the conceptual framework. We then describe the two
metrics of network embeddedness and our modeling approach.
The subsequent section delineates the data, empirical analysis,
and managerial implications. We conclude by summarizing the
contributions and limitations of this research, as well as
suggesting avenues for future research.
2. Conceptual framework
2.1. Film industrial network and functional roles
Prior research focuses on the impact of product character-
istics and consumer responses on box office (e.g. Eliashberg
et al., 2006). By focusing on the film development team, we
expand this literature and aim to provide some answers to one
of the most challenging questions facing the motion picture
industrycore team composition. Relevant to this inquiry, the
literature on new product development (NPD) suggests that
NPD team members' functional diversity (Sethi, Smith, & Park,
2001) or specific cognitive skills (Madhavan & Grover, 1998)
2G. Packard et al. / Intern. J. of Research in Marketing xx (2015) xxxxxx
Please cite this article as: Packard, G., et al., The role of network embeddedness in lm success, Intern. J. of Research in Marketing (2015), http://dx.doi.org/10.1016/
j.ijresmar.2015.06.007
impact team performance. Moreover, when NPD teams are
constructed and dissolved fluidly on a project-by-project basis,
team members benefit from their prior collaborations in a
variety of ways, such as gaining information, reputation,
knowledge, skills, and/or support that can be applied to future
projects (Cattani & Ferriani, 2008; Delmestri, Montanari, &
Usai, 2005). That is, team members' structural positions in
a collaborative network can critically impact new product
success.
Of central interest to us are more nuanced aspects of these
relationships, which have been advocated as important directions
for future research (e.g. Ahuja et al., 2003; Grewal et al., 2006).
Particularly, creative relationships should be examined at the
team level beyond a single member or functional role (e.g.
director in Delmestri et al., 2005). Our cross-functional role
approach may address a vital yet unanswered questionhow
should a film producer assemble a revenue-maximizing movie
team?
To accomplish this, we employ social network analysis. This
approach examines the interdependence of persons in a
structured environment (i.e. network) to identify opportunities
for, or constraints on, resources and actions (Wasserman &
Faust, 1994). Most relevant to our work is prior research on
collaborative networks that involve groups of individuals
working together to achieve a common goal. In such networks,
individuals are related to one another through a collaborative
activity (e.g. a film project); and activities are related to one
another through common collaborators (Faust, 1997; see
Appendix A for a demonstrative example). Beyond the sheer
number of a person's ties (i.e. volume of past experience), the
potential impact of an individual's embeddedness in a
collaboration network should be informed by the nature of
with whomone collaborates and the functional role they play
in these collaborations.
According to Baker and Faulkner (1993),arolecan be
considered a resource used to pursue interests, enact positions,
and claim, bargain for, or gain group membership. It grants
access to unique social, cultural, and material capital to be
exploited for group interests. We examine a group of
individuals widely regarded as the coreof a film team by
the literature (e.g. Cattani & Ferriani, 2008) and based on our
conversations with studio executives and producers who recruit
team members. The core members are commonly classified into
two broad roles: the principal cast (lead actor, lead actress,
supporting actor, supporting actress
4
) and crew (director,
cinematographer, and production designer). The actors and
actresses interpret the dramatic characters on-camera under the
guidance of the director. The director controls and collaborates
with other crew members on the film's creative and technical
aspects. The cinematographer, also known as the director of
photography, is responsible for artistic and technical decisions
related to the film's visual image. Finally, the production
designer identifies and acquires the locations, settings, and
styles that help visually tell the movie's story.
While the movie marketing literature has documented the
revenue impact of a star cast member, often including it as a
control variable operationalized as a power ranking or Oscars
dummy (e.g. Ainslie, Drèze, & Zufryden, 2005; Basuroy,
Chatterjee, & Ravid, 2003; Elberse & Eliashberg, 2003), it has
not examined the impact of the crew or differential contribu-
tions across roles. Hence, it cannot speak to one of the most
critical decisions facing the industrythe composition of a
film's core team. It also views cast members as isolated
individuals instead of ones embedded in an elaborate social
network. Our research intends to fill these gaps.
2.2. Impact of PE and JE by functional role and studio as
a moderator
Positional embeddedness (PE) indicates the extent to which a
person is associated with well-connected others in the network
(i.e. others who possess high PE). Such connections may
engender several benefits to a film, such as enhanced publicity
opportunities. How likely these benefits are accrued depends in
part on the person's functional role. Consider, a film's box
office is partly influenced by the attention that its actors and
actresses can attract from the media and general public. By
definition, those who enjoy high PE (e.g. George Clooney and
Gwyneth Paltrow) should be associated with other powerful,
well-connected individuals in the industry (e.g. directors Steven
Soderbergh and Robert Zemeckis). These associations may lead
to enhanced visibility and broader media coverage, stronger
audience appeal, and more effective promotional campaigns for
the film. Producers are known to value prominent stars as they
generate greater media attention, especially around the releases
of their movies (Albert, 1998). Consumers also remember and
respond more favorably to advertising that features well-known
actors, leading to demonstrable economic benefits to the product
(Agrawal & Kamakura, 1995; Erdogan, 1999). Furthermore,
high PE actors and actresses may signal a movie's quality to
financers and exhibitors, mitigate negative critics' reviews
(Basuroy et al., 2003; Eliashberg & Shugan, 1997) and enhance
a movie's brand equity through their marquee appeal (Desai &
Basuroy, 2005; Luo et al., 2010)
In contrast, high PE may be less important for the crew due
to their relatively low profile in behind-the-scenes work. For
example, while cinematographer Roger Deakins and produc-
tion designer Therese DePrez are both winners of multiple
technical awards in the industry and possess high JE (as shown
in Table 3 later), they are less likely to enhance a film's
financing or marketability to the same extent as a high PE cast.
In summary, we predict that the cast's PE will have a more
positive effect on box office than the crew's PE.
High JE professionals bridge weakly linked clusters or
sub-components of a network (Burt, 2000, 2002). Those with
higher JE may benefit from the greater diversity in information
and resources that they can draw from the collaboration network.
They are expected to have greater access to unique and valuable
knowledge, skills, and resources that may emerge outside the
4
We use the highest listed cast members in the lm credit database on
Oscars.org, reecting the importance, not the alphabetic order, of the cast in a
lm. This list is also consistent with the one on imdb.com, arguably the best
known movie database.
3G. Packard et al. / Intern. J. of Research in Marketing xx (2015) xxxxxx
Please cite this article as: Packard, G., et al., The role of network embeddedness in lm success, Intern. J. of Research in Marketing (2015), http://dx.doi.org/10.1016/
j.ijresmar.2015.06.007
core of a network (e.g. Cattani & Ferriani, 2008; Cross &
Cummings, 2004). Furthermore, those with high JE have been
exposed to a broader array of concepts, developmental processes,
and collaborative styles (Arranz & Fdez De Arroyabe, 2012). A
crew with more diverse experiences may also offer greater
novelty and breadth in their abilities to apply unconventional
ideas, leading to competitive advantages (Cattani & Ferriani,
2008). Thus, we suggest that high JE should enable a crew to
identify and apply movie-making innovations that occur both in
the core and the more avant-garde indie or foreign film regions of
the industry network. For instance, director Quentin Tarantino is
known for borrowing techniques from foreign and indie films
(Armstrong, 2013), such as the Japanese animation styles used in
Kill Bill. In contrast, high JE is less likely to enhance the cast's
reputation or value. While being connected with both the core
and more peripheral communities may enhance a cast member's
artistry, such a position does not necessarily elevate his/her media
profile or marquee appeal. In summary, we predict that the crew's
JE has a more positive effect on box office than the cast's JE.
We further expect that a film's studio affiliation may
moderate the relationship between box office and the cast's or
crew's network embeddedness. Film studios enjoy varied
degrees of brand recognition and production, marketing, and
distribution resources. Studios are commonly classified into
majors (including mini-majors in our empirical analysis) versus
independents (i.e. indies;Vogel, 2004). Majors release a
large number of films each year and command approximately
90% of North American box office revenues. The Big Six
majors include the 20th Century Fox, Buena Vista/Disney,
Sony Columbia, Paramount, Universal, and Warner Brothers.
They also have subsidiaries concentrating on art house or niche
films, such as Fox Searchlight. Besides the Big Six, well-
known mini-majors include studios such as Lionsgate and
MGM/UA, which are larger than indies and attempt to compete
directly with the Big Six (Variety, 2012).
Indies sometimes get their projects picked up by majors after
progress toward film completion has been made (Vogel, 2004).
They also manage distribution themselves, especially in local and
regional markets that are not well covered by majors and
mini-majors. As a result, brand recognition is critical for indies
when competing for desirable release dates and negotiations for
wider distribution. When a studio lacks a strong brand, investors,
exhibitors, and consumers resort to the cast and crew's
professional brands to assess the film's quality and potential for
success (Bettman, Luce, & Payne, 1998). Hence, a cast and crew
with strong PE may be particularly important to indie films that
are in greater need of brand recognition. We thus propose that
higher PE among the cast and crewwill add extra benefits to indie
films. In contrast, because the behind-the-scene advantages
offered by high JE team members do not contribute to brand
recognition, we do not expect that the benefits of JE will interact
with studio affiliation.
2.3. Summary of predictions
To summarize, team members' abilities to contribute
knowledge and skills to new film projects depend on their
embeddedness in the industrial network and their functional
roles. We predict that (i) high PE is more valuable to the cast;
(ii) high JE is more critical for the crew; and (iii) high PE
among both the cast and crew will offer incremental benefits to
indie studios.
3. Measures and modeling
In our empirical analysis, the collaborative network consists
of each film's core team members: the top four cast and the top
three crew (director, cinematographer, and production design-
er). A tie is formed between any dyad of individuals regardless
of functional roles, iand i, if they have collaborated on at least
one film in the ten years prior to the focal film's release year.
We then use PEimto denote positional embeddedness and JEim
junctional embeddedness of individual iworking on movie m.
For a movie released in year t, the network used to compute
PEimand JEimis constructed from the collaborations on movies
released between year (t1) and year (t10).
We capture positional embeddedness (PE) by using a measure
of eigenvector centrality (Bonacich, 1987), which captures how
well a person is tied to well-connected others in a social network.
PE captures not only the number of a person's direct ties,
5
but
weighs these ties according to their importance in the larger
ecosystem of the global network (Jackson, 2008,p.40).Inthis
sense, a tie to a person connected to many others is worth more
than a tie to a person who is not as well-connected. Following
Bonacich's (1987) formulation of eigenvector centrality, we
estimate PEimas proportional to the total PE of individual i's past
collaborators ion prior movies m,i0m0PEi0m0over the 10 years
prior to the release of movie m:
λPEim¼Xi0
m0
PEi0m0;ð1Þ
where λis a proportionality factor between 0 and 1 to ensure a
non-zero solution to Eq. (1). The equation is ultimately
self-referential in that i
m'
s PE depends on the PE of i's past
collaborators i
m
, whose PE depends on the PE of their
collaborators; and so on throughout the entire network. The
value, λand PEim, for each individual iin movie mare derived by
solving a simultaneous linear equation system in the standard
eigenvector-eigenvalue formulation:
λPE ¼ePE:ð2Þ
Here, PE is a column vector of dimension [n×1]thatconsists
of eigenvector centralities of all individuals in the network, where
5
The number of a person's direct ties can be described as his or her
unweighted degree centrality. While degree is a commonly used social network
measure, when applied to collaborative networks with teams that are similar in
size, it approximates a simple count of prior collaborations; that is, how many
movies that person has worked on. When included together with PE in
preliminary models, degree centrality was not signicant, despite being
signicant in the absence of PE. A fourth commonly used measure of network
embeddedness is closeness centrality. To our knowledge, there is no theoretical
support or prior examination of this variable in a context similar to the present
research. Our preliminary analysis found it non-signicant in relation to box
ofce.
4G. Packard et al. / Intern. J. of Research in Marketing xx (2015) xxxxxx
Please cite this article as: Packard, G., et al., The role of network embeddedness in lm success, Intern. J. of Research in Marketing (2015), http://dx.doi.org/10.1016/
j.ijresmar.2015.06.007
nis the total number of individuals in the network, and eis a
[n×n] symmetric adjacency matrix capturing all prior collabo-
rations of all nindividuals in the network. The diagonal elements
of eare zero and each off-diagonal element in eis a binary
indicator
6
(1 or 0) of whether each person iin movie mhas
collaborated with another person iin any movies released in the
decade before m. In the language of matrix algebra, λis the
largest eigenvalue associated with the adjacency matrix e,andPE
is its corresponding eigenvector.
7
For JE, we adapt betweenness centrality from network
theory (Freeman, 1979) to accommodate our team-level
analysis, operationalizing i's JE as
JEim¼Xjm0km0:imjm0km0
ðÞ
Pi jm0km0
ðÞ=Pj
m0km0
ðÞ
ngm
ðÞngm1ðÞ=2:ð3Þ
Here Pi(j
m
k
m
) denotes the number of shortest paths between
collaborators jand kon an earlier movie mthat run through i,
P(j
m
k
m
) the total number of shortest paths between jand k;g
m
the number of team members on movie m,andnthe total number
of individuals in the network. We extend the Freeman (1979)
equation to our team context by normalizing this proportion by
the total number of pairs of individuals in the network (excluding
i
m
and all others working on movie m) in the denominator of
Eq. (3). The intuition behind this JE measure is that information
and resources accrued to a given movie team are likely to travel
through the social ties established by the team members via prior
collaborations. The extent of one's exclusivity over such social
paths in the network connotes his/her JE (see Appendix A for an
illustration).
We use the igraph package of the R statistical language to
calculate PE and JE.
8
When inputting the observed ties to the
package, we further account for (a) the number of prior
collaborations in a dyad, since one may expect a stronger bond
between two individuals from repeated collaborations (fre-
quency); and (b) temporal discounting of the collaborations that
took place farther in the past (recency).
9
While (a) is relatively
common in examining social and economic networks (Brandes,
2001; Jackson, 2008), (b) is less so. For (b), we use the discount
function, e
β(t1)
, where tis the year lapse (e.g. t= 1 means
the collaboration occurred last year) and βa discount
parameter. In our context, βshould be fairly small such that
the network effects do not dissipate rapidly over the 10-year
window. We also performed a grid search with different values
of βand find that, indeed, large discount rates weaken the
effects of JE, but not PE, on box office. This is consistent with
the argument that tie values below 1 will statistically
over-punish paths through only negligibly weaker ties
(Granovetter, 1973; Opsahl, Agneessens, & Skvoretz, 2010).
We use β=0.05 in our analysis, which results in a discount
factor of 0.64 for collaborations that occurred 10 years prior.
To assess PE's and JE's impact on box office, we link the PE
and JE values to the logarithm of movie m's cumulative box
office in inflation-adjusted U.S. dollars, R
m
, as:
Rm¼αþz0
mθþPE0
mτ1þJE0
mτ2þPE0
mImτ3þJE0
mImτ4
þεm;ð4Þ
where αis an intercept if movie mis affiliated with an indie studio;
and z
m
includes control variables commonly used in the movie
literature (e.g. Ainslie et al., 2005; Sawhney & Eliashberg, 1996)
such as sequel and genre, MPAA rating, Oscars, critics' and
consumers' ratings. PE
m
(JE
m
) consists of the average PE (JE) of
movie m's cast and crew after the frequency and recency weighted
PEim(JEim) is calculated for each individual ias discussed earlier.
Hence τ
1
and τ
2
capture the main effects of PE and JE,
respectively, on box office. This approach both addresses our
research questions directly and reduces potential multi-collinearity
in individual PE and JE. The grouping of the cast versus crew is
further validated by factor analysis which shows that the PEs (and
JEs) of the director, cinematographer, and production designer
load on one dimension, while those of the actors and actresses load
on a second dimension. The scalar dummy I
m
= 1 if movie mis
affiliated with an indie studio, and thus τ
3
and τ
4
examine whether
the relationship between box office and network embeddedness
varies across majors/mini-majors versus indie studios.
Despite accounting for critics' ratings, consumer ratings,
and Oscar nominations above, we may not have adequately
captured the heterogeneity in movie quality. A movie with
higher quality and financial potential has a greater chance of
attracting a cast and crew of higher caliber, leading to higher
box office revenue. Failing to properly control for quality
heterogeneity can lead to omitted variable bias or potential
endogeneity between the movie's box office and the network
embeddedness of its team members. To address this potential
endogeneity, prior work suggests exploiting the panel data
structure and incorporating movie-level fixed effects (Elberse,
2007; Gopinath, Chintagunta, & Venkataraman, 2013). How-
ever, only one observation of the cumulative revenue exists for
each movie. PE and JE also vary by movie, not by time or
geographic area. As a result, using more disaggregate data such
as weekly or regional revenues is not plausible. Another pos-
sible approach is to use instruments for network embeddedness.
However, it is challenging to identify adequately strong
instruments for PE and JEvariables that are highly correlated
with PE and JE but not with box office revenue.
10
Prior
6
We later discuss weighting of this indicator to account for repeated
collaborations and temporal discounting of past collaborations.
7
Readers interested in the standard eigenvector-eigenvalue formulation in
matrix algebra may refer to Krishnan (1984) or Abadir and Magnus (2005) for a
more detailed, step-by-step derivation. Appendix B also offers a brief, general
example of this derivation.
8
Other network analysis software packages available to facilitate the
calculation of the network statistics include the CENTPOW module for Stata,
Gephi, Pajek, UCINET, and SocNetV.
9
The key results also sustain when simple binary (1 = collaborated; 0 = not),
instead of weighted collaborations, are analyzed.
10
For example, potential instruments for PE are family or social connections
with well-established individuals in the industry. These connections may lead to
movie collaborations with higher PE individuals. However, these connections
also likely affect an individual's ability to generate strong box ofce revenues.
Familial connections, unlike PE, also do not vary over time. As for JE, potential
instruments include individuals' career diversity (e.g. work in different elds of
entertainment, such as music, Broadway, etc.). However, this variable can also
have a direct impact on a movie's box ofce.
5G. Packard et al. / Intern. J. of Research in Marketing xx (2015) xxxxxx
Please cite this article as: Packard, G., et al., The role of network embeddedness in lm success, Intern. J. of Research in Marketing (2015), http://dx.doi.org/10.1016/
j.ijresmar.2015.06.007
research suggests that using weak instruments not highly
correlated with the endogenous variable can lead to larger
inconsistencies in the estimates of the endogenous variable than
a model that properly controls for the potential source of
endogeneity (Bound, Jaeger, & Baker, 1995; Rossi, 2014). We
therefore include multiple control variables in the model to best
capture quality heterogeneity across movies.
First, we follow prior research that suggests the decay rate of
weekly revenues from the first to second week of release as an
indicator of film quality (e.g. Krider & Weinberg, 1998). We
include in the vector z
m
in Eq. (4) a quality decay variable
calculated as the difference between the logarithm of a movie's
first- and second-week revenues. Second, each movie project is
affiliated with a particular studio (Vogel, 2004). These studios
vary drastically in their abilities to finance and market films,
with major and mini-major studios enjoying far greater
resources than indie studios (Scott, 2005; Waterman, 2005).
Greater resources increase the majors' abilities to produce
higher quality movies and promote them more effectively to the
public. Given that the indie studios we observe (N = 223)
produce a much smaller number of movies (73% only produced
one or two movies), we include studio fixed effects for the
major and mini-major studios (N = 10) to capture heterogene-
ity in movie quality and financial support.
Finally, production budget may be included to further control
for heterogeneity in movie quality and financial support. Budget
was not available, however, for a large percentage (72%) of the
indie films in the data. If analysis is limited to only movies with
budgets, there is insufficient variation in PE and JE to identify
their contributions.
11
Considering that a substantial part of a
movie's budget is driven by the salaries of the core cast and crew
(Forbes, 2014), we include popularity of the cast and crew, as
measured by the cast's and crew's temporally discounted average
cumulative box office over the prior decade, as another set of
control variables. We use the temporal discount function e
β(t1)
to be consistent with the discounted PE and JE measures. As team
members who generated higher revenues in the past tend to
command higher salaries, the popularity measures help further
capture heterogeneity in movie quality and financial support,
thereby alleviating the endogeneity issue. Moreover, since high
PE and JE members may also be popular, these quality measures
also ensure that the network effects are not confounded with cast
or crew popularity
.12
4. Empirical analysis
4.1. Data
We examine the box office revenues of 2110 movies released
in the U.S. over a six-year period (1999 to 2004 inclusive) that
earned at least $1000. As new movies are developed and new
collaborations established, the network dynamically evolves.
Thus, we use a lagged rolling-window approach to define a
collaborative network for each of the six release years under
investigation. For example, for each movie released in 2004, we
use the movies released during the prior decade (19942003
inclusive) to construct the collaborative network and compute PE
and JE for the cast and crew involved in those 2004 releases.
Excluding the focal movie's release year from the network
alleviates potential simultaneity between box office and network
the statistics. Table 1 provides the descriptive statistics of the
variables used in our analysis.
4.2. Network analysis
While this research takes the perspective of the producers
who assess the cast and crew's potential contributions when
assembling the core movie teams, and thus producers' PE and
JE are not key predictors in the model, producers' ties to the
cast and crew are also part of the network. We believe that it is
important to include producers' ties as the cast's and crew's
relationships with producers play a crucial role in determining
the cast's and crew's network positions, and hence their PE and
JE. Also, for the 5.8% of 16,891 persons in the data that took on
more than one role on a particular team, we assign their
network embeddedness to each role performed.
Table 2 displays the summary statistics of the six networks
analyzed. Each network involves nearly 3000 movies and over
9000 individuals, forming a giant componentthat connects
over 85% of all potential collaborators in the industry.
Unsurprisingly, further inspection of the data indicates that
Hollywood is at the core of this component, while non-U.S.
productions and a few isolated U.S. film teams operate outside
this dominant invisible college(see Appendix C for a
sample visualization of the 19942003 network used for 2004
releases).
We also observe that an individual wishing to reach a
potential collaborator through the latter's prior collaborators
would on average need to engage only about four others. That
is, the mean path lengthis 4, varying between 3.99 to 4.24
across the six networks. Moreover, we report the clustering
coefficient (Watts & Strogatz, 1998) as an indicator of the
density of ties, or the proportion of the cases where a
collaborator of my collaborator was also my collaborator.This
coefficient is 21% in our data, higher than what would be
observed in randomly generated networks of the same size.
The above combination of short path lengths and high
clustering coefficients confirms that the film industry can be
characterized as a small-worldnetwork (Watts & Strogatz,
1998). That is, an enormous network (e.g. 928611,857 indi-
viduals per network in our case) can be quickly traversed
through ties among a small number of individuals (e.g. 4 in our
data). Such networks tend to be highly conducive to social
transmission of information, resources, or influence.
Table 2 summarizes the properties of the six ten-year
networks. The giant component statistic describes the proportion
of the individuals who have connections in the largest connected
cluster in the network; the average degree indicates the average
number of past collaborators; the average path length captures the
11
We estimated the proposed model (Eq. (4)) using only those movies with
budgets and indeed could not uncover the effects of network embeddedness.
12
We thank the Associate Editor for this suggestion.
6G. Packard et al. / Intern. J. of Research in Marketing xx (2015) xxxxxx
Please cite this article as: Packard, G., et al., The role of network embeddedness in lm success, Intern. J. of Research in Marketing (2015), http://dx.doi.org/10.1016/
j.ijresmar.2015.06.007
number of steps between any two individuals in the network; and
the clustering coefficient suggests the tendency of individuals to
cluster together such that the collaborator of a collaborator is
also my collaborator.
There are several noteworthy temporal dynamics in the
networks. In particular, positive yearly trends appear in the
number of films released, number of unique cast and crew
members, average path length, and clustering coefficient.
Decreasing over time are the proportion of the individuals in
the network's fully-connected giant component and the average
number of direct collaboration ties held by an individual. Overall,
these findings support the notion that the Hollywood core has
become increasingly exclusive (e.g. Scott, 2005). However, they
also indicate a growing number of less connected or less
experienced individuals entering the more independent sub-
communities of the industry. A cursory manual examination of
the data suggests the rise of productions from outside North
America, such as India's Bollywood, as a driver of this change.
To offer more concrete examples of PE and JE at the
individual level, we list the 25 cast with the highest PE and 25
crew with the highest JE in the 2004 releases with the 19942003
network (Table 3).
13
For example, while actors such as Nicolas
Cage and Samuel L. Jackson may not spring to mind as among
the top 10 on-camera talents of 2004, they held some of the
highest PE (and JE) at that time. This is likely due to their
exceptional productivity as actors, often in supporting roles, and
their collaborations with both diverse (JE) and well-connected
(PE) others. For example, Nicolas Cage was credited for 29
movies over the entire observation period, including a diverse
range of Hollywood blockbusters (e.g. National Treasure),
small-budget, artistic independent projects (e.g. Leaving Las
Vegas), B-movies (e.g. Kiss of Death), and foreign productions
(e.g. Tempo di uccidere,Zandalee).
Turning to the list of top crew by JE, we spotlight
cinematographer Christopher Doyle, whose incredibly diverse
experience is expected to propel his creative and technical
contribution to a movie's success. Doyle's variety of experiences
across the industry's sub-communities is evident in his work
on movies appealing to English, Cantonese, Mandarin, and
French language markets, including major studio films (e.g. the
1998 Hollywood re-make of Psycho and 2006's Lady in the
Water with director M. Night Shyamalan), a number of notable
Chinese-language films, unusual genre films such as the
Japanese-German co-production of pink-filmUnderwater
Love, and several North American indie films (e.g. Paranoid
Park,Passion Play).
4.3. Model comparison
To demonstrate the contributions of the core cast's and
crew's network embeddedness to box office, we estimate a
series of models. Building upon the commonly used models in
the movie literature that account for product characteristics (e.g.
13
The values of PE and JE by year for all 16,891 individuals across the six
collaboration networks are available from the rst author on request.
Table 1
Descriptive statistics.
All movies Major studio
movies
Indie movies
(n = 2110) (n = 1229) (n = 881)
Mean S.D. Mean S.D. Mean S.D.
Box office ($MM) 20.444 41.688 32.904 49.000 3.062 17.183
Sequel 0.104 0.305 0.146 0.353 0.045 0.208
Foreign movie 0.201 0.401 0.087 0.282 0.360 0.480
Action 0.063 0.243 0.090 0.286 0.026 0.160
Adventure 0.011 0.106 0.013 0.113 0.009 0.095
Animated 0.035 0.184 0.048 0.214 0.017 0.129
Biography/documentary 0.069 0.253 0.023 0.151 0.105 0.306
Black comedy 0.010 0.099 0.009 0.094 0.011 0.106
Comedy 0.225 0.418 0.256 0.437 0.182 0.386
Crime 0.009 0.097 0.005 0.070 0.016 0.125
Drama 0.380 0.486 0.327 0.469 0.454 0.498
Fantasy 0.008 0.089 0.011 0.102 0.005 0.067
Horror 0.030 0.170 0.033 0.180 0.025 0.156
Musical 0.009 0.092 0.006 0.075 0.012 0.111
Suspense/thriller/mystery 0.050 0.218 0.068 0.251 0.025 0.156
Romantic comedy 0.054 0.226 0.072 0.258 0.030 0.169
Science fiction 0.020 0.141 0.028 0.166 0.009 0.095
Western 0.006 0.075 0.007 0.085 0.003 0.058
G-rated 0.027 0.161 0.035 0.184 0.015 0.121
PG13-rated 0.080 0.271 0.107 0.309 0.042 0.201
PG-rated 0.243 0.429 0.359 0.480 0.082 0.274
R-rated 0.475 0.499 0.496 0.500 0.446 0.497
NC17-rated 0.002 0.049 0.002 0.049 0.002 0.048
Consumer rating 6.305 1.149 6.199 1.155 6.453 1.125
Critics rating 5.786 1.310 5.617 1.373 6.020 1.178
Oscar nominated 0.043 0.202 0.066 0.248 0.010 0.101
PE of cast 0.066 0.076 0.092 0.083 0.031 0.043
PE of crew 0.057 0.077 0.083 0.088 0.022 0.037
JE of cast 0.028 0.034 0.039 0.036 0.013 0.023
JE of crew 0.024 0.029 0.034 0.030 0.011 0.020
Popularity of cast 18.376 18.619 25.506 18.496 8.429 13.542
Popularity of crew 14.351 20.505 22.024 22.986 3.646 8.567
Note: a movie is coded as 1 if it belongs to one of the genre categories (such as
Drama) or MPAA ratings (such as R for restricted) listed in the data collected from
Oscars.org. The average consumers' rating and average critics' rating for each film
are from imdb.com and rottentomatoes.com, respectively, both on a 010 point
scale where 10 = best rated. For Oscar nominations, a movieis coded as 1 ifit was
nominated for one of the six major award categories: best picture, director, actor,
actress, supporting actor, and supporting actress.
Table 2
Summary statistics of the six collaboration networks.
Movie
released
(inclusive)
Movies
in network
Persons
in network
% in giant
component
Mean
degree
Mean
path
length
Clustering
coefficient
19942003 3,268 11,857 0.858 13.11 4.24 0.217
19932002 3,195 11,473 0.868 13.34 4.18 0.215
19922001 3,066 10,850 0.886 13.70 4.15 0.212
19912000 2,900 10,166 0.895 13.88 4.08 0.211
19901999 2,809 9,776 0.904 14.01 4.07 0.211
19891998 2,693 9,286 0.894 14.20 3.99 0.209
7G. Packard et al. / Intern. J. of Research in Marketing xx (2015) xxxxxx
Please cite this article as: Packard, G., et al., The role of network embeddedness in lm success, Intern. J. of Research in Marketing (2015), http://dx.doi.org/10.1016/
j.ijresmar.2015.06.007
Ainslie et al., 2005; Sawhney & Eliashberg, 1996), Model 1
(baseline) includes the studio fixed effects and other quality
measures described earlier, such as critics' and audience's
ratings, Oscar nominations, and the revenue decay. Model 2
integrates the cast's and crew's popularity effects without
their network embeddedness. Models 3 and 4 add the main
effects and interaction effects of network embeddedness,
respectively.
Table 4 shows that accounting for cast and crew popularity
(Model 2: adjusted R-square = .720) improves model fit beyond
the movie characteristics commonly used in the literature (Model
1: adjusted R-square = .683). Importantly, the main effects of
network embeddedness explain the variations in box office above
and beyond popularity (Model 3: adjusted R-square = .729), and
the interaction effects of network embeddedness further improve
model fit (Model 4: adjusted R-square = .731). The PE, JE, and
popularity measures in Models 24 account for frequency and
recency discounting using the discount function, e
β(t1)
.We
performed a grid search by varying the values of βfrom 0.01 to
0.75 for both the network and popularity effects. The best
model fit with the same βfor both effects is β= .05. Model fit
gets worse as βbecomes greater or smaller than .05. As a
robustness check, we also estimate and report Model 5 where
PE and JE are weighted by the number of prior collaborations
between any two persons (frequency), but not the temporal
discounting of these collaborations (recency). Model 5 also
includes the annual inflation discounted popularity measures of
the cast and crew. Overall, we see that the same pattern of
results holds. However, Model 4 (.731) fits slightly better than
Model 5 (.729).
14
4.4. Parameter estimates
4.4.1. Effects of movie characteristics
Parameters of movie characteristics make intuitive sense
across all models: sequels, MPAA rated, Oscar nominated, and
those receiving favorable consumers' and critics' reviews
accrue higher revenues. In contrast, foreign films, crime genre
films, and those with faster revenue decay generate lower
revenues. All of the studio fixed effects except for that of
United Artists, are significant and positive, confirming our
expectation that movies released by larger studios accumulate
higher box office. We omit reporting the studio fixed effects for
simplicity of exposition. Popularity of the cast and crew
significantly affects movie box office (Model 2).
4.4.2. Effects of PE
To assess the relationship between network embeddedness
and box office, we start with Model 3. Note that since PE and
JE are standardized in the analysis, we can directly compare the
magnitude of their effects within and across functional roles.
Model 3 reveals that the PE effect for the cast is positive
(τ
1,cast
= .287) and significant at 0.05, after controlling for the
cast and crew's popularities, indicating that higher PE for the
cast is associated with elevated revenues. However, the PE
effect for the crew is not significant. The positive PE effect
persists in Model 4 where interaction effects between network
embeddedness and the type of studio are taken into account.
These findings indicate that, again, PE of the cast, but not of the
crew, contributes to revenues. Echoing our earlier discussions,
we attribute this result to cross-functional differences such that
ties to well-connected others provide the cast with heightened
image and reputation, which in turn may enhance media
attention and marquee appeal. However, such capabilities are
significantly less important for the crew.
4.4.3. Effects of JE
Model 3 shows that the effect of the crew's JE (τ
2,crew
=
.186) is positive and significant at 0.05, while the cast's JE
is non-significant. These results persist even when the
interaction effects are accounted for in Model 4. These findings
reveal that the crew's, but not the cast's, JE contributes to
box office success. As reasoned earlier, a crew occupying a
position that bridges sub-communities of the network may draw
greater technical knowledge, creativity, and methods from
more varied sources, potentially boosting product quality to a
higher level.
14
Although not reported in Table 4, two additional models were estimated:
Model 1 plus the main effects of PE and JE, and Model 1 plus the main and
interaction effects of PE and JE. Comparing these two models with Models 3
and 4 shows that when popularity effects are considered, unsurprisingly, the
effects of PE remain signicant, although they become smaller in size.
Table 3
Top 25 cast by PE and Top 25 crew by JE in 2004 releases.
Top 25 cast by PE Top 25 crew by JE
Rank Person PE JE Rank Person JE PE
1 Danny Devito .406 .610 1 Eduardo Serra .763 .034
2 Gene Hackman .231 .196 2 Giorgos Arvanitis .761 .009
3 Kevin Spacey .216 .359 3 Thierry Arbogast .741 .041
4 Samuel L Jackson .182 .659 4 Christopher Doyle .687 .019
5 Ben Stiller .174 .138 5 Elliot Davis .564 .110
6 Nicolas Cage .159 .431 6 Benoit Delhomme .529 .011
7 Robert De Niro .154 .357 7 XavierPerezGrobet .398 .006
8 John Travolta .151 .289 8 Andrew Dunn .379 .089
9 Julianne Moore .150 .507 9 Robert Richardson .371 .093
10 Meryl Streep .149 .201 10 Dante E Spinotti .354 .116
11 Bruce Willis .148 .429 11 Giles Nuttgens .343 .012
12 George Clooney .147 .165 12 Therese Deprez .335 .063
13 Morgan Freeman .140 .234 13 David Wasco .334 .102
14 Julia Roberts .122 .161 14 Paul J Peters .320 .059
15 Jim Carrey .121 .175 15 Denis Lenoir .306 .020
16 Gwyneth Paltrow .120 .341 16 Maryse Alberti .297 .031
17 Laura Linney .118 .068 17 Ashley Rowe .293 .021
18 Robin Williams .116 .459 18 Ellen Kuras .290 .047
19 Bill Paxton .115 .084 19 William Chang .289 .001
20 Drew Barrymore .115 .264 20 Adam Biddle .279 .069
21 Billy Bob Thornton .114 .237 21 Dick Pope .268 .033
22 Tim Robbins .113 .202 22 Jane Ann Stewart .266 .016
23 James Garner .112 .048 23 Bob Ziembicki .263 .068
24 Eddie Murphy .106 .255 24 Kevin Thompson .255 .048
25 Kevin Bacon .106 .384 25 Declan Quinn .255 .052
8G. Packard et al. / Intern. J. of Research in Marketing xx (2015) xxxxxx
Please cite this article as: Packard, G., et al., The role of network embeddedness in lm success, Intern. J. of Research in Marketing (2015), http://dx.doi.org/10.1016/
j.ijresmar.2015.06.007
4.4.4. Moderation by studio affiliation
As predicted, we observe a significant and positive interaction
between the crew's PE and studio affiliation (e.g., τ
3,crew
=.441
in Model 4). This result indicates that the crew's PE provides a
much needed extra signal of a film's quality for indie films that
lack the brand recognition enjoyed by major studio films.
However, we did not find the predicted interaction of PE for an
indie film's cast, suggesting that the cast's connections to
well-connected others (PE) are important regardless of the studio's
overall marketing resources. In other words, PE of an indie film's
cast does not add extra benefit beyond its main effect contribution
to box office. Lastly, as expected, we did not find interaction
effects of the studio affiliation and JE of the cast or crew.
In summary, this analysis reveals that a film achieves greater
box office if developed by a high PE cast who has collaborated
with well-connected others and a high JE crew who bridges
diverse sub-communities in the industry. While the movie
literature has focused on the effects of product- and consumer-
related factors on box office, we demonstrate the important
contributions of the movie's core development team, whereby
each team member draws knowledge and skills through prior
collaborations to support his or her role-driven contribution to
a film's revenues. These previously undocumented findings
represent important considerations for critical managerial deci-
sions on product team formation before millions of dollars in
development costs are incurred.
Table 4
Parameter estimates.
Baseline (1) + popularity (2) + network
main effects
(3) + network
interaction effects
(4) based on # collaboration-
weighted PE and JE
(1) (2) (3) (4) (5)
Intercept: indie studios 10.249⁎⁎ 9.972⁎⁎ 10.308⁎⁎ 10.525⁎⁎ 10.540⁎⁎
Sequel 1.618⁎⁎ 1.284⁎⁎ 1.321⁎⁎ 1.311⁎⁎ 1.317⁎⁎
Foreign film 0.952⁎⁎ 0.575⁎⁎ 0.487⁎⁎ 0.470⁎⁎ 0.478⁎⁎
Action 1.389⁎⁎ 0.960⁎⁎ 0.974⁎⁎ 1.022⁎⁎ 1.024⁎⁎
Adventure 0.628 0.403 0.413 0.371 0.353
Animated 0.587⁎⁎ 0.252 0.4400.4440.428
Black comedy 0.870⁎⁎ 0.6590.541 0.544 0.569
Comedy 0.404⁎⁎ 0.2670.2520.2790.272
Crime 1.184⁎⁎ 1.425⁎⁎ 1.451⁎⁎ 1.435⁎⁎ 1.427⁎⁎
Drama/romance 0.162 0.020 0.027 0.005 0.027
Fantasy 0.736 0.248 0.107 0.036 0.054
Horror 1.555⁎⁎ 1.646⁎⁎ 1.733⁎⁎ 1.737⁎⁎ 1.741⁎⁎
Musical 0.392 0.382 0.272 0.312 0.322
Romantic comedy 0.870⁎⁎ 0.684⁎⁎ 0.592⁎⁎ 0.637⁎⁎ 0.642⁎⁎
Suspense/thriller/mystery 1.180⁎⁎ 0.896⁎⁎ 0.794⁎⁎ 0.822⁎⁎ 0.805⁎⁎
Sci-fi 1.196⁎⁎ 0.705⁎⁎ 0.661⁎⁎ 0.660⁎⁎ 0.645⁎⁎
Western 0.9570.210 0.210 0.223 0.309
G-rated 1.765⁎⁎ 1.566⁎⁎ 1.680⁎⁎ 1.613⁎⁎ 1.620⁎⁎
PG13-rated 1.639⁎⁎ 1.448⁎⁎ 1.495⁎⁎ 1.325⁎⁎ 1.386⁎⁎
PG-rated 1.767⁎⁎ 1.447⁎⁎ 1.381⁎⁎ 1.429⁎⁎ 1.339⁎⁎
R-rated 0.613⁎⁎ 0.569⁎⁎ 0.551⁎⁎ 0.473⁎⁎ 0.461⁎⁎
NC17-rated 0.894 0.850 0.811 0.724 0.644
Consumer rating 0.129⁎⁎ 0.107⁎⁎ 0.0860.0920.103⁎⁎
Critics rating 0.113⁎⁎ 0.146⁎⁎ 0.159⁎⁎ 0.156⁎⁎ 0.147⁎⁎
Oscar nomination 1.470⁎⁎ 1.171⁎⁎ 1.072⁎⁎ 1.068⁎⁎ 1.135⁎⁎
Quality decay 0.285⁎⁎ 0.281⁎⁎ 0.275⁎⁎ 0.273⁎⁎ 0.277⁎⁎
Studio fixed effects Y Y Y Y Y
PE: cast 0.287⁎⁎ 0.265⁎⁎ 0.157⁎⁎
PE: crew 0.076 0.040 0.016
JE: cast 0.073 0.059 0.000
JE: crew 0.186⁎⁎ 0.169⁎⁎ 0.225⁎⁎
PE: cast × indie 0.216 0.055
PE: crew × indie 0.441⁎⁎ 0.813⁎⁎
JE: cast × indie 0.111 0.033
JE: crew × indie 0.010 0.165
Popularity: cast 0.024⁎⁎ 0.019⁎⁎ 0.018⁎⁎ 0.015⁎⁎
Popularity: crew 0.023⁎⁎ 0.019⁎⁎ 0.019⁎⁎ 0.014⁎⁎
Adjusted R-square 0.683 0.720 0.729 0.731 0.729
Notes:
1
Both PE and JE are standardized so that their corresponding parameters are comparable.
2
Significance at 0.05 is denoted by ⁎⁎ and at 0.10 by .
3
The baseline genre is biography/documentary. The baseline MPAA rating is unrated.
9G. Packard et al. / Intern. J. of Research in Marketing xx (2015) xxxxxx
Please cite this article as: Packard, G., et al., The role of network embeddedness in lm success, Intern. J. of Research in Marketing (2015), http://dx.doi.org/10.1016/
j.ijresmar.2015.06.007
4.5. Managerial implications
The proposed conceptual framework and methodology lead
to important and practical guidance to film studios and talents,
and more broadly, for new product team assembly in other
industries. First, faced with a large and constant flux of talents,
how do producers (or senior managers) assess the cost/benefit
involved in hiring a new-comer (i.e., a person with limited
network embeddedness) versus an old hand(i.e., a person
with high network embeddedness) in the industry? Our
approach offers a model-based evaluation of this and related
tradeoffs by predicting the cumulative revenues based on either
scenario. In the same vein, when one talent becomes unavailable
and alternatives are considered, our approach can readily
forecast the potential revenue gain or shortfall when considering
alternative team members.
For a second example specific to the film industry, when
deciding among a roster of potential candidates for the cast and
crew, producers may utilize the proposed approach as an
effective decision aid to assemble a dream teamthat
complements auditions, interviews and the recommendations
of professional talent agencies. With insider information on
budget, salary cap, and negotiation stance, a producer who has
a revenue goal in mind may conduct a tradeoff analysis or
optimization exercise to derive a team with a minimum salary
and maximum box office potential.
Related to the above, a third question is whether the
producer faced with skyrocketing salaries should resort to a
star strategyfocusing only on a star cast or a more balanced
strategy involving a more modest cast (lower PE) but high-
value (higher JE) crew? Our research suggests the potential of
the latter strategy to help producers assemble an optimal movie
team in this cost environment.
In addition to offering managerial guidance to studio execu-
tives and producers, our findings shed light on career manage-
ment by the cast and crew themselves in a highly competitive
industry. Theoretically and empirically, this research reveals that
an actor or actress should focus on collaborating with well-
connected others, while a crewmember may be better-off seeking
diverse collaborations. Thus, when selecting which film projects
might maximize one's own career trajectory, an industry pro-
fessional should be cognizant of how his/her potential team
mates' collaborative history could influence his/her own future
success.
5. Discussion
This research contributes to the literature on movie
marketing, collaborative networks, and new product develop-
ment along several important dimensions. Theoretically, our
conceptual framework accentuates the importance of the
development team to product success, moving beyond the
conventional focus on product or consumer traits in the movie
marketing literature. It takes a network perspective by
proposing that team members' contributions to a film arise
from their positions in the industrial network, and thus their
opportunities and capabilities to draw knowledge and skills
accrued from past collaborative experiences. The conceptual
framework also reveals an important, potentially divergent
relationship between box office and network embeddedness of
the cast versus the crew. In doing so, it expands the marketing
and sociology literatures' focus on a single function and allows
us to address a key managerial challenge of team assembly. It
further proposes and partially validates a moderator (studio
affiliation) in the relationship between box office and a team's
network embeddedness. While past research offers evidence of
the value of a star cast, this research reveals a more nuanced
picture, suggesting a crew that has worked in diverse regions
of the industry can be as important as a well-connected cast.
From a substantive perspective, the proposed methodolog-
ical framework provides producers and movie studios with a
new decision making tool in assembling an optimal movie
team. The conceptual framework and methodology may also
be generalized to other entertainment, media, and technology
industries, or firms sharing characteristics similar to the movie
industry, such as relatively fluid formation and dissolution of
product development teams and distinct roles within each
team.
Despite these contributions, this research has limitations
and thus points to promising avenues of future research. For
example, future research may investigate the evolution of net-
work embeddedness within an individual and further address
self-selection into teams. This is a complex yet intriguing area
of research as it involves dynamic and endogenous network
evolution, a challenging topic that is receiving growing
research attention in the marketing and statistics communities.
Our research is also limited in scope by focusing on a team
member's connections to others outside, instead of within, the
team. This focus was driven by the existence of research that
has already examined past collaborations among team members
in collaboration networks (i.e. team cohesion; Mehra, Dixon, &
Brass, 2006; Sparrowe, Liden, Wayne, & Kraimer, 2001; Uzzi
& Spiro, 2005).
Furthermore, while our modeling tactics alleviate endogeneity
of the network measures, additional control variables such as
advertising spending were not available for analysis; other
potential sources of endogeneity may exist as well. Readers
therefore should keep in mind that our results may remain subject
to some endogeneity bias. Nonetheless, we believe that this
research takes an important step toward quantifying team
members' contributions as they arise from their network positions
and across functional roles, shedding a critical light on film (and
more generally, new product) team formation in the early stages
of product development.
Appendix A. Illustrative collaboration network and
calculation of network statistics
As is typical in the analysis of large social networks, the
complexity of the data we observe makes it cumbersome to
demonstrate how our statistics of network embeddedness are
derived from the actual data. For brevity, we offer an illustrative
example of a collaboration network focusing on two hypothet-
ical movies released in 2004 (Movies A and B) by extracting a
10 G. Packard et al. / Intern. J. of Research in Marketing xx (2015) xxxxxx
Please cite this article as: Packard, G., et al., The role of network embeddedness in lm success, Intern. J. of Research in Marketing (2015), http://dx.doi.org/10.1016/
j.ijresmar.2015.06.007
collaboration history for these movies and their NPD team
members from four hypothetical movie released in the lagged
10-year network over 19942003 (Movies CF). Figure A1
presents the hypothetical data observed and PE and JE that
would result from this data set. Figure A2 presents visualiza-
tions of the two- and one-mode networks generated from this
data. The two-modevisualization connects people (circles) to
the movie teams on which they collaborated (squares). The
one-mode projection on persons (circles) presents ties between
persons who have worked together on at least one movie. The
one-mode projection on movies (squares) connects movies that
share at least one team member.
Illustrative Data of A Collaboration Network
Raw Data 2004 Individual-level Embeddedness
Person Name Movie Year Person Name JE PE
1 Smith A 2004 1 Smith 0.03 0.70
2 Wong A 2004 2 Wong 0.43 1.00
3 Fleur A 2004 3 Fleur 0.00 0.42
4 Li B 2004 4 Li 0.00 0.30
5 James B 2004 5 James 0.00 0.50
6 Ortega B 2004 6 Ortega 0.00 0.59
1 Smith C 2002
7 Page C 2002 2004 Team-level Embeddeddness
8 Nayar C 2002 Movie JE PE
1 Smith D 1999 A 0.15 0.71
2 Wong D 1999 B 0.00 0.46
9 Gold D 1999
8 Nayar D 1999
2 Wong E 1997
4 Li E 1997
6 Ortega E 1997
2 Wong F 1995
4 Li F 1995
5 James F 1995
Fig. A.1. Illustrative data of a collaboration network.
Fig. A.2. Visualizations of the two- and one-mode networks generated from the illustrative data.
11G. Packard et al. / Intern. J. of Research in Marketing xx (2015) xxxxxx
Please cite this article as: Packard, G., et al., The role of network embeddedness in lm success, Intern. J. of Research in Marketing (2015), http://dx.doi.org/10.1016/
j.ijresmar.2015.06.007
Junctional embeddedness (JE)
To calculate JE in Eq. (3) for persons on the Movie A team, we
first find the proportion of the shortest paths between all pairs of
persons (i.e. dyads) who are not members of the Movie A team
that pass through Movie A's team members. The shortest paths
are those that require the fewest steps between any dyad
independent of Movie A's team members. For example, the
twoshortestpathsbetweenPersons4and7are4-2-1-7and
4-2-8-7(with path length = 3). Movie A's team member,
Smith (Person 1), lies on the shortest path for three dyads (the
paths connecting Persons 4 to 7, 5 to 7, and 6 to 7). For each of
these three dyads, Smith is on 50% of the shortest paths (the rest
go through Nayar (Person 8)), providing the numerator in Eq. (3)
for Smith. Calculation of Smith's denominator in this equation
requires the number of persons in the network (n= 9) and Movie
A's team size (g= 3). It is hence (9 3) × (9 31) / 2 = 15.
Following Eq. (3),Smith'sJE is 3 × (.5 / 15) = .03. As can be
observed in the one-mode projection for persons in Figure A.2,
Wong (Person 2) holds an even stronger junctional position in the
network than Smith as Wong lies on shortest paths for nearly all
collaborations bridging the two sides of this network. In contrast,
all other persons lie on the outside edgesof the network, and do
not bridge other collaborators.
Positional embeddedness (PE)
Since a simultaneous linear equation system is used to
produce the standard eigenvalue and eigenvector calculations
underlying PE in Eq. (2), it is not feasible to manually
demonstrate the development of this measure. However,
intuition for this measure can be gained by comparing the
individual statistics for PE presented in Figure A.1 against the
one-mode (person) visualization in Figure A.2. For instance,
Wong (Person 2) holds the maximal positional embeddedness
in this network (PE = 1) due to both the number of
collaborations he holds (ties = 7) and the connectednessof
his ties (e.g. Persons 1 and 6 also possess high PE). In contrast,
the person with the lowest PE, Li (Person 4), has several ties
(ties = 4), but has collaborated with poorly-connected others.
In most physics-based network visualizations, nodes with high
PE (or other eigenvector-based centrality measures) will appear
deep in the network's core, as can be observed for Wong
(Person 2) in Figure A.2.
Appendix B. Calculating eigenvalues and eigenvectors
Let ebe an n×nmatrix. And λis an eigenvalue of eif there
exists a non-zero vector vsuch that
ev ¼v:
In this case, vector v(or PE in our context) is called an
eigenvector of ecorresponding to λ. We can rewrite the
condition ev = v as follows:
eIðÞv¼0;
where Iis the n×nidentity matrix. For a non-zero vector v to
satisfy this equation, eImust not be invertible. That is, the
determinant of eImust equal 0. Call p(λ) = det (eλI) the
characteristic polynomial pof e. The eigenvalues of eare the
roots of the characteristic polynomial of e.
For example,
Let e ¼24
11

:
Then p λðÞ¼ det 2λ4
11λ

¼2λðÞ1λðÞ−−4ðÞ1ðÞ
¼λ2λ6
¼λ3ðÞλþ2ðÞ
Thus, λ
1
= 3 and λ
2
=2 are the eigenvalues of e.
To find the eigenvectors corresponding to these eigenvalues,
solve the system of linear equations given by
eλIðÞv¼0:
For example, to solve for the eigenvectors corresponding to
λ
1
= 3, let v=½v1
v2. Then (e3I)v= 0 gives us
234
113

v1
v2

¼0
0

;
from which we obtain the duplicate equations
v14v2¼0
v14v2¼0:
If we let v
2
=t, then v
1
=4t. All eigenvectors corre-
sponding to λ
1
= 3 are multiples of ½4
1and thus the
eigenspace corresponding to λ
1
= 3 is given by the span of
½4
1.
Appendix C. Visualization of the 19942003 network
The graph visualization below shows the 19942003
network used to evaluate the impact of network embeddedness
on the revenues of 2004 movie releases. The image is
a one-mode graph projection of movies (n = 3268; see
Appendix A for alternative mode examples). Here, movies are
represented as black dots, with grey lines linking movies shared
by common collaborators. The visualization is physics-based
(OpenOrd using Gephi); that is, the distance between any two
movies depends on the number of collaboration ties among the
core team members on those two movies. Labels describe
selected examples of major visible clusters in the network.
Movies for which no core team members have worked on a
movie project with others in the network appear as isolated
dots.
12 G. Packard et al. / Intern. J. of Research in Marketing xx (2015) xxxxxx
Please cite this article as: Packard, G., et al., The role of network embeddedness in lm success, Intern. J. of Research in Marketing (2015), http://dx.doi.org/10.1016/
j.ijresmar.2015.06.007
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... To address these items, we study piracy quality in the context of motion pictures, a focal area in marketing research (e.g., Dhar and Weinberg 2016;Packard et al. 2016) for several reasons. First, the effect of piracy and consumption may be clearer in movies than in other information goods (Lu, Wang, and Bendle 2020), as few movies are seen by a consumer multiple times in theaters, but illegally downloaded music or software may be consumed repeatedly. ...
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