Network Leadership and Team Creativity: An Exploratory Study of New York City Jazz Bands

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NETWORK LEADERSHIP AND TEAM CREATIVITY:
AN EXPLORATORY STUDY OF NEW YORK CITY JAZZ BANDS
FLOOR VAN DEN BORN
Amsterdam, The Netherlands
fvandenborn@gmail.com
AJAY MEHRA
Gatton College of Business and Economics, Room 335 A
LINKS Center, University of Kentucky
phone: 859.257.8268. email: ajay.mehra@uky.edu
MARTIN KILDUFF
UCL School of Management
UCL
phone: +44 (0)20 3108 6021; email: m.kilduff@ucl.ac.uk
Acknowledgements. We thank Steve Borgatti, Joanna Burr, Dan Halgin, Marissa King, Robert Krause,
Edward Lyman, Zuzana Sasovova, Stoyan Sgourev, J.P. Vergne, and Kevin Yong for their advice and
assistance. Participants of the 2018 Network Evolution Conference (INSEAD) provided useful feedback.
The first author acknowledges financial support from the Paris Chamber of Commerce and HEC, Paris,
and thanks the jazz musicians who contributed to this project. We thank our editor, Jochen Menges, and
the anonymous reviewers, for their guidance. We, the authors, are solely responsible for any errors.
This is a draft version of a paper that is forthcoming in Academy of Management Discoveries.

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NETWORK LEADERSHIP AND TEAM CREATIVITY:
AN EXPLORATORY STUDY OF NEW YORK CITY JAZZ BANDS
ABSTRACT
Jazz bands exemplify the creative economy of teams engaged in flexible and precarious work.
Theory is conflicted concerning how leadership of such audience-facing organizations affects
outcomes. For the 346 New York City jazz bands active in 2010, we explored how formal and
network leadership related to music creativity and popularity; as well as to band longevity
through the year 2021. Formal leadership may direct band members toward joint creative
outcomes. Or such leadership may harm the free-flowing energy that fuels creative performance.
Network leaders engage in brokering connections across the network of jazz musicians; or
building status through connections to central people. The network in this case consisted of ties
between people who had overlapping band membership. We found that formal leadership
negated band creativity but made no difference to band popularity or longevity. Network
leadership, defined as status, facilitated both creativity and popularity, whereas brokerage had no
discernible effects. Interestingly, creative bands were less likely to endure. In the creative
industries, formalized hierarchy may be less important for a team’s creative output than
representation in the external market for talent and aesthetic judgment that well-connected
network leaders bring.
KEYWORDS: SOCIAL NETWORKS; CREATIVITY; LEADERSHIP; JAZZ; TEAMS; GIG
ECONOMY.

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The question of whether and how teams in the creative industries benefit from leadership
is an important one to address. These industries contribute significantly to the economies of
many countries. In the USA, for example, the annual contribution of the creative industries is
estimated at $700 billion, with employment estimated to be around 5 million people (Dodd,
2015). In many of the creative industry sectors such as dance (Harrison & Rouse, 2015), classical
music (Murnighan & Conlon, 1991), and haute cuisine (Tan, 2015), creative production is
organized through audience-facing, self-managing teams in highly competitive markets.
Creativity is integral to the success and viability of these small team organizations that include
the New York City jazz bands that we examine in this article (Umney & Kretsos, 2015).
However, the role of leaders in these audience-facing, self-managing teams is poorly
understood. Whether we consider formal leadership that derives from occupation of a designated
leadership position or network leadership that derives from occupation of a central social
network position in the competitive field, existing research offers a confusing picture of how
leadership relates to important outcomes such as team creativity, popularity, and longevity.
To explore the question of how leadership relates to these outcomes, we gathered data on
jazz teams active in New York City. A key advantage of the jazz-band setting for the emergence
of theory concerning leadership is the variation in leadership structure. These bands can be
leader-led or leaderless; and the members of a band can vary greatly in terms of their positions in
the network connecting musicians across the competitive field. Further, jazz music is frequently
hailed as a metaphor for organizing (e.g., De Pree, 1992; Hatch, 1998) and jazz bands are studied
as exemplars of creative endeavor (e.g., Bougon, Weick, & Brinkhorst, 1977). Jazz bands are
“particularly intense workgroups” (Murnighan & Conlon, 1991: 165) that are designed for
constant innovation (Barrett, 2012).

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The need for exploratory research on the leadership of jazz bands and other audience-
facing competitive teams is clear from the limitations of the existing literature. Formal leadership
research has focused on settings where creativity is “a relatively less fundamental aspect of
organizational activity” (Mainemelis, Epitropaki, & Kark, 2021:106) compared with settings in
which creativity is a primary consideration. Much of the research emphasizes
charismatic/transformational leadership, an emphasis that some have questioned because it
defines leadership in terms of its effectiveness (van Knippenberg & Sitkin, 2013). The relevance
of this research for our context – jazz bands competing for gigs and resources in New York City
– is unclear.
From the perspective of social network research, network leaders are identified by their
connections in the social arena (Carter, DeChurch, Braun, & Contractor, 2015: 603). Network
leaders can contribute to team creativity by bringing resources, timely information, and influence
opportunities from their interactions across the field of endeavor. But here again, there is
confusion as to whether the kind of network leadership that matters for creativity derives from
spanning across gaps in social structure to gather novel ideas (Burt, 2004) or is, instead, the
result of the status and influence that derives from connections to the elite circles in which ideas,
resources, and opportunities flow (e.g., Ibarra, 1993).
Formal Leaders
In organizational behavior research, the case for the importance of formal leadership, not
specific to teams, is clearly stated: “leadership makes a difference in the nature and success of
creative efforts” (Mumford & Licuanan, 2004: 164; see also, Amabile & Khaire, 2008). Formal
leaders are needed to facilitate, direct, and synthesize creative activity across a range of complex,
ill-defined problems where performance requires novel yet useful solutions (Mumford, Scott,

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Gaddis, & Strange, 2002). Formal leaders stimulate, motivate, and support followers to
overcome the uncertainty and stress involved in creative work (e.g., Eisenbeiss, van
Knippenberg, & Boerner, 2008). There is a need for leadership that provides people with “a
common process or method of finding and defining problems” (Basadur, 2004: 111). Leaders act
as team facilitators in brainstorming sessions (Rickards & Moger, 2000: 276). They facilitate the
creativity of others, act as primary sources of creative thinking in directing the work of others,
and they help synthesize the contributions of individuals into an integrated process (Mainemelis,
Kark, & Epitropaki, 2015).
But this literature concerning the benefits of formal leadership has focused on settings
where creativity is “a relatively less fundamental aspect of organizational activity” (Mainemelis,
Epitropaki, & Kark, 2021:106) compared with settings in which creativity is a primary
consideration. Indeed, research on how the leadership of teams affects creativity or innovation
has been described as small and “relatively fragmented and scattered, with little integration or
cohesion” (Rietzschel, Rus, & Wisse, 2021:129). Much research on the creativity of teams
within organizations emphasizes team autonomy that involves coaching and sharing (reviewed in
Liang, van Knippenberg, & Gu, 2021). Team-member autonomy stimulates information
elaboration within the team, as well as team member empowerment. From this perspective
formal, directive leadership of teams within organizations promotes team efficiency but is
negatively related to team creativity (Li, Liu, & Luo, 2018).
Indeed, creative teams may benefit from the absence of formal leadership processes
because these processes are likely to impede the self-organization that fuels creativity. The high
level of expertise among team members can make formal leadership redundant according to
leader substitutes theory (Kerr & Jermier, 1978). Formal leadership is theorized to impede

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creativity, given that creative teams lend themselves to coordination with a minimum of formal
rules (Barrett, 2012; Sawyer, 2010).
When we consider leaders of teams in the creative industries, including chefs at high-end
restaurants (Bouty & Gomez, 2010) and conductors of orchestras (Marotto, Roos, & Victor,
2007), the limited research that we have, paints a picture of the creative leader “as the primary
source of creative thinking and behavior… a master-creator who directs the implementation of
their creative vision by other collaborators” (Mainemelis et al., 2021: 106). In these contexts,
“the identity of the leader is often closely tied to the outcome… directive leaders see their role as
ensuring followers produce a high-quality outcome” (Abecassis-Moedas & Gilson, 2017: 125).
In these mainly qualitative studies, centralized formal leadership is far from being redundant
(Rouse & Harrison, in press).
Thus, prior research is inconsistent in sometimes highlighting the positive effects on team
creativity of formal leadership (e.g., Hughes, Lee, Tian, Newman, & Legood, 2018) and
sometimes highlighting the negative effects (Li et al., 2018). And this research typically neglects
the iconic case of teams in creative industries for whom creativity is a primary output and for
whom commercial success depends on pleasing audiences. Our first exploratory research
question, therefore, follows: Does formal leadership of teams in creative industries affect
outcomes, which include creativity and audience popularity, positively, negatively, or not at all?
Network Leadership
Leadership of teams in the creative industries involves not just the coordination of team
members but also resource acquisition from external environments. These teams compete for
resources that include personnel, customers, and new ideas. Thus, jazz teams in New York City
strive to be creative while competing for gigs, record deals, airtime, and consumer purchases.

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Some formal team leaders are active as boundary spanners in the organizational field outside of
their specific team domain (e.g., Ancona & Caldwell, 1990). Informal leaders within teams (e.g.,
Wheelan & Johnston, 1996) are also sometimes active linking together teams within
organizations (Guo, Heidl, Hollenbeck, Yu, & Howe, 2022). But the literature on team
leadership has tended to focus on leadership within the team rather than exploring the role of
leadership in the context of teams competing for resources. This focus on internal team
leadership extends both to the role of formal leaders (e.g., Rouse & Harrison, in press); and
informal leaders, who monitor and manage relationships within the team (e.g., Schaubroeck,
Peng, Hannah, Ma, & Cianci, 2021). There has been a neglect of network leaders, people who
may have no formal authority within a group, but who are nonetheless influential by virtue of
their centrality in the broader field (Carter, DeChurch, Braun, & Contractor, 2015: 603). These
externally well-connected leaders gather ideas and other resources of use to their small
organizations from the environment of competing team organizations. In this process, people can
gauge the extent to which team members are central players in the relevant community
(Banerjee, Chandrasekhar, Duflo, & Jackson, 2019). Team members attribute leadership to those
colleagues who bring resources that help the team achieve its goals (Carnabuci, Emery, &
Brinberg, 2018).
There are two main accounts of network leaders, one that emphasizes network brokerage
whereas the other emphasizes network status (Kenney et al., 2012). Brokerage is key to
structural hole theory: the broker across structural holes is a critical player in the response to
disorder (Burt, 1992: 116) given that “much of business leadership is about bringing together ill-
connected functions, organizations, or market segments” (Burt, 2002:171). Good ideas and other
resources accrue to social network brokers whose contacts are disconnected from each other

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(Burt, Kilduff, & Tasselli, 2013). The more heterogenous the contacts, the less redundancy there
is in terms of knowledge, and the more likely the broker is to garner diverse ideas, opportunities,
and resources (Burt, 2004; Hargadon & Sutton, 1997). The broker fuels creativity by supplying
good ideas but also spots opportunities such as gigs, record deals, and other chances that
facilitate commercial success (Long Lingo & O’Mahony, 2010). The act of leadership consists,
in part, of moving complex information from a place where it may be seen as quite mundane to
the network leader’s team where it has value (Burt, 2021). And in this process, the information
itself is likely to be changed to be more relevant to the home team.
An alternative conception emphasizes the status of network leaders rather than their
brokerage. High-status leaders are well-respected in the field as indicated by their connections to
well-connected people (Heinz & Laumann, 1982). Applied to the context of teams in the creative
industries, it is the leader’s credibility in the field of experts that facilitates the transfer of new
ideas and opportunities to teams. Without this field-based legitimacy, the contributions of
individuals are likely to be disregarded (Burt, 1992) given the resistance to new ideas
characteristic of teams and organizations in general (Mueller, Melwani, & Goncalo, 2011).
People with ties to well-connected others across the industry are better positioned to bring to
their teams the resources that foster creativity and enhance commercial success. In the gig
economy of music production, connectedness between teams is facilitated by overlapping
membership (Uzzi & Spiro, 2005).
The presence of a high-status person within a team can trigger a self-reinforcing process
by which colleagues within the team confer leadership on the individual; and the individual
develops an identity as a leader (Emery, Daniloski, & Hamby, 2011). But the presence of these
network leaders within a team can constrain the emergence of other innovators (Kehoe &

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Tzabbar, 2015), crowd out valuable contributions from team members, and disrupt team
chemistry (Groysberg, Polzer, & Elfenbein, 2011), thereby impeding the creativity that is so
essential for teams in creative industries (Asgari et al., 2021). There is, therefore, the potential
for both positive and negative effects of well-connected network leaders on creative outcomes.
And a recent meta-analysis found no support for this type of global connectedness on the
performance of teams (Brennecke & Stoemmer, 2018).
Network leadership can, of course, overlap with formal leadership (e.g., Ancona, 1990)
and with emergent informal leadership – the provision of help and advice to team members
(Neubert & Taggar, 2004). But current research provides little guidance concerning how network
leadership – defined as network centrality in the organizational field within which audience-
facing teams compete -- affects creative teams in the creative industries in terms of longevity,
creativity, and popularity. Our second, two-part research question follows: Are the outcomes of
teams in the creative industries positively affected by the presence of network leaders in the
team? The subsidiary question concerns how to conceptualize network leadership, whether in
terms of brokerage in the creative field, or in terms of the status that derives from connectedness
to the well-connected.
In the spirit of exploratory research, we address not only the performance outcomes of
creativity and audience popularity for the jazz bands in our sample. We also examine an outcome
that has been of concern to team researchers (Balkundi & Harrison, 2006) and features in an
iconic study of music groups (Murnighan & Conlon, 1991) as well as research on entrepreneurial
teams (Vedres & Stark, 2010), namely the longevity of the team. Prior research would support
the idea of a positive relationship between leadership that facilitates a team’s popular success and
the longevity of the team: for small organizations, success in the marketplace is necessary for

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survival (Barnett, 1997). But the relationship between creativity and team longevity is less clear,
necessitating an exploratory investigation. On the one hand, creativity can deepen relationships
at work, thereby fostering commitment to the collective (as suggested by Goncalo, Katz,
Vincent, Krause, & Yang, 2021). Collective creativity can induce deeply rewarding flow-like
states that provide motivation for team members to stay together (Sawyer, 2010). But there are
also centrifugal pressures on the members of creative bands. Members of creative bands, like
members of successful startups (Saxenian, 2007), find it relatively easy to form or join new
ensembles or pursue solo careers.
METHODS
Setting
We drew on survey, interview, and archival data to stitch together the social network
among 596 professional jazz musicians in New York City circa 2010. Of the 346 bands in our
sample, 96 had no formal leader. Data on creativity came directly from jazz experts, who coded
creativity based on audio samples from records released by the bands. The judges were kept
purposefully blind as to the origins and authorship of the music because such knowledge is
known to distort how people hear a tune (Babon, 2006; Phillips, 2013). Band popularity was
assessed by the extent of album sharing on an online platform. We measured band longevity as
the number of years since data collection in 2010 until the last band performance we could find.
Our questions focus on team outcomes as affected by leadership rather than on the outcomes of
individuals within teams (cf. Cattani & Ferriani, 2008).
Jazz music is produced across the world, but its roots are quintessentially American. Its
pre-history is often traced to the city of New Orleans in the early 19th century. At that time, due
to a range of historical circumstances—slavery, war, economic trade—New Orleans comprised a

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heterogeneous mix of people from Africa, the Caribbean, and Europe. This cultural diversity
spawned several musical hybrids, including the syncopated and Blues inflected sounds that
prefigure jazz. Indeed, it has been argued that the “rhythms of ragtime, the bent notes and chord
patterns of the Blues, and an instrumentation drawn from New Orleans brass bands and string
ensembles” that gave early jazz its signature sound originated in the polyrhythms of the people
who occupied the margins of New Orleans society (Gioia, 2011: 34).
Jazz has undergone many transformations and changes since the first jazz recording, in
1917, by the Original Dixieland Jazz Band of New Orleans. Several different styles—e.g., swing,
bebop, hard bop, free jazz, acid jazz— have risen to prominence over the years1. But rather than
fading away, these past styles have become simultaneously available, resulting in a field that is
characterized by hybridity and synthesis (Szwed, 2000). Contemporary jazz music borrows
freely from the remnants of past traditions while disdaining “hierarchies and pomposity” (Szwed,
2000: 9). Given the ready availability of even the most arcane historical recordings, and efforts
by neo-traditionalists — most recognizably, the virtuoso trumpet player, Wynton Marsalis — to
revive public interest in the jazz repertory, contemporary jazz musicians seeking to make a
creative contribution must struggle not just with their current competitors but with the
increasingly vocal ghosts of musicians past. New York City has long been one of the epicenters
of jazz, having been home to such legends as Charlie Parker, Miles Davis, and Lester Young.
The city is home to numerous musical training academies, and it features many venues that
feature live jazz performances.
Data Collection and Model Specification
1 A detailed genealogy of jazz is beyond the scope of this paper (but see, e.g., Giddins &
DeVeaux, 2009; Gioia, 2011; Szwed, 2000).

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We endeavored to map the full network of connections among all jazz musicians active in
New York City in the 2010 calendar year. A key challenge in social network research is
boundary specification – deciding which ties and which people to include (Kilduff & Brass,
2010). In some settings it is clear which people should be included in the network— monks in a
monastery, for example (Sampson, 1969). But in settings like ours the boundary can be harder to
discern. In our research, we defined a tie as existing between two musicians if they were
members of the same band. In the world of jazz musicians, players tend to have multiple
“gigs”—i.e., they belong to more than one band2. To determine membership in the active jazz
band community, we followed a respondent-driven sampling method (see Borgatti, Everett, &
Johnson, 2013: 32-35; Wasserman & Faust, 1994: 45-50). This approach utilized a combination
of interviews, free-lists, and archival data from online magazines and websites. The network data
we collected eventually encompassed 596 musicians, based in NYC, spread across 346 jazz
bands.
Specifically, the identification of bands and band members proceeded as follows. Before
entering the field, we conducted a search in Factiva looking for jazz groups in New York City
that were currently producing jazz. The initial list contained 25 musicians. The first author
contacted each musician by mail in which she introduced herself, briefly explained the subject
and purpose of the research, and asked the musician for an interview. The first author obtained
informed consent from each interviewee and explained that any information they provided that
2 For example, one of the most prolific jazz musicians in our sample, Mary Halvorson, was, at
the time of data collection, associated with the Anthony Braxton Diamond Curtain Wall Trio,
Ingrid Laubrock Anti-House, Crackleknob, a duo with Daniel Levin, Map, Marc Ribot Trio, a
duo with Jessica Pavone, a duo with Weasel Walter, the Mary Halvorson Quintet, the Mary
Halvorson Trio, the Taylor Ho Bynum Sextet, the Taylor Ho Bynum Trio, the Anthony Braxton
Septet, the Anthony Braxton twelvetet, Thirteenth Assembly, the Tom Rainey Trio, and a quintet
called Yore.

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was not already in the public domain would be anonymous. During the interviews, musicians
recommended relevant others and provided contact details. To supplement this procedure, the
first author also documented notices of performances scheduled during the period of data
collection. Between November 2009 and June 2010, she contacted 106 New York City (NYC)
based jazz musicians. Three musicians declined interviews, 32 never replied, 71 agreed to be
interviewed, and 60 interviews were arranged with 61 musicians (there was one double
interview). As part of the interview, she asked respondents to list the bands of which they were
members and to list their collaborators.
Overall, this set of processes provided a list of 288 jazz bands from which we excluded
12 bands who were either not associated with jazz, were not operating at a professional level
(college bands or bands impossible to trace on the internet) or were not based in NYC. Based on
concert agendas published in relevant magazines, such as TimeOut New York, we added another
70 NYC–based bands. The resulting network represented joint membership in 346 jazz bands of
596 jazz musicians based in NYC. We then used the “affiliations 2-mode to 1-mode” procedure
in the software program UCINET to convert this people-by-bands (2-mode) network into a
musician-by-musician (1-mode) network in which a tie indicated that two people were members
of the same band (Borgatti, Everett, and Freeman, 2002).
Variables
Band creativity. We drew on the consensual assessment approach to the measurement of
creativity: products are creative to the extent that appropriate observers agree that they are
creative (Amabile, 1996: 33). We recruited three judges, living in Paris, France, with deep

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domain-specific knowledge of jazz3 to evaluate the creativity of the 203 (of 346) teams that
produced at least one album in the previous three years. The three male judges were, in 2010,
aged 47, 55, and 64 respectively, and each had over 20 years of experience in the jazz industry,
including experience producing jazz recordings.
For each band in our sample, we selected the most recently released album, and from
each album, we randomly selected a song to list in randomized playlists that were evaluated by
the judges. All visible identifiers (band name, song name, and release year) were removed from
these playlists to exclude any possible biased appraisal of the music (e.g., Phillips, 2011).
After reviewing the items previously developed by Amabile (1996: 41-59) to examine
artistic creativity, we adapted six items for use in our creativity scale. Each judge used the scale
to independently evaluate (1= “Not at all”; to 5 = “Very much”) the extent to which a piece of
jazz music: (1) sounded “original and fresh”; (2) “inspiring to you as a connoisseur”; (3) “takes
you by surprise”; (4) “matches your understanding of jazz and its possibilities”; (5) “coheres as a
unity”; and (6) “reflects technical virtuosity and/or precision.”
----------------------------
Table 1 about here
-----------------------------
Table 1 shows that the six-item scale exhibited two underlying factors, which we labeled
“novelty” and “mastery of convention”4. Inter-rater reliability (Cohen’s kappa) for the novelty
3 Following Amabile’s advice, the judges were not “preselected on any dimension other than
their familiarity with the domain” (1996: 42).
4 For the second dimension, we chose the label “mastery of convention” rather than the more
commonly used label of “usefulness” (Amabile, 1996) because the notion of usefulness is
misleading in the context of creative music. Music is not more or less useful; it is more or less
technically sophisticated in the sense of displaying a mastery of musical conventions (Becker,
1982; Godart, Seong, & Phillips, 2020).

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and mastery of convention items was .94 (Z < .001) and .93 (Z < .01), respectively, well above
the accepted threshold of .61 (Kvalseth, 1989). Cronbach’s alpha for the scale was 0.83. Given
our view of creativity as consensually determined, inter-judge reliability here is akin to construct
validity: “if appropriate judges independently agree that a given product is highly creative, then
it can and must be accepted as such” (Amabile, 1996: 43). We computed an overall score for
band creativity by adding the average scores for “novelty” and “mastery of conventions.” To get
a sense of the language the judges used to anchor their judgments of creativity, see the
Appendix.
Band popularity. Our measure of band popularity was based on the extent to which a
band’s most recent album was shared among consumers on Soulseek, an online music-sharing
platform. In comparison to other such platforms operating around 2019, such as Isohunt or
KAT, that primarily focused on film and television content, Soulseek only offered audio file
sharing. We counted the number of times an album was shared among users of the platform. We
used the log of the variable to address high kurtosis.
Band longevity. This measure is a count of years since 2010 (when the network data on
the bands were collected) that the band mounted its most recent performance. We obtained these
data by querying, in December 2021, a contemporary jazz website, popular with jazz musicians
and fans: www.allaboutjazz.com.
Formal leader. It is common practice in jazz to name the band after its formal leader.
Thus, this variable was coded as 1 if the band had a formal leader and as 0 otherwise. Of the 346
teams in our sample, 250 had formal leaders. To determine whether a band had a formal leader
we first checked to see if the band was named after a particular musician in the band (e.g., Mary
Halvorson Quintet). In cases where it was unclear whether the band had a formal leader, we

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examined record reviews and checked to see if the review mentioned a band leader or noted that
the band was a collaborative enterprise with no formal leader. Duos -- bands comprised of two
members -- may constitute a special kind of collaboration. Except for one duo in which one
member was formally mentioned as a leader, we coded formal leadership as 0 for duos.
Network Leaders
Given the exploratory focus of our investigation, network leadership was conceptualized
in two different ways, as status and as brokerage. We computed status in terms of eigenvector
centrality (Bonacich, 1987)5 and brokerage in terms of betweenness centrality6 from the one-
mode musician-by-musician network.
Eigenvector centrality considers both direct and indirect connections in a recursive
procedure that captures the extent to which an individual is connected to well-connected others
(Bonacich, 2007). A high eigenvector centrality score indicates that the individual is connected
to individuals who are themselves well-connected. Thus, an actor’s eigenvector centrality is
proportional to the sum of centralities of the actors to which the actor is connected. Eigenvector
centrality scores are only interpretable if they are based on a connected network, so we
confirmed that the network of ties between musicians was fully connected. The eigenvector
centrality score is interpretable as a measure of reputability and status in information and
resource exchange networks (e.g., Ballinger, Cross, & Holtom, 2016; Bonacich & Lloyd, 2015;
Burt & Merluzzi, 2014; Mehra et al., 2006). We measured eigenvector centralities using the
network analysis package UCINET (Borgatti, Everett, & Freeman, 2002).
5 For the formula, see Borgatti, Everett, & Freeman (1992).
6 See Freeman (1979) for rationale and formula.

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The betweenness of an actor in a network is the extent to which an actor falls along the
shortest paths between all other pairs of actors in a network (Freeman, 1979). An individual who
has a high betweenness centrality is akin to a bridge connecting others in the field. Previous
work shows that the betweenness of individuals predicts innovative performance (Mehra,
Kilduff, & Brass, 2001); and employees whose bosses occupy bridging positions in the network
of bosses exhibit radical creativity (Venkataramani, Richter, & Clarke, 2014).
Number of network leaders in the band. We coded an individual as a network leader if
she or he had a centrality score in the top five percent of our sample (for a similar approach to
identifying those with exceptional network connectedness, see Grigoriou & Rothaermel, 2014).
We measured network leadership in two different ways as betweenness centrality and as
eigenvector centrality, corresponding to the two alternative conceptualizations of network
leadership. Thus, this variable was represented by two different measures7. We used the log of
the measures to address high kurtosis.
Formal leader is network leader. For bands with formal leaders, we scored the formal
leader as a network leader if the formal leader also scored in the top 5 percent of centrality scores
(variable = 1; otherwise = 0) for betweenness centrality (brokerage network leadership) or
eigenvector centrality (status network leadership).
7 In our sample, network leaders we identified using the eigenvector-based approach included
Mary Halvorson, Taylor Ho Bynum, Jessica Pavone, and Nate Wooley. Network leaders using
the betweenness based approach included Dan Weiss, Nate Wooley, David Smith, and Loren
Stillman.

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Control Variables
Band visibility. This was measured as the total number of times a band was mentioned in
the public press over the period December 2005 to December 2010. We used the log of the
variable to address high kurtosis.
Band experience. Previous research shows that experience working as a team influences
the creativity of teams (Gino, Argote, Miron-Spektor, & Todorova, 2010; Taylor & Greve,
2006). Band experience was measured as the total number of concerts played by the band before
2010 (dating back to 2005) as indicated on performance agendas published in daily newspapers,
magazines, and specialized press (e.g., JazzTimes and All About Jazz) available through
LexisNexis. We used the log of the variable to address high kurtosis.
Band size. The effects of team size on team outcomes are well-documented (e.g.,
Cummings, Kiesler, Bosagh Zadeh, & Balakrishnan, 2013). Out of the 346 bands, there were 46
duos, 96 trios, 92 quartets, 60 quintets, 24 sextets, 9 septet, 9 octets, 5 nonets, and 5 “big” bands
with 10 members or more. We calculated band size as the total number of musicians in a team,
logged to address high kurtosis.
Inverse Mills ratio. Sample selection bias refers to problems where the dependent
variable is observed for only a restricted, nonrandom sample. A potential selection bias might
exist in our regression analysis because the unit of analysis is the album (n = 203), whereas many
teams (n = 143) included in the full network had not yet released any albums. All teams that
produced a record were selected non-randomly from the population of teams. Following prior
research (e.g., Kilduff, Crossland, Tsai, & Bowers, 2016), we used a Heckman two-stage
approach (Heckman, 1979) to correct potential bias. First, using a Probit model, we regressed the
binary variable “album” (whether a team had released an album or not) on three different

19
variables that seemed likely to affect album production: (1) a categorical variable that reflected
whether a team had a formal leader; (2) a variable reflecting team size; and (3) a variable that
captured prior team experience, as reflected in previous concerts performed as a team. A variable
that measured the number of past reviews acted as our instrument (Bascle, 2008). Based on the
results of the Probit regression, we calculated the inverse Mills ratio. Second, we included the
inverse Mills ratio as a control in our analysis.
ANALYSIS
For analyses predicting band creativity and band popularity, we used OLS regression to
derive coefficients. Given the possibility for multicollinearity between our measures of network
leadership, we confirmed that VIF scores were acceptable (scores were less than 2.5 for the
network leadership variables and less than 4.3 for other variables). Given the non-random nature
of our network sample, standard significance tests can produce misleading results (Borgatti,
Everett, & Johnson, 2013: 144). We therefore relied on a permutation-based node-level
regression routine in the software package UCINET 6 (Borgatti et al., 2013: 157-158). This
routine uses ordinary least squares regression to derive coefficients whose significance is then
assessed using a permutation-based procedure (Borgatti et al., 2013: 144-147)8. Band longevity
is an over-dispersed count variable. We therefore used negative binomial regression—a
generalization of the Poisson model that accounts for overdispersion— for analyses predicting
band longevity (Greene, 1997).
8 The pattern of significance was the same irrespective of whether we computed p-values using
the permutation-based approach or the standard OLS approach.

20
FINDINGS
What kind of leadership promotes the creativity, popularity, and longevity of audience-
facing teams in the creative industries? Does formal leadership promote these outcomes? Or is it
what we term network leadership, that is, leadership that taps into resources accessible through
the relationships that connect people across different teams? And if it is network leadership, is
this best understood as brokerage across gaps in the network, or the status that derives from
connections to the best-connected people in the creative field?
----------------------------
Table 2 about here
-----------------------------
Table 2 provides preliminary answers to our research questions in terms of descriptive
statistics and correlations among the variables. Table 2 shows that jazz bands with formal leaders
tended to be less creative than jazz bands without formal leaders (r = -.29, p < .001). Further, it
was high status rather than brokerage that characterized effective network leaders: jazz bands
with high-status members (as measured by the number of band members with high eigenvector
centrality scores) tended to be creative (r = .26, p < .001) and popular (r = .25, p < .001) whereas
the presence of highly-ranked brokers in a band (as measured by the number of band members
with high betweenness centrality scores) did not significantly affect a band’s creativity or
popularity.
Band Creativity
Were the regression results, which controlled for the effects of several theoretically
relevant variables, consistent with these correlations? The answer is: yes. The results of OLS
regressions predicting band creativity are shown in Table 3. Model 1 shows that bands were
more creative if they were experienced (b = 0.45, p < .01) and smaller in size (b = -0.94, p <

21
.001). Model 2 shows that, accounting for the effects of these control variables, bands with
formal leaders, relative to those without formal leaders, produced music that was deemed less
creative by judges (b = -0.39, p < .05).
----------------------------
Table 3 about here
-----------------------------
One formal band leader in our sample made remarks that help explain the negative effects
of directive leadership on team creativity: “I tend to bring in a composition once I have a very
clear idea of what I want it to sound like and what I want to achieve by playing the piece, so that
I can then articulate it to everyone else in the group and communicate it.” Another formal band
leader told us: “I just give them a new page and say this is the tempo.” By contrast, a member of
a leaderless band told us: “We all write compositions for the group and then bring them in and
then collectively make changes and rearrange them.” When teams are comprised of musicians or
other creative people, formal leadership may interfere with the self-organization and
coordination that help teams achieve creativity.
Irrespective of whether the jazz band had a formal leader or not, the question arises as to
whether the number of network leaders in a band affected the band’s creativity. The results in
Model 3 of Table 3 show that the number of status-based network leaders in a band predicted the
extent to which the band produced creative music (b = 0.52, p < .001). Brokerage-based network
leadership did not significantly affect band creativity (b = 0.02, ns). Model 4 shows that if a band
did have a formal leader, it was more creative to the extent that the formal leader was a high-
status network leader among New York City jazz bands (b = 0.42, p < .10).
Band Popularity

22
Model 2 in Table 4 shows that the presence of a formal leader was not a significant
predictor of jazz band popularity (b = 0.02, ns). Model 3 shows that the presence of status-based
network leaders in a band predicted the band’s popularity (b = 0.50, p < .001) but there was no
significant effect of the presence of network leaders in a band when network leadership was
defined as brokerage (b = -0.09, ns).
----------------------------
Table 4 about here
-----------------------------
Band Longevity
As shown by the non-significant results across all models in Table 5, there was no
evidence that formal leadership by itself affected jazz band longevity. But, as Model 5, Table 5
shows, formal leaders who also had status in the creative field as network leaders did positively
affect band longevity (b = 1.32, p < .01). Moreover, bands with a history of popularity tended to
survive (b = 0.44, p < .01) whereas creative bands were less likely to endure (b = -0.65, p <
.001)9.
----------------------------
Table 5 about here
-----------------------------
Auxiliary Tests
In our dataset, a musician can belong to multiple bands. Indeed, it is this membership of
individuals across bands that leads to the emergent network structure of the field in which some
individuals are well-connected, and others are not. It is possible that the ties between bands
9 The size of the coefficient in a negative binomial model represents the effect of the variable on
the logarithm of the dependent count variable.

23
create a situation where the errors in our regression are not independent. Of course, we cannot
test this directly (because we cannot observe the errors) but we can test for autocorrelation in our
residuals. To do this, we estimated a band-level regression model, collected the residuals, and
then ran a spatial autocorrelation test using Geary’s C (Cliff & Ord, 1972) to determine whether
bands with ties to each other tended to have more similar residuals. Geary’s C varies between 0
and infinity, with 1 indicating independence and values closer to zero indicating positive
autocorrelation. This was paired with a QAP permutation test to determine significance. A
separate test was conducted for each dependent variable.
We found no evidence of autocorrelation when the dependent variable was band
creativity (Geary’s C = 0.58, ns.) or band longevity (Geary’s C = 0.57, ns). However, there was
some evidence of network autocorrelation when the dependent variable was band popularity
(Geary’s C = 0.49, p = .03). This suggests that the results predicting band popularity should be
interpreted with caution. However, the extent of autocorrelation is modest: a QAP regression
using band-to-band ties to predict squared differences in residuals explained only 0.1 percent of
the variance.10
One of the implications of using the eigenvector-based approach to measure network
leadership in the context of 2-mode data is that musicians who played in larger bands were more
likely to be assigned higher eigenvector centrality scores. Could network leadership be the
straightforward result of playing in larger bands? To examine this possibility, we computed, for
each musician, a measure capturing the average size of the bands the musician played in and
entered it as a control variable in our regression models. This measure, as expected, was
10 With 346 bands, there were 59,685 dyadic observations in the autocorrelation tests. This large
sample size explains in part why a modest level of autocorrelation can be significant.

24
significantly correlated with the eigenvector-based measure of network leadership (r = 0.29, p <
.001). However, this variable was not a significant predictor of band creativity (b = 0.02, p =
.76), band popularity (b = -0.01, p = .93), or band longevity (b = -0.11, p = .41) and its inclusion
did not alter the pattern of support for the effects of eigenvector-based network leadership on the
band’s creativity and the band’s longevity. These results suggest that network leadership was not
merely a matter of playing in large bands; it also mattered how well connected the people one
played with were.
We coded team creativity based on judges’ evaluations of one randomly selected song
from each album being judged. As an alternative approach to coding the creativity of an album,
we selected the most popular song from each album using data from the free music streaming
site Last.fm. After assembling the playlist, we randomized and anonymized each list and asked
our three judges to code each song’s creativity using the six-item, 5-point creativity scale
described above. This alternative measure of creativity was significantly correlated (r = 0.58, p <
.001) with our original measure of band creativity. We re-ran the regression models in Table 3
using this alternative coding of creativity. The pattern of significant results was the same for all
three dependent variables, with two exceptions: the effects of having a formal band leader went
from being significant (b = -0.46, p < .001) to marginally significant (b = -0.36, p = .07); and the
effects of a formal leader also being a status-based network leader went from being marginally
significant (b = 0.42, p = .07) to not significant (b = 0.41, p = .16). The number of network
leaders in a band remained a strong predictor of the band’s creativity (b = .60, p < .01).
We failed to find evidence that brokerage-based network leadership affected creativity of
jazz bands. However, it could be argued that the creative benefits for jazz bands of network
leaders who span across gaps in the social structure are only available if these network leaders

25
have the status that ensures their ideas are regarded by their colleagues as legitimate (Burt,
1992). We created a new measure that identified the number of people in each band who both
scored in the top five percent for eigenvector centrality and the top five percent for betweenness
centrality. This new variable was not a significant predictor of band creativity (b = .04, p = .66);
and the inclusion of this variable did not change the results. It was the presence of individuals
who were network leaders in the sense of being well connected to individuals who were
themselves well connected that was positively associated with a band’s creativity (b = .43, p <
.05).
One could argue that duos—teams of two persons—represent a special kind of team in
which collaborative interactions tend to be especially intimate and intense (Rouse, 2016). Our
sample included 46 duos, of which 15 produced an album. We included a dummy variable that
was coded as 1 if the team was a duo. This variable was not significant in any of the regression
models, except when predicting band longevity: Duos were marginally less likely than non-Duos
to persist (b = -0.88, p = .08). The inclusion of this additional control variable did not change the
pattern of results reported in the tables.
Teams composed of demographically diverse individuals can be more creative than
homogenous teams. To account for this possibility, we used Blau’s (1977) index of heterogeneity
to assess the demographic diversity present in each team. We focused on race and gender
because both are readily visible attributes and have implications for emergent team processes
(such as cooperation and conflict) that relate to team outcomes. This heterogeneity index was not
a significant predictor of creativity (b = -0.48, p = .26); and its inclusion did not change the
pattern of results reported in Table 3.

26
We checked to see if accounting for differences in the level of attention given to an
album by the media changed the pattern of significance reported in Tables 3, 4, and 5. The
inclusion of a control variable that counted the number of reviews that the album received in the
press was not a significant predictor of band creativity (b = .01, p = .83) and the inclusion of this
variable did not change the pattern of results reported in the tables. Media attention predicted
band popularity (b = 0.11, p < .01). Even with this additional control in the regression model,
however, the number of network leaders in a band (as measured by eigenvector centrality) was a
significant predictor of band popularity (b = 0.50, p < .01). Media attention was not a significant
predictor of band longevity (b = -0.14, p = 0.30), and its inclusion did not change the pattern of
results reported in Table 5.
Our approach to network leadership has focused on the best-connected people in an
overall field. We used a cutoff of 5 percent to identify network leaders (for a similar approach to
identifying network stars, see Grigoriou & Rothaermel, 2014). We found the same pattern of
results, albeit a little weaker, using an alternative 10 percent cutoff. Last, we checked for, and
ruled out, the possibility of a curvilinear relationship between the number of network leaders in a
band and the band’s creativity, popularity, and longevity.
DISCUSSION
Through a study of jazz bands in New York City, we sought answers to exploratory
questions concerning the leadership of creative teams. First, we asked how formal leadership of
these teams affected creativity and band popularity? We found that teams with formal leaders,
compared to those without formal leaders, were less creative. Formal leadership was unrelated to
band popularity. Second, we asked how the presence of network leaders affected these
outcomes? We found that the presence of network leaders in jazz bands had positive effects on

27
both creativity and popularity. The subsidiary question concerned how to conceptualize network
leadership in the context of team creativity. Our research showed that network leadership in
terms of the status that derives from connectedness to the well-connected positively affected
creativity and popularity whereas network leadership in terms of brokerage did not.
----------------------------
Figure 1 about here
-----------------------------
Our findings are summarized in Figure 1, which represents a template for future research
rather than a set of conclusive results 11. Figure 1 suggests that network leaders fuel the creativity
of the teams to which they belong. And this network leadership derives from the connectedness
of the network leader across the competitive landscape of self-managed team organizations
rather than from brokerage across disconnects. The status that comes from being well-connected
across the field of small organizations is more important in the context of these creative teams
than the brokerage across structural holes that has been shown to be important for formal leaders
of teams within the more siloed world of large organizations (e.g., Venkataramani et al., 2014).
The timely movement of knowledge and information from one place to another emphasized in
structural hole theory (Burt, 1992) is important in managing the process by which separated
professional groups are coordinated (e.g., Kellogg, 2014); but this kind of brokerage may be less
important for creativity and popularity in the creative industries than access to the elite people
who control resources and set trends (e.g., Friedman & Laurison, 2020).
11 In a path analysis, we found the model depicted in Figure 1 fit the data well (Chi-square =
8.92, df = 9, p = .45). All control variables, and the betweenness based measure of network
leadership, were included in the path analysis but are not depicted in the figure to enhance visual
clarity.

28
A recent review noted the absence of work on interconnectedness across teams and called
for researchers to examine whether teams composed of individuals who are exceptionally well-
connected outside their team outperform others (Park, Grosser, & Roebuck, 2020). Our findings
suggest that the creativity of teams was enhanced by the presence of more than one well-
connected network leader, in contrast to the situation within organizations where too many
network stars within a team (as identified by the members of the teams themselves) can inhibit
learning and experimentation (Li et al., 2020). In contrast to work suggesting that stars can
hinder the emergence of other leaders in a team (Kehoe & Tzabbar, 2015) and stifle the
contributions of others, we found that the presence of many network leaders in a team did not
spoil the tune. Our findings align with research on self-organized entrepreneurial teams that
suggests that new trends and tacit knowledge are available to people who belong to more than
one team. These network leaders are “multiple insiders” who contribute to the creative
dynamism within teams whereas brokers who span across structural holes do not (Vedres &
Stark, 2010).
Our finding that formal leadership negatively affects team creativity contrasts with the
large literature on the positive benefits of leadership for teams in which creativity is not the
primary outcome (see the review by Hughes et al., 2018). Our findings are also discrepant with
prior research concerning how maestros in fields such as classical music (Marotto, et al., 2007),
high-end restaurants (Bouty & Gomez, 2010), and dance troupes (Rouse & Harrison, in press)
drive peak performance through their dominance of team members. We did find that formal
leaders who were also network leaders positively affected the creativity of their teams, a finding
which is compatible with research on three famous band leaders (Duke Ellington, Art Blakey,

29
Miles Davis) who combined a developmental, collaborative leadership style with high status in
the creative field (Humphreys, Ucbasaran, Lockett, Colville, Brown, & Pye, 2012).
As Figure 1 reminds us, it is not just creativity that the network leadership of small team
organizations in the creative industries facilitates but also the popularity of the creative product
with audiences. There has long been tension within the creative industries between the desire for
creativity and the necessity of audience popularity, between innovation and commerce (Negus,
1995). The pursuit of creative work often involves a departure from tradition and a focus on
originality and technical prowess. Historically, bebop jazz musicians were criticized as rebels
who had thrown over the earlier swing tradition and whose music was such that, as an article in
Collier’s complained, “You can’t sing it. You can’t dance it. Maybe you can’t even stand it”
(Gioia, 2011: 200). The path that enhances creativity, as the beboppers were well-aware, can be a
different one than that which enhances a band’s popularity.
Given this tension between the pursuit of popularity and the engagement with creativity,
the tentative but significant findings summarized in Figure 1 are encouraging: the creativity of
teams, we suggest, helps rather than hurts the popularity of team creative products. Given the
statistical results, the path between creativity and popularity in Figure 1 could be drawn in either
direction. Taking into consideration the literature on the tendency of organizations enjoying
success to persist rather than innovate (Levinthal & March, 1993), we intuited that creativity
drove popularity rather than the other way round, a conjecture that invites future research.
The story concerning the longevity of teams is different: the higher the creativity of the
team, the shorter its longevity, perhaps because creativity can engender a range of negative
outcomes in teams including disinhibition leading to dishonesty, cognitive depletion, and work-
life imbalance (Khessina, Krause, & Goncalo, 2018). Moreover, creative workers, whether in

30
jazz bands or in Silicon Valley startups, may exhibit a greater willingness to move across
organizational boundaries compared with employees negotiating careers within internal labor
markets (Saxenian, 1996).
Limitations and Boundary Conditions
The challenges that jazz bands face as they strive to create new music are different from
the challenges facing teams in large corporations. As others have noted (e.g., Heath & Sitkin,
2001), research that focuses on large organizations can limit our understanding of concepts
central to organizational behavior, especially given that most people are employed in small
organizations (Granovetter, 1984). Similarly, the teams we studied compete directly for audience
attention in the marketplace and are therefore different from advertising teams that create
products for clients; and from corporate inventor teams that present ideas for patenting (see
Perry-Smith & Mannucci, 2017). Our findings have limited generalizability for these kinds of
teams within bureaucratic settings and for those teams whose efforts are focused purely on
routine tasks (e.g., Clarke, Richter, & Kilduff, 2021).
Our research is limited in being unable to trace the emergent leadership within teams that
has been the focus of much research within bureaucratic contexts (e.g., Hanna, Smith, Kirkman,
& Griffin, 2021). In the absence of formal leadership of small organizational creative outfits, if
the network leader also plays the role of coordinator within the team, does this boost or harm
team processes? Current research on emergent leadership is scattered across many different
literatures that focus on internal dynamics rather than on the external status or brokerage of
resource providers (Cox, Madison, & Eva, 2022; Lungeanu, DeChurch, & Contractor, 202). Our
portrayal of network leaders has been positive, but it is also possible that some network leaders
use their influence to damage individuals’ careers rather than to enhance them. We need more

31
research on how externally focused leadership affects outcomes that include not just team
creativity and popularity but also the careers of individuals within and across teams (e.g.,
Kilduff, Crossland, Tsai, & Bowers, 2016).
We examined network leadership exclusively in terms of network connections. However,
one could also be a leader in terms of previous exemplary performance or creativity (e.g., Li et
al., 2020). Are these bases of leadership substitutes for one another when it comes to their effects
on team creativity? We lacked historical performance data, so we were unable to examine how
team creativity was shaped by leadership grounded in social networks, relative to leadership
grounded in prior performance (Grigoriou & Rothaermel, 2014).
Our measure of band longevity focused on a band’s most recent performance (since
2010). We were not able to distinguish between a disbanded jazz band and a dormant one that
might re-form and play at a future time. Creative groups can go through periods of dormancy
before performing again.
We lacked the data necessary to unpack the temporal linkages between network
leadership at the individual level and creativity at the team level. Perhaps the individuals who
were exceptionally well-connected in the field were themselves exceptionally creative
individuals, and it was their exceptional creativity, rather than their exceptional connectedness in
the field, that was the foundation of their team’s creativity. We suspect that the direction of
causality runs in both directions. It is likely that creative musicians are pulled into various bands
(“projects,” as they are often described by jazz musicians) with well-connected musicians, so
that, over time, they, too, become well connected. However, not every collaboration is equally
creative. There is an emergent quality to team creativity that makes it more than just a sum of the
creativity of its members. It is not uncommon in the creative industries to find a group of people

32
who are creative together but whose subsequent efforts, solo or with a different cast of
individuals, fail to shine (Sawyer, 2010:11). Moreover, just as creative players contribute to the
bands they play in, playing with other musicians contributes, over time, to one’s own creativity.
A coevolutionary approach seems best suited to understanding this complex dynamic between
the creativity of individuals, their location in the field’s network, and the creativity of the bands
to which they belong.
Future Research
Jazz bands have inspired organizational theorists to speculate about the processes of
organizing for innovation (e.g., Organization Science special issue on jazz, 1998) but we need
new research to see the extent to which our results concerning leadership of jazz bands
generalize to contexts within organizations. Contemporary work teams, like the jazz bands we
examined, may contain individuals who are members of more than one team at the same time.
Our exploratory findings suggest that the presence of externally well-connected individuals may
enhance team creativity but may also erode team viability. Individuals who are connected to
many teams may find themselves stretched thin and unable to give the band appropriate
attention. For example, Vijay Iyer, a well-known New York City jazz musician, noted that the
various ongoing endeavors of members of the leader-less (“collective”) band, Fieldwork, made it
difficult for the band to continue playing: “each of us is pursuing our own individual projects,
and it’s made it hard for us to connect… [and this] ends up competing with the collective”
(McGuire, 2011).
Our findings on jazz bands suggest new research on the network leadership of
entrepreneurial teams that are involved in new product development and launch (Conlon & Jehn,
2009). These entrepreneurs resemble jazz musician in that they experience setbacks in uncertain

33
environments in the form of roadblocks, failures, and disappointments that erode resilience
(Blatt, 2009). Jazz musicians and other creative workers often struggle for years to gain visibility
while experiencing periods of unemployment and uncertainty (Caves, 2000; Friedman &
Laurison, 2019). Future research can investigate the extent to which teams in entrepreneurial
contexts in which people belong to several teams (e.g., the global video game industry -- de
Vaan, Stark, & Vedres, 2015) benefit from network leadership in terms of positive outcomes
such as creativity; but also suffer from negative outcomes such as reduced team longevity (e.g.,
Vedres & Stark, 2010) and career outcomes (e.g., Kilduff, Crossland, Tsai, & Bowers, 2015).
We need more research on how network leadership evolves (e.g., Carnabuci, Emery, &
Brinberg, 2018). Qualitative studies may be particularly useful for identifying the mechanisms
by which network leadership influences team creativity and popularity. We have emphasized that
status, derived from exceptional connectedness within the field, contributes to band creativity
because high status players are able to provide access to resources and ideas shared among elite
artists. It is also possible that the connectedness of network leaders inspires team members to
produce their finest work. As one of the jazz legend Art Blakey’s sidemen once noted, “how
could one not become intoxicated” with the awareness of how connected Blakey was to other
famous figures in jazz (Goldsher, 2002: 12)? It seems likely that the influence tactics needed in
settings such as jazz bands and entrepreneurial teams differ from those used in more formal
organizational settings (Mumford et al., 2002). The annals of jazz history contain extensive
accounts of how different band leaders coaxed creativity out of their bands, but we lack accounts
of how network leaders who were not formal leaders inspired creativity. Collecting systematic
data on influence tactics used by such network leaders represents a future research opportunity.

34
Our focus in this paper is on active ties. However, “ghost ties” (Kilduff, Tsai, & Hanke,
2006) to team members from the past may influence the creativity of actors today. Networks
change and evolve over time, so it is possible that current networks do not fully account for
observed outcomes. The creativity of a band today can be shaped by members who have come
and gone but whose influence lingers. The trumpeter Kenny Dorham’s creativity and finesse in
running through chord changes that produced his melodic, airy sound were such that, long after
his death, Dorham continued to shape the distinctive sound of the Jazz Messengers (Goldsher,
2002). Ties from the past, from this perspective, can produce a relational residue, a “network
memory,” that “project[s] a structural overhang over the present, much like a shadow of the past”
(Soda, Usai, & Zaheer, 2004: 893). Perhaps what matters therefore is not just how well-
connected one is in the field today but also how well-connected an individual is to important
figures in the past. Team members are, at least to some extent, aware of one another’s relational
histories. Status and prestige can derive from connections to high-status others from the past,
especially those forged during particularly formative periods in an individual’s professional
development (e.g., Halgin et al., 2020). The relative influence of past and present connectedness
on team creativity is a topic ripe for inquiry.
The question of network leadership itself demands further research in the light of our
finding that brokerage leadership had no effects on the outcomes of interest despite prior work
on the extent to which brokerage relates to creative ideas (Burt, 2004) and creative production
(Long Lingo & O’Mahony, 2010). Our research suggests that it is the status of the people you
are in contact with that facilitates access to the resources that fuel your team’s creative and
productive output. The context we investigated was similar to the bio-tech network in which
innovation by individual firms depended on access to the flow of ideas and resources between

35
organizations (Powell, Koput, & Smith-Doerr, 1996). Our context was less similar to networks
within organizations in which brokerage helps managers compete for promotions and bonuses
(Burt, 1992). Creativity and innovation are not always well served by brokerage across separated
units (e.g., Ahuja, 2000). Network leaders, to succeed in helping the several teams to which they
belong, may need to invest in the onerous process of building and maintaining trust across teams
that exhibit different cultures and priorities (Tasselli & Kilduff, 2018).
Implications for Practice
Our study is exploratory, so implications for practice are necessarily tentative. The
findings suggest that leaders of small organizations in the creative industries or in entrepreneurial
contexts may unintentionally stifle the very creativity they hope to engender if their leadership is
based solely on their formal role. Absent the connectedness in the artistic field that provides not
only legitimacy but also the possibility of idea recombination and resource access, formal
leaders’ influence may negate creative endeavors. Further, despite the importance of brokerage
for the creativity of individuals within large corporations (e.g., Burt, 2004), leadership that
derives from individuals spanning across the landscape of self-organized creative teams—
of which the jazz band is an exemplar (e.g., De Pree, 1992)— may prove ineffective in
facilitating either creativity or popular success. Finally, our provisional results suggest an
unexpected outcome from the successful accomplishment of team creativity: a greater likelihood
of non-survival due perhaps to conflicts within teams (e.g., Murnighan & Conlon, 1991) or the
availability of opportunities in the wider competitive arena (e.g., Saxenian, 1996). Taken
together, our results suggest that the leadership of creative teams is a balancing act involving
robust tradeoffs over time between the pursuit of creativity, the practical necessity of some
popular success, and the ability of the band to avoid being pulled apart.

36
Conclusion
Currently, there is a paucity of research concerning whether leadership matters for small
team organizations competing in the creative industries. These teams are typically composed of
skilled workers who collaborate intensively in the context of creative projects (Mainemelis et al.,
2015). Despite the history of research showing the importance of leadership in formal
organizations and in teams, formal supervisory behaviors that may be appropriate for the
encouragement of outcomes in non-creative jobs may inhibit creativity in contexts such as jazz
bands.
Our study of jazz musicians, exemplars of workers in the creative economy, found that
the presence of formal leaders suppresses team creativity whereas the presence of network
leaders, i.e., people with connections to the well-connected in the creative field, enhances team
creativity. The network leader, by playing a part in more than one team, occupies a multiple
insider role that facilitates the recombination of ideas and resources necessary for fueling not
only team creativity but also the popularity that helps teams endure.

37
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46
TABLE 1
Factor Analysis of Scale Measuring Band Creativity
Team creativity Novelty
Mastery of
Convention
Originality 0.94
-0.04
Inspiring 0.81
0.24
Unexpected 0.96
-0.10
Conforms to jazz genre 0.11
0.72
Coherence -0.19
0.73
Technicality 0.20
0.51
a Rotated factor loadings, oblique promax. Cronbach’s alpha: 0.83.

47
TABLE 2
Means, Standard Deviations, and Correlations
Mean
(s.d.)
1
2
3
4
5
6
7
8
9
10
11
1. Brand Creativity
6.41
(0.89)
2. Brand Popularity
1.25
(0.90)
0.28***
3. Brand Longevity
1.42
(2.93)
-0.18*
0.19*
4. Brand Visibility
1.07
(1.30)
0.09
0.37***
0.12+
5. Brand Experience
0.48
(0.74)
0.09
0.36***
0.21**
0.41***
6. Brand Size
1.59
(0.31)
-0.33***
-0.03
0.09
0.11+
0.11*
7. Inverse Mills
0.15
(0.09)
0.04
-0.22**
-0.16*
-0.17**
-0.84***
-0.14**
8. Formal Leader? (Yes
= 1)
0.72
(0.45)
-0.29***
-0.02
0.05
0.16**
0.13*
0.40***
-0.21***
9. Number of Network
Leaders (Eigen.)
0.38
(0.53)
0.26***
0.25***
-0.12
-0.06
-0.06
0.10+
-0.04
0.01
10. Number of Network
Leaders (Bet.)
0.47
(0.46)
0.09
0.11
-0.15*
0.01
0.01
0.20***
-0.04
-0.07
0.55***
11. Formal Leader in a
Network Leader
(Eigen.; Yes = 1)
0.11
(0.32)
0.21**
0.20**
0.03
0.05
0.03
0.09
-0.11*
0.22***
0.59***
0.28***
12. Formal Leader in a
Network Leader (Bet.;
Yes = 1)
0.11
(0.31)
0.08
0.12+
-0.04
0.08
0.09
0.10
-0.14*
0.22***
0.25***
0.40**
0.42***
Note: N = 346, except for analyses involving Band Creativity (n = 203) and Band Longevity (n = 222); *** p < .001; ** p < .01; *p < .05; +p < .10 (two-tailed tests).

48
TABLE 3
Linear Regression Models Predicting Band Creativity
Model 1 Model 2 Model 3 Model 4
Band Visibility 0.01
(0.05) 0.01
(0.05) -0.01
(0.05) -0.01
(0.05)
Band Experience
0.45**
(0.15)
0.40**
(0.15)
0.51**
(0.14)
0.51***
(0.14)
Band Size
-0.94***
(0.20)
-0.77***
(0.20)
-0.78***
(0.20)
-0.78***
(0.20)
Inverse Mills Ratio
3.02**
(1.11)
2.55*
(1.12)
3.49**
(1.08)
3.63***
(1.08)
Formal Leader Yes=1 -0.39*
(0.16) -0.39*
(0.15) -0.46**
(0.15)
Num. of Network Leaders
(Eigen.) 0.52***
(0.13) 0.34*
(0.16)
Num. of Network Leaders
(Bet.)
0.02
(0.14)
0.00
(0.15)
Is Formal Leader a Network
Leader (Eigen.) 0.42
+
(0.23)
Is Formal Leader a Network
Leader (Bet.)
0.11
(0.20)
F 9.05*** 8.63*** 10.15*** 8.53***
Adj. R squared .20 .19 .25 .25
*** p < .001; ** p < .01; *p < .05; +p < .10 (two-tailed tests).

49
TABLE 4
Linear Regression Models Predicting Band Popularity
Model 1 Model 2 Model 3 Model 4
Band Visibility 0.18***
(0.05) 0.18***
(0.05) 0.16***
(0.05) 0.16**
(0.05)
Band Experience
0.48***
(0.14)
0.48***
(0.14)
0.57***
(0.14)
0.57***
(0.14)
Band Size
-0.13
(0.19)
-0.13
(0.20)
-0.10
(0.20)
-0.10
(0.16)
Inverse Mills Ratio
1.59
(1.09)
1.61
(1.11)
2.44*
(1.08)
2.48*
(1.09)
Formal Leader (Yes = 1) 0.02
(0.16) 0.01
(0.15) -0.02
(0.16)
Num. of Network Leaders
(Eigen.) 0.50***
(0.13) 0.47**
(0.16)
Num. of Network Leaders
(Bet.)
-0.09
(0.14)
-0.12
(0.15)
Is Formal Leader a Network
Leader (Eigen.)
0.07
(0.23)
Is Formal Leader a Network
Leader (Bet.)
0.09
(0.20)
F 13.36*** 10.64*** 10.82*** 8.40***
Adj. R squared .20 .19 .25 .25
*** p < .001; ** p < 01; *p < 05 (two-tailed tests).

50
TABLE 5
Negative Binomial Regression Models Predicting Band Longevity
Model 1 Model 2 Model 3 Model 4 Model 5
Band Visibility
-0.00
(0.08)
-0.00
(0.08)
0.02
(0.08)
0.03
(0.08)
0.06
(0.10)
Band Experience
0.52*
(0.23)
0.56*
(0.24)
0.56*
(0.24)
0.49*
(0.24)
0.32
(0.29)
Band Size
0.65*
(0.33)
0.58+
(0.34)
0.87*
(0.36)
0.83*
(0.36)
0.34
(0.43)
Inverse Mills Ratio
0.68
(1.67)
1.01
(1.74)
0.97
(1.79)
0.33
(1.82)
0.42
(2.09)
Formal Leader (Yes = 1)
0.16
(0.25)
0.06
(0.26)
-0.04
(0.27)
-0.53
(0.32)
Num. of Network Leaders
(Eigen.)
-0.22
(0.22)
-1.08**
(0.34)
-0.74+
(0.41)
Num. of Network Leaders
(Bet.)
-0.82***
(0.24)
-0.61*
(0.27)
-0.36
(0.32)
Is Formal Leader a Network
Leader (Eigen.)
1.64***
(0.49)
1.32*
(0.60)
Is Formal Leader a Network
Leader (Bet.)
-0.28
(0.37)
-0.24
(0.46)
Past Creativity
-0.65***
(0.18)
Past Popularity
0.44**
(0.16)
Pearson Chi Square 574.27 574.14 542.70 481.86 403.04
Log Likelihood -352.65 -352.46 -342.10 -335.76 -243.45
Likelihood Ratio Chi
Square
24.02*** 24.40*** 45.13*** 57.80*** 56.40***
*** p < 001; ** p < .01; *p ≤ .05; +p < .10 (two-tailed tests).

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Figure 1
An Emergent Model
Note: Network leadership is based on eigenvector centrality. Betweenness centrality network leadership and all control variables were included in the path analysis but are not
shown here to simplify the diagram. Betweenness based network leadership was not a significant predictor of team creativity, team popularity, or team longevity.

52
Appendix
Sample of Jazz Experts’ Aesthetic Judgments of Music

53
Floor van den Born (fvandenborn@gmail.com) is an adjunct faculty member of the University of Amsterdam and the VU
University, Amsterdam. Her research examines creativity and social networks, with a focus on the NYC jazz scene. In
addition to her academic activities, she runs an organic market garden in the Netherlands.
Ajay Mehra (ajay.mehra@uky.edu) is Professor and Chellgren Chair in the Gatton College of Business and Economics. His
research examines how psychological forces influence the creation, perception, and utilization of social networks. Ajay is
a jazz enthusiast, currently infatuated with the music of the trumpeter, Wadada Leo Smith, and the pianist, Vijay Ayer.
Martin Kilduff (m.kilduff@ucl.ac.uk) is Professor and Director of Research at UCL School of Management; and the 2021-
2022 chair of the OMT Division of the Academy of Management. Current research focuses on how social network
brokerage, multiplexity, agency, and elite connections relate to gender, self-monitoring personality, burnout, and
likelihood of being fired.