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The principles of open collaboration for innovation (and production), once distinctive to open source software, are now found in many other ventures. Some of these ventures are internet-based: Wikipedia, online forums and communities. Others are off-line: in medicine, science, and everyday life. Such ventures have been affecting traditional firms, and may represent a new organizational form. Despite the impact of such ventures, questions remain about their operating principles and performance. Here we define open collaboration (OC), the underlying set of principles, and propose that it is a robust engine for innovation and production. First, we review multiple OC ventures and identify four defining principles. In all instances, participants create goods and services of economic value, they exchange and reuse each other's work, they labor purposefully with just loose coordination, and they permit anyone to contribute and consume. These principles distinguish OC from other organizational forms, such as firms or cooperatives. Next, we turn to performance. To understand the performance of OC, we develop a computational model, combining innovation theory with recent evidence on human cooperation. We identify and investigate three elements that affect performance: the cooperativeness of participants, the diversity of their needs, and the degree to which the goods are rival (subtractable). Through computational experiments, we find that OC performs well even in seemingly harsh environments: when cooperators are a minority, free riders are present, diversity is lacking, or goods are rival. We conclude that OC is viable and likely to expand into new domains. The findings also inform the discussion on new organizational forms, collaborative and communal.
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Organization Science
Articles in Advance, pp. 1–20
ISSN 1047-7039 (print) ISSN 1526-5455 (online)
© 2013 INFORMS
Open Collaboration for Innovation:
Principles and Performance
Sheen S. Levine
Columbia University, New York, New York 10027,
Michael J. Prietula
Emory University, Atlanta, Georgia 30322,
The principles of open collaboration for innovation (and production), once distinctive to open source software, are
now found in many other ventures. Some of these ventures are Internet based: for example, Wikipedia and online
communities. Others are off-line: they are found in medicine, science, and everyday life. Such ventures have been affecting
traditional firms and may represent a new organizational form. Despite the impact of such ventures, their operating principles
and performance are not well understood. Here we define open collaboration (OC), the underlying set of principles, and
propose that it is a robust engine for innovation and production. First, we review multiple OC ventures and identify four
defining principles. In all instances, participants create goods and services of economic value, they exchange and reuse each
other’s work, they labor purposefully with just loose coordination, and they permit anyone to contribute and consume. These
principles distinguish OC from other organizational forms, such as firms or cooperatives. Next, we turn to performance.
To understand the performance of OC, we develop a computational model, combining innovation theory with recent
evidence on human cooperation. We identify and investigate three elements that affect performance: the cooperativeness of
participants, the diversity of their needs, and the degree to which the goods are rival (subtractable). Through computational
experiments, we find that OC performs well even in seemingly harsh environments: when cooperators are a minority, free
riders are present, diversity is lacking, or goods are rival. We conclude that OC is viable and likely to expand into new
domains. The findings also inform the discussion on new organizational forms, collaborative and communal.
Key words: innovation; entrepreneurship; strategy; performance; simulation; model; software; open source;
crowdsourcing; Wikipedia; community; economics; psychology
History: Published online in Articles in Advance.
Open source software is booming. Once the domain of
hobbyists and hackers, it has gained acceptance with
consumers, corporations, and governments. Some exem-
plars of open source, such as the Linux and Android
operating systems, are now commonplace, operating
millions of devices. Together with other products of
open source software, they have been creating billions of
dollars in economic value (European Commission 2006).
Yet the same patterns of collaboration, innovation, and
production can now be found beyond software (Baldwin
and von Hippel 2011, Benkler 2006, von Hippel 2005b).
For example, people collaborate, sometimes with com-
plete strangers, in user-to-user forums (Lakhani and von
Hippel 2003), mailing lists (Jarvenpaa and Majchrzak
2008), and online communities (Faraj and Johnson
2010). Some share openly (and occasionally illegally)
digital media: music, movies, TV programs, and soft-
ware (Levine 2001). People also share processing power
and Internet bandwidth, enabling free services such as
Skype (Benkler 2006, pp. 83–87). In the physical world,
off the Internet, people give, receive, and share tools and
appliances (Goodman 2010, Nelson et al. 2007, Willer
et al. 2012), even host strangers overnight (Lauterbach
et al. 2009, Perlroth 2011)—all without payments or
barter. Such ventures exemplify what we call open col-
laboration (OC), a shorthand inspired by Baldwin and
von Hippel (2011). Here, we define its principles and
explore its performance.
Firms have been affected by open collaboration,
some negatively, others positively. The free encyclopedia
Wikipedia, a prime example of such collaboration, has
come to match the quality of Encyclopædia Britannica
(Giles 2005), which, after 244 years in circulation, has
ceased printing. Other firms have been thriving by facil-
itating open collaborations, hosting forums and commu-
nities. This is how firms such as Amazon, an Internet
retailer, and TripAdvisor, a review site for hotels and
restaurants, established a flow of “user-generated con-
tent”: reviews, advice, photos, and video clips. Fellow
users may benefit from such information, and the firms
economize on wage-free, royalty-free content (Chevalier
and Mayzlin 2006, Mudambi and Schuff 2010). The
effect of open collaboration may be nascent, but it has
already required established firms to tweak their strategy,
operations, and marketing (Chen and Xie 2008, Scott
and Orlikowski 2012). It also has an impact outside the
commercial world.
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Levine and Prietula: Open Collaboration for Innovation
2Organization Science, Articles in Advance, pp. 1–20, © 2013 INFORMS
Open collaboration fits well with the scientific ethos
(David 1998), and, not surprisingly, it has been ben-
efiting scientific endeavors. For instance, thousands of
volunteers, each contributing just a fraction of a solu-
tion, have been discovering and solving problems too
immense for traditional organizations (Benkler 2004,
Partha and David 1994). Contributors classify celestial
objects in Galaxy Zoo, decipher planetary images in the
Mars Public Mapping Project (Benkler 2006, p. 69), and
labor over terrestrial maps (Helft 2007). Similar patterns
that are labeled with the adjectival “open” have appeared
in medicine (Ortí et al. 2009, Rai 2005), engineering
(e.g., “open design”), and biotechnology (Henkel and
Maurer 2007, 2009). In scientific publication, the open
science movement aims to disperse authority and expand
collaboration (Lin 2012).
Scholars have been attracted to these novel patterns of
innovation and production. Open source software, a har-
binger of open collaboration, was the topic of several
edited volumes (e.g., Feller et al. 2005, West and Gallagher
2006) and special issues of journals (von Krogh and
von Hippel 2003, 2006). The use of “open source” as
a scholarly term has been growing dramatically, from
just 32 appearances in 1999 to 687 times a decade later
(see the electronic companion, available as supplemen-
tal material at
We build on these efforts. Here, we extend and general-
ize what prior research called open collaborative inno-
vation projects (Baldwin and von Hippel 2011), peer
production (Benkler 2002), a community-based innova-
tion system (Franke and Shah 2003), Wikinomics, and
mass collaboration (Tapscott and Williams 2006), as
well as instances of collaborative communities (Adler
et al. 2008), transaction-free zones (Baldwin 2008),
crowdsourcing (Afuah and Tucci 2012), collaborative
consumption (Goodman 2010), electronic networks of
practice or online communities (Faraj et al. 2011,
Kollock 1999, Wasko and Faraj 2005), and open inno-
vation (West and Gallagher 2006).
Such open collaborations have drawn scholarly inter-
est because of their social and economic impact. How-
ever, what affects their performance—even why they are
viable—remains a puzzle. We begin by identifying some
defining principles: a system of innovation or production
that relies on goal-oriented yet loosely coordinated par-
ticipants who interact to create a product (or service) of
economic value, which is made available to contributors
and noncontributors alike.
Next, we examine performance. We investigate several
elements that affect the performance of open collabora-
tion. One element is cooperative behavior of contributors
who willingly share their work (or property) with non-
contributors. Performance can benefit with people bene-
fit others at cost to themselves, even if reciprocity is not
guaranteed. But such behavior, desirable as it may seem,
is inherently risky—contributors can be overwhelmed by
free riders, as in the classic tragedy of the commons
(Hardin 1968, Olson 1965). Thus, we draw on recent
evidence of human cooperation to answer a fundamental
question: Why do people share the fruits of cooperation
with noncontributors?
Cooperativeness may be the life-giving element of
OC, but it is not the only element affecting performance.
We add to cooperation, a characteristic of interaction,
two other elements taken from the economics and inno-
vation literature. One is a characteristic of the partici-
pants: need heterogeneity, which is the extent to which
they have heterogeneous (diverse) needs. Another a char-
acteristic of the goods: rivalry (or subtractability; see
Hess and Ostrom 2006), which is the extent to which
one’s consumption of a good interferes with another’s.
To assess the performance of OC, we combine
the three elements—cooperativeness, need heterogene-
ity, and rivalry—in an agent-based model. Because we
envision OC as a general system for innovation and pro-
duction, we use a fundamental measure of economic
performance: efficiency in turning inputs to outputs.
We find surprising results: OC can thrive even in
seemingly harsh environments, reaching much more
broadly than observers assumed or observed. It performs
robustly even when cooperators are a fraction of partic-
ipants, free riders are present, goods are rival, or partic-
ipant needs are homogeneous (nondiverse).
The results suggest what affects the performance of
OC and—equally important—what does not. Some con-
ditions are commonly assumed necessary but they are
not. First, OC can thrive even if participants are not an
exclusive bunch of cooperators but just a random sam-
ple from the human population, where cooperators are
a small minority. Second, it is not required that par-
ticipants derive immediate benefits from contribution,
such as monetary gains, enhanced professional reputa-
tion, or pleasure. Third, OC is not derailed when the
resources shared are rival or when participant needs are
highly similar. It can perform well even with rival goods
or homogeneous needs; performance suffers only when
the two are concurrent. The model also explains some
intriguing observations, such as the extreme disparity in
contributions to OC, where a small core contributes the
most and many contribute little.
The findings imply that OC is likely to grow and
spread into new domains. They also inform the dis-
cussion on new organizational forms, collaborative and
communal (e.g., Adler et al. 2008, Benkler 2011, Faraj
et al. 2011, Heckscher and Adler 2006). Human efforts,
it seems, can be harnessed in previously unthought-of
of ways: by relying on goal-oriented yet loosely coor-
dinated participants who interact to create products of
economic value, which they then offer to anyone—
contributors and free riders alike.
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Levine and Prietula: Open Collaboration for Innovation
Organization Science, Articles in Advance, pp. 1–20, © 2013 INFORMS 3
Open Collaboration: Definition, Benefits,
and Puzzles
Open Collaboration Defined
We define open collaboration as any system of innova-
tion or production that relies on goal-oriented yet loosely
coordinated participants who interact to create a prod-
uct (or service) of economic value, which they make
available to contributors and noncontributors alike. This
definition captures multiple instances, all joined by sim-
ilar principles, which are detailed in Table 1. For exam-
ple, all of the elements are present in an open source
software project, in Wikipedia, or in a user forum or
community. They can also be present in a commercial
website that is based on user-generated content. In all of
these instances of OC, anyone can contribute and any-
one can freely partake in the fruits of sharing, which
are produced by interacting participants who are loosely
Economic value is manifested by the substitution of
for-profit services, such as commercial software, Ency-
clopædia Britannica, or technical consultants. Value is
also manifested by the content that open collabora-
tions create for firms such as Amazon or TripAdvisor.
This content affects other firms, such as book publish-
ers and hotel operators, whose fortunes grow or shrink
Table 1 Definitional Elements of Open Collaboration
Corresponding model
Elements Description manipulation Theoretical and empirical referents
Create goods of
economic value
The main purpose is the
creation of products
and services of
economic value.
The dependent variable is a
fundamental measure of
economic performance:
efficiency in turning inputs to
Benkler (2002, 2006), Cooley (1909),
Shah (2005), von Hippel and
von Krogh (2003), von Krogh and
von Hippel (2003)
Open access to
Participants can freely
contribute but also
consume, regardless of
their contribution.
Any agent can contribute or
seek others’ contributions
without exclusion.
Faraj and Johnson (2010), Kollock
(1999), Lakhani and von Hippel
(2003), Raymond (1999), von Krogh
and von Hippel (2003), Zeitlyn (2003)
Interaction and
exchange is centrala
Participants interact,
exchange, and reuse
each other’s work, all
while engaging in own
Agents search and engage
others to transfer resources.
Anthony et al. (2009, p. 283), Benkler
(2004, p. 1110), Faraj and Johnson
(2010), Faraj et al. (2011), Häfliger
et al. (2008, p. 180), Jeppesen and
Lakhani (2010), von Krogh et al.
Participants labor
purposefully yet
loosely coordinated
Coordination, structure,
and hierarchy are
emergent and less
specified than in other
organizational forms
(e.g., firms, cooperatives).
Agents have individualistic goals
for resources and coordinate
only when engaging in
exchange events. There is no
predefined structure or
Dahlander and O’Mahony (2011), Kuk
(2006), Lee and Cole (2003),
MacCormack et al. (2006), Mockus
et al. (2005), O’Mahony and Ferraro
(2007), Shah (2006), von Krogh et al.
aSome open collaboration systems involve interaction and exchange toward a coordinated creation of an artifact (software code, ency-
clopedia article, etc.), but in others purposeful interaction and exchange are themselves the end goal. This is the case, for example, in
many user-to-user interactions described in prior work (Franke and von Hippel 2003; Lakhani and von Hippel 2003; von Hippel 2005a,
pp. 33–43; Wasko and Faraj 2005). Because interaction is a prerequisite to the creation of any open collaboration artifact, we see it as a
necessary element in the definition. The model can accommodate instances of open collaboration with or without a coordinated artifact.
It merely affects the interpretation of the performance measure: without an artifact, it is an aggregation of individual performance; with an
artifact, it is a measure of collective performance.
with online reviews (Chen and Xie 2008, Chevalier and
Mayzlin 2006, Mudambi and Schuff 2010, Scott and
Orlikowski 2012).
The definition also delineates what OC is not. It
excludes traditional firms (because of the lack of open
access or loose coordination) and markets where individ-
ual agents work independently (no open access and/or
no interaction). It also excludes primary groups (Cooley
1909) such as social clubs and some online communi-
ties, where the raison d’être is social interaction, not
innovation or production.
The Benefits of Open Collaboration
OC provides unique benefits to participants, firms, and
society. One benefit is the ability to build on others’
work in a direct way, because contributors can directly
view the architecture of the product (Kumar et al. 2011,
MacCormack et al. 2006). In software, it means that
they can reuse code, economizing on skills, time, and
cost (Häfliger et al. 2008). Contributors can also interact
directly with others, many of them strangers, to share
and integrate knowledge and other resources. Such vast
exchange characterizes OC, whether in Wikipedia, an
Internet user forum, or a mailing list. However, unlike
in a traditional organization, here contributions come
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Levine and Prietula: Open Collaboration for Innovation
4Organization Science, Articles in Advance, pp. 1–20, © 2013 INFORMS
from many casual participants, who give as much as
they wish, with no up-front commitment (Benkler 2002,
Jeppesen and Lakhani 2010). Such casual contributions
can lead to better products: in Wikipedia, the most reli-
able contributions are made not by the few prominent
writers but by the many who contributed only once
(Anthony et al. 2009, p. 283). Open source software has
been shown to be safer than pricier commercial alterna-
tives (Bambauer and Day 2011).
When firms engage with OC, they can reap Schum-
peterian (innovation) rents (Nelson and Winter 1982)
that stem not from their employees, a traditional source
of innovation, but from their users (Bonaccorsi et al.
2006, Chatterji and Fabrizio 2012, von Hippel 2005a,
West 2003). For instance, many Internet security prod-
ucts were improved by probing users, not the (some-
times dismayed) engineers who designed the products
(Bambauer and Day 2011).
The benefits of engaging users in innovation are multi-
ple. Firms economize on research and development costs
by pooling or externalizing them (West and Gallagher
2006), enjoy higher customer satisfaction (Franke and
von Hippel 2003), and benefit from more favorable
beliefs and greater trust from users (Dahlander and
Wallin 2006, Porter and Donthu 2008).
For society, OC can not only improve economic and
social welfare (Bambauer and Day 2011, Benkler 2004,
Maurer and Scotchmer 2006, Strandburg 2009) but also
heighten morality: “Foster virtue by creating a context
or a setting that is conducive to virtuous engagement and
practice” (Benkler and Nissenbaum 2006, p. 403).
OC offers many benefits, impacts economically and
socially, but it is not well understood. Extant theories
of firm-based and market-based innovation are unfit-
ting. Although OC may complement firm-based and
market-based innovation, it differs greatly from them
(Baldwin and von Hippel 2011, Lee and Cole 2003,
von Hippel and von Krogh 2003). Differences can
be found in the motivation to participate, organiza-
tion, and governance (Dahlander and O’Mahony 2011,
Faraj et al. 2011, O’Mahony and Ferraro 2007, Shah
2006); product design and production (MacCormack
et al. 2006, Mockus et al. 2005); and market behavior
(Casadesus-Masanell and Ghemawat 2006, Economides
and Katsamakas 2006).
Because OC appears so different from other sources
of innovation, some pundits dismissed it as an oddity.
For instance, a prominent practitioner opined that open
source software “goes against the grain of everything
I know about the software field”; therefore, “any change
[it fosters] will be limited to one or a few cults emerging
from a niche culture” (Glass 2000, pp. 104–105). Such a
view is not preposterous; it is not clear how OC survives,
let alone thrives. OC is not only voluntary and infor-
mal but also open. Fisheries, cooperatives, kibbutzim,
and research consortia can be voluntary and informal,
yet they are closed and their membership is bound and
stable (Ouchi 1980, p. 129). In all of them, participants
band to produce jointly, but those who share the benefits
must share the costs (Cornes and Sandler 1986, p. 159).
In such arrangements, only contributors may consume
the fruits of cooperation, and they endeavor to deter free
riders. Free riding must be controlled, the thinking goes,
or it will unleash the tragedy of the commons, the the-
oretical expectation that shared property, such as pas-
tureland, will eventually vanish as self-interested users
abuse it (Hardin 1968, Olson 1965).
In contrast, OC operates with vague membership and
porous boundaries, where anyone can consume, contrib-
utor or not. As emphatically open as it is, OC is even
more puzzling than Hardin’s pastoral illustration. Unlike
in a pastureland, here a secure fence can be built and a
sign can be hung above the bolted gate: “Members only.
Such exclusion of noncontributors is especially easy on
the Internet; it can be done simply by screening users
to allow contributors and block free riders. But in many
instances of OC, numerous users free ride. Even con-
tributors vary widely in their efforts. Empirical accounts
repeatedly show that few participants provide much of
the work, others contribute occasionally, and many oth-
ers contribute little or nothing (Anthony et al. 2009, Kuk
2006, Lancashire 2001, Lerner and Tirole 2005, Madey
et al. 2004, Mockus et al. 2005). But despite free rid-
ing and vastly unequal contributions, collaboration does
not collapse. The major contributors do not revolt. “Who
would ever have imagined that innovation could flourish
under conditions like those?” wondered von Krogh and
von Hippel (2006, p. 975).
The performance of OC has been a puzzle for partici-
pants and scholars alike, and a variety of conjectures and
explanations have been proposed. An early participant
and observer, Raymond (1999) offered a long list of intu-
itive explanations to why participants contribute freely:
perhaps they develop for personal use, derive plea-
sure (“scratching a developer’s personal itch”; p. 23),
enhance their professional reputation, or engage in reci-
procity, referred to as “gift exchange. Ghosh (1998),
another participant-cum-observer, seconded that OC may
be based on reciprocity, which he called “cooking pot
markets, where participants simultaneously contribute
and consume others’ contribution. Ghosh ruminated
about other possible explanations, including a diversity
of needs and contributions, reputation, and the nonrival
nature of the goods.
Scholars have been more parsimonious. Some in-
stances of OC fit with self-interest, it was suggested,
because an innovator can sometimes benefit from
freely revealing an innovation. Revealing may be lucra-
tive thanks to complementarities and diffusion of the
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Levine and Prietula: Open Collaboration for Innovation
Organization Science, Articles in Advance, pp. 1–20, © 2013 INFORMS 5
innovation (e.g., Harhoff et al. 2003, Henkel 2006),
which can concur with private benefits such as enhanced
professional prestige (Lerner and Tirole 2002), learning,
and pleasure (von Hippel and von Krogh 2003).
The theoretical model we present here, which focuses
on behavior, fits with prior explanations. It incorpo-
rates them in a general way by referring to charac-
teristics of the interaction (e.g., reciprocal cooperation,
altruism), characteristics of the participants (e.g., diverse
needs), and characteristics of the goods (e.g., nonrival).
By focusing on behavior, it complements prior explana-
tions that turn on intentions and motivations, which are
more difficult to ascertain than behavior. For instance,
cooperative behavior can stem from immediate private
gain, but it can also come from altruism or social move-
ment ideology (O’Mahony and Bechky 2008). More-
over, cooperation often emerges even without immediate
private gains, such as when contributions are unglam-
orous or even anonymous, such as when participants
respond to others’ questions in a user forum (Lakhani
and von Hippel 2003) or engage in mundane soft-
ware maintenance (Glass 2000), Wikipedia administra-
tion (Zittrain 2008, pp. 127–148), or illegal file sharing
(Levine 2001). Because the model focuses on behavior,
it can also account for contributions motivated by plea-
sure or “fun,” but it does not necessitate this explanation
(which can be tautological).
Next, we review some recent evidence on a central
element in the performance of OC: cooperation. The
performance of OC is also affected by two other ele-
ments: the diversity (heterogeneity) of participants and
their needs, and the rivalry of goods and resources. We
discuss these two elements in turn, followed by the
Table 2 Performance-Related Elements of Open Collaboration
Corresponding Theoretical and
Level of analysis Element Description model elements empirical referents
Individual Cooperativeness
(Study I)
Individuals can be typified by
their tendency to cooperate.
When individuals are
aggregated, the composition
of cooperative types
distinguishes one population
from another.
Agents are defined as
cooperative types in terms
of likelihood to contribute.
Agent populations are
defined by the distribution
of cooperative types in
Kurzban and Houser (2005),
Lerner and Tirole (2005),
Mockus et al. (2005), Shah
(2006, p. 1005)
Group Diversity in needs
(Studies II and
Participants can have similar or
diverse needs, which implies
that they differ on the
resources they seek.
A resource can be desired by
many or few.
Need heterogeneity, the
extent to which agents
have dissimilar needs and
therefore seek a different
resource, is varied from
homogeneity to
Anthony et al. (2009), Baldwin
et al. (2006), Jeppesen and
Lakhani (2010), von Hippel
(2005a, p. 33)
Goods Rivalry
(Studies II and
Goods differ in the extent to
which one’s consumption
affects another’s; e.g., a
perfectly rival good diminishes
completely when used or
The extent to which a
resource is a rival good
varied from nonrival to
completely rival.
Baldwin and Clark (2006),
Cornes and Sandler
(1986), Hess and Ostrom
(2006), von Hippel and
von Krogh (2003)
model, which incorporates all three elements (detailed
in Table 2).
What Affects the Performance of Open Collaboration:
The Human Tendency to Cooperate
Cooperation is central for open collaboration, and it
often takes the form of reciprocity (i.e., “I contribute
because I benefited from others’ contributions”). In an
early study, users who helped others with technical
advice were asked to complete the following sentence:
“I was motivated to answer because0 0 0 0 A variety of
motivations were reported, but the top three reasons were
similar. They all stressed reciprocity: “I help now so
I will be helped in the future,” “I have been helped
before [here]—so I reciprocate,” and “I have been helped
[elsewhere] before—so I reciprocate” (Lakhani and von
Hippel 2003, p. 937). Indeed, recent evidence suggests
that people generally cooperate and reciprocate even
without direct benefits. Benefiting from others’ contri-
butions can trigger one’s contribution. We turn to review
the evidence accumulated in evolutionary biology, eco-
nomics, and psychology.
Early theoreticians presumed that, without safeguards,
cooperation would be displaced by free riding. The
prevalent presumption was that “every agent is actu-
ated only by self-interest” (Edgeworth 1881, p. 16).
When OC emerged, scholars sought to explain it as self-
interest. Participants must have some direct and imme-
diate benefit from doing so, the thinking went, or else
they never would have contributed.
However, self-interest is common but not omnipresent.
Scholars, including economists, have long questioned
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Levine and Prietula: Open Collaboration for Innovation
6Organization Science, Articles in Advance, pp. 1–20, © 2013 INFORMS
whether humans are truly defined by narrow self-interest
(e.g., Dawes and Thaler 1988, Sen 1977). In recent
years, a more nuanced picture has emerged. Experiments
in the laboratory and the field have demonstrated that
humans are willing to collaborate, bear costs, and defer
self-interest for the greater good. Reviews concluded that
humans are “super cooperators” (Nowak and Highfield
2011) and “human altruism is a powerful force” (Fehr
and Fischbacher 2003, p. 785). Altruism and coopera-
tion are central to human existence and have been fun-
damental in our evolution, distinguishing humans from
other species (Alexander 1987). Cooperation can per-
sist without contracts or quid pro quo. As the early
accounts of open source hinted, cooperation can emerge
even without direct reciprocity, i.e., a future gain from
the beneficiary (Baker and Levine 2013, Fehr and
Fischbacher 2003).
Aiming to quantify the frequency and distribution of
cooperation and reciprocity, Kurzban and Houser (2005)
used an elaborate experimental design to study cooper-
ation between and within individuals. They found that
people are consistent in the extent of their cooperation
in different situations. People’s behavioral types are so
stable as to allow an accurate prediction: A group’s
cooperative outcomes can be remarkably well predicted
if one knows its type composition” (Kurzban and Houser
2005, p. 1803). The general human population has been
estimated to consist of three cooperative types:
1. Cooperators (13% of the general population) con-
tribute to others at a cost to themselves, uninfluenced by
others’ contribution. This behavior resembles pure altru-
ism, one suggested cause of OC.
2. Reciprocators (63%) may contribute to others at a
cost to themselves, but only insomuch that others are
also contributing. Such behavior suggests reciprocity,
also suggested as a cause of OC.
3. Free riders (20%) contribute at a low rate, regard-
less of whether others contribute. Empirically, free rid-
ers (perhaps better called “easy riders”; see Cornes and
Sandler 1984) contribute significantly less than others
(but usually more than nothing). This behavior fits the
image of a narrowly self-interested individual.
The remaining 4% are too inconsistent to be categorized.
The findings have been replicated, for example, in
non-Western and field settings (Ishii and Kurzban 2008,
Rustagi et al. 2010), leading a recent review to deter-
mine that “we now have a fairly clear picture about the
preference heterogeneity among participants and the pre-
ponderance of conditional cooperators [reciprocators]”
(Chaudhuri 2010, p. 77).
Reciprocators are of special importance, not only
because they are the largest group in the human
population (see also Fischbacher and Gächter 2010,
Fischbacher et al. 2001) but also because they match
the behavior of those around them. Whereas cooperators
and free riders behave predictably, reciprocators mimic
those around them. This alternating behavior introduces
a dynamic element in which past experiences affect cur-
rent behavior, leading to a virtuous or a vicious cycle:
If you benefited from others’ contributions, you adjust
your behavior so that you benefit others alike; if you
were exploited, you reduce contributions. Such virtuous
cycles are behind the popular concept of “pay it for-
ward” and the scientific notions of generalized exchange
(Baker and Levine 2013, Molm et al. 2007, Willer et al.
2012) and indirect reciprocity (Alexander 1987, Nowak
and Roch 2007, Nowak and Sigmund 1998). The spread
of cooperation has been documented empirically (Bolton
et al. 2005, Fowler and Christakis 2010, Weber and
Murnighan 2008).
What Affects the Performance of Open
Collaboration: Diversity and Rivalry
Cooperation is central to open collaboration. But we pro-
pose that two other elements also affect performance.
Diversity of Participants and Their Needs. OC par-
ticipants are diverse, empirical accounts show, and
differ in the resources they seek and goods they pro-
duce (e.g., Anthony et al. 2009, p. 283; Benkler 2004,
p. 1110; Jeppesen and Lakhani 2010; Madey et al.
2004). Researchers have documented the pattern across
a variety of products and services: people’s needs are
often homogeneous (for a review, see Franke et al. 2009;
von Hippel 2005a, pp. 33–43).
How diversity affects OC performance was of con-
siderable discussion. Some academics (Bonaccorsi and
Rossi 2003, p. 1244) and practitioners (Ghosh 1998)
argued that diversity supports OC. Higher heterogene-
ity of needs, they proposed, leads to better performance
(von Hippel 2005a, pp. 33–43). It appears plausible;
as Platt (1973) pointed out, the tragedy of the com-
mons occurs because too many individuals seek the same
good; the tragic outcome is caused by low need hetero-
geneity. When people seek a diversity of goods, the com-
mons can thrive. Not incidentally, need heterogeneity
is necessary for realizing gains from trade: exchange is
lucrative if people have nonoverlapping demands (Kemp
1987). Otherwise, why trade?
Although it seems plausible that need heterogeneity
increases performance, at least one study suggested the
opposite (Baldwin et al. 2006). It described users who
shared knowledge, modeled as a nonrival good, and
found that homogeneous user needs led to better perfor-
mance. Similar needs, it was argued, allow users to solve
a problem only once and share the solution, avoiding
duplicate efforts. In our experiment, we consider main
and interaction effects separately, so we can show how
these seemingly differing propositions about diversity
and performance are actually compatible.
Rivalry of Goods and Resources. A good is defined
as nonrival if more consumption of it requires no addi-
tional cost (Cornes and Sandler 1986). People can simul-
taneously enjoy air and sunlight, watch television, and
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listen to radio. Adding consumers does not add cost;
one’s enjoyment does not interfere with another’s. Few
goods are perfectly nonrival. Many goods that are non-
rival initially, such as road use, become less nonrival
(and more rival) as congestion creeps in (Leach 2004,
pp. 155–156).
To early observers, software was a pure nonrival good.
Once software was produced, the cost of sharing it
appeared minuscule, because a contributor could keep
the software while providing a perfect copy to some-
one else. Because one’s benefit does not interfere with
another’s, “you never lose from letting your product
free,” exclaimed Ghosh (1998). Thus, nonrivalry became
central in the discussion of open source software, an
early instance of OC. Nonrivalry was frequently cited in
discussing and modeling of the phenomenon (Baldwin
et al. 2006; Harhoff et al. 2003, pp. 1759–1767). Some
scholars have argued that OC performs well because it
produces “antirival goods” (Weber 2004), which benefit
those who share them.
But others have recognized that OC can involve goods
that are somewhat rival, even if just because contribu-
tions can require attention, time, and effort (Shah 2006,
p. 1005). If we consider that sharing may require an
effort, then even software may not be the pure nonrival
good it seems (Baldwin and Clark 2006, Marengo and
Pasquali 2010).
To accommodate a variety of goods and resources,
we refrain from modeling them as binary: entirely rival
or nonrival. Instead, we employ a continuous range
of rivalry. The results suggest that rivalry matters less
than expected. We find that OC can perform even with
rival goods, let alone with goods that are nonrival or
Modeling the Performance of
Open Collaboration
Computational models are a useful method to study
complex social and organizational phenomena (Burton
and Obel 2011, Davis et al. 2007, Harrison et al.
2007, Levine and Prietula 2012, Macy and Willer 2002,
Prietula et al. 1998) and were employed in some pivotal
studies in organizational theory (e.g., Cyert and March
1963, Levinthal and March 1981, Nelson and Winter
1982). By building an agent-based model, a form of
computational model, we heed Benkler’s (2002, p. 424)
call to study OC through “artificial life-type modeling,”
and we extend earlier models (Bonaccorsi and Rossi
2003, Madey et al. 2004). Agent-based models are par-
ticularly useful here because they excel in determin-
ing boundary conditions and explicating mechanisms
(Baldwin et al. 2006, Prietula 2011). Aspects such as
contagious behavior are difficult to represent using tradi-
tional analytic models but are easily captured by agent-
based models. We use the agent-based model to conduct
computational experiments: varying elements and rules
while observing interactions and outcomes, unraveling
processes that are unobservable (or nonmanipulable) in
the field or the laboratory.
To identify salient elements of OC, we reviewed
empirical accounts and studied theories in organizational
theory, economics, sociology, and psychology, many of
which we review above. We used these sources to decide
which elements should be modeled. We then constructed
the model: abstracting from human behavior, we phrased
rules to describe, parsimoniously and unambiguously,
how agents behave. We then conducted computational
experiments, varying conditions and observing agent
behavior and its outcomes.
The model involves a setting common in many OC
instances: a crowd of agents is working on somewhat
similar tasks. To accommodate a broad variety of phe-
nomena, we focus on behavior and assume little about
the source of the tasks or the agents’ motivation. The
tasks can be equally thought of as assigned by an admin-
istrator, emerging from intrinsic motivation, advancing
self-interested goals, or stemming from any other source.
We do not assume that the agents are particularly moti-
vated to share or help the collective. Rather, we cau-
tiously assume they work primarily on their personal
tasks. While pursuing its own tasks, an agent may be
willing to assist others by providing resources, but only
if asked and in extent commensurate with its cooper-
ative type (cooperator, reciprocator, or free rider). To
remain cautious, we do not assume organizational fea-
tures such as coordination, division of labor, or manage-
rial function.
The model is abstract and general. It applies in many
settings where agents can complete their tasks indepen-
dently but benefit from cooperation, which allows them
to complete the tasks with fewer resources (i.e., quicker,
cheaper, with less individual expertise, etc.). The bene-
fits of cooperation are captured in the measure of per-
formance, which can be thought of as the equivalent
of achieving an organizational goal or increasing col-
lective welfare. The model can represent a highly coor-
dinated effort to create a single artifact, such as in a
software project (e.g., O’Mahony and Ferraro 2007), but
it can also describe a crowd, where each pursues his
own needs, as in online communities or forums (Baldwin
et al. 2006, Faraj and Johnson 2010, Lakhani and von
Hippel 2003). The following overview of the model is
complemented by the electronic companion, which con-
tains a full description of the algorithm, assumptions,
parameters and values, and alternative performance mea-
sures. It also offers examples, a flow chart, pseudocode,
sensitivity analyses, and a glossary.
The Model
Structure. The model consists of a population of 100
agents, each of which has a set of 100 personal tasks.
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Each task requires different resources. One of the ele-
ments manipulated, Diversity, determines the extent to
which a given task requires specific (as opposed to fun-
gible) resources. The other, Rivalry, affects the cost that
an agent bears when cooperating. The tasks are inde-
pendent, so the agents work asynchronously. A task is
completed when an agent attains a sufficient level of
a resource needed for a task. For instance, a resource
could be knowledge of a computer language and a task
could be the writing of a specific computer code. An
agent can access a resource in three ways: producing
the resource through work (the costliest option), receiv-
ing it from another, or already possessing the resource.
For instance, one can obtain knowledge about computer
programming (a resource) by learning-by-doing (self-
production), receiving help from a peer (cooperation), or
using knowledge gained previously (prior possession).
Dynamics. Each agent starts with a set of randomly
endowed resources and a set of randomly assigned tasks.
Each agent also has a cooperative type:
1. The model iterates over periods, where each can
represent any fixed time length (e.g., hour, day, week).
In each period, each agent can work on its assigned tasks
and interact with other agents.
2. An agent first attempts to complete its tasks by
using the resources it already possesses. But if it does not
have a needed resource, the agent (hereafter, “seeker”)
seeks it by looking for another that has the resource
(hereafter, a “source”). Because cooperation is cheaper
than self-production, a seeker economizes by obtaining
a resource from another. In searching for a source, the
seeker relies on a simple index that shows “who knows
what” (Levine and Prietula 2012), a representation of
transactive memory (Hollingshead 2001, Jarvenpaa and
Majchrzak 2008, Lewis et al. 2005, Wegner 1986).
3. If a potential source is found, the seeker requests
the resource it needs.
3.1. Whether and how much of the resource is pro-
vided is determined by the source’s cooperative type and
availability (see items 3.4 and 4 below), neither of which
the seeker knows beforehand.
3.2. An agent may provide to another any resource
it holds, regardless of whether the resource was obtained
through self-production, transfer, or prior possession.
Thus, although agents produce only for their own use,
never by request of another, they may transfer resources
created for own use.
3.3. If the source obliges, it bears a cost that is
proportional to the Rivalry of the resource it contributes.
The seeker can immediately use the contributed resource
for its tasks and provide it to others.
3.4. If the source does not oblige, the seeker con-
tinues searching until it either obtains the resource
or inquires all of the agents, whatever comes first. If
unsuccessful, the seeker begins self-production of the
resource. During production, the agent is unavailable for
other tasks or requests.
4. When an agent completes all of its tasks, it be-
comes dormant but responds to requests. When all
agents complete their tasks, the run is complete.
Cooperation. Axiomatically, it is always cheaper to
achieve a resource through cooperation than through
self-production. Cooperation is desirable because the
benefit to the beneficiary is greater than the cost to the
benefactor. Cooperation increases welfare because it is
a positive-sum, not a zero-sum, game. People cooperate
when it leads to an advantage; people do not cooperate
if they can achieve the same outcome by working alone
(Axelrod 1984, von Neumann and Morgenstern 1944).
For instance, Mark saves times by asking Rosemarie, a
colleague, for help with graphics software. If Mark could
have obtained the same knowledge quicker by reading
a manual, he would not have asked for help. Because
cooperation can economize on cost, people’s ability to
cooperate improves their collective performance. This
effect is captured in the definition of performance.
Performance. If OC is to serve as a general system of
innovation and production, the performance of the entire
population matters. Therefore, we do not measure the
performance of an individual agent but the cumulative
performance in using resources to complete tasks. Even-
tually, all tasks are completed, some through cooperation
(Tc) and some through self-production (Ts). We simply
define performance (P) in terms of the proportion con-
tributed through cooperation:
The divisor is determined by the number of agents and
the number of tasks allocated to each. In each run, the
agents complete 10,000 tasks, a quantity that constitutes
a fixed goal. The dividend is a measure of input, and it
is sensitive to cooperation. Cooperation economizes on
inputs, because when an agent completes a task through
cooperation, assisted by another’s resources, it spends
less. Thus, when the dividend increases, it means that
a given outcome was achieved using fewer resources or
that the same resources have accomplished a greater out-
come. Because the divisor is fixed, a greater dividend
means higher performance.
Performance here can be thought of as a measure
of productivity: how much in resources is required to
accomplish a goal. The higher the ratio is, the more effi-
ciently (or cheaply, quickly, with less knowledge, etc.)
the tasks were completed. When interpreting the results,
higher performance always means higher productivity.
It matches common measures of economic productivity
and procedural rationality (Simon 1976). This measure
accounts for both outputs and inputs, so it allows com-
parison across conditions.
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This measure of performance also accounts for the
potential drawbacks of cooperating (Levine and Prietula
2012). For example, when a seeker obtains a resource
from a source, the seeker saves time and effort, but the
source may suffer. When providing a resource that is rival
(to any extent), the source loses at least some of it. If the
source needs this resource in a future period, the source
must obtain it anew by seeking or producing it. This can
be a drag on collective performance. Another potential
drag is wasted effort: an agent can spend time searching
for a resource but ultimately fail to obtain it because it is
held by free riders who would not provide it.
Modeling the Elements that Shape Performance
Cooperative Types. A population can be composed of
cooperative types in varying ratios. Kurzban and Houser
(2005) estimated the prevalence of the three cooperative
types in the general population, but one can imagine sub-
populations with differing ratios, such as a handpicked
group of cooperators. To examine the effect on perfor-
mance, we vary the ratios of the three types.
When modeling cooperative types, we follow care-
fully the empirical findings. As in the studies on which
we rely, all agents may contribute but in varying quan-
tities. As these studies found, the amount of each con-
tribution is a combination of a base rate, determined
by the agent’s type, and some variation (Kurzban and
Houser 2005, pp. 1804–1805). When asked to provide a
resource, a Cooperator agent contributes with a base rate
that is at least half its own endowment. The exact pro-
portion is determined for each transfer by a draw from
a Gaussian distribution (=0075, =00125). When
faced with a similar request, a Free rider agent con-
tributes strictly below half of its own endowment. The
exact proportion is similarly determined by a draw from
a Gaussian distribution (=0010, =0005). Reciproca-
tor agents adjust their behavior to match the population
trend. The trend is determined by comparing the pop-
ulation contributions () in the prior two periods (p1
and p2). If the population contribution is decreasing
(p1/p2<1), then Reciprocators act as if they were
Free riders; if it is nondecreasing (p1/p21), these
agents act as Cooperators. Reciprocator behavior is ran-
dom in the first two periods, after which a trend is
Resource-Need Heterogeneity 4Diversity5.We vary
the Need Heterogeneity of the population by varying
the tasks assigned to the agents thereby varying their
resource needs. An agent’s needs are determined by
the tasks assigned to it. Need Heterogeneity rises when
the agents in the population aim to complete different
tasks; it is maximal when all agents are assigned com-
pletely different tasks. Need Heterogeneity drops when
some agents seek to complete the same tasks; it is
minimal when all agents are assigned the same task. Ini-
tial resource levels are drawn from a random uniform
distribution (see Table EC1 in the electronic companion).
Rivalry. Recall that the model allows for a gamut of
rivalry, not just a binary state. Whenever an agent uses
a resource or contributes to another, the quantity of that
resource decreases according to its degree of rivalry. For
example, if a source contributes a nonrival resource, it
suffers no loss; but if it contributes a resource that is
50% rival, then the source’s stock of that resource is
Cautious Bias in Model. The model is based on cau-
tious assumptions. We likely underestimate performance
because we omit some mechanisms that enhance coop-
eration: knowledge of others’ cooperative tendencies
(Chaudhuri and Paichayontvijit 2006), participants sort-
ing themselves into collectives (“communities”) accord-
ing to types (Gächter and Thöni 2005, Page et al. 2005,
Shen and Monge 2011), communication between par-
ticipants (Dawes et al. 1977), punishment (Fehr and
Gächter 2000, Gächter et al. 2008, Robins and Beer
2001), shunning (Dreber et al. 2008), and ostracism
(Cinyabuguma et al. 2005). When it comes to rivalry,
we include resources that range from nonrival to com-
pletely rival. For caution, we do not include goods that
are “antirival” (Weber 2004). Also, performance is likely
underestimated because the model does not presume
performance-enhancing institutions such as administra-
tion, coordination, or division of labor. Each agent pro-
duces resources based on its tasks only.
Computational Studies and Results
We begin by investigating how performance is affected
by the composition of cooperative types in the popula-
tion (Study I). Next, we examine the performance con-
sequences of rivalry and need heterogeneity in several
populations (Study II). Finally, we study how perfor-
mance is affected by rivalry and need heterogeneity in
the most relevant environment: the general population
(Study III).
Study I: How Cooperation Affects the Performance
of Open Collaboration
Kurzban and Houser (2005) identified the distribution
of cooperative types in the general population. But,
as noted, the composition can be different in some
groups—or can be made different. For instance, leaders
can interview prospective participants to sieve cooper-
ators from the others (Gächter and Thöni 2005, Page
et al. 2005). They can expose free riders by facilitat-
ing communication (Dawes et al. 1977), tracking reputa-
tion, and enabling punishment (Fehr and Gächter 2000,
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Gächter et al. 2008, Robins and Beer 2001) and shun-
ning (Dreber et al. 2008). All these can increase coop-
eration, but they come at a cost. If a leader screens
potential participants to admit cooperators, she must bear
costs: for example, conduct interviews, administer tests,
follow on referrals, review work (Baldwin 2008). Simi-
larly, if one tracks reputation or facilitates punishment to
expose free riders, one has to build suitable mechanisms.
Here, we explore questions about the optimal investment
in promoting cooperation: Are more cooperators always
better or is there a point of decreasing returns? How does
one decide how much to invest in bettering cooperation?
Because this experiment focuses on the effects of
cooperative types, other elements were held constant.
Rivalry was set to “none" by defining all resources
as nonrival. Need Heterogeneity was set to "high" by
defining all tasks as completely unique, thereby ensur-
ing that agents do not compete for resources. We
investigated 144 population compositions: the propor-
tion of Cooperators was varied between 12 points
(1%15%110%120%10001100%). The Cooperator pro-
portion was fixed at each point, and the remainder of
the population was varied from all Reciprocators to all
Free riders in 12 steps (99%195%190%180%100010%).
In all of the studies, the number of replications was such
that we could detect absolute effect sizes at =0005 for
all main and interaction effects with power of at least
0.80 (Lenth 2001). In this study, we repeated the exper-
iment with 100 replications in each step for a total of
14,400 runs.
The results are plotted as performance means for each
level of Cooperators in the population (the solid line in
Figure 1 Mean Performance by Proportion of Cooperators in the Population (Solid Line) with 95% Confidence Intervals
1 5 10 20 30 40 50 60 70 80 90 100
Cooperators in population (%)
Performance (%)
Cooperators: 5%
Reciprocators: 95%
Free riders: 0%
Cooperators: 5%
Reciprocators: 40%
Free riders: 40%
Cooperators: 5%
Reciprocators: 0%
Notes. Bars show the marginal improvement in performance. The callout shows the effect of Reciprocator and Free rider composition when
Cooperator proportion is fixed at 5%. The star signifies the ratio of cooperators in the general population (13%). Held constant were Rivalry
(none) and Need Heterogeneity (high).
Figure 1). An increase in Cooperators increases perfor-
mance. This (expected) main effect confirms intuition
and supports the validity of the model.
Proposition 1A: Cooperators Improve Perfor-
mance. More Cooperators bring better performance
over a range of population compositions.
Although the performance plot is generally increas-
ing, there is a distinct concavity in the graph. The results
show that higher Cooperator proportion improves perfor-
mance but at a decreasing rate. Large gains occur at the
lowest levels of Cooperators, e.g., when the proportion
increases from 1% to 5% (see the bar graph in Figure 1).
Proposition 1B: The Benefits of Cooperators
Are Diminishing. Increasing the proportion of Cooper-
ators has a diminishing marginal effect on performance.
Therefore, costly efforts to increase cooperation, such
as by screening participants or setting incentive schemes,
may be counterproductive. Efforts to increase cooper-
ation can backfire, reducing overall performance. The
optimum depends on the population composition and the
cost of increasing cooperation. For instance, the general
population, with its average of 13% Cooperators, can
reach a performance level of above 50%, whereas popu-
lations with vastly more cooperators show just an uptick
in performance; e.g., increasing cooperators from 60%
to 90% or even to 100% hardly affects performance.
Even if adding cooperators leads to diminishing bene-
fits in performance, we find that adding cooperators low-
ers variance in performance, as visible in the narrowing
confidence intervals (see Figure 1).
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Proposition 1C: Cooperators Reduce Variation.
More Cooperators reduce the effect of other types on
Cooperators benefit performance by decreasing vari-
ation, thereby increasing predictability. To understand
the mechanism behind the stabilizing effect of coop-
erators, we tracked the change in performance as we
fixed the proportion of Cooperators and varied the ratio
between Reciprocators and Free riders. This follow-up
experiment also serves as a sensitivity analysis: it tells
how the results are affected by shifts in the proportions
of the two other types. The detailed performance plot
(the callout in Figure 1) shows the effect as the popu-
lation composition changes from a majority of Recipro-
cators (decreasing from left to right) to a majority of
Free riders (increasing from left to right). Not surpris-
ingly, performance decreases with fewer Reciprocators
and more Free riders. However, the effect of Free riders
is nonlinear—when few, they hardly affect performance.
For instance, when the ratio of Free riders jumps from
0% to 40%, performance drops just by 3%. Only when
Free riders increase substantially does performance suf-
fer noticeably.
Proposition 1D: Free Rider Tipping Point. Perfor-
mance is hardly affected by Free riders until a threshold
is crossed, beyond which performance collapses.
The effect is driven by the change in the ratio of
free riders to reciprocators. Recall that reciprocators
are influenced by those around them. When cooper-
ators are numerous (toward the right side of the x
axis in Figure 1), reciprocators are shielded from the
influence of free riders. As a result, variation in per-
formance decreases. When cooperators are fewer (left
side of the xaxis) and reciprocators are more numer-
ous, a bigger chunk of the population is ready to
switch between cooperation and free riding. The ulti-
mate behavior of reciprocators is determined by the
group that they mimic: cooperators or free riders. As
the proportion of free riders grows, they are more likely
to serve as role models, thereby pushing reciprocators
toward lower contributions. This is a path-dependent,
self-reinforcing process (Sydow et al. 2009): as more
reciprocators behave similar to free riders, the popu-
lation’s overall level of contribution decreases, which
reinforces free riding. This process is inherent in social
traps (Platt 1973, Schelling 1978), and similar behavioral
contagion was shown in laboratory experiments (Bolton
et al. 2005, p. 1464; Fowler and Christakis 2010; Weber
and Murnighan 2008).
The accelerated process leads to the rapid decrease—
indeed, a collapse—in performance. How likely are such
collapses? We conducted analysis of tipping-point behav-
ior for the most susceptible populations: where coopera-
tors are a minority. We found that even with populations
that contain as little as 5% or even 1% Cooperators, far
less than in the general population, the tipping point is
extreme—it occurs only when Free riders exceed 70%
(details are in the electronic companion). When Coop-
erators are more numerous, the effect is much reduced.
Tipping-point behavior is altogether eliminated when
Cooperators are more than 20% of the population. In
sum, we find that free riders matter little in many practi-
cal situations.
Reciprocators Substitute for Cooperators. The find-
ings imply that reciprocators, the most widespread type,
can take the place of cooperators, the rarest type. Because
reciprocators are very common, leaders of open col-
laboration ventures will likely find it easier to recruit
reciprocators rather than seek cooperators. For example,
reaching a performance level of 50% is easy at the 10%
Cooperator level but difficult with just 5% Cooperators
(see Figure 1). However, even if an OC venture begins
with such a low level of cooperators, less than half of
the average level in the general population, it can still
reach 50% performance with a simple step—increasing
the ratio of Reciprocators (see the callout in Figure 1).
A group consisting of 5% Cooperators and 40% Recipro-
cators can achieve that level of performance. Because of
this substitution effect, equivalent levels of performance
can be achieved with a range of population compositions.
Some of these compositions are easier to obtain.
Study II: How Cooperation, Rivalry, and
Need Diversity Interact to Affect Performance
In Study I, we fixed rivalry and need diversity (hetero-
geneity). Here, we complement it by examining how
cooperation interacts with these two elements to affect
performance. We study three populations:
1. Cooperator population, which is composed of 98%
Cooperators, 1% Reciprocators, and 1% Free riders.
2. Reciprocator population, composed of 1% Cooper-
ators, 98% Reciprocators, and 1% Free riders.
3. The general population, composed of 13% Coop-
erators, 63% Reciprocators, 20% Free riders, and 4%
We examined performance by crossing each population
with low (0%) and high (100%) levels of Rivalry and
Need Heterogeneity. We conducted 100 simulation runs
for each combination for a total of 1,200 runs.
The most important finding is that the performance
impact of Rivalry and Need Heterogeneity is not simple.
Rather, the impact depends on population composition
(Proposition 2C). We also find two main effects (Propo-
sitions 2A and 2B) that confirm intuition and match prior
predictions, thereby validating the model.1
Proposition 2A: Rival Goods Hurt Performance.
The lower the rivalry (subtractability) of resources, the
better the performance.
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Intuitively, rivalry adds a cost to transferring
resources. When contributing a rival resource, the source
suffers a reduction in the quantity it holds. If in a sub-
sequent period the source requires the resource it con-
tributed, it has to obtain it anew by production, which
is costly, or by seeking it from another, which may be
futile because of free riding. When resources are rival,
cooperators suffer and so does performance, but when
resources are nonrival, even massive free riding does not
affect availability or performance.
Proposition 2B: Diverse Needs Increase Perfor-
mance. The higher the heterogeneity in needs, the better
the performance.
Intuitively, when participant needs are diverse, com-
petition for resources is lower. Because participants
seek different resources, it is more likely that a needed
resource is held (unused) by another agent. Sharing
of resources is more frequent, gains from trade ensue,
and performance is higher. In contrast, when participant
needs are similar, sharing is still possible, but when a
source contributes a resource, it may need this resource
Figure 2 How Elements Interact to Affect Performance
Notes. Panel (A) shows the main effect of Rivalry and its interaction with Need Heterogeneity over a combination of three populations:
cooperators, the general population, and reciprocators. Panel (B) shows the main effect of Need Heterogeneity and its interaction with
Rivalry over the same populations. Panel (C) shows the interactions between Rivalry,Need Heterogeneity, and population. Free rider
populations (not plotted) showed lower performance (<10% in all conditions; p < 00001).
∗∗A change in Rivalry leads to a statistically significant difference in performance for this combination of population and heterogeneity
(p < 00001).
in a future round. So those who cooperate may endure
a constant search for resources, harming performance.
It may appear intuitive that diverse needs promote per-
formance, but, as we noted above, at least one study sug-
gested the opposite: performance benefits from similar
needs as duplicate efforts are eliminated. As we probed
to understand the differing views, we found their source:
the performance consequences of Rivalry and Need Het-
erogeneity are dependent on the levels of each element—
and on the composition of the population.2
Proposition 2C: Rivalry, Need Heterogeneity,
and Cooperation Interact. The performance impact
of each of the three elements is partly dependent on the
other two.
Rivalry can hamper performance (Proposition 2A), but
not always. Performance is affected not only by rivalry
but also by the interaction of rivalry and need hetero-
geneity (Figure 2, panel (A)). For instance, when rivalry
increases from low to high, it can cause major harm or
just a dent in performance, depending on the level of need
heterogeneity. Similarly, need heterogeneity generally
improves performance (Proposition 2B), but its effect
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Levine and Prietula: Open Collaboration for Innovation
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depends on the level of rivalry. When rivalry is high, a
change in need heterogeneity has a vast effect on per-
formance, but when rivalry is low, a change in need
heterogeneity matters little (Figure 2, panel (B)).
These findings suggest that OC can perform well even
with rival goods. They also explain the differing pre-
dictions on the effect of high need heterogeneity: high
rivalry generally undermines performance, but the effect
is mitigated when high rivalry is combined with high
need diversity (see the +line in Figure 2, panel (A)).
On the other hand, because the performance impact of
need heterogeneity is similarly affected by rivalry (see
the ×line in Figure 2, panel (B)), then the combina-
tion of low rivalry and high heterogeneity (similar needs)
begets high performance.
The performance impact of either rivalry or hetero-
geneity also depends on the cooperative composition of
the population (Figure 2, panel (C)). With a Cooper-
ator population, which can be thought of as a group
of altruistic individuals, rivalry hardly affects perfor-
mance (see the “open circle” line in Figure 2, panel
(C)). Even in the general population, which lacks such
a concentration of cooperators, rivalry has just a mod-
erate impact (the “open triangle” line). Performance
drops sharply only when high rivalry coincides with
homogeneous needs (“solid circle,” “solid triangle, and
“solid square” lines). Intuitively, performance suffers
because rivalry affects the cost of contribution. Need
heterogeneity affects the availability of sources. When
resources are rival and needs are similar, cooperating
agents bear a cost and may struggle to find an unused
resource from another. In such an environment, even if
most participants are cooperators, the aggregate gains
from cooperation are small. However, even in such a
harsh environment, all three populations reach perfor-
mance levels of 20%–40%. In some situations, this may
Study III: Open Collaboration Performs Robustly in
the General Population
We now turn to a detailed investigation in a particularly
important environment—the general human population,
composed of 13% Cooperators, 63% Reciprocators, 20%
Free riders, and 4% inconsistent. In the prior study, we
examined just the extreme values of rivalry and need
heterogeneity. Here, we provide a more elaborate inves-
tigation, varying Rivalry and Need Heterogeneity from
0.0 to 1.0 in increments of 0.1 while observing the effect
on performance. Each combination ran 100 times, yield-
ing 12,100 runs.
By examining the interior values, not just the
extremes, we replicate the finding in Proposition 2C:
in the general population, neither element has a simple
effect on performance. Rather, the effect of each is partly
dependent on the value of the other elements.
Proposition 3A: In the General Population, Need
Heterogeneity and Rivalry Interact Nonlin-
early. The performance impact of need heterogeneity is
partly dependent on rivalry, and the performance impact
of rivalry is partly dependent on need heterogeneity.
This nonlinear relationship is evident, for example,
at the highest level of Need Heterogeneity. There, a
decrease in Rivalry does not lead to improvement in
performance (in Figure 3, trace edge from point 1
to 2). In contrast, at the lowest level of Need Hetero-
geneity, a similar decrease in Rivalry leads to a marked
improvement in performance, a rise that begins slowly
but then accelerates (trace edge from point 4 to 3).
And between these two extreme points, the performance
effect of change in Rivalry at a given level of Need Het-
erogeneity is nonlinear (compare some of the lines that
connect points on edge to edge ).
This nonlinear relationship is evident also in the
performance effects of Rivalry over levels of Need
Heterogeneity.3For instance, at the highest level of
Rivalry, an increase in Need Heterogeneity leads to an
increase in performance that is linear (edge in Fig-
ure 3). But at the lowest level of rivalry, a similar
increase in Need Heterogeneity brings no improvement
in performance (edge ). In between, the effect is some-
times linear, sometimes not. To see that, trace the lines
that connect any point in edge to edge : some are
linearly increasing, others are flat.
In the general population, a change in the level of
either element brings a nonobvious impact on perfor-
mance. The combination of low Rivalry, assumed to
benefit performance, and low Need Heterogeneity, often
assumed to harm it, generates high performance (point 3)
Figure 3 Performance of the General Population at Various
Combinations of Need Heterogeneity and Rivalry
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Levine and Prietula: Open Collaboration for Innovation
14 Organization Science, Articles in Advance, pp. 1–20, © 2013 INFORMS
at a level remarkably close to the supposedly ideal com-
bination of high Need Heterogeneity and low Rivalry
(point 2). As we saw earlier, when rivalry is low, changes
in need heterogeneity have little effect on performance.
Another counterintuitive result: the combination of
high Rivalry, assumed to harm performance, and high
Need Heterogeneity, generally assumed to benefit it, gen-
erates performance that is on par with the other combi-
nations (point 1). Once again, only when high Rivalry
is combined with low Need Heterogeneity is perfor-
mance low (point 4). Hence, this leads to the following
Proposition 3B: In the General Population, Ri-
valry and Need Heterogeneity Compensate for
Each Other. When one element undermines perfor-
mance, the other can compensate.
To grasp the underlying mechanisms, consider the fol-
lowing scenarios. As we noted above, when Rivalry is
high and Need Heterogeneity is low (point 4), cooper-
ation is costly and search is often futile. Those who
cooperate can be depleted of resources and fail to obtain
them from others, and so they must turn to costly self-
production. In such an environment, cooperation brings
puny gains. For OC, such an environment may be the
From that low point, an increase in the diversity of
needs (in Figure 3, follow edge from point 4 toward 1)
leads to a performance boost. As Need Heterogeneity
increases, contributions are more likely to boost per-
formance because agents are not seeking identical
resources. Sources are less likely to discover (belatedly)
that a resource they have contributed is needed for their
own tasks. Even as high Rivalry makes each contribu-
tion costlier, gains from collaboration increase perfor-
mance. Thus, performance is so high that decreasing
Rivalry does not improve it much (follow edge toward
point 2). Performance level at the extreme (point 1),
where maximum Rivalry meets high Need Heterogene-
ity, represents some instances of OC in the physi-
cal world, such as Freecycle, where participants share
rival goods, including furniture, clothes, computers, and
office supplies (Willer et al. 2012). Sharing rival goods
may appear improbable, but when participant needs are
diverse, performance is so high that decreasing Rivalry
improves it little (follow edge toward point 2).
Another way to increase performance is by reducing
rivalry. As Rivalry decreases, the cost of contribution
decreases too, even with need homogeneity (in Figure 3,
follow edge from point 4). When Rivalry approaches
zero (point 3) and needs are homogeneous, few
contributors suffice to provide the needs of a large
population. At this point, even if Need Heterogeneity
increases, it has little consequence because performance
is already very high (follow edge ). Performance at the
extreme (point 3), where needs are homogeneous and
rivalry is low, is similar to the sharing of design ideas
by user-innovators. There, performance benefits because
a single good design can be shared cheaply and benefit
a multitude (Baldwin et al. 2006).
There are a few practical implications here. When a
leader faces a combination of high rivalry and low need
heterogeneity (similar participant needs), she should
remember that rivalry decreases performance dramat-
ically once it passes an inflection point (see along
edge ). If the leader attempts to reduce high rivalry,
she will see modest performance gains. A bigger boost
in performance will come from increasing need hetero-
geneity, because its relationship to performance is linear.
On the other hand, if rivalry is low, an increase will lead
to a substantial drop in performance.
Discussion and Next Steps
Open collaboration is a growing source of innovation
and production, perhaps a new organizational form. It
embodies uncommon combinations: goal-oriented yet
loosely coordinated participants who cooperate volun-
tarily to create freely distributed products and services,
creating an economic impact. Innovators, scientists, and
jurists have described the benefits of openness and urged
support for it (Benkler 2011, Henkel and Maurer 2009,
Lessig 2005, Madison et al. 2010, von Hippel 2005a,
Zittrain 2008). Yet, despite the economic and social
impact of OC, its principles are vague and its perfor-
mance remains a puzzle.
As much as scholars have done for firms, we aim
to identify some principles of operation and determi-
nants of performance for open collaboration, whether a
software venture, file sharing collective, or an advice
forum. The model, structured as a typical scientific the-
ory, is akin to other theories of performance (Merton
1967, Sutton and Staw 1995). We begin with micropro-
cesses of individual behavior and proceed to identify
underlying principles, elements that affect performance,
and connections among them. We then focus on the per-
formance of the entire system of innovation or produc-
tion. The theory is phrased as a formal model of human
behavior (DiMaggio 1995), which specifies processes
and uses an agent-based model to generate distributions
of outcomes. A robust theory should also possess pre-
dictive power (Popper 1959, 1963), being able to fore-
tell, for instance, what kinds of goods can be produced
efficiently in open collaboration or where it is likely to
complete with firms. This is what we aim to achieve. The
theory, expressed in the model, marries prior accounts
of OC with recent evidence on human cooperation. We
investigate the performance impact of variations in coop-
eration, rivalry, and need heterogeneity. We show that
OC is a robust engine for innovation and production, one
that performs well even in unfavorable environments.
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Levine and Prietula: Open Collaboration for Innovation
Organization Science, Articles in Advance, pp. 1–20, © 2013 INFORMS 15
The model shows some main effects that serve to val-
idate it and supports assertions made by others: coopera-
tors improve performance (Proposition 1A), rival goods
hurt it (Proposition 2A), and need diversity helps (Propo-
sition 2B). But the model uncovers some novel effects.
First, as cooperators increase, the benefits they bring
taper off (Proposition 1B). Thus, OC can thrive even if
cooperators are just a sliver of the population. Because
a majority of altruists is not essential, OC can prosper
beyond the realm of those who are naturally inclined to
cooperate, such as hobbyists and hackers. The need core
of cooperators can be found even in a random sample of
the general population.
A majority of cooperators is not essential for high
performance, but they aid by reducing variance (Propo-
sition 1C). When reliable performance is needed, as in
industrial production, many cooperators are beneficial.
The two propositions can assist in leaders in designing
and organizing OC ventures. Based on the specific of
the initiatives, a leader can contemplate the benefits and
costs of recruiting cooperators.
As much as assumptions about cooperators can be
relaxed, so are assumptions about free riders. We expand
prior arguments (Baldwin 2008, Baldwin and Clark
2006) by showing that free riders matter little, even
when goods are rival. Because they do little harm, efforts
to sieve free riders seem inefficient. Now we can under-
stand why many OC ventures appear so unconcerned
with free riders.
When we model the role of rivalry and need het-
erogeneity in performance, we find that these elements
interact with each other and with the population. The
performance impact of rivalry and need heterogeneity
varies with the composition of cooperative types in a
population (Proposition 2C). The interaction suggests
again that OC can thrive even when goods are rival.
The impact of rivalry on performance is not simple but
depends on the cooperative composition and need het-
erogeneity in the population. The needs of users are
often diverse, prior research has found. Thus, OC may
perform well in the general population. In this environ-
ment, performance is stable even if rivalry increases (see
general population in Figure 1 and follow edge in Fig-
ure 2). Currently, OC may appear restricted to nonrival
goods, but we propose that it can expand into rival ones.
Further guidance to leaders of OC comes from the
third experiment, simulating the general population.
Need heterogeneity and rivalry interact to create a com-
plex effect on performance (Proposition 3A), so changes
to either one will have a varying impact on performance.
Leaders should tread carefully because changes can be
costly: if a leader attempts to increase performance
by reducing rivalry, for instance, he has to find ways
to abate congestion or reduce contribution cost. But
because the effect of rivalry reduction is not obvious,
he should be aware of the trade-off before acting. Fur-
thermore, performance can be improved without reduc-
ing rivalry or altering need heterogeneity, because either
one can compensate for the other (Proposition 3B). High
performance is possible with high rivalry if the needs
are heterogeneous. And OC can perform well with low
need heterogeneity, depending on the level of rivalry. In
sum, open collaboration can emerge in more places than
currently observed.
The findings serve to explain baffling prior find-
ings, such as the skewed contributions to OC. It is
not a defect, as some observers suspected. The general
population features a handful of people who contribute
unconditionally (cooperators) and two larger groups that
contribute conditionally (reciprocators) or little (free rid-
ers). Disparities in contributions can be expected. The
core of cooperators serves as a “critical mass” (Wasko
and Faraj 2005, p. 52): it sparks contributions from
reciprocators, the largest group. It may be why, when
surveyed or observed, contributors explain their contri-
butions not as paying back to a specific individual, but
rather speak of a generalized reciprocity (Ekeh 1974,
Yamagishi and Kiyonari 2000). As we noted, in their
survey of helpful users, Lakhani and von Hippel (2003,
p. 937) found that many of them generalized reciprocity
as a motivation: “I have been helped before [in these
user forums] 000so I reciprocate” (also see Bagozzi and
Dholakia 2006; Wasko and Faraj 2005, p. 51).
Limitations and Extensions
To build the model, we made assumptions about coop-
eration, search, modularity, diversity, and performance.
Some of the assumptions are consciously cautious, lead-
ing to a likely underestimation of performance (as
detailed in the Cautious Bias in Model section). Others
are merely realistic, based on accounts of open collab-
oration in the literature. Some assumptions are oppor-
tunities for extensions. The findings on cooperation, on
which the model relies, assume that a participant has
information about the contribution trend in the popula-
tion. This is true in many cases, such as when a par-
ticipant observes the growth of a Wikipedia entry, the
multiplying lines of codes in a software project, or the
growing pool of shared files. If a user has only partial
information on others’ contributions, performance may
suffer (Levati et al. 2009). The model requires a simple
index of “who knows what, akin to transactive mem-
ory, to facilitate search. Such an index often emerges
naturally in the minds of participants (Wegner 1986).
It can also be set up easily in the form of a computer
system (Jarvenpaa and Majchrzak 2008), but without
it, performance will suffer (Levine and Prietula 2012).
When it comes to diversity, resource-need heterogene-
ity is one form of diversity that has been hypothesized
to affect performance (Baldwin et al. 2006; von Hippel
2005a, pp. 33–43). Inclusion of other forms of diversity,
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Levine and Prietula: Open Collaboration for Innovation
16 Organization Science, Articles in Advance, pp. 1–20, © 2013 INFORMS
such as skill (Hong and Page 2004) or network struc-
ture (Santos et al. 2008), may be a useful extension. In
the model, performance is defined as substantive ratio-
nality (Simon 1976, pp. 130–131; Weber 1947, pp. 184–
186): efficiency in turning inputs to outputs through
collaboration. Procedural performance measures, such
as how correct or neutral Wikipedia is (Giles 2005,
Greenstein and Zhu 2012), are complementary. Finally,
the model excludes, by design, some elements of open
source that received empirical description elsewhere,
such as recruiting, administration, and governance (e.g.,
Dahlander and O’Mahony 2011, O’Mahony and Bechky
2008, O’Mahony and Ferraro 2007).
Open collaboration is an exemplar of human coop-
eration: people, often strangers, working in concert to
achieve specific goals, even without direct benefits. The-
oreticians regard such ventures as the toughest test
of human cooperation: “The problem of transient and
anonymous exchange is not only a matter of consider-
able practical interest; it is also one of the most theo-
retically compelling social traps” (Macy and Skvoretz
1998, p. 639). Our investigation suggests that com-
mon assumptions about barriers to cooperation are too
gloomy. Some were surprised that OC could thrive on
the Internet, which is devoid of “the social signaling,
cues, and relationships that tend toward moderation in
the absence of law” (Zittrain 2008, p. 130; also see
von Krogh and von Hippel 2003). Here, we show that
open collaboration can thrive without close personal
relationships. Furthermore, it can scale up, countering
predictions that cooperation would break down in larger
groups (e.g., Olson 1965, Raub 1988).
Writing in Science, Vollan and Ostrom (2010, p. 924)
called for more research to explain variation in human
cooperation. We can advance “a behavioral theory of
human action,” they wrote, by “[u]sing multiple methods
to identify the relevant ‘microsituational’ and broader
contextual variables” and linking them to “differences in
behavior and real-world outcomes. For such investiga-
tions, we suggest, open collaborations can be natural lab-
oratories for field studies and experiments. For instance,
researchers utilized Wikipedia to show how group size
is directly related to voluntary contributions (Zhang and
Zhu 2011).
Where can open collaboration thrive? For the perfor-
mance of open collaboration, hard-to-find cooperators
are not all important, and free riders will not necessar-
ily doom performance. Open collaboration can withstand
both rivalry and a lack of diversity in needs. It will per-
form surprisingly well even with a bunch of ordinary
people. It can thrive far and wide, we suggest.
Supplemental Material
Supplemental material to this paper is available at http://dx.doi
The authors are thankful to Carliss Baldwin, Gregory
Berns, Ramon Casadesus-Masanell, Eric von Hippel, Lars Bo
Jeppesen, Chengwei Liu, and Sarah M. G. Otner for their
comments. The authors also thank Ian Lim for his research
support. The authors acknowledge the comments received at
the following conferences: American Sociological Associa-
tion 2006 in Montréal; Academy of Management 2008 in
Anaheim, California; User and Open Innovation workshop
2009 in Hamburg; Mid-Atlantic Strategy Colloquium 2010 at
the University of Maryland; Open Source Software 2010 in
Notre Dame, Indiana; Israel Strategy Conference 2010 at the
Technion; Society for the Advancement of Socio-Economics
2012 in Cambridge; and Academy of Management 2012 in
Boston. The first author thanks the Sloan School of Manage-
ment of the Massachusetts Institute of Technology, where the
manuscript was written. The second author acknowledges a
Summer Research grant from the Goizueta Business School,
Emory University; discussions at the Human Social, Culture
and Behavior Modeling Program meetings of the Office of
Naval Research; and a grant from the Air Force Office of Sci-
entific Research through the Office of Naval Research [Grant
1The overall main effects for Rivalry and Need Heterogene-
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4171102 (p < 00001), respectively. The interaction was
F 411115845=91289 (p < 00001), and a post hoc Tukey analy-
sis indicated that all means differed significantly (p < 00001).
2Rivalry by Need Heterogeneity by Cooperative Types interac-
tion: (F 431115845=1149104, p < 00001).
3A linear model is not a perfect fit for either effect, although
it fits Need Heterogeneity over Rivalry values better (r2=
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(r2=00149). Nonlinear regression fitting Rivalry over Need
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