Open Collaboration for Innovation: Principles and Performance

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Open Collaboration for Innovation: Principles andPerformanceSheen S. Levine, Michael J. Prietula

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Open Collaboration for Innovation:Principles and Performance

Sheen S. LevineColumbia University, New York, New York 10027, [email protected]

Michael J. PrietulaEmory University, Atlanta, Georgia 30322, [email protected]

The principles of open collaboration for innovation (and production), once distinctive to open source software, arenow 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 affectingtraditional firms and may represent a new organizational form. Despite the impact of such ventures, their operating principlesand performance are not well understood. Here we define open collaboration (OC), the underlying set of principles, andpropose that it is a robust engine for innovation and production. First, we review multiple OC ventures and identify fourdefining principles. In all instances, participants create goods and services of economic value, they exchange and reuse eachother’s work, they labor purposefully with just loose coordination, and they permit anyone to contribute and consume. Theseprinciples 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 recentevidence on human cooperation. We identify and investigate three elements that affect performance: the cooperativeness ofparticipants, the diversity of their needs, and the degree to which the goods are rival (subtractable). Through computationalexperiments, we find that OC performs well even in seemingly harsh environments: when cooperators are a minority, freeriders are present, diversity is lacking, or goods are rival. We conclude that OC is viable and likely to expand into newdomains. 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 ofhobbyists and hackers, it has gained acceptance withconsumers, corporations, and governments. Some exem-plars of open source, such as the Linux and Androidoperating systems, are now commonplace, operatingmillions of devices. Together with other products ofopen source software, they have been creating billions ofdollars in economic value (European Commission 2006).

Yet the same patterns of collaboration, innovation, andproduction can now be found beyond software (Baldwinand von Hippel 2011, Benkler 2006, von Hippel 2005b).For example, people collaborate, sometimes with com-plete strangers, in user-to-user forums (Lakhani and vonHippel 2003), mailing lists (Jarvenpaa and Majchrzak2008), and online communities (Faraj and Johnson2010). Some share openly (and occasionally illegally)digital media: music, movies, TV programs, and soft-ware (Levine 2001). People also share processing powerand Internet bandwidth, enabling free services such asSkype (Benkler 2006, pp. 83–87). In the physical world,off the Internet, people give, receive, and share tools andappliances (Goodman 2010, Nelson et al. 2007, Willeret al. 2012), even host strangers overnight (Lauterbachet al. 2009, Perlroth 2011)—all without payments or

barter. Such ventures exemplify what we call open col-laboration (OC), a shorthand inspired by Baldwin andvon Hippel (2011). Here, we define its principles andexplore its performance.

Firms have been affected by open collaboration,some negatively, others positively. The free encyclopediaWikipedia, a prime example of such collaboration, hascome to match the quality of Encyclopædia Britannica(Giles 2005), which, after 244 years in circulation, hasceased printing. Other firms have been thriving by facil-itating open collaborations, hosting forums and commu-nities. This is how firms such as Amazon, an Internetretailer, and TripAdvisor, a review site for hotels andrestaurants, established a flow of “user-generated con-tent”: reviews, advice, photos, and video clips. Fellowusers may benefit from such information, and the firmseconomize on wage-free, royalty-free content (Chevalierand Mayzlin 2006, Mudambi and Schuff 2010). Theeffect of open collaboration may be nascent, but it hasalready required established firms to tweak their strategy,operations, and marketing (Chen and Xie 2008, Scottand Orlikowski 2012). It also has an impact outside thecommercial world.

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Levine and Prietula: Open Collaboration for Innovation2 Organization 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 ofvolunteers, each contributing just a fraction of a solu-tion, have been discovering and solving problems tooimmense for traditional organizations (Benkler 2004,Partha and David 1994). Contributors classify celestialobjects in Galaxy Zoo, decipher planetary images in theMars Public Mapping Project (Benkler 2006, p. 69), andlabor over terrestrial maps (Helft 2007). Similar patternsthat are labeled with the adjectival “open” have appearedin medicine (Ortí et al. 2009, Rai 2005), engineering(e.g., “open design”), and biotechnology (Henkel andMaurer 2007, 2009). In scientific publication, the openscience movement aims to disperse authority and expandcollaboration (Lin 2012).

Scholars have been attracted to these novel patterns ofinnovation and production. Open source software, a har-binger of open collaboration, was the topic of severaledited volumes (e.g., Feller et al. 2005, West and Gallagher2006) and special issues of journals (von Krogh andvon Hippel 2003, 2006). The use of “open source” asa scholarly term has been growing dramatically, fromjust 32 appearances in 1999 to 687 times a decade later(see the electronic companion, available as supplemen-tal material at http://dx.doi.org/10.1287/orsc.2013.0872).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), peerproduction (Benkler 2002), a community-based innova-tion system (Franke and Shah 2003), Wikinomics, andmass collaboration (Tapscott and Williams 2006), aswell as instances of collaborative communities (Adleret al. 2008), transaction-free zones (Baldwin 2008),crowdsourcing (Afuah and Tucci 2012), collaborativeconsumption (Goodman 2010), electronic networks ofpractice 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 areviable—remains a puzzle. We begin by identifying somedefining principles: a system of innovation or productionthat relies on goal-oriented yet loosely coordinated par-ticipants who interact to create a product (or service) ofeconomic value, which is made available to contributorsand noncontributors alike.

Next, we examine performance. We investigate severalelements that affect the performance of open collabora-tion. One element is cooperative behavior of contributorswho 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 notguaranteed. 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 recentevidence of human cooperation to answer a fundamentalquestion: Why do people share the fruits of cooperationwith noncontributors?

Cooperativeness may be the life-giving element ofOC, 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 whichthey have heterogeneous (diverse) needs. Another a char-acteristic of the goods: rivalry (or subtractability; seeHess and Ostrom 2006), which is the extent to whichone’s consumption of a good interferes with another’s.

To assess the performance of OC, we combinethe three elements—cooperativeness, need heterogene-ity, and rivalry—in an agent-based model. Because weenvision OC as a general system for innovation and pro-duction, we use a fundamental measure of economicperformance: efficiency in turning inputs to outputs.

We find surprising results: OC can thrive even inseemingly harsh environments, reaching much morebroadly than observers assumed or observed. It performsrobustly 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 ofOC and—equally important—what does not. Some con-ditions are commonly assumed necessary but they arenot. First, OC can thrive even if participants are not anexclusive bunch of cooperators but just a random sam-ple from the human population, where cooperators area 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 theresources shared are rival or when participant needs arehighly similar. It can perform well even with rival goodsor homogeneous needs; performance suffers only whenthe two are concurrent. The model also explains someintriguing observations, such as the extreme disparity incontributions to OC, where a small core contributes themost and many contribute little.

The findings imply that OC is likely to grow andspread into new domains. They also inform the dis-cussion on new organizational forms, collaborative andcommunal (e.g., Adler et al. 2008, Benkler 2011, Farajet al. 2011, Heckscher and Adler 2006). Human efforts,it seems, can be harnessed in previously unthought-ofof ways: by relying on goal-oriented yet loosely coor-dinated participants who interact to create products ofeconomic value, which they then offer to anyone—contributors and free riders alike.

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Levine and Prietula: Open Collaboration for InnovationOrganization Science, Articles in Advance, pp. 1–20, © 2013 INFORMS 3

Open Collaboration: Definition, Benefits,and PuzzlesOpen Collaboration DefinedWe define open collaboration as any system of innova-tion or production that relies on goal-oriented yet looselycoordinated participants who interact to create a prod-uct (or service) of economic value, which they makeavailable to contributors and noncontributors alike. Thisdefinition 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 sourcesoftware project, in Wikipedia, or in a user forum orcommunity. They can also be present in a commercialwebsite that is based on user-generated content. In all ofthese instances of OC, anyone can contribute and any-one can freely partake in the fruits of sharing, whichare produced by interacting participants who are looselycoordinated.

Economic value is manifested by the substitution offor-profit services, such as commercial software, Ency-clopædia Britannica, or technical consultants. Value isalso 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 modelElements Description manipulation Theoretical and empirical referents

Create goods ofeconomic value

The main purpose is thecreation of productsand services ofeconomic value.

The dependent variable is afundamental measure ofeconomic performance:efficiency in turning inputs tooutputs.

Benkler (2002, 2006), Cooley (1909),Shah (2005), von Hippel andvon Krogh (2003), von Krogh andvon Hippel (2003)

Open access tocontribute—andconsume

Participants can freelycontribute but alsoconsume, regardless oftheir contribution.

Any agent can contribute orseek others’ contributionswithout exclusion.

Faraj and Johnson (2010), Kollock(1999), Lakhani and von Hippel(2003), Raymond (1999), von Kroghand von Hippel (2003), Zeitlyn (2003)

Interaction andexchange is centrala

Participants interact,exchange, and reuseeach other’s work, allwhile engaging in ownwork.

Agents search and engageothers to transfer resources.

Anthony et al. (2009, p. 283), Benkler(2004, p. 1110), Faraj and Johnson(2010), Faraj et al. (2011), Häfligeret al. (2008, p. 180), Jeppesen andLakhani (2010), von Krogh et al.(2003)

Participants laborpurposefully yetloosely coordinated

Coordination, structure,and hierarchy areemergent and lessspecified than in otherorganizational forms(e.g., firms, cooperatives).

Agents have individualistic goalsfor resources and coordinateonly when engaging inexchange events. There is nopredefined structure orhierarchy.

Dahlander and O’Mahony (2011), Kuk(2006), Lee and Cole (2003),MacCormack et al. (2006), Mockuset al. (2005), O’Mahony and Ferraro(2007), Shah (2006), von Krogh et al.(2003)

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, inmany 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 anecessary 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 anartifact, it is a measure of collective performance.

with online reviews (Chen and Xie 2008, Chevalier andMayzlin 2006, Mudambi and Schuff 2010, Scott andOrlikowski 2012).

The definition also delineates what OC is not. Itexcludes traditional firms (because of the lack of openaccess or loose coordination) and markets where individ-ual agents work independently (no open access and/orno interaction). It also excludes primary groups (Cooley1909) such as social clubs and some online communi-ties, where the raison d’être is social interaction, notinnovation or production.

The Benefits of Open CollaborationOC provides unique benefits to participants, firms, andsociety. One benefit is the ability to build on others’work in a direct way, because contributors can directlyview the architecture of the product (Kumar et al. 2011,MacCormack et al. 2006). In software, it means thatthey can reuse code, economizing on skills, time, andcost (Häfliger et al. 2008). Contributors can also interactdirectly with others, many of them strangers, to shareand integrate knowledge and other resources. Such vastexchange characterizes OC, whether in Wikipedia, anInternet user forum, or a mailing list. However, unlikein a traditional organization, here contributions come

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from many casual participants, who give as much asthey wish, with no up-front commitment (Benkler 2002,Jeppesen and Lakhani 2010). Such casual contributionscan lead to better products: in Wikipedia, the most reli-able contributions are made not by the few prominentwriters but by the many who contributed only once(Anthony et al. 2009, p. 283). Open source software hasbeen 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 sourceof 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 costsby pooling or externalizing them (West and Gallagher2006), enjoy higher customer satisfaction (Franke andvon Hippel 2003), and benefit from more favorablebeliefs and greater trust from users (Dahlander andWallin 2006, Porter and Donthu 2008).

For society, OC can not only improve economic andsocial welfare (Bambauer and Day 2011, Benkler 2004,Maurer and Scotchmer 2006, Strandburg 2009) but alsoheighten morality: “Foster virtue by creating a contextor a setting that is conducive to virtuous engagement andpractice” (Benkler and Nissenbaum 2006, p. 403).

PuzzlesOC offers many benefits, impacts economically andsocially, but it is not well understood. Extant theoriesof firm-based and market-based innovation are unfit-ting. Although OC may complement firm-based andmarket-based innovation, it differs greatly from them(Baldwin and von Hippel 2011, Lee and Cole 2003,von Hippel and von Krogh 2003). Differences canbe found in the motivation to participate, organiza-tion, and governance (Dahlander and O’Mahony 2011,Faraj et al. 2011, O’Mahony and Ferraro 2007, Shah2006); product design and production (MacCormacket al. 2006, Mockus et al. 2005); and market behavior(Casadesus-Masanell and Ghemawat 2006, Economidesand Katsamakas 2006).

Because OC appears so different from other sourcesof innovation, some pundits dismissed it as an oddity.For instance, a prominent practitioner opined that opensource software “goes against the grain of everythingI know about the software field”; therefore, “any change[it fosters] will be limited to one or a few cults emergingfrom a niche culture” (Glass 2000, pp. 104–105). Such aview 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 andstable (Ouchi 1980, p. 129). In all of them, participantsband to produce jointly, but those who share the benefitsmust share the costs (Cornes and Sandler 1986, p. 159).In such arrangements, only contributors may consumethe fruits of cooperation, and they endeavor to deter freeriders. 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 usersabuse it (Hardin 1968, Olson 1965).

In contrast, OC operates with vague membership andporous boundaries, where anyone can consume, contrib-utor or not. As emphatically open as it is, OC is evenmore puzzling than Hardin’s pastoral illustration. Unlikein a pastureland, here a secure fence can be built and asign can be hung above the bolted gate: “Members only.”Such exclusion of noncontributors is especially easy onthe Internet; it can be done simply by screening usersto allow contributors and block free riders. But in manyinstances of OC, numerous users free ride. Even con-tributors vary widely in their efforts. Empirical accountsrepeatedly show that few participants provide much ofthe work, others contribute occasionally, and many oth-ers contribute little or nothing (Anthony et al. 2009, Kuk2006, Lancashire 2001, Lerner and Tirole 2005, Madeyet al. 2004, Mockus et al. 2005). But despite free rid-ing and vastly unequal contributions, collaboration doesnot collapse. The major contributors do not revolt. “Whowould ever have imagined that innovation could flourishunder conditions like those?” wondered von Krogh andvon Hippel (2006, p. 975).

The performance of OC has been a puzzle for partici-pants and scholars alike, and a variety of conjectures andexplanations have been proposed. An early participantand 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 maybe based on reciprocity, which he called “cooking potmarkets,” where participants simultaneously contributeand consume others’ contribution. Ghosh ruminatedabout other possible explanations, including a diversityof needs and contributions, reputation, and the nonrivalnature 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 fromfreely revealing an innovation. Revealing may be lucra-tive thanks to complementarities and diffusion of the

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innovation (e.g., Harhoff et al. 2003, Henkel 2006),which can concur with private benefits such as enhancedprofessional prestige (Lerner and Tirole 2002), learning,and pleasure (von Hippel and von Krogh 2003).

The theoretical model we present here, which focuseson 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., diverseneeds), and characteristics of the goods (e.g., nonrival).By focusing on behavior, it complements prior explana-tions that turn on intentions and motivations, which aremore difficult to ascertain than behavior. For instance,cooperative behavior can stem from immediate privategain, but it can also come from altruism or social move-ment ideology (O’Mahony and Bechky 2008). More-over, cooperation often emerges even without immediateprivate gains, such as when contributions are unglam-orous or even anonymous, such as when participantsrespond to others’ questions in a user forum (Lakhaniand 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 centralelement in the performance of OC: cooperation. Theperformance of OC is also affected by two other ele-ments: the diversity (heterogeneity) of participants andtheir needs, and the rivalry of goods and resources. Wediscuss these two elements in turn, followed by the

Table 2 Performance-Related Elements of Open Collaboration

Corresponding Theoretical andLevel of analysis Element Description model elements empirical referents

Individual Cooperativeness(Study I)

Individuals can be typified bytheir tendency to cooperate.When individuals areaggregated, the compositionof cooperative typesdistinguishes one populationfrom another.

Agents are defined ascooperative types in termsof likelihood to contribute.Agent populations aredefined by the distributionof cooperative types inthem.

Kurzban and Houser (2005),Lerner and Tirole (2005),Mockus et al. (2005), Shah(2006, p. 1005)

Group Diversity in needs(Studies II andIII)

Participants can have similar ordiverse needs, which impliesthat they differ on theresources they seek.A resource can be desired bymany or few.

Need heterogeneity, theextent to which agentshave dissimilar needs andtherefore seek a differentresource, is varied fromhomogeneity toheterogeneity.

Anthony et al. (2009), Baldwinet al. (2006), Jeppesen andLakhani (2010), von Hippel(2005a, p. 33)

Goods Rivalry(subtractability)(Studies II andIII)

Goods differ in the extent towhich one’s consumptionaffects another’s; e.g., aperfectly rival good diminishescompletely when used orcontributed.

The extent to which aresource is a rival goodvaried from nonrival tocompletely rival.

Baldwin and Clark (2006),Cornes and Sandler(1986), Hess and Ostrom(2006), von Hippel andvon Krogh (2003)

model, which incorporates all three elements (detailedin Table 2).

What Affects the Performance of Open Collaboration:The Human Tendency to CooperateCooperation is central for open collaboration, and itoften takes the form of reciprocity (i.e., “I contributebecause I benefited from others’ contributions”). In anearly study, users who helped others with technicaladvice were asked to complete the following sentence:“I was motivated to answer because 0 0 0 0” A variety ofmotivations were reported, but the top three reasons weresimilar. They all stressed reciprocity: “I help now soI will be helped in the future,” “I have been helpedbefore [here]—so I reciprocate,” and “I have been helped[elsewhere] before—so I reciprocate” (Lakhani and vonHippel 2003, p. 937). Indeed, recent evidence suggeststhat people generally cooperate and reciprocate evenwithout direct benefits. Benefiting from others’ contri-butions can trigger one’s contribution. We turn to reviewthe evidence accumulated in evolutionary biology, eco-nomics, and psychology.

Early theoreticians presumed that, without safeguards,cooperation would be displaced by free riding. Theprevalent 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 elsethey never would have contributed.

However, self-interest is common but not omnipresent.Scholars, including economists, have long questioned

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whether humans are truly defined by narrow self-interest(e.g., Dawes and Thaler 1988, Sen 1977). In recentyears, a more nuanced picture has emerged. Experimentsin the laboratory and the field have demonstrated thathumans are willing to collaborate, bear costs, and deferself-interest for the greater good. Reviews concluded thathumans are “super cooperators” (Nowak and Highfield2011) and “human altruism is a powerful force” (Fehrand Fischbacher 2003, p. 785). Altruism and coopera-tion are central to human existence and have been fun-damental in our evolution, distinguishing humans fromother species (Alexander 1987). Cooperation can per-sist without contracts or quid pro quo. As the earlyaccounts of open source hinted, cooperation can emergeeven without direct reciprocity, i.e., a future gain fromthe beneficiary (Baker and Levine 2013, Fehr andFischbacher 2003).

Aiming to quantify the frequency and distribution ofcooperation and reciprocity, Kurzban and Houser (2005)used an elaborate experimental design to study cooper-ation between and within individuals. They found thatpeople are consistent in the extent of their cooperationin different situations. People’s behavioral types are sostable as to allow an accurate prediction: “A group’scooperative outcomes can be remarkably well predictedif one knows its type composition” (Kurzban and Houser2005, p. 1803). The general human population has beenestimated to consist of three cooperative types:

1. Cooperators (13% of the general population) con-tribute to others at a cost to themselves, uninfluenced byothers’ contribution. This behavior resembles pure altru-ism, one suggested cause of OC.

2. Reciprocators (63%) may contribute to others at acost to themselves, but only insomuch that others arealso 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 andSandler 1984) contribute significantly less than others(but usually more than nothing). This behavior fits theimage of a narrowly self-interested individual.The remaining 4% are too inconsistent to be categorized.

The findings have been replicated, for example, innon-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 thepreference heterogeneity among participants and the pre-ponderance of conditional cooperators [reciprocators]”(Chaudhuri 2010, p. 77).

Reciprocators are of special importance, not onlybecause they are the largest group in the humanpopulation (see also Fischbacher and Gächter 2010,Fischbacher et al. 2001) but also because they matchthe behavior of those around them. Whereas cooperatorsand free riders behave predictably, reciprocators mimicthose 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 adjustyour behavior so that you benefit others alike; if youwere exploited, you reduce contributions. Such virtuouscycles 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, Nowakand Roch 2007, Nowak and Sigmund 1998). The spreadof cooperation has been documented empirically (Boltonet al. 2005, Fowler and Christakis 2010, Weber andMurnighan 2008).

What Affects the Performance of OpenCollaboration: Diversity and RivalryCooperation 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, anddiffer 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 acrossa variety of products and services: people’s needs areoften 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 andRossi 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 samegood; 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 heterogeneityis necessary for realizing gains from trade: exchange islucrative if people have nonoverlapping demands (Kemp1987). Otherwise, why trade?

Although it seems plausible that need heterogeneityincreases performance, at least one study suggested theopposite (Baldwin et al. 2006). It described users whoshared knowledge, modeled as a nonrival good, andfound that homogeneous user needs led to better perfor-mance. Similar needs, it was argued, allow users to solvea problem only once and share the solution, avoidingduplicate efforts. In our experiment, we consider mainand interaction effects separately, so we can show howthese seemingly differing propositions about diversityand performance are actually compatible.

Rivalry of Goods and Resources. A good is definedas 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. Fewgoods 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 itappeared minuscule, because a contributor could keepthe software while providing a perfect copy to some-one else. Because one’s benefit does not interfere withanother’s, “you never lose from letting your productfree,” exclaimed Ghosh (1998). Thus, nonrivalry becamecentral in the discussion of open source software, anearly instance of OC. Nonrivalry was frequently cited indiscussing and modeling of the phenomenon (Baldwinet al. 2006; Harhoff et al. 2003, pp. 1759–1767). Somescholars have argued that OC performs well because itproduces “antirival goods” (Weber 2004), which benefitthose who share them.

But others have recognized that OC can involve goodsthat 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 aneffort, then even software may not be the pure nonrivalgood it seems (Baldwin and Clark 2006, Marengo andPasquali 2010).

To accommodate a variety of goods and resources,we refrain from modeling them as binary: entirely rivalor nonrival. Instead, we employ a continuous rangeof rivalry. The results suggest that rivalry matters lessthan expected. We find that OC can perform even withrival goods, let alone with goods that are nonrival or“antirival.”

Modeling the Performance ofOpen CollaborationComputational models are a useful method to studycomplex social and organizational phenomena (Burtonand 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 pivotalstudies in organizational theory (e.g., Cyert and March1963, Levinthal and March 1981, Nelson and Winter1982). By building an agent-based model, a form ofcomputational model, we heed Benkler’s (2002, p. 424)call to study OC through “artificial life-type modeling,”and we extend earlier models (Bonaccorsi and Rossi2003, 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 ascontagious 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 ruleswhile observing interactions and outcomes, unravelingprocesses that are unobservable (or nonmanipulable) inthe field or the laboratory.

To identify salient elements of OC, we reviewedempirical accounts and studied theories in organizationaltheory, economics, sociology, and psychology, many ofwhich we review above. We used these sources to decidewhich elements should be modeled. We then constructedthe model: abstracting from human behavior, we phrasedrules to describe, parsimoniously and unambiguously,how agents behave. We then conducted computationalexperiments, varying conditions and observing agentbehavior and its outcomes.

The model involves a setting common in many OCinstances: a crowd of agents is working on somewhatsimilar tasks. To accommodate a broad variety of phe-nomena, we focus on behavior and assume little aboutthe source of the tasks or the agents’ motivation. Thetasks can be equally thought of as assigned by an admin-istrator, emerging from intrinsic motivation, advancingself-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 personaltasks. While pursuing its own tasks, an agent may bewilling to assist others by providing resources, but onlyif asked and in extent commensurate with its cooper-ative type (cooperator, reciprocator, or free rider). Toremain 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 manysettings where agents can complete their tasks indepen-dently but benefit from cooperation, which allows themto 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 equivalentof 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 asoftware project (e.g., O’Mahony and Ferraro 2007), butit can also describe a crowd, where each pursues hisown needs, as in online communities or forums (Baldwinet al. 2006, Faraj and Johnson 2010, Lakhani and vonHippel 2003). The following overview of the model iscomplemented 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 100agents, 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 towhich a given task requires specific (as opposed to fun-gible) resources. The other, Rivalry, affects the cost thatan agent bears when cooperating. The tasks are inde-pendent, so the agents work asynchronously. A task iscompleted when an agent attains a sufficient level ofa resource needed for a task. For instance, a resourcecould be knowledge of a computer language and a taskcould be the writing of a specific computer code. Anagent can access a resource in three ways: producingthe resource through work (the costliest option), receiv-ing it from another, or already possessing the resource.For instance, one can obtain knowledge about computerprogramming (a resource) by learning-by-doing (self-production), receiving help from a peer (cooperation), orusing knowledge gained previously (prior possession).

Dynamics. Each agent starts with a set of randomlyendowed resources and a set of randomly assigned tasks.Each agent also has a cooperative type:

1. The model iterates over periods, where each canrepresent any fixed time length (e.g., hour, day, week).In each period, each agent can work on its assigned tasksand interact with other agents.

2. An agent first attempts to complete its tasks byusing the resources it already possesses. But if it does nothave a needed resource, the agent (hereafter, “seeker”)seeks it by looking for another that has the resource(hereafter, a “source”). Because cooperation is cheaperthan self-production, a seeker economizes by obtaininga resource from another. In searching for a source, theseeker relies on a simple index that shows “who knowswhat” (Levine and Prietula 2012), a representation oftransactive memory (Hollingshead 2001, Jarvenpaa andMajchrzak 2008, Lewis et al. 2005, Wegner 1986).

3. If a potential source is found, the seeker requeststhe resource it needs.

3.1. Whether and how much of the resource is pro-vided is determined by the source’s cooperative type andavailability (see items 3.4 and 4 below), neither of whichthe seeker knows beforehand.

3.2. An agent may provide to another any resourceit holds, regardless of whether the resource was obtainedthrough self-production, transfer, or prior possession.Thus, although agents produce only for their own use,never by request of another, they may transfer resourcescreated for own use.

3.3. If the source obliges, it bears a cost that isproportional to the Rivalry of the resource it contributes.The seeker can immediately use the contributed resourcefor 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 resourceor inquires all of the agents, whatever comes first. Ifunsuccessful, the seeker begins self-production of the

resource. During production, the agent is unavailable forother tasks or requests.

4. When an agent completes all of its tasks, it be-comes dormant but responds to requests. When allagents complete their tasks, the run is complete.

Cooperation. Axiomatically, it is always cheaper toachieve a resource through cooperation than throughself-production. Cooperation is desirable because thebenefit to the beneficiary is greater than the cost to thebenefactor. Cooperation increases welfare because it isa positive-sum, not a zero-sum, game. People cooperatewhen it leads to an advantage; people do not cooperateif they can achieve the same outcome by working alone(Axelrod 1984, von Neumann and Morgenstern 1944).For instance, Mark saves times by asking Rosemarie, acolleague, for help with graphics software. If Mark couldhave obtained the same knowledge quicker by readinga manual, he would not have asked for help. Becausecooperation can economize on cost, people’s ability tocooperate improves their collective performance. Thiseffect is captured in the definition of performance.

Performance. If OC is to serve as a general system ofinnovation and production, the performance of the entirepopulation matters. Therefore, we do not measure theperformance of an individual agent but the cumulativeperformance in using resources to complete tasks. Even-tually, all tasks are completed, some through cooperation(Tc) and some through self-production (Ts). We simplydefine performance (P ) in terms of the proportion con-tributed through cooperation:

P = Tc/4Tc + Ts50

The divisor is determined by the number of agents andthe number of tasks allocated to each. In each run, theagents complete 10,000 tasks, a quantity that constitutesa fixed goal. The dividend is a measure of input, and itis sensitive to cooperation. Cooperation economizes oninputs, because when an agent completes a task throughcooperation, assisted by another’s resources, it spendsless. Thus, when the dividend increases, it means thata given outcome was achieved using fewer resources orthat the same resources have accomplished a greater out-come. Because the divisor is fixed, a greater dividendmeans higher performance.

Performance here can be thought of as a measureof productivity: how much in resources is required toaccomplish 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 productivityand procedural rationality (Simon 1976). This measureaccounts for both outputs and inputs, so it allows com-parison across conditions.

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This measure of performance also accounts for thepotential drawbacks of cooperating (Levine and Prietula2012). For example, when a seeker obtains a resourcefrom a source, the seeker saves time and effort, but thesource may suffer. When providing a resource that is rival(to any extent), the source loses at least some of it. If thesource needs this resource in a future period, the sourcemust obtain it anew by seeking or producing it. This canbe a drag on collective performance. Another potentialdrag is wasted effort: an agent can spend time searchingfor a resource but ultimately fail to obtain it because it isheld by free riders who would not provide it.

Modeling the Elements that Shape Performance

Cooperative Types. A population can be composed ofcooperative types in varying ratios. Kurzban and Houser(2005) estimated the prevalence of the three cooperativetypes in the general population, but one can imagine sub-populations with differing ratios, such as a handpickedgroup 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 whichwe 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, determinedby the agent’s type, and some variation (Kurzban andHouser 2005, pp. 1804–1805). When asked to provide aresource, a Cooperator agent contributes with a base ratethat is at least half its own endowment. The exact pro-portion is determined for each transfer by a draw froma Gaussian distribution (� = 0075, � = 00125). Whenfaced with a similar request, a Free rider agent con-tributes strictly below half of its own endowment. Theexact proportion is similarly determined by a draw froma Gaussian distribution (�= 0010, � = 0005). Reciproca-tor agents adjust their behavior to match the populationtrend. The trend is determined by comparing the pop-ulation contributions (�) in the prior two periods (�p−1and �p−2). If the population contribution is decreasing(�p−1/�p−2 < 1), then Reciprocators act as if they wereFree riders; if it is nondecreasing (�p−1/�p−2 ≥ 1), theseagents act as Cooperators. Reciprocator behavior is ran-dom in the first two periods, after which a trend isestablished.

Resource-Need Heterogeneity 4Diversity5. We varythe Need Heterogeneity of the population by varyingthe tasks assigned to the agents thereby varying theirresource needs. An agent’s needs are determined bythe tasks assigned to it. Need Heterogeneity rises whenthe agents in the population aim to complete differenttasks; it is maximal when all agents are assigned com-pletely different tasks. Need Heterogeneity drops whensome 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 uniformdistribution (see Table EC1 in the electronic companion).

Rivalry. Recall that the model allows for a gamut ofrivalry, not just a binary state. Whenever an agent usesa resource or contributes to another, the quantity of thatresource decreases according to its degree of rivalry. Forexample, if a source contributes a nonrival resource, itsuffers no loss; but if it contributes a resource that is50% rival, then the source’s stock of that resource ishalved.

Cautious Bias in Model. The model is based on cau-tious assumptions. We likely underestimate performancebecause 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 andGächter 2000, Gächter et al. 2008, Robins and Beer2001), 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 thatare “antirival” (Weber 2004). Also, performance is likelyunderestimated because the model does not presumeperformance-enhancing institutions such as administra-tion, coordination, or division of labor. Each agent pro-duces resources based on its tasks only.

Computational Studies and ResultsWe begin by investigating how performance is affectedby the composition of cooperative types in the popula-tion (Study I). Next, we examine the performance con-sequences of rivalry and need heterogeneity in severalpopulations (Study II). Finally, we study how perfor-mance is affected by rivalry and need heterogeneity inthe most relevant environment: the general population(Study III).

Study I: How Cooperation Affects the Performanceof Open CollaborationKurzban and Houser (2005) identified the distributionof cooperative types in the general population. But,as noted, the composition can be different in somegroups—or can be made different. For instance, leaderscan interview prospective participants to sieve cooper-ators from the others (Gächter and Thöni 2005, Pageet 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 screenspotential participants to admit cooperators, she must bearcosts: for example, conduct interviews, administer tests,follow on referrals, review work (Baldwin 2008). Simi-larly, if one tracks reputation or facilitates punishment toexpose free riders, one has to build suitable mechanisms.Here, we explore questions about the optimal investmentin promoting cooperation: Are more cooperators alwaysbetter or is there a point of decreasing returns? How doesone decide how much to invest in bettering cooperation?

Because this experiment focuses on the effects ofcooperative types, other elements were held constant.Rivalry was set to “none" by defining all resourcesas nonrival. Need Heterogeneity was set to "high" bydefining all tasks as completely unique, thereby ensur-ing that agents do not compete for resources. Weinvestigated 144 population compositions: the propor-tion of Cooperators was varied between 12 points(1%15%110%120%1 0 0 0 1100%). The Cooperator pro-portion was fixed at each point, and the remainder ofthe population was varied from all Reciprocators to allFree riders in 12 steps (99%195%190%180%1 0 0 0 10%).In all of the studies, the number of replications was suchthat we could detect absolute effect sizes at �= 0005 forall main and interaction effects with power of at least0.80 (Lenth 2001). In this study, we repeated the exper-iment with 100 replications in each step for a total of14,400 runs.

The results are plotted as performance means for eachlevel 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

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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 whenCooperator 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 intuitionand supports the validity of the model.

Proposition 1A: Cooperators Improve Perfor-mance. More Cooperators bring better performanceover a range of population compositions.

Although the performance plot is generally increas-ing, there is a distinct concavity in the graph. The resultsshow that higher Cooperator proportion improves perfor-mance but at a decreasing rate. Large gains occur at thelowest levels of Cooperators, e.g., when the proportionincreases from 1% to 5% (see the bar graph in Figure 1).

Proposition 1B: The Benefits of CooperatorsAre Diminishing. Increasing the proportion of Cooper-ators has a diminishing marginal effect on performance.

Therefore, costly efforts to increase cooperation, suchas by screening participants or setting incentive schemes,may be counterproductive. Efforts to increase cooper-ation can backfire, reducing overall performance. Theoptimum depends on the population composition and thecost of increasing cooperation. For instance, the generalpopulation, with its average of 13% Cooperators, canreach a performance level of above 50%, whereas popu-lations with vastly more cooperators show just an uptickin 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 narrowingconfidence intervals (see Figure 1).

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Proposition 1C: Cooperators Reduce Variation.More Cooperators reduce the effect of other types onperformance.

Cooperators benefit performance by decreasing vari-ation, thereby increasing predictability. To understandthe mechanism behind the stabilizing effect of coop-erators, we tracked the change in performance as wefixed the proportion of Cooperators and varied the ratiobetween Reciprocators and Free riders. This follow-upexperiment also serves as a sensitivity analysis: it tellshow the results are affected by shifts in the proportionsof 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 ofFree riders (increasing from left to right). Not surpris-ingly, performance decreases with fewer Reciprocatorsand more Free riders. However, the effect of Free ridersis nonlinear—when few, they hardly affect performance.For instance, when the ratio of Free riders jumps from0% to 40%, performance drops just by 3%. Only whenFree riders increase substantially does performance suf-fer noticeably.

Proposition 1D: Free Rider Tipping Point. Perfor-mance is hardly affected by Free riders until a thresholdis crossed, beyond which performance collapses.

The effect is driven by the change in the ratio offree riders to reciprocators. Recall that reciprocatorsare influenced by those around them. When cooper-ators are numerous (toward the right side of the xaxis in Figure 1), reciprocators are shielded from theinfluence of free riders. As a result, variation in per-formance decreases. When cooperators are fewer (leftside of the x axis) and reciprocators are more numer-ous, a bigger chunk of the population is ready toswitch between cooperation and free riding. The ulti-mate behavior of reciprocators is determined by thegroup that they mimic: cooperators or free riders. Asthe proportion of free riders grows, they are more likelyto serve as role models, thereby pushing reciprocatorstoward lower contributions. This is a path-dependent,self-reinforcing process (Sydow et al. 2009): as morereciprocators behave similar to free riders, the popu-lation’s overall level of contribution decreases, whichreinforces free riding. This process is inherent in socialtraps (Platt 1973, Schelling 1978), and similar behavioralcontagion was shown in laboratory experiments (Boltonet al. 2005, p. 1464; Fowler and Christakis 2010; Weberand Murnighan 2008).

The accelerated process leads to the rapid decrease—indeed, a collapse—in performance. How likely are suchcollapses? 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, farless than in the general population, the tipping point isextreme—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 whenCooperators are more than 20% of the population. Insum, 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. Becausereciprocators are very common, leaders of open col-laboration ventures will likely find it easier to recruitreciprocators 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 beginswith such a low level of cooperators, less than half ofthe average level in the general population, it can stillreach 50% performance with a simple step—increasingthe 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 ofthis substitution effect, equivalent levels of performancecan be achieved with a range of population compositions.Some of these compositions are easier to obtain.

Study II: How Cooperation, Rivalry, andNeed Diversity Interact to Affect PerformanceIn Study I, we fixed rivalry and need diversity (hetero-geneity). Here, we complement it by examining howcooperation interacts with these two elements to affectperformance. 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%inconsistent.We examined performance by crossing each populationwith low (0%) and high (100%) levels of Rivalry andNeed Heterogeneity. We conducted 100 simulation runsfor each combination for a total of 1,200 runs.

The most important finding is that the performanceimpact 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 priorpredictions, thereby validating the model.1

Proposition 2A: Rival Goods Hurt Performance.The lower the rivalry (subtractability) of resources, thebetter the performance.

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Intuitively, rivalry adds a cost to transferringresources. When contributing a rival resource, the sourcesuffers 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, whichis costly, or by seeking it from another, which may befutile because of free riding. When resources are rival,cooperators suffer and so does performance, but whenresources are nonrival, even massive free riding does notaffect availability or performance.

Proposition 2B: Diverse Needs Increase Perfor-mance. The higher the heterogeneity in needs, the betterthe performance.

Intuitively, when participant needs are diverse, com-petition for resources is lower. Because participantsseek different resources, it is more likely that a neededresource is held (unused) by another agent. Sharingof resources is more frequent, gains from trade ensue,and performance is higher. In contrast, when participantneeds are similar, sharing is still possible, but when asource 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 withRivalry over the same populations. Panel (C) shows the interactions between Rivalry, Need Heterogeneity, and population. Free riderpopulations (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 endurea 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 similarneeds as duplicate efforts are eliminated. As we probedto 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 impactof each of the three elements is partly dependent on theother two.

Rivalry can hamper performance (Proposition 2A), butnot always. Performance is affected not only by rivalrybut also by the interaction of rivalry and need hetero-geneity (Figure 2, panel (A)). For instance, when rivalryincreases from low to high, it can cause major harm orjust a dent in performance, depending on the level of needheterogeneity. Similarly, need heterogeneity generallyimproves performance (Proposition 2B), but its effect

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depends on the level of rivalry. When rivalry is high, achange in need heterogeneity has a vast effect on per-formance, but when rivalry is low, a change in needheterogeneity matters little (Figure 2, panel (B)).

These findings suggest that OC can perform well evenwith rival goods. They also explain the differing pre-dictions on the effect of high need heterogeneity: highrivalry generally undermines performance, but the effectis mitigated when high rivalry is combined with highneed diversity (see the + line in Figure 2, panel (A)).On the other hand, because the performance impact ofneed heterogeneity is similarly affected by rivalry (seethe × 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 ofthe population (Figure 2, panel (C)). With a Cooper-ator population, which can be thought of as a groupof altruistic individuals, rivalry hardly affects perfor-mance (see the “open circle” line in Figure 2, panel(C)). Even in the general population, which lacks sucha concentration of cooperators, rivalry has just a mod-erate impact (the “open triangle” line). Performancedrops sharply only when high rivalry coincides withhomogeneous needs (“solid circle,” “solid triangle,” and“solid square” lines). Intuitively, performance suffersbecause rivalry affects the cost of contribution. Needheterogeneity affects the availability of sources. Whenresources are rival and needs are similar, cooperatingagents bear a cost and may struggle to find an unusedresource from another. In such an environment, even ifmost participants are cooperators, the aggregate gainsfrom cooperation are small. However, even in such aharsh environment, all three populations reach perfor-mance levels of 20%–40%. In some situations, this maysuffice.

Study III: Open Collaboration Performs Robustly inthe General PopulationWe now turn to a detailed investigation in a particularlyimportant environment—the general human population,composed of 13% Cooperators, 63% Reciprocators, 20%Free riders, and 4% inconsistent. In the prior study, weexamined just the extreme values of rivalry and needheterogeneity. Here, we provide a more elaborate inves-tigation, varying Rivalry and Need Heterogeneity from0.0 to 1.0 in increments of 0.1 while observing the effecton performance. Each combination ran 100 times, yield-ing 12,100 runs.

By examining the interior values, not just theextremes, we replicate the finding in Proposition 2C:in the general population, neither element has a simpleeffect on performance. Rather, the effect of each is partlydependent on the value of the other elements.

Proposition 3A: In the General Population, NeedHeterogeneity and Rivalry Interact Nonlin-early. The performance impact of need heterogeneity ispartly dependent on rivalry, and the performance impactof rivalry is partly dependent on need heterogeneity.

This nonlinear relationship is evident, for example,at the highest level of Need Heterogeneity. There, adecrease in Rivalry does not lead to improvement inperformance (in Figure 3, trace edge � from point 1to 2). In contrast, at the lowest level of Need Hetero-geneity, a similar decrease in Rivalry leads to a markedimprovement in performance, a rise that begins slowlybut then accelerates (trace edge � from point 4 to 3).And between these two extreme points, the performanceeffect of change in Rivalry at a given level of Need Het-erogeneity is nonlinear (compare some of the lines thatconnect points on edge � to edge �).

This nonlinear relationship is evident also in theperformance effects of Rivalry over levels of NeedHeterogeneity.3 For instance, at the highest level ofRivalry, an increase in Need Heterogeneity leads to anincrease in performance that is linear (edge � in Fig-ure 3). But at the lowest level of rivalry, a similarincrease in Need Heterogeneity brings no improvementin performance (edge �). In between, the effect is some-times linear, sometimes not. To see that, trace the linesthat connect any point in edge � to edge �: some arelinearly increasing, others are flat.

In the general population, a change in the level ofeither element brings a nonobvious impact on perfor-mance. The combination of low Rivalry, assumed tobenefit performance, and low Need Heterogeneity, oftenassumed to harm it, generates high performance (point 3)

Figure 3 Performance of the General Population at VariousCombinations of Need Heterogeneity and Rivalry

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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, changesin need heterogeneity have little effect on performance.

Another counterintuitive result: the combination ofhigh Rivalry, assumed to harm performance, and highNeed 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 Rivalryis combined with low Need Heterogeneity is perfor-mance low (point 4). Hence, this leads to the followingproposition.

Proposition 3B: In the General Population, Ri-valry and Need Heterogeneity Compensate forEach 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 ishigh and Need Heterogeneity is low (point 4), cooper-ation is costly and search is often futile. Those whocooperate can be depleted of resources and fail to obtainthem from others, and so they must turn to costly self-production. In such an environment, cooperation bringspuny gains. For OC, such an environment may be theharshest.

From that low point, an increase in the diversity ofneeds (in Figure 3, follow edge � from point 4 toward 1)leads to a performance boost. As Need Heterogeneityincreases, contributions are more likely to boost per-formance because agents are not seeking identicalresources. Sources are less likely to discover (belatedly)that a resource they have contributed is needed for theirown tasks. Even as high Rivalry makes each contribu-tion costlier, gains from collaboration increase perfor-mance. Thus, performance is so high that decreasingRivalry does not improve it much (follow edge � towardpoint 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 sharerival goods, including furniture, clothes, computers, andoffice supplies (Willer et al. 2012). Sharing rival goodsmay appear improbable, but when participant needs arediverse, performance is so high that decreasing Rivalryimproves it little (follow edge � toward point 2).

Another way to increase performance is by reducingrivalry. As Rivalry decreases, the cost of contributiondecreases too, even with need homogeneity (in Figure 3,follow edge � from point 4). When Rivalry approacheszero (point 3) and needs are homogeneous, fewcontributors suffice to provide the needs of a largepopulation. At this point, even if Need Heterogeneityincreases, it has little consequence because performanceis already very high (follow edge �). Performance at theextreme (point 3), where needs are homogeneous and

rivalry is low, is similar to the sharing of design ideasby user-innovators. There, performance benefits becausea single good design can be shared cheaply and benefita multitude (Baldwin et al. 2006).

There are a few practical implications here. When aleader faces a combination of high rivalry and low needheterogeneity (similar participant needs), she shouldremember that rivalry decreases performance dramat-ically once it passes an inflection point (see alongedge �). If the leader attempts to reduce high rivalry,she will see modest performance gains. A bigger boostin 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 leadto a substantial drop in performance.

Discussion and Next StepsOpen collaboration is a growing source of innovationand production, perhaps a new organizational form. Itembodies uncommon combinations: goal-oriented yetloosely coordinated participants who cooperate volun-tarily to create freely distributed products and services,creating an economic impact. Innovators, scientists, andjurists have described the benefits of openness and urgedsupport for it (Benkler 2011, Henkel and Maurer 2009,Lessig 2005, Madison et al. 2010, von Hippel 2005a,Zittrain 2008). Yet, despite the economic and socialimpact of OC, its principles are vague and its perfor-mance remains a puzzle.

As much as scholars have done for firms, we aimto identify some principles of operation and determi-nants of performance for open collaboration, whether asoftware venture, file sharing collective, or an adviceforum. The model, structured as a typical scientific the-ory, is akin to other theories of performance (Merton1967, Sutton and Staw 1995). We begin with micropro-cesses of individual behavior and proceed to identifyunderlying 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 humanbehavior (DiMaggio 1995), which specifies processesand uses an agent-based model to generate distributionsof 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 producedefficiently in open collaboration or where it is likely tocomplete with firms. This is what we aim to achieve. Thetheory, expressed in the model, marries prior accountsof OC with recent evidence on human cooperation. Weinvestigate the performance impact of variations in coop-eration, rivalry, and need heterogeneity. We show thatOC is a robust engine for innovation and production, onethat performs well even in unfavorable environments.

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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 goodshurt it (Proposition 2A), and need diversity helps (Propo-sition 2B). But the model uncovers some novel effects.First, as cooperators increase, the benefits they bringtaper off (Proposition 1B). Thus, OC can thrive even ifcooperators are just a sliver of the population. Becausea majority of altruists is not essential, OC can prosperbeyond the realm of those who are naturally inclined tocooperate, such as hobbyists and hackers. The need coreof cooperators can be found even in a random sample ofthe general population.

A majority of cooperators is not essential for highperformance, but they aid by reducing variance (Propo-sition 1C). When reliable performance is needed, as inindustrial production, many cooperators are beneficial.The two propositions can assist in leaders in designingand organizing OC ventures. Based on the specific ofthe initiatives, a leader can contemplate the benefits andcosts of recruiting cooperators.

As much as assumptions about cooperators can berelaxed, so are assumptions about free riders. We expandprior arguments (Baldwin 2008, Baldwin and Clark2006) by showing that free riders matter little, evenwhen goods are rival. Because they do little harm, effortsto sieve free riders seem inefficient. Now we can under-stand why many OC ventures appear so unconcernedwith free riders.

When we model the role of rivalry and need het-erogeneity in performance, we find that these elementsinteract with each other and with the population. Theperformance impact of rivalry and need heterogeneityvaries with the composition of cooperative types in apopulation (Proposition 2C). The interaction suggestsagain that OC can thrive even when goods are rival.The impact of rivalry on performance is not simple butdepends on the cooperative composition and need het-erogeneity in the population. The needs of users areoften diverse, prior research has found. Thus, OC mayperform well in the general population. In this environ-ment, performance is stable even if rivalry increases (seegeneral population in Figure 1 and follow edge � in Fig-ure 2). Currently, OC may appear restricted to nonrivalgoods, but we propose that it can expand into rival ones.

Further guidance to leaders of OC comes from thethird experiment, simulating the general population.Need heterogeneity and rivalry interact to create a com-plex effect on performance (Proposition 3A), so changesto either one will have a varying impact on performance.Leaders should tread carefully because changes can becostly: if a leader attempts to increase performanceby reducing rivalry, for instance, he has to find waysto abate congestion or reduce contribution cost. Butbecause 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 eitherone can compensate for the other (Proposition 3B). Highperformance is possible with high rivalry if the needsare heterogeneous. And OC can perform well with lowneed heterogeneity, depending on the level of rivalry. Insum, open collaboration can emerge in more places thancurrently observed.

The findings serve to explain baffling prior find-ings, such as the skewed contributions to OC. It isnot a defect, as some observers suspected. The generalpopulation features a handful of people who contributeunconditionally (cooperators) and two larger groups thatcontribute conditionally (reciprocators) or little (free rid-ers). Disparities in contributions can be expected. Thecore of cooperators serves as a “critical mass” (Waskoand Faraj 2005, p. 52): it sparks contributions fromreciprocators, the largest group. It may be why, whensurveyed or observed, contributors explain their contri-butions not as paying back to a specific individual, butrather speak of a generalized reciprocity (Ekeh 1974,Yamagishi and Kiyonari 2000). As we noted, in theirsurvey of helpful users, Lakhani and von Hippel (2003,p. 937) found that many of them generalized reciprocityas a motivation: “I have been helped before [in theseuser forums] 0 0 0 so I reciprocate” (also see Bagozzi andDholakia 2006; Wasko and Faraj 2005, p. 51).

Limitations and ExtensionsTo 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 (asdetailed in the Cautious Bias in Model section). Othersare merely realistic, based on accounts of open collab-oration in the literature. Some assumptions are oppor-tunities for extensions. The findings on cooperation, onwhich the model relies, assume that a participant hasinformation 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, themultiplying lines of codes in a software project, or thegrowing pool of shared files. If a user has only partialinformation on others’ contributions, performance maysuffer (Levati et al. 2009). The model requires a simpleindex of “who knows what,” akin to transactive mem-ory, to facilitate search. Such an index often emergesnaturally in the minds of participants (Wegner 1986).It can also be set up easily in the form of a computersystem (Jarvenpaa and Majchrzak 2008), but withoutit, performance will suffer (Levine and Prietula 2012).When it comes to diversity, resource-need heterogene-ity is one form of diversity that has been hypothesizedto affect performance (Baldwin et al. 2006; von Hippel2005a, pp. 33–43). Inclusion of other forms of diversity,

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such as skill (Hong and Page 2004) or network struc-ture (Santos et al. 2008), may be a useful extension. Inthe model, performance is defined as substantive ratio-nality (Simon 1976, pp. 130–131; Weber 1947, pp. 184–186): efficiency in turning inputs to outputs throughcollaboration. Procedural performance measures, suchas how correct or neutral Wikipedia is (Giles 2005,Greenstein and Zhu 2012), are complementary. Finally,the model excludes, by design, some elements of opensource that received empirical description elsewhere,such as recruiting, administration, and governance (e.g.,Dahlander and O’Mahony 2011, O’Mahony and Bechky2008, O’Mahony and Ferraro 2007).

Open collaboration is an exemplar of human coop-eration: people, often strangers, working in concert toachieve specific goals, even without direct benefits. The-oreticians regard such ventures as the toughest testof human cooperation: “The problem of transient andanonymous 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 Skvoretz1998, p. 639). Our investigation suggests that com-mon assumptions about barriers to cooperation are toogloomy. Some were surprised that OC could thrive onthe Internet, which is devoid of “the social signaling,cues, and relationships that tend toward moderation inthe absence of law” (Zittrain 2008, p. 130; also seevon Krogh and von Hippel 2003). Here, we show thatopen collaboration can thrive without close personalrelationships. Furthermore, it can scale up, counteringpredictions that cooperation would break down in largergroups (e.g., Olson 1965, Raub 1988).

Writing in Science, Vollan and Ostrom (2010, p. 924)called for more research to explain variation in humancooperation. We can advance “a behavioral theory ofhuman action,” they wrote, by “[u]sing multiple methodsto identify the relevant ‘microsituational’ and broadercontextual variables” and linking them to “differences inbehavior 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 sizeis directly related to voluntary contributions (Zhang andZhu 2011).

Where can open collaboration thrive? For the perfor-mance of open collaboration, hard-to-find cooperatorsare not all important, and free riders will not necessar-ily doom performance. Open collaboration can withstandboth rivalry and a lack of diversity in needs. It will per-form surprisingly well even with a bunch of ordinarypeople. It can thrive far and wide, we suggest.

Supplemental MaterialSupplemental material to this paper is available at http://dx.doi.org/10.1287/orsc.2013.0872.

AcknowledgmentsThe authors are thankful to Carliss Baldwin, GregoryBerns, Ramon Casadesus-Masanell, Eric von Hippel, Lars BoJeppesen, Chengwei Liu, and Sarah M. G. Otner for theircomments. The authors also thank Ian Lim for his researchsupport. The authors acknowledge the comments received atthe following conferences: American Sociological Associa-tion 2006 in Montréal; Academy of Management 2008 inAnaheim, California; User and Open Innovation workshop2009 in Hamburg; Mid-Atlantic Strategy Colloquium 2010 atthe University of Maryland; Open Source Software 2010 inNotre Dame, Indiana; Israel Strategy Conference 2010 at theTechnion; Society for the Advancement of Socio-Economics2012 in Cambridge; and Academy of Management 2012 inBoston. The first author thanks the Sloan School of Manage-ment of the Massachusetts Institute of Technology, where themanuscript was written. The second author acknowledges aSummer Research grant from the Goizueta Business School,Emory University; discussions at the Human Social, Cultureand Behavior Modeling Program meetings of the Office ofNaval Research; and a grant from the Air Force Office of Sci-entific Research through the Office of Naval Research [GrantN000140910912].

Endnotes1The overall main effects for Rivalry and Need Heterogene-ity were F 411115845= 211561 (p < 00001) and F 411115845=

4171102 (p < 00001), respectively. The interaction wasF 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, althoughit fits Need Heterogeneity over Rivalry values better (r2 =

00618) than it fits Rivalry over Need Heterogeneity values(r2 = 00149). Nonlinear regression fitting Rivalry over NeedHeterogeneity also had a poor fit (second-order polynomial,r2 = 0005; third-order polynomial, r2 = 0001).

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Michael J. Prietula is a professor at the Goizueta Busi-ness School at Emory University. He has been on the fac-ulties of Dartmouth, Carnegie Mellon, and Johns Hopkins.He received his Ph.D. from the University of Minnesota. Hisresearch focuses on computational models of groups and insti-tutions and on the neuroscience of belief and choice. He is afaculty member of the Center for Neuropolicy at Emory andis a visiting research associate at the Judge Business Schoolat the University of Cambridge.

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Documents

Open Collaboration for Innovation: Principles and Performance