technology adoption in online social networks

Technology Adoption in Online Social Networks*Gang Peng and Jifeng Mu

With the advances of information technology, online social networks are becoming increasingly important venues fortechnology adoption. However, although the dynamics of technology adoption in real world social networks have beenwell documented, technology adoption in online social networks remains relatively under-explored. This study iden-tifies the differences between online and offline social networks and proposes a framework to investigate the dynamicsof technology adoption in online social networks. To illustrate the proposed research framework, this study employsbehavior-link panel data obtained from an open source software (OSS) development network to examine how onlinesocial networks affect the adoption of Subversion, the latest OSS version control technology. Based on social networktheory, co-membership is used to construct online social networks within the OSS development network. Method-ologically, this study takes advantage of the panel dataset and addresses the issues of simultaneity and individualheterogeneity that frequently confound the relationship between network structure and adoption decision, and as aresult it demonstrates a more compelling relationship between social networks and technology adoption. The resultsof this study reveal that social networks are major conduits for technology adoption in an online social network interms of imitation, leadership, lock-in, similarity, recency, and team size effects. In online social networks, one’sdecision to adopt a new technology is strongly influenced by the actions of the connected others. Project leaders havea stronger influence over other members in technology adoption decision making, even in informal virtual teams wheretraditional governance structures do not apply. Older projects exhibit stronger inertia and thus lack innovativeness.Similarities among projects facilitate faster adoption, and the effect of leadership attenuates in the networks withincreasing project dissimilarity. Recent adoptions of technology within the networks, rather than more distant ones,have a stronger impact on subsequent adoption, implying the salience of memory over usage confidence, and increasedsize of a project team accelerates the rate of adoption. These results help in understanding the dynamics of technologyadoption in online social networks, and provide useful guidelines for firms to promote technology and productinnovation.

Introduction

S tudies on technology adoption have traditionallyexamined real world contexts wherein individualactors typically interact with each other through

face-to-face, word-of-mouth, or traditional media such asmail and telephone (e.g., Rogers, 2003). The advent ofcomputers and the Internet has fundamentally changedthe way individuals communicate with each other, andbrought new forms of organization, such as online com-munities (e.g., Lange, McDade, and Oliva, 2004; Waarts,van Everdingen, and Van Hillegersberg, 2002). Increas-ing evidence suggests that these new forms of organiza-

tion are potentially important venues for technologyadoption (e.g., Godes and Mayzlin, 2004).

Much of the research on the dynamics of technologyadoption to date was produced before the ubiquity of theInternet. Little is known about the dynamics of technol-ogy adoption in online social networks. In fact, evenextant understanding of technology adoption in realworld networks is largely drawn from unreliable orincomplete data such as verbal reportage or geographicalproxies that only indirectly capture interpersonal socialnetworks (Kalnins, 2004; see Freeman and Romney[1987] for a discussion of the “accuracy” of network datacollected through questioning informants). Thus, thenetwork effect claimed in these studies remains at best aconjecture rather than an empirically tested truth(Valente, 2005). As such, the objective of this study istwofold. First, to identify the differences between dynam-ics of online and offline social networks on technologyadoption; and second, to assess the extent to which thefindings of studies on technology adoption performed in

Address correspondence to: Gang Peng, Department of Management,Williamson College of Business Administration, Youngstown State Univer-sity, One University Plaza, Youngstown, OH 44555. Tel: (330) 941-3271.Fax: (330) 941-1459. E-mail: [email protected].

* The authors express gratitude to the two anonymous reviewers andeditor C. Anthony Di Benedetto for their comments and suggestions, whichhave greatly improved this article.

J PROD INNOV MANAG 2011;28(S1):133–145© 2011 Product Development & Management Association

real world situations are valid for this radically differentcontext of online collaboration. As suggested by Wasser-man and Faust (1994) that more work needs to be done oncollecting behavioral or observational network data, thisstudy employs open source software (OSS) developercollaboration data to examine how online social networksaffect the adoption of Subversion (SVN), the latest OSSversion control technology, to illustrate the proposedresearch framework.

A software version control technology for OSS devel-opment serves as a code repository and, more impor-tantly, an effective coordination technology for codecontribution (Fogel, 2006). In OSS, geographically dis-tributed project members contribute their codes via theInternet, and no single person could ever be expected totrace, organize, and integrate the huge volume of codeinputs. It is the version control technology that makesOSS a reality. The OSS developer collaboration data hasseveral advantages for this study. First, the OSS develop-ment community is one of the largest and most prominentvirtual communities in the world, and has gained increas-ing attention from researchers (von Hippel and vonKrogh, 2003). Second, open innovation is considered tobe the innovation model of the 21st century. In fact, thehuge success of OSS projects such as Linux, Apache,MySQL, Perl, PHP, and Mozilla has led many to believethat the open and collaborative nature of OSS develop-ment model could potentially revolutionize the innova-tion process in other fields, such as biology and medicine(e.g., Rai, 2005; von Hippel and von Krogh, 2003).

This study adopts the theoretical lens of socialnetwork theory. In social network research, the analysis isintrinsically cross-level, i.e., it draws causal relationships

between constructs across different levels of analysiswith the aim to better understand the impact of networks(Rousseau, 1985). More importantly, theories and con-structs initially developed for the study of interpersonalrelationships are frequently applied to the analysis ofinterorganizational/inter-project/inter-team linkages as“micro and macro can be very similar theoretically andmethodologically” (Borgatti and Foster, 2003). Thus, thepresent study acknowledges its cross-level nature andtherefore ranges over and across three levels of analysis:the individual level, the project level, and the networklevel.

Another key issue to network research is the construc-tion of social networks. In real world contexts, socialnetworks are often built on geographical proximity, andcontiguous actors are called neighbors (Freeman andRomney, 1987). However, in online settings, individualscan virtually reach others anywhere in the world throughthe Internet. Thus, the term “neighbors” takes on anentirely different meaning. In this study, we define“project members” or peers as the developers who workon the same project, and “co-members” as members whoserve on two different projects. Correspondingly, “neigh-bors” or neighboring projects are defined as OSS projectsthat share one or more co-members. We believe thatco-membership is one of the most important mechanismsthrough which online social networks are built and onlinecommunication occurs.

This study makes several contributions to the literatureon technology adoption as well as to the broader field ofsocial network research. First, we develop a theoreticalframework to model the phenomenon of online technol-ogy adoption. We delineate the factors affecting technol-ogy adoption in online as contrasted to offlineenvironments. Second, we utilize behavior-link data(Wasserman and Faust, 1994) obtained from OSS devel-oper collaboration history rather than self-reported(survey) data used in most other studies. Third, weemploy panel data to address the simultaneity and het-erogeneity issues plaguing many social network research(e.g., King, 2007). Therefore, results from this studyshow a more compelling relationship between social net-works and technology adoption.

This study finds that social networks are major con-duits for online technology adoption. In online collabo-ration projects such as the OSS, project leaders have astronger influence over other members, even in informalvirtual teams where traditional governance structures donot apply. Older projects exhibit stronger inertia and thuslack innovativeness. Similarities among projects facilitatefaster adoption, and the effect of leadership attenuates in

BIOGRAPHICAL SKETCHES

Dr. Gang Peng is Associate Professor of Management at theYoungstownState University in Youngstown, OH. He received his Ph.D. in Informa-tion Systems from the University of Washington, Seattle. He holds abachelor’s degree in economics and a master’s degree in businessadministration. His research interests include technology adoption anddiffusion, product innovation and management, and open source soft-ware development.

Dr. Jifeng Mu is Assistant Professor of Marketing at Alabama A&MUniversity. He earned a Ph.D. in Marketing from the University ofWashington, and a Ph.D. in Management Science and Engineering fromXi’an Jiaotong University. Also, he was a visiting professor at theUniversity of Washington. His research interests focus on product inno-vation, marketing strategy, and marketing research. His research articleshave appeared in numerous top Chinese business academic journals andinternational journals such as Expert Systems with Application, Techno-vation, and Journal of Knowledge Management, among others.

TECHNOLOGY ADOPTION IN ONLINE SOCIAL NETWORKS J PROD INNOV MANAG 1342011;28(S1):133–145

the networks with increasing project dissimilarity. Recentadoptions of technology within the community, ratherthan more distant ones, have a stronger impact on subse-quent adoption, implying the salience of memory overusage confidence. We also find that increased size of aproject team accelerates the rate of adoption.

Theory and Hypotheses

Technology adoption in real world situations has receivedconsiderable attention from scholars in different disci-plines (e.g., Davis, Bagozzi, and Warshaw, 1989; Shihand Venkatesh, 2004; Wejnert, 2002). A rich and growingliterature on this phenomenon documented the impor-tance of interpersonal contacts (e.g., Rogers, 2003),explored the nature of network linkages (Granovetter,1985), analyzed the effects of opinion leaders (e.g., Burt,1987), and probed how the adoption decision was influ-enced by the social networks in which potential adopterswere embedded (e.g., King, 2007). These studies havegreatly enhanced our understanding of the process oftechnology adoption. However, far less research is avail-able on the dynamics that drive technology adoption inonline social networks. The proposed research frame-work is depicted in Figure 1.

Imitation Effect

Organizational actions, including technology adop-tion, are embedded in networks of relationships (e.g.,

Granovetter, 1985). A few early adopters within a com-munity can trigger a cascade of interest in a technology.Prior studies have found that potential adopters tend toimitate their technology-adopting neighbors when theycontemplate future adoption—i.e., proximity can poten-tially strengthen a local social network and increase thefrequency of communication and interaction betweenpotential adopters and their technology-adopting neigh-bors (Coleman, Katz, and Menzel, 1966). Cascades occurwhen the probability of adopting a technology dependson the rate of adoption by others in a community (Gra-novetter, 1985). A cascade of technology adoption deci-sions can be furthered if technologies are associated withpositive network externalities (Shapiro and Varian, 1999)because people tend to imitate others’ adoption patternsin order to economize on their learning costs.

During an OSS development process, members of afocal project may work on multiple projects concurrently.These members serve as linkages among differentprojects. Naturally, they enable knowledge developedor information available in one project to be reapplied orassimilated by other projects. First, under conditions ofhigh uncertainty, project members learn from the actionsof others, and take into consideration the experiences ofothers in deciding what to do next. For example, ifone project successfully adopts a new technology andother projects hear of the promise of the technology,then they will be likely to adopt the same techno-logy because they tend to believe that adopting the tech-nology will help them to gain similar success (Fleming,1999). Second, in a world of uncertainty and ambiguity,learning from others’ experiences can economize the costof decision making and save scarce cognitive efforts andresources (DiMaggio and Powell, 1983). Therefore,through this learning and imitation process, as moreneighboring projects adopt a technology, more informa-tion and knowledge about the technology will be accu-mulated, and it becomes more likely that the newtechnology will be communicated to the focal project.This, in turn, should cause faster adoption of the technol-ogy. Thus, we propose:

H1: The more the neighboring projects that have adopteda new technology, the faster the focal project will adoptthe new technology.

Similarity Effect

Literature on technology adoption suggests that contactsoccur at a higher rate among similar people than dissimi-lar ones (Van den Bulte and Wuyts, 2007). The term

Project Similarity

Imitation Effect

Size Effect

Recency Effect

Lock-in Effect

Technology Adoption

Leadership Effect

Similarity Effect

Figure 1. Research Framework

135 J PROD INNOV MANAG GANG PENG AND JIFENG MU2011;28(S1):133–145

“homophily” is used to describe this phenomenon. It isone of the important properties or measures of socialnetwork ties that capture the tendency for actors toconnect and mingle with those who have similar socialattributes (e.g., Zenger and Lawrence, 1989). It simplymeans similarity breeds connection (as in “birds of afeather flock together”). Homophily is important becauseit has significant implications for the information socialactors receive, the attitudes they form, and the interac-tions they experience (e.g., McPherson, Smith-Lovin, andCook, 2001).

Homophily not only applies to individuals but also toother social actors such as teams and organizations(Joshi, 2006; Reagans and McEvily, 2003). Social actorsabsorb cues and information that guide their behavior bylooking at those whom they perceive to be similar. Simi-larity leads to the evolution of shared perceptions, whichin turn define cognitive boundaries around specificactions. In the context of OSS development, projectsdiffer substantially on multiple dimensions, and thosesharing similar traits tend to make similar decisions(Lerner and Tirole, 2005; for more details about thedifference/similarity between OSS projects, please referto the Estimation Model and Results section). Accord-ingly, OSS projects will be more likely to imitate andlearn from neighbors that are similar to them to sustaintheir judgments about technological trends and superior-ity. Similarity among projects facilitates more contacts,making it easier for them to transfer or apply new ideas toother projects, thereby inducing faster adoption of a newtechnology. Therefore:

H2: The greater the similarity the focal project shareswith its neighboring projects that have adopted a newtechnology, the faster the focal project will adopt thesame technology.

Leadership Effect

An OSS project is typically initiated by an individual or asmall group of individuals, who most often try to solvetheir own problems (Raymond, 1999; von Hippel and vonKrogh, 2003). These individuals share initial softwarecode bases, recruit programmers and other contributors,and guide the future project direction (Lerner and Tirole,2005; Raymond, 1999). Naturally, they become projectleaders during the project development process.

Prior theoretical studies suggest that governance struc-tures in online social networks are flat and less hierarchi-cal, and network members have equal importance ininfluencing the behavior of others (von Hippel and von

Krogh, 2003). In contrast, members in offline social net-works are typically not uniform, and they can be classi-fied in accordance with their status and influence in thenetworks. For example, in the physically collocatedworld, participation inequality is well-known (Bales,1950). It underlines a social and psychological phenom-enon that distribution of participation is not random buthierarchical, with a few members dominating the partici-pation even when conditions encourage equal participa-tion. This leads to the speculation that though projectleaders in OSS development do not hold absolute author-ity and power as do leaders in traditional teams, they stillplay paramount roles.

First, through participation in prolonged deliberatepractice, project leaders gain extensive knowledge onhow to organize OSS projects, and learn how to identifymeaningful patterns in complex arrays of events or trends(e.g., Matlin, 2005). Second, if the project leaders arehighly persuasive and can impart contagious enthusiasmto their disciples, they will be able to shepherd othermembers/disciples to the visionary future. The high con-nectivity and interactivity among project leaders, inter-mediated by disciples, will lead the team as a whole torefresh its belief toward the vision. Third, if projectleaders are highly knowledgeable and can posit the mostinteresting and creative ideas, they will be more likely tocatch attention and can persuade others to participate inthe knowledge creation and sharing activities. Due tothese various reasons, the substantive knowledge andcontributions of the project leaders may influence the wayothers perform their tasks as well as their decisionmaking in the future. This may ultimately endow projectleaders with access to and control of relevant resourcesand more decision-making authority (Salancik andPfeffer, 1977). Hence:

H3a: Project leaders have a stronger influence on theadoption of a new technology as compared with othermembers.

In the section on similarity effect above, we discussedthat imitation occurs most frequently between projectsthat are similar or homophilious. Here, we furtherpropose that the effect of project leaders in influencingthe adoption of technology hinges on the similaritybetween the focal project and its neighboring projects. Aleader’s role in guiding and informing the team, as wellas leveraging resources and information, will be moreeffective when projects share more similarities. When theenvironments of neighboring projects are different fromthe focal project, the expertise and experiences accumu-


lated by the project leaders from participating in theactivities of the neighboring projects will be less trans-ferable. In this case, project leaders will face increaseddifficulties and obstacles in interpreting and applying theknowledge obtained from neighboring projects. Expertiseconfers authority (Fleming and Waguespack, 2007). Thatis, if the project environments change considerably,leaders’ accumulated expertise will be greatly discounted.Consequently, their roles in facilitating the adoption oftechnology for the focal project will be substantially con-strained. Accordingly:

H3b: The effect of leadership on new technology adop-tion attenuates with the dissimilarity between the focalproject and its neighboring projects.

Lock-in Effect

Quite often, there are situations in which social actorshave to choose among two or more technologies thatcompete for dominance. These situations are typicallycharacterized by high uncertainty and rapid environmen-tal changes. In an uncertain and ambiguous environment,decision making that depends on history and experienceis a good approach (March, 1988). The more uncertainthe environment and the more complex the technology,the greater the value of history and experience will be.OSS projects follow the same logic.

First, increasing rewards of experience lead projectleaders to refine what they already know about a technol-ogy, and this in turn leads to stronger absorptive capacityrelating to the existing technology (Cohen and Levinthal,1990), thus boosting confidence in the technology.Second, existing technology is relatively stable andmature, with more available resources and a wider userand support network. Cognitive embeddedness andcommon network linkages among projects adopting thesame technology create shared understanding andmeaning (Podolny and Baron, 1997), shared codes andlanguages (Nahapiet and Ghoshal, 1998), and collectivethinking (Weick and Roberts, 1993), which make theexisting technology more valuable for the projects. Forthese reasons, an existing technology has the advantageof legitimacy in the eyes of OSS project members andother stakeholders.

Third, once a certain technology is chosen, switchingto another technology is costly because new investmentshave to be made (Katz and Shapiro, 1994). Thus, if acertain technology has already been adopted for a project,the adoption pattern will be reinforced even if a compet-ing technology is superior in terms of performance

(Arthur, 1989). Therefore, projects are likely to dwellwith the technology with which they are already familiarfor its stability and maturity. With high uncertainty oftechnology and environment, the foregoing factors tendto reinforce each other and, combined with past success-ful experiences, should have a synergistic effect uponprojects in implementing the existing technology.Thereby:

H4: Project experiences with the existing technology willnegatively affect the adoption of a new technology.

Recency Effect

Within a social network, the intensity and tightness ofconnections among members is referred to as “tiestrength” (Granovetter, 1973), which is a multidimen-sional construct. One of the distinguishable dimensionsof tie strength is “duration”—time spent in a relationship(e.g., Marsden and Campbell, 1984; Van den Bulte andWuyts, 2007). Researchers have recorded that recentstimuli or observations have disproportionate salience ofinfluence on human decision making—the “recencyeffect” (e.g., Deese and Kaufman, 1957), which promptsus to wonder how the recency of prior technology adop-tion in a network affects future adoption. In this study wecapture “duration” using the average time elapsed sinceneighboring projects have adopted a new technology.Possibly, when the duration is longer, projects memberswho have experienced the new technology tend to gainmore confidence about the technology, thus leading to aquicker adoption. We postulate that when the duration isshorter or neighbors’ adoption is more recent, thememory of experiencing the technology is more salientand the effect of word-of-mouth would be stronger, thusleading to a quicker adoption.

We believe the recency effect is more salient in onlinesettings such as OSS communities, in which basic cuesabout personalities and social roles to which individualsare accustomed in the physical world are absent (Brettet al., 2007). Therefore, the memory of the informationdelivered online tends to fade more quickly and the effec-tiveness of the information is largely discounted as timepasses. That is, information received more recently willloom larger than information received at a more distanttime in online settings. Thus, we predict:

H5: The more recent its neighboring projects adopted anew technology, the faster the focal project will adopt thesame technology.


Size Effect

In general, the size of a social actor is positively related totechnology adoption because larger organizations orteams have more slack resources and expertise (e.g.,Eveland and Tornatzky, 1990), although contrary predic-tions exist (e.g., Utterback, 1974; Hannan and Freeman,1984). The issue is more complicated in online scenarios.Some researchers have argued that team size may affectvirtual teams differently from traditional teams sincetechnology can mitigate the negative effect of size foundin face-to-face decision making (e.g., Butler, 2001; Leen-ders, van Engelen, and Kratzer, 2003). Thus, the negativeeffect of team size, if any, would be mitigated by the useof various technologies in virtual teams.

We argue here that a contingency perspective would bemore meaningful in examining the effect of size on tech-nology adoption. The effect of team size on technologyadoption depends on the nature of the tasks and the tech-nology used to support the tasks—the better the fitbetween the technology and the tasks that the technologysupports, the faster the technology will be adopted. Forexample, group decision-support systems are more usefulfor distributed teams to solve nonroutine, complex deci-sion problems (e.g., Majchrzak, Malhotra, and John,2005). In OSS development, SVN, as a software versioncontrol technology, is particularly useful for large andcomplex projects in nature; it is of very limited use if aproject has a single programmer. But as the teambecomes larger and software gets increasingly compli-cated, SVN provides a handy and indispensable solutionsince it can manage the concurrent update conflicts andkeep track of when and who contributed what to the codebase (Fogel, 2006). Thus, we propose:

H6: Larger projects tend to adopt a new technologyfaster than smaller projects.

Data Sources and Variable Definitions

The data set used in this research is from Source-Forge.net, the world’s largest OSS project hostingwebsite (von Hippel and von Krogh, 2003). Softwareversion control technologies, like SVN, are the very foun-dation of OSS development. In OSS, geographically dis-tributed project members contribute their codes,including bug fixes, via the Internet, and no single personcould be expected to organize and integrate this largevolume of information. A version control technologyserves as the code repository for developers and endusers, and also an effective coordination technology for

code contribution (Bar and Fogel, 2003). Before SVN,OSS projects used CVS (Concurrent Versions System) ascode coordination technology. The precursors to CVS arediff and patch. Diff is a Unix program that produces thedifference between two files: if one take the diff betweena file after and before the modification, the output willconsist of only the files that have been modified. A trainedprogrammer can just look at the diff and know whatexactly happened to the original file. Patch is the oppositeof diff: patch can construct the modified file from com-bining the diff and the original file. But diff and patchhave their own limitations: sometimes programmers needto roll back the software to its earlier version if it containsflaws. The job becomes incredibly difficult withoutkeeping track of who contributed to what and when.

A solution to this problem is the Revision ControlSystem (RCS). However, RCS also suffers from severallimitations, one of which is its “lock-modify-unlock” stylesimilar to database concurrency control. With few con-tributors and well coordinated updating, RCS works fine.But RCS turns out to be fatal for OSS development, whichrequires simultaneous access and much faster responses.To address this issue, CVS came into being in the late1980s. Because CVS supports networked access, simulta-neous development, and intelligent automated merging ofthe code contribution, it quickly gained dominance in OSSdevelopment as well as in closed-source projects.Although CVS has been the de facto standard in versioncontrol technologies for the past few years (Collins-Sussman, Fitzpatrick, and Pilato, 2005), developers havenoticed some limitations from the beginning. For example,CVS only tracks the history of individual files, not thewhole directory tree of the software, and it does notsupport atomic update of codes either (Collins-Sussmanet al., 2005; Worth and Greenough, 2005). In data and filemanagement terminology, atomic update means either allor none of the proposed changes will be committed whenupdating a database or file system. SVN was designed tosolve these problems, and it started to gain popularity.

SourceForge has supported SVN since December2005, and the first adopter appeared in January 2006. Weobtained a ten-month panel data set from January 2006 toOctober 2006 tracing the adoption of SVN in the com-munity. The data set represents monthly snapshots of theproject characteristics, including SVN adoption status,members on each of the projects, and the social networkstructures among the projects. We restrict our sample to asubset of the whole projects: the Java foundry projects. AtSourceForge, a foundry is a subset of projects that use aparticular technology (Grewal, Lilien, and Mallapragada,2006).


The online social network is built based on the devel-opers’ collaboration history. At SourceForge, through co-membership among projects, members and projects forman affiliation network that can be represented as a bipar-tite graph (see Figure 2), in which circles representprojects, squares represent members, and links betweenprojects and members represent the participation rela-tionships (Xu, Christley, and Madey, 2006).

The dependent variable, Use_SVN, is a binary vari-able: whether a project adopted SVN or not by the end ofeach month. For each project, variable SVN_total is thetotal number of neighboring projects that have adoptedSVN. SVN_total captures the imitation effect. Admin_SVN is the total number of the SVN-adopting neighborsthat have the leaders of the focal project on their teams.Admin_SVN captures the role of project leaders in adopt-ing SVN (leadership effect). The derivations of the abovetwo variables are illustrated in Figure 2: project 1, P1, hasthree members, m1, m2, and m3, who concurrently serveon six other projects, P2 to P7; m1 and m2 are leaders onP1 (denoted by L), and m1 further serves on two otherprojects, P2 and P3; P2 uses SVN but P3 does not. Therole of m2 and m3 on the other projects can be interpretedsimilarly. Thus for the focal project, P1, SVN_total = 4.Out of the four projects using SVN (P2, P4, P5, and P6),three of them (P2, P4, and P5) have at least one of thefocal project leaders (m1 or m2) serving on their teams,thus Admin_SVN = 3. We also calculate SVN_total andAdmin_SVN as percentages, which are used for lateranalysis. When calculated in percentage, SVN_total isthe percentage of SVN-adopting neighbors out of all

the neighbors of the focal project, and Admin_SVN is thepercentage of SVN-adopting neighbors that have theleaders of the focal project on their teams. They equal to4/6 and 3/4 in Figure 2.

The similarity between focal project i and its SVN-adopting neighbors is measured as the following. First,based on the three major project characteristics of an OSSproject—intended audience (IA), operating system (OS),and project topic (TP)—we calculate the Euclidean dis-tance between project i and its jth SVN-adopting neigh-bor as dij, i � j (Reagans and McEvily, 2003). Since thenumber of SVN-adopting neighbors varies for each focalproject, dij is averaged over project i’s Ni SVN-adoptingneighbors as the final distance between project i and its

SVN-adoption neighbors: Distancei ij

N

id Ni

= ∑1

. The

higher the distance, the lower the similarity will be.Aggregating member properties as a team property is awidely adopted practice in social network research (e.g.,Joshi, 2006).

Lock-in effect is captured with the variable Experi-ence, which is measured by the length of time (in months)since the focal project registered with SourceForge. Aswe explained previously, all projects used CVS beforethey adopted SVN. Recency effect is measured by thevariable Duration, and size effect is captured by the vari-able Size. Both Duration and Size are obtained directlyfrom the data set. We also control for variables includekey project status in this study. There are six possiblestatuses of an active project: Planning, Pre-Alpha, Alpha,Beta, Production, and Mature. Other control variablesinclude key project characteristics such as intended audi-ence, operating systems, and project topics. These vari-ables are shown to be influential in decision making forOSS development (Lerner and Tirole, 2005). Detaileddefinitions of the variables are provided in Table 1.

Estimation Model and Results

The data structure of this study can be best described astime-to-event data—the history of each project can betraced, and the adoption of SVN represents the event tohappen. As suggested by Greene (2003), we applied sur-vival analysis to test our hypotheses. There are two impor-tant identification issues that need to be addressed indetecting the existence and estimation of the magnitude ofsocial network effect (King, 2007). The first is the simul-taneity issue: network structure affects the behavior ofsocial actors and, in response, actors can strategically takeactions to alter their positions within the social network.Therefore, the network structure is altered subsequently.

m1

P2

P3

LSVN

P4

P7

P6

P5

P1

L

SVN

SVN

SVNm2

m3

Figure 2. Project Membership Bipartite Graph


In this study, a focal project’s decision to adopt SVN isinfluenced by other projects’ adoption decisions or thecurrent network structure. At the same time, it is possiblethat the decision of the focal project to adopt SVN influ-ences the choices by other projects in the network. Thus,the value of network measures change. To explicitlyaccount for the simultaneity issue in examining networkinfluence, we employ a strategy similar to Reagans andMcEvily (2003) and Lee (2007) by using independentvariables in the prior periods in the estimation model. Bydoing this, we are able to specify that the choice ofadopting SVN at time t is determined by the social networkstructure at time t - 1, and not vice versa.

The second issue we need to address is unobservedproject heterogeneity, which could cause biased estima-tion results (Greene, 2003). Social network effect, bydefinition, implies that the behavior of participants in agiven network tends to be correlated. The observed corre-lation in the behavior of network participants by itself,however, does not necessarily suggest that any partici-pant’s action has a causal effect on the actions of the othersin the network. It is possible that network participants withsimilar tastes tend to aggregate, thus forming the socialnetwork. To mitigate this confounding effect, we need tocontrol for unobserved project heterogeneity. The paneldataset allows us to do so by including project-specificeffects in the estimation model. Thus, we apply the fol-lowing Weibull model with unobserved heterogeneity forthe analysis (Greene, 2003; Wooldridge, 2002):

λ γt ptij ip

ij( ) = ( )−1 exp ,X bb (1)

where l is the hazard function, tij is the duration tillproject i adopts SVN in the jth period, p is ancillary

parameter, gi is the project heterogeneity with variance q,and Xij is a vector of independent variables at time t - 1,which include characteristics of project i and the networkmeasures. More details about the model are availablefrom Greene (2003) and Wooldridge (2002).

The summary statistics and correlation matrix of thevariables in October 2006 is provided in Table 2. Asexpected, SVN_total, Admin_SVN, and Size are positivelycorrelated with Use_SVN, and Experience and Distanceare negatively correlated with Use_SVN.

The estimation results for equation 1 are shown inTable 3. The results in model 1 are used to test all thehypotheses except H3b. First, the coefficient of SVN_to-tal is positive and highly significant. This supports H1,the imitation effect. The coefficient of Distance is nega-tive and highly significant, demonstrating that as the dis-tance between the focal project and its SVN-adoptingneighbors decreases or the more similarities they share,the focal project tends to adopt SVN faster. This con-firms H2. H3a, the leadership effect, is supported aswell, as evidenced by the positive and highly significantcoefficient of Admin_SVN. Furthermore, the negativeand significant coefficient of Experience shows thatmore experience with prior technology will slow downthe adoption of SVN. Thus, H4, the lock-in effect, isconfirmed. In addition, the neighbors’ duration of adopt-ing SVN, Duration, negatively affects the speed withwhich the focal project adopts SVN. This supports H5,the recency effect. Finally, team Size is highly significantand positive. Thus H6, the size effect, is supported.Model 2 adds the interaction term, Admin_SVN ¥Distance, and its coefficient is negative and significant.This supports H3b that as distance increases, the lead-ership effect attenuates.

Table 1. Definitions of Variables

Variables Definitions

Use_SVN Dummy variable for adoption of SVN. It equals 1 if the focal project adopts SVN, and 0 otherwise.SVN_total The total number of neighboring projects that have adopted SVN.Admin_SVN The number of the SVN-adopting neighbors that have the leaders of the focal project on their projects.

Distance Distancei ij

N

id Ni

= ∑1

, d IA IA OS OS TP TPij il jll

im jmm

in jnn

= −( ) + −( ) + −( )∑ ∑ ∑2 2 2 where l, m, and n are the possible categories

of intended audience, operating systems, and project topics for the projects. IAil is a dummy variable and it equals 1 if projecti is intended for the lth category of audience (e.g., end users, developers, etc.); OSim and TPin are interpreted similarly.

Experience The age of the projects since their registration at SourceForge.net (in months).Duration The average time elapsed since the adoption of SVN by the neighboring projects.Size The log of the total number of developers on the focal project.Pre-Alpha Dummy variable. It equals 1 if the focal project is in pre-Alpha status, and 0 otherwise.Alpha Dummy variable. It equals 1 if the focal project is in Alpha status, and 0 otherwise.Beta Dummy variable. It equals 1 if the focal project is in Beta status, and 0 otherwise.Production Dummy variable. It equals 1 if the focal project is in Production/stable status, and 0 otherwise.Mature Dummy variable. It equals 1 if the focal project is in Mature status, and 0 otherwise.


As a robustness test, we also report in models 3 and 4the results when percentage measurements are used forSVN_total and Admin_SVN. Different from the results inthe first two models, Admin_SVN in model 3, as well as theinteraction term, Admin_SVN ¥ Distance, in model 4, isnot significant. The results show that, in the case of SVNadoption, it is actually the absolute number of contacts,rather than the percentage of contacts, that affects groupdecisions. This finding is similar to that of Carley et al.(2006) that when a population is exposed to a disease, thepotency of the disease and the speed with which thedisease is spread depend upon the dose of exposure or thenumber of diseased patients rather than the percentage ofthe patients who have contracted the disease.

Finally, Table 3 also shows the results of model specifi-cation tests: the likelihood-ratio tests of q = 0 are rejected(p < 0.01) in all four models. This shows significant hetero-geneity among the projects and supports the specification inour models (Gutierrez, Carter, and Drukker, 2001).

Discussions and Implications

In this paper we propose a framework to examine howsocial network dynamics affect online technology adop-

tion. We use behavior-link data to test the hypothesesafter accounting for the simultaneity and heterogeneityissues that have plagued identification of network influ-ence. Not only do the results show that the social networkmechanism is salient in an online setting, but we alsodemonstrate the differences between online and real worldsocial networks on technology adoption. The findingsfrom our research have several theoretical and practicalimplications.

Theoretical Implications

Our findings from this study suggest significant influenceof reference groups: one’s decision to adopt a new tech-nology is strongly influenced by the actions of the othersin the group. In choosing a version control technologybetween two alternatives, CVS and SVN, in situationswhere a clear course of action is not obvious for OSSprojects, the better choice is to mimic the decisions ofneighbors, leading to what has been termed outcome-based imitation (Haunschild and Miner, 1997). Ourresults also demonstrate that technology inertia leads to alack of innovativeness. In the SVN adoption process, weobserve a significant lock-in effect, which locks a system

Table 2. Descriptive Statistics and Correlation Matrix

VariablesMean(s.d) 1 2 3 4 5 6 7 8 9 10 11

1. Use_SVN 0.139 —(0.345)

2. SVN_total 2.046 0.243** —(2.054)

3. Admin_SVN 1.465 0.226** 0.699** —(1.518)

4. Experience 48.959 -0.085** 0.102** 0.039* —(16.050)

5. Distance 3.918 -0.144** -0.034* -0.200** 0.262** —(0.869)

6. Duration 4.553 0.046** 0.064** 0.060** 0.013 -0.039* —(2.212)

7. Size 0.874 0.028 0.303** -0.050** 0.261** 0.320*** 0.021 —(0.876)

8. Pre-Alpha 0.110 -0.012 -0.062** -0.020 -0.103** -0.062* 0.043** -0.047** —(0.313)

9. Alpha 0.162 -0.031 -0.048** -0.025 -0.050** -0.017 -0.029 -0.045* -0.154** —(0.368)

10. Beta 0.259 -0.013 -0.025 0.007 -0.010 -0.003 -0.017 -0.030 -0.208** -0.260** —(0.438)

11. Production 0.336 0.098** 0.113** 0.020 0.139** 0.074** 0.001 0.103** -0.250** -0.313** -0.421** —(0.472)

12. Mature 0.041 -0.025 0.055** 0.020 0.095** 0.050** 0.004 0.071** -0.073** -0.091** -0.123* -0.147**(0.199)

Note: N = 5,589.*p < 0.05, **p < 0.01.


in a self-reinforcing process that strengthens familiarityand thereby hampers transition to the latest technology.However, the effect of social influence in our analysisoffsets that of technology inertia caused by the lock-ineffect. It is possible that in an OSS community, the dis-semination of knowledge among projects throughnetwork relationships is quite powerful and efficient.Projects in the community can peruse and evaluatewhether a new technology has a high chance of successcompared to others and make their own adoption deci-sions in an informed manner.

Theoretical analyses in online social network researchtypically assume that networks are composed of memberswith equal importance in influencing the behavior ofothers or altering the network structure (von Hippel andvon Krogh, 2003). Our study, however, provides empiri-cal evidence that project leaders play more importantroles in the decision to adopt SVN than other members of

a project do. This result enriches our understanding thatthough leaders of OSS projects have no formalpower—that is, they can not coerce anyone to actually doanything—their leadership often has considerable realauthority (Aghion and Tirole, 1997). We find that a fewcore group members, the leaders, have a disproportionateamount of influence.

The hypothesis that similarity in social environmentsfacilitates faster adoption is strongly supported. Thisview is shared by other theories. For example, the asso-ciative learning theory argues that it is easier for knowl-edge to be transferred between groups with similarbackground situations (e.g., Reagans and McEvily,2003). Social comparison theory holds that individuals ororganizations tend to compare with others that are similarto themselves, and they tend to imitate the actions ofothers similar to them (e.g., Mussweiler, 2003). Thenetwork theory articulated that similarity in position, or

Table 3. Estimation Results of Adoption of SVN

Independent Variables Model 1 Model 2 Model 3 Model 4

SVN_total 0.207** 0.204** 1.194** 1.253*(0.038) (0.039) (0.387) (0.392)

Distance -0.325** -0.114 -0.193 -0.378(0.065) (0.082) (0.109) (0.211)

Admin_SVN 0.317** 0.852** 0.064 -0.875(0.048) (0.127) (0.181) (0.944)

Admin_SVN ¥ Distance -0.154** 0.217(0.035) (0.215)

Experience -0.040** -0.040** -0.037** -0.037**(0.004) (0.004) (0.004) (0.004)

Duration -0.711** -0.701** -0.632** -0.633**(0.034) (0.034) (0.032) (0.032)

Size 0.446** 0.449** 0.612** 0.618**(0.083) (0.083) (0.088) (0.087)

Development status:Pre_Alpha 0.738* 0.733* 0.796* 0.796**

(0.320) (0.321) (0.322) (0.322)Alpha 0.863** 0.869** 0.941** 0.938**

(0.301) (0.302) (0.303) (0.303)Beta 1.315** 1.330** 1.407** 1.402**

(0.281) (0.283) (0.283) (0.282)Production/Stable 1.858** 1.852** 2.017** 2.014**

(0.276) (0.277) (0.278) (0.278)Mature 1.251** 1.244** 1.480** 1.474**

(0.404) (0.405) (0.404) (0.405)Log likelihood -2195.027 -2185.570 -2282.232 -2281.741p 4.161** 4.215** 4.175** 4.174**

(0.159) (0.160) (0.154) (0.154)q 41.822** 43.970** 47.570** 47.660**

(11.325) (11.889) (12.167) (12.206)Likelihood-ratio test of q = 0 105.53** 108.99** 143.33** 142.62**

Notes: N = 5,589. Estimated coefficients and their associated standard errors (in parentheses) are listed under each model.*p < 0.05, **p < 0.01. SVN_total and Admin_SVN are measured in percentages in model 3 and model 4. For brevity, the coefficients of the control variablesare not shown.


structural equivalence, is salient for adoption decisionmaking (Burt, 1987). Within a similar virtual socialspace, interaction and communication among networkactors lead their behavior to categorical similarity. Inother words, if there exists a cultural understanding thatsocial actors belong to a common social category, adop-tion should be rapid (Strang and Meyer, 1993).

We also find that in the context of online technologyadoption, recent adoptions have a stronger impact onsubsequent adoption than distant ones. This might be dueto the fact that recent technological events loom larger orweigh more in online settings. This finding is differentfrom that of the real world social network, wherein trustand confidence are developed through ongoing socialinteraction among actors and a technology gets adoptedwhen a trusted contact recommends it. Therefore, dura-tion is positively correlated with adoption (e.g., Van denBulte and Wuyts, 2007). In contrast, our results suggestthat OSS projects readily trust the source of recent infor-mation they receive and seem to weigh the expediency oftrying the latest technology over the trust that requirestime to build within social relationships. We also find thatan increase in the size of a project team leads to anaccelerated rate of adoption. In real world social net-works, as team size grows, the cost of coordinatingmembers could escalate to deter technology adoption.For technology adoption in an online setting, however,the negative effect of team size seems to be offset by theease of collaboration and communication facilitated byvarious technologies.

Practical Implications

Our findings from this study have important practicalimplications. Online social networks represent anincreasingly important channel for individuals and orga-nizations to exchange views and gain valuable informa-tion (Godes and Mayzlin, 2004). Firms continue tolaunch online marketing campaigns to expand their cus-tomer bases (Hill, Provost, and Volinsky, 2006). Ourresults can possibly provide helpful guidance to suchefforts.

First, managers and practitioners need to understandthat imitation is not unique to offline social networks.Rather, it is also a strong factor affecting social behavior,including technology adoption, in online social networks.Imitation produces cascading moves toward adopting anew technology and is amplified when social actors aresubjected to pressure from specific subsets of their neigh-bors and the presence of network linkages among neigh-boring projects. Imitation from neighboring projects can

be simply regarded as a way of interpreting reality andacting in accordance with it.

Second, it is important to realize that even in onlinecommunities wherein the governance structures are pre-sumed to be flat and less hierarchical, asymmetric peereffects still exist: some members tend to have more influ-ence than others. The implication is that firms wishing toinfluence online customers’ preferences have to identifythe de facto leaders in such online networks to impart thenew ideas and accelerate the rate of technology orproduct adoption.

Third, the finding that recent adoptions are more influ-ential than distant ones provides new directions for futureonline marketing and technology or product adoption.Prior research showed that the duration of media expo-sure can affect individual behavior either way. In onlinesettings, it is the information and knowledge communi-cated more recently that have a stronger influence onindividual behavior.

Fourth, many researchers have suggested that quickadoption and assimilation of technology and innovationare of paramount importance for firms to gain competi-tive advantages (e.g., Majchrzak et al., 2005). Our resultsindicate that, for firms using virtual teams to performvarious tasks, it is important for them to coordinateteams’ online activities to facilitate the technology adop-tion process. This view is consistent with the need-basedmodels of technology adoption such as the “push-pull”model (Zmud, 1984).

Finally, our findings suggest the necessity of consid-ering multiple factors in studying online technology orproduct adoption. For example, imitation as a process ofsocial learning is not the only mechanism that influencesthe adoption decision. Other factors, either individuallyor collectively, can influence adoption choices in anetwork. Thus, firms have to take into account a variety offactors, including findings in this research, to speed-uptechnology or product adoption.

Limitations and Future Research

This study has its limitations that warrant further inquiry.First, we build our network measures based on the devel-opers’ collaboration information. Potentially, the commu-nication frequency might vary among project members.Thus, future research may investigate the social networkeffect further if data sets include the actual word-of-mouth information—who’s talking to whom, and thenature of the information exchange. It would be interest-ing to study the content of the email correspondencebetween developers to supplement the inferences we


make about the impact of online social networks on tech-nology adoption. Second, we do not consider the impactof marketing factors. As discussed earlier, one of thepractical implications of our research is that online socialnetworks have the potential to influence customer basesand marketing campaigns. Firms’ marketing activitiescould instigate marketing externalities within the onlinesocial networks. The context of this research context doesnot allow us to examine this important question. Futurestudies incorporating marketing activities can providefurther insights into how firms’ actions can affect onlinetechnology or product adoption. Third, the data we usedwere from OSS development projects. The uniqueness ofthe data set allows us to test the proposed research frame-work. However, one should be cautious in interpreting theresults and generalizing the results to other contexts.Future research should validate the findings from thisresearch in other contexts.

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