EMPIRICAL INVESTIGATION
A dissertation submitted to the
Kent State University Graduate School of Management in partial fulfillment of the requirements
for the degree of Doctor of Philosophy
by Jing Wang May, 2007
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B.A. Nankai University
, 1994 M.A. Bowling Green State University, 2001M.B.A. Kent State University, 2003 Ph.D. Kent State University, 2007
Approved by
Dr. Murali S. Shanker Chair, Doctoral Dissertation Committee
Dr. Marvin D. Troutt
Dr. Michael Y. Hu Members, Doctoral Dissertation Committee
Accepted by
Dr. James W. Boyd Doctoral Director, Graduate School of
Management
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support of numerous people. Thus, my sincere gratitude humbly goes to my doctoral
advisors, my husband, and my parents, for their support, patience, and faith in me over
this long journey.
I wish to thank my doctoral advisors, Dr. Michael Y. Hu, Dr. Murali Shanker and
Dr. Marvin D. Troutt for their guidance, inspiration, and most importantly, their devotion
at every stage of my dissertation. It was with their inspiration and the challenges they laid
before me that I gained so much drive and an ability to confront challenging research
issues head on.
I would also like to thank my husband and my best friend, Khole, for his support,
encouragement and patience. His tolerance of my occasional annoying moods is a
testament in itself of his unwavering devotion and love.
Finally, my special thanks go to my parents for their faith in me and for allowing
me to be ambitious.
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OPEN-SOURCE SOFTWARE AND ITS DEVELOPMENT MODEL ... 1
OSS SUCCESS AND WHY IS IT IMPORTANT TO STUDY IT? ... 4
CHARACTERISTICS OF OSS COMMUNITY AND THE IMPORTANCE OF SOCIAL NETWORKS IN OSS SUCCESS ... 6
PROBLEM STATEMENT ... 8
PURPOSES AND SCOPE OF THIS DISSERTATION ... 10
STRUCTURE OF THE DISSERTATION ... 13
Chapter 2 - Literature Review on OSS Research ... 14
OVERVIEW OF RESEARCH ON OSS SUCCESS... 14
OTHER RESEARCH ISSUES IN THE OSS LITERATURE ... 16
Inputs ... 17
Process ... 19
Outputs ... 21
Environment ... 21
Chapter 3 - Conceptual Framework and Research Hypotheses ... 26
OVERVIEW OF THIS CHAPTER ... 26
THE CONCEPTUAL FOUNDATIONS ... 30
Social Network Theories ... 31
IO Economics as a Framework for the Role of Macro-level Network Structures ... 32
RBV as a Framework for the Role of Project-level Network Structures ... 34
The Strategic Choice Perspective as a Framework for the Role of OSS Projects’ Conduct ... 37
PROPOSED MODEL ... 38
Contextual Boundary of the Proposed Model ... 39
Constructs ... 40
Hypotheses Development ... 42
Chapter 4 - Methodology, Analysis, and Results ... 69
RESEARCH SETTING AND DATA ... 69
EXTRACTING NETWORK STRUCTURAL PATTERNS ... 71
SAMPLE SELECTION ... 73
VARIABLES AND THEIR MEASURES ... 75
Dependent Variables ... 75
Project Conduct ... 76
Independent Variables (Network Structural Variables) ... 77
Control Variables ... 79
MODEL SPECIFICATION AND TESTING PROCEDURE ... 80
Testing for the Effects of Macro- and Project-level Structural Variables ... 80
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The Moderating Effects of Other Contextual Variables ... 96
Chapter 5 - Discussion, Implications and Conclusions ... 139
OVERVIEW ... 139
DISCUSSIONS OF THE FINDINGS ... 140
Network Size ... 140
Network Density ... 140
Network Diversity ... 142
Project Degree Centrality ... 143
Developer Degree Centrality ... 144
Other Structural Variables ... 145
Project Conduct ... 146
Contingency Factors ... 146
CONTRIBUTIONS AND IMPLICATIONS ... 148
LIMITATIONS AND SUGGESTIONS FOR FUTURE RESEARCH ... 150
CONCLUSION ... 153
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Table 4.1 The proportion of projects that have gone through multiple development phases ... 99
Table 4.2 The Percentage Composition of All SourceForge.net Projects as of July 2006 ... 100
Table 4.3 Percentage Composition for Projects in Difference Phases within Each Domain ... 101
Table 4.4 Application Domain Composition for the Largest 55 Sub-networks ... 102
Table 4.5 The Number of Missing Values for the Success Measures ... 103
Table 4.6 Variables and their Operational Definitions ... 104
Table 4.7 Variable Names and Their Notations ... 106
Table 4.8 Means Standard Deviations, and Correlations ... 107
Table 4.9 OLS Estimation of Regression Models ... 109
Effects of Macro-level Structural Variables on OSS Project Conduct ... 109
Table 4.10 OLS Estimation of Regression Models ... 110
Effects of Macro-level Structural Variables and Conduct Variables on OSS Project Success ... 110
Table 4.11 Multiple Comparisons between Groups of Various Network Size ... 112
Table 4.12 Multiple Comparisons between Groups of Various Network Density ... 113
Table 4.13: Frequencies of Projects in Open and Closed Networks ... 114
Table 4.14 Independent Samples t-Test for Closed and Open Networks ... 115
Table 4.15 Multiple Comparisons between Groups of Various Network Diversity ... 116
Table 4.16 OLS Estimation of Regression Models ... 117
Effects of Project-level Structural Variables on OSS Project Conduct ... 117
Table 4.17 OLS Estimation of Regression Models ... 118
Effects of Project-level Structural Variables and Conduct Variables on OSS Project Conduct ... 118
Table 4.18 Multiple Comparisons between Groups of Various Project Degree Centrality ... 120
Table 4.19 Multiple Comparisons between Groups of Various Developer Degree Centrality ... 121
Table 4.20 Moderating Effects of Network Structures on Conduct-Performance Relationship ... 122
Table 4.21 Moderating Effects of Development Phase and Domain on Structure-Conduct Relationship .. 124
Table 4.22 Moderating Effects of Development Phase and Domain on Structure-Performance Relationship ... 126
Table 4.23 Moderating Effects of Development Phase and Domain on Conduct-Performance Relationship ... 128
Table 4.24 Hypotheses Testing Using OLS ... 130
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Figure 3.1 Affiliation Networks and Collaboration Networks ... 64
Figure 3.2 Conceptual Foundations of the Proposed Framework ... 65
Figure 3.3 A Network Model of OSS Project Success ... 66
Figure 3.4 An Illustrative Example for Calculating Macro-level Network Structural Variables ... 67
Figure 3.5 An Illustration of Cohesive Sub-groups ... 68
Figure 4.1. Projects Spanning Multiple Phases ... 132
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Chart 4.2 Histogram of Network Density ... 135
Chart 4.3 Histogram of Network Diversity ... 136
Chart 4.4 Histogram of Project Degree Centrality ... 137
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Chapter 1
Introduction
OPEN-SOURCE SOFTWARE AND ITS DEVELOPMENT MODEL
Open-source software (OSS) refers to any computer software whose source code is made available under differing OSS licensing formats that bestow the users the freedom to use, modify, and improve the software, and to redistribute the software in modified or unmodified form. Raymond (2001, 1997) compares the OSS development model with the traditional software development model and describes the former as the bazaar development style as opposed to the latter, the cathedral style. In the traditional model, software development takes place in a centralized manner and is similar to building a cathedral. Roles are clearly defined among people including systems design, project management, programming, implementation etc. In the bazaar model, however, software is developed in a decentralized, voluntary, public, and collaborative manner and roles are not clearly defined (Raymond, 2001). In the extremely distributed environment enabled by the internet, OSS developers from geographically dispersed locations voluntarily collaborate to develop and produce various software products. Distinguishing characteristics of this open-source bazaar model include: 1) users as co-developers, 2) voluntary collaboration and cooperation among community members, 3) early releases, 4) frequent integration and frequent releases, 5) several versions, and 6) high modularization (Raymond,
2001; von Hippel & von Krogh, 2003).
THE SIGNIFICANCE OF OSS RESEARCH
OSS projects are emerging as a significant economic, social, and cultural phenomenon (von Hippel & von Krogh, 2003). The number of open-source software (OSS) projects is rapidly growing. A significant amount of software developed by commercial firms is being released
under open-source licenses. With little or no marketing, open-source software is finding its way into the information technology (IT) arsenal of a variety of companies and has become the dominant platform for many categories of business applications. According to a survey by Netcraft, as of February 2006, 68% of the web server market has been captured by the free, open-source Apache software, compared to 20% by the proprietary Microsoft web server (Netcraft, 2006). Other successful examples of open-source software include the GNU/Linux operating system, Sendmail, and the Perl programming language (von Hippel & von Krogh, 2003).
While the software industry has struggled to discover methods of developing quality software products, vocal advocates of OSS have been quick to point to the commercial success enjoyed by Linux and the Apache server and the potential of the OSS approach to produce reliable, flexible, and high quality software quickly and inexpensively (Dempsey, Weiss, Jones, & Greenberg, 2002; von Hippel, 2001). Some proponents of the OSS development model argue that its bazaar style possesses inherent advantages over the cathedral style. As Raymond (2001, 1997) nicely puts it, “Given enough eyeballs, all bugs are shallow.” Due to these advantages, they claim that OSS development has the potential to compete successfully, and in many cases displace traditional commercial development methods (Mockus, Fielding, & Herbsleb, 2002). Others assert that OSS helps companies to achieve greater market penetration, offer opportunities to establish an industry standard, and create increased competitive advantages over their competitors (Portelli, 2000). Still others vision that OSS presents an alternative economic and innovation model for engendering and managing robust software that will ultimately reshape the multi-billion commercial software industry (Dempsey et al., 2002; Sharma, Sugumaran, & Rajagopalan, 2002).
While skeptics challenge the aforementioned assertions, few would dispute that the manner by which OSS is developed and distributed has rich implications for both practitioners
and researchers. In practice, the development of OSS lacks central control and traditional coordination mechanisms such as plans, system-level design, schedules, and defined processes that are generally considered important for software development (Mockus et al., 2002; von Hippel & von Krogh, 2003). Yet the OSS community has been consistently producing products equivalent, if not superior, to software developed using traditional approaches. It subverts the traditional software engineering and project management principles and provides extreme and successful cases of distributed development (Madey, Freeh, & Tynan, 2002; Mockus et al., 2002). With little formal motivational mechanism, the OSS practice poses a serious challenge to the basic presumptions of economic theories that innovation is driven by protection of intellectual property and by individual and corporate financial wealth. Open-source software contributors are rarely paid for their services, and the licenses and hacker culture make it difficult, if not impossible for the contributors to appropriate returns from their products. Yet thousands of programmers keep contributing freely to make the OSS movement a success (von Krogh, 2003). As a fundamentally new process to develop software, the OSS practice also threatens existing proprietary software business strategies. As Mockus et al. (2002) assert, this threat is not the sort posed by a new competitor that operates according to the same rules but strives to do it faster, better, and cheaper. This threat is more fundamental, and goes to the basic motivations, economics, market structure, and philosophy of the institutions that develop, market, and use software.
Therefore, from a practical standpoint, the OSS movement provides important management lessons regarding alternative strategies to nurture knowledge creation (G. Lee & Cole, 2003), to structure and implement innovations and new product development (von Hippel & von Krogh, 2003; von Krogh, 2003), and to take advantage of the opportunities offered by the OSS movement to gain competitive advantages (von Krogh, 2003). Theoretically, the practice of
OSS will be intriguing to researchers working in various fields for one simple reason: the OSS movement presents a phenomenon that challenges many prevailing theories in economics (von Hippel, 2001), knowledge management (G. Lee & Cole, 2003), software engineering (Madey et al., 2002), business strategy (Madey et al., 2002), and information systems (IS) and innovation management, (Madey et al., 2002; von Hippel & von Krogh, 2003).
Given the practical and theoretical challenges and implications of OSS, it is vital that we understand and evaluate this phenomenon (Mockus et al., 2002). Nonetheless, there has been relatively little systematic theorizing and empirical work to provide insights into the various puzzles generated by this phenomenon. How do hundreds of contributors from all over the world effectively coordinate with each other and manage the process in the absence of traditional hierarchical structure and control mechanisms? Is the OSS movement setting the stage for a structural transformation in the software industry or is it another fad? What role do successful large-scale open-source projects such as the Linux operating system play in shaping the OSS community in particular, and the whole software industry in general? What are the social and economical sources of influences that shape the development, evolution, and deployment of successful or not so successful OSS projects? How should IT managers and national policy makers develop more effective strategies to embrace the opportunities and handle the threats brought about by the OSS practice?
OSS SUCCESS AND WHY IS IT IMPORTANT TO STUDY IT?
Although the investigation of each of the aforementioned issues is warranted, in this dissertation, we are interested in identifying factors that are important to the success of open-source software systems. Despite the major commercial success enjoyed by some OSS projects, not all OSS initiatives have been successful. Examples in point include SourceXchange and Eazel.
Therefore, studies of this nature should shed light on issues regarding the conditions under which OSS projects are likely to succeed, how they can be managed most successfully, and how they influence and are influenced by their social context. If the OSS movement is going to set the stage to transform the current software industry, it is imperative that practitioners learn from the successful and not so successful examples of OSS. By examining factors relating to the success of OSS projects, we can provide guidelines on how to produce OSS products that have a greater chance of being deployed, how to promote OSS diffusion, and how to prevent them from deterioration. Studies of this nature will also help commercial software firms learn how to structure their software development teams more effectively in order to produce high quality products. From a broader perspective, the OSS development model has rich implications for innovation management and new product development in general. This model has been referred to as “user-driven innovation” (Lerner & Tirole, 2002) and community-based innovation (von Hippel & von Krogh, 2003). Studying the critical success factors of OSS projects will also improve our understanding of how to structure and nurture innovative communities.
Many companies such as IBM, Google, Sun, and Oracle have considered open-source (or variations of this approach) as integral to their business operation and many others like Red Hat and SuSe, Novell are aggressively seeking business opportunities provided by successful open-source products such as Linux (von Krogh, 2003). Further, public institutions and governmental agencies, particularly governments of developing countries are increasingly interested in open-source software because the increasing reliance on software systems has generated a number of concerns over software security, trusted computing platforms, and the increasing digital divide between the developed and under-developed world. As the long-term viability and legitimacy of the OSS movement largely depends on the level of success achieved by OSS projects, studying
OSS success should also help practitioners and national policy makers better formulate their strategies with the opportunities and threats raised by the OSS phenomenon.
CHARACTERISTICS OF OSS COMMUNITY AND THE IMPORTANCE OF
SOCIAL NETWORKS IN OSS SUCCESS
The OSS community has some unique features. As previously noted, one distinguished feature of the OSS development model is the cooperation and collaboration among the community members (including both developers and users). In the process of cooperation, participations, and collaborations, various social networks emerge (Grewal, Lilien, & Mallapragada, 2006; Kuk, 2006). To some extent, the OSS community is a more networked world than the traditional organizational communities. Although the formation of alliances and partnerships has become a ubiquitous strategy for contemporary organizations, formal boundaries still exist between the participating firms. In the OSS community, programmers can join, participate, and leave any project at any point in time and developers collaborate not only within the same project team but also across teams. Therefore, OSS projects represent extremely fluid organization forms with blurred boundaries and high linkages among them. Researchers have recognized that OSS projects are connected with each other into networks of relationships because their developers are participating in multiple projects and are collaborating both within and across projects (Grewal et al., 2006; Kuk, 2006).
Such connectedness and relationship networks can be enduring and of strategic importance for the success of OSS projects. Although the significance of social networks to OSS project success remains largely under-explored, there is a rich strand of research in sociology, organizational behavior, and strategic management that has devoted effort to explaining how the performance of individuals, groups, or organizations, may be influenced by the social structure of
relationships within which they are embedded (Collins & Clark, 2003; C. Lee, Lee, & Pennings, 2001; Lynall, Golden, & Hillman, 2003; McDonald & Westphal, 2003). Researchers suggest that networks of relationships can be important sources of competitive advantages due to the valuable resource availability and control benefits bestowed by social networks (Burt, 2000; Granovetter, 1983, 1985). The distinct social structural patterns in exchange relations have the potential to shape the flow of resources. Empirical work provides further support for the postulation that networks of ties and relationships help explain the differential performances of actors (Collins & Clark, 2003; C. Lee et al., 2001; Lynall et al., 2003; McDonald & Westphal, 2003). Similarly, the success of OSS projects may also be influenced by the social structure of relationships within which they are situated. OSS projects discover, acquire, and create information, knowledge, and resources through their interactions in the OSS community. The distinct social structural patterns in OSS projects’ exchange relations have the potential to shape the flow of such resources. Projects located at an important position on the network may have better access to various resources and may be able to control the flow of such resources. This in turn may influence the projects’ success by providing the projects with asymmetric access to information, resources, and human talents; and with advantages from learning (Grewal et al., 2006).
Another important characteristic of the OSS community is the lack of formal governance and control mechanisms (Mockus et al., 2002; von Hippel & von Krogh, 2003). In conventional organizations, well-defined governance mechanisms are present including hierarchical structures, clear role definitions, and formal contract agreements to ensure the smooth functioning and success of the organization. However, in the case of OSS projects, explicit governance structures are absent due to the voluntary and distributed nature of the OSS development process. In the absence of formal mechanisms, how can OSS projects then function effectively and efficiently? Researchers have long found that social relational assets such as social networks, social norms,
and trust could function as substitutes for complex and explicit contract arrangements, particularly when it is impossible or too costly to establish formal governance structures (Granovetter, 1985; Larson, 1992; Poppo & Zenger, 2002; Uzzi, 1997). For instance, studies have suggested that trust acts as an alternative for traditional control mechanisms in virtual organizations where vertical control, hierarchical authority, and formalized organizational procedures and policies are typically absent (Morris & McManus, 2002; Sheppard & Tuchinsky, 1996). Similar arguments can be extended to the OSS community: the social networks of an OSS project may serve as an alternative to hierarchical structures and formal control mechanisms to ensure its day-to-day functioning and long-term success. For instance, studies indicate that reciprocity is common in the OSS community. As a result, some developers strategically nurture their social networks by sharing knowledge with other teams so that they are also likely to receive help when needed (Kuk, 2006).
To encapsulate, due to the widely observed inter-connectivity among OSS projects, and the fact that social networks could function as substitutes of formal governance mechanism, an awareness of the role of social networks could be central towards understanding OSS projects’ performance and success. A complete understanding of the factors that are important to OSS project success requires us to take into consideration the networks of the relationships in which OSS projects are embedded and the performance implications of such networks.
PROBLEM STATEMENT
Nevertheless, much of prior research on OSS success ignores the inherent inter-connectivity in the OSS community. The current OSS success literature usually focuses on the project as the unit of analysis and has tried to identify the attributes of projects that influence their performances. While these prior efforts have led to valuable insights on the behavior and
performance of OSS projects, they have typically conceptualized each OSS project as independent entities and assumed that a project’s success is neither influenced by the actions of other projects nor by the relationships in which the project is already embedded. Such a view is inadequate in addressing issues relating to the OSS community because as previously highlighted, OSS projects are inherently interrelated through their developers’ participation and collaboration. An independent view falls short in taking into account the relationship networks within which each OSS project is embedded and the performance consequences of such exchange relationships. Given the proliferation and the potential importance of various forms of social links and exchanges in the OSS community, neglecting the networks in which OSS projects are embedded can lead to an incomplete understanding of project conduct and performance. Therefore, it is our belief that a network perspective is imperative in examining issues related to OSS project success. In response to the limitations of the independent view which currently dominates the OSS success literature, we argue in this dissertation that there is a clear need for a shift in conceptualization when seeking to identify factors influencing the performance and success of OSS projects. We need to go beyond assuming that OSS projects exist as independent entities and focusing on the projects as the unit of analysis. We need to shift to understanding the larger networks in which OSS projects are embedded and the effects of such networks on the performance of the projects embedded in them. Although researchers suggest that an array of elements in social networks including structural, cognitive, institutional, and cultural, could potentially influence the behavior and performance of participating actors (Zukin & DiMaggio, 1990), in this dissertation, we focus on the social structural context and seek to explore how the structural patterns of OSS projects’ relationship networks influence their success. Specifically we aim to answer three intrinsically related questions: 1) How do the structural patterns of OSS projects’ social network environment influence their success? 2) Do OSS projects make use of strategies to respond to the
(dis)advantages of their social network environments to influence their performance? 3) How do OSS projects’ social networks and the strategies they use interact to influence their success?
PURPOSES AND SCOPE OF THIS DISSERTATION
In answering the above three questions, we aim to achieve two specific goals. First, we draw on research in industrial organization (IO) economics, strategic choice perspective, resource-based view (RBV), and the social network theories and advance an a conceptual model for project success in OSS systems. Second, we empirically evaluate the proposed model by analyzing panels of data from the world’s largest OSS development data repository SourceForge.net.
Social network theories enable us to adopt a network perspective and conceptualize each OSS project as existing in a larger network of many related projects. We posit that such relationship networks provide the projects with both opportunities and constraints and can have rich implications for their conduct and performance.
In addition to focusing on highlighting the importance of a networked perspective in understanding important factors shaping the performance of OSS projects, we further draw on research in RBV and IO economics to provide a theoretical explanation in our conceptual model on why OSS project success is influenced by the social structure of ties and relationships within which the projects are embedded. IO economics and the RBV have provided two competing theoretical foundations for strategic management research into the determinants of firm performance and it is our belief that they could also shed light on factors that are significant in explaining OSS projects’ differential performance. IO economists consider industry as the main unit of analysis and the main driver behind firm performance differentials (Porter, 1980). Within this stream of research, the structure–conduct–performance (SCP) model is the most popular
theoretical framework, which proposes that the structural characteristics of industries could either enable or constrain the strategies/conduct of the member firms, which in turn influences their performances. While IO economics considers industry as the primary determinants of performance, the RBV focuses on the firm itself and posit that firm idiosyncratic resources and capabilities are the explanation for heterogeneity in firm performance (Barney, 1991). Although earlier work adopting the RBV concentrates on resources and capabilities internal to the firm, more recent development in this area argues that firms establish alliances and partnerships with each other, hence, their value-creating resources and capabilities extend far beyond their boundaries into the social networks the firms are embedded in.
Despite their seemingly opposite positions on sources of performance advantage, researchers have recognized that IO economics and the RBV are indeed natural complements of each other (Foss, 1996; Spanos & Lioukas, 2001). With IO economics solely focusing on the macro-industrial forces whereas the RBV exclusively on micro- firm-specific factors, each of these frameworks only offers part of the story. Researchers have found that the macro-level industrial forces and the micro-level firm resources are both important sources of competitive advantage, but explain different dimensions of performance (Spanos & Lioukas, 2001; Spanos, Zaralis, & Lioukas, 2004). Hence, these two frameworks can and should be integrated, because together, they present a more complete picture. In this dissertation, we intend to explore the role of network structures in OSS projects’ success. There are differing levels of network structures, each of which could be significant in influencing the performance of OSS projects. Therefore, drawing on both the IO economics framework and the RBV allows us to investigate why and the extent to which the network properties at both the macro- and micro-level affect OSS project success. Adopting the IO economics lens, we can focus on the macro-environment (including the industry and the overall social networks) as the unit of analysis and explore how an OSS project’s
success is shaped by the distinct structural patterns of its macro-environment. Although the IO economics and SCP were originally intended as a research paradigm for the study of the performance effects of market structures, we believe that social network structures play a similar role as market structures. Specifically, the distinct structural features of network ties provide both opportunities and constraints for the member projects and hence have rich implications for their behaviors and performances.
On the other hand, the RBV enables us to focus on the OSS project as the unit of analysis and study the performance implications of structural characteristics that are specific to each OSS project. Following the basic tenet of the RBV, we conjecture that OSS projects’ performance is intimately linked to their resources and capabilities. However, due to the blurred boundary delineation among OSS projects, the success of OSS projects depends not only on the resources and capabilities internal to the project, but also on all the resources and capabilities that are made available through their network ties and relationships. Therefore, at the project level, structural features such as the projects’ distinct locations in their social network influence the extent to which valuable resources and capabilities are available to each project in the social network and the extent to which each project is able to mobilize and exploit such resources and capabilities.
Moreover, OSS projects do not simply passively accept whatever positive or negative influences are exerted by their social network environment. On the contrary, they actively act upon the environment and constantly use various strategic responses to overcome the structural disadvantages and create favorable conditions to positively influence their performance. Therefore, drawing upon the Strategic Choice Perspective, we conjecture that OSS project conduct is another important source of performance variation. By integrating the social network theory, IO economics, the RBV, and the Strategic Choice Perspective, our proposed conceptual
model provides a systematic framework on how OSS project strategies and the network structural characteristics at both the macro- and micro-level could affect OSS projects’ performance.
STRUCTURE OF THE DISSERTATION
This dissertation unfolds as follows. Chapter 2 provides an overview of the work done in the field of open-source software. Chapter 3 begins with the conceptual foundations on which the theoretical framework of this study is based. We then provide an in-depth theoretical conceptualization of why and how project conduct and social networks play an essential role in the success of open-source software systems. A series of appropriate and theoretically grounded hypotheses are then developed. In chapter 4, we specify the methodology utilized to test the proposed framework and present the results. Finally, we discuss the findings, implications, and limitations of this dissertation and give suggestions for future research.
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Chapter 2
Literature Review on OSS Research
OVERVIEW OF RESEARCH ON OSS SUCCESS
The robust functioning of open-source products in the marketplace and its novel modes of operation pose important and exciting new questions for scholars in various fields. Yet literature in this area is still in its infancy, probably reflecting the distributed and emerging nature of this phenomenon. One stream of research which is most relevant to this dissertation examines what factors have contributed to the success of OSS projects. Elements that are commonly cited as significant to the success of OSS projects include the devotion of developers (Bonaccorsi & Rossi, 2003), a critical mass of developers (Mockus et al., 2002), market structure (Bonaccorsi & Rossi, 2003), license strategy (Stewart, Ammeter, & Maruping, 2006), team leadership (Bonaccorsi & Rossi, 2003), team composition, collective identity (Stewart & Gosain, 2006), and trust (Stewart & Gosain, 2006). Earlier work in this area focuses on well-known OSS projects such as Apache, Mozilla, and Linux to identify elements that are important to their success. For example, Mockus et al. (2002) conduct two case studies. Their findings indicate that a critical mass of 10 to 15 core developers is crucial to the success of Apache and Mozilla. Peripheral developers who provide feedback and participate in bug fixing and service supporting are also important to project success. Bonaccorsi et al. (2003) suggest that market structure plays a critical role in the differential performance between Apache and Linux. They believe that open-source systems are less successful in the client market than in the server market because the former has already been dominated by Microsoft products. Therefore, there is a negative network externality effect, and market structure functions as a barrier to entry for the client-end OSS products. In the presence of significant and negative network externality, the value of Linux innovation decreases with the number of adopters who use Microsoft product and other items that
are compatible with Microsoft products. Valuable as these efforts have been in improving our understanding of an emerging phenomenon like OSS, it is not clear whether or not findings based on well-known projects can be generalized to the larger OSS population.
More recently, studies with a larger sample size have begun to emerge. For example, some researchers conceptualize the OSS communities as a network of relationships because developers are connected through the projects they participate in (Grewal et al., 2006). They examine how the network structure affects the success of open source projects. Their findings suggest that project network embeddedness positively influences both project technical success and commercial success. Nevertheless, the effects of embeddedness are much stronger for technical success than for commercial success (Grewal et al., 2006). Others seek to address the issue of what leads to the effectiveness of OSS development teams in the absence of formal control. Stewart and Gosain (2006) develop a framework of the OSS community ideology (including specific norms, beliefs, and values) and a theoretical model suggesting that adherence to components of the ideology impacts effectiveness in OSS teams. Using survey and SourceForge panel data, the authors find that team members' adherence to the tenets of the OSS community ideology influences OSS team effectiveness by enhancing trust and communication quality, but different components impact effectiveness in different ways (Stewart & Gosain, 2006). Stewart et al. (2006) examine how license restrictiveness influences user interest in a particular OSS project and find that users are most attracted to projects that employ nonrestrictive licenses.
Recent progress notwithstanding, important gaps remain. In spite of the increasing interest in OSS research, there is little theory or rigorous empirical work regarding what has contributed to the success of open-source software. Empirically, many studies adopt a case-study approach, limiting us from generalizing their findings to the larger OSS population. From a
theoretical stand point, the discussion of the critical success factors of OSS has been dominated by a perspective focusing on empirical assessment, and this discussion, while practically useful, is theoretically deficient. Even those that strive to guide their investigations with theoretic development, most of them adopt a view that considers OSS projects as independent entities that are isolated from the larger systems within which they are situated. Such a perspective ignores the fact that each OSS project is indeed embedded in networks of relationships with other OSS projects and such networks of relationships could be enduring and of strategic significance to the performance of OSS projects.
Further, open-source communities represent fluid and dynamic organizational forms that change frequently in response to the changing environment. The developer and user base of OSS projects keep changing. The general purposes of open-source applications undergo constant modification and improvement in order to reflect local conditions of the users’ task and work environment. Nevertheless, most of the current established models of open-source project success employ a static view and assume that factors influencing the success of OSS projects and the effects of such factors do not change over time. Such a static view falls short in accounting for the dynamic and on-going processes of how open-source systems and their performance evolve over time. A good understanding of the success of open-source projects hence requires longitudinal investigations that can trace ongoing dynamics of OSS improvement. Therefore, future research that longitudinally investigates the evolution of OSS projects should shed new insights into the factors that are important to the success of OSS projects.
OTHER RESEARCH ISSUES IN THE OSS LITERATURE
Parallel to the research stream which investigates the critical success factors of OSS projects, several other schools have also recently emerged, probing into other important research
issues in the OSS area. Although these issues are not directly relevant to this dissertation, we summarize them in this section so that readers could have a more complete picture of the current state of the OSS literature. We categorize and explore these issues using the economics input-process-output framework. Within this framework, OSS projects are viewed as processes that take in different inputs ranging from developers with differing skills, motivation, and licensing formats and yield different outcomes including productivity, product quality, and market share. We structure papers published under a general typology of four major areas, i.e., input, process, output, and environment. Figure 2.1 provides an overview of the classification of each topic area. Table 2.1 summarizes work done in these areas.
Inputs
We classify whatever goes into an open-source project to achieve an outcome as its input. In the case of open-source software, input can take both tangible and intangible forms. It includes but is not limited to the skills of developers, size of the user/developer base, motivations of contributors, and licensing format of the software.
Motivations of Contributors: One central question is why thousands of top-notch programmers are willing to contribute freely to the provision of a public good, open-source software (Hertel, Niedner, & Herrmann, 2003; Lerner & Tirole, 2002; von Krogh & von Hippel, 2003). A number of studies have begun to investigate this important research question and their findings suggest that altruism alone is not sufficient in explaining contributors’ behavior. Using the case of Apache, Lerner and Tirole (2002) argue that reputations that accrue from successful contributions to open-source projects play an important motivational role for top-notch programmers. Hertel, Niedner, and Herrmann (2003) conduct an internet survey of 141 contributors to the Linux Kernel and suggest that identification as Linux developers, need to
improve their own software, and perceived indispensability of the team are three key factors influencing contributors’ involvement with the Linux Kernel. Franke & von Hippel (2003) find that in the case of Apache security software, contributors’ dissatisfaction with off-the-shelf “closed” software and their need to customize the product are the key determinants of their contribution to the open-source projects. Using archival and survey data on the Apache projects, Roberts, Hann, and Slaughter (2006) suggest that extrinsic incentives such as pay and career concerns do not compromise contributors’ intrinsic gratification from OSS participation. Egocentric rewards such as public recognition and status positively influence OSS participation and performance (Roberts et al., 2006). Shah (2006) finds that developers initially participate in OSS projects due to their own needs to improve the software. Then a majority of the developers leave after their needs are satisfied and only a small subset remains involved because participation becomes a hobby for them.
Licensing: Some scholars postulate that the form of licensing arrangements has an important impact on the landscape of software markets (Välimäki & Oksanen, 2005). Using Microsoft Windows, Apple OS X, and GNU/Linux as examples, Välimäki and Oksanen (2005) demonstrate that open-source and free software licensing has been one of the most important agents of changes in the microcomputer operating systems markets, including new entrants in the relatively closed market and renewed business models by incumbents. According to OpenSource.org, there are more than 50 different OSS licensing formats (http://www.opensource.org/licenses). Examples of OSS licensing formats include Apache license, BSD license, GNU General Public License (GPL), MIT license, and GNU Library or "Lesser" General Public License (LGPL). These license formats differ in terms of the degree of restrictions they impose on the use of the software. For example, GPL is the most restrictive among all the licensing formats. Under GPL licensing, any software that cites other GPLed
software or simply includes lines of code released under GPL, must be distributed under the same licensing terms. The BSD license is much less restrictive. Under BSD, licensees are allowed to modify the software or derive proprietary versions of the software (Comino, Manenti, & Parisi, 2007). Researchers suggest that OSS developers are more attracted to participate in projects with less restrictive licensing formats (Stewart et al., 2006).
Design Modularity: Due to the distributed nature of open-source development, much research has highlighted the critical role of modular design in the success of OSS products. A number of researchers have compared the degree of modularity of open-source code and that of the proprietary code. They find that open-source products are significantly more modular than their commercial counterparts. However, they also find that commercial products can be redesigned with comparable degree of modularity if a purposeful effort is made (MacCormack, Rusnak, & Baldwin, 2006).
Process
The development process of commercial software has been a major issue in the literature of information systems, software engineering, management, and organizational science. Research suggests that tight management of the development process such as subcontracting, resource allocation, and careful project planning and scheduling results in faster cycle time, lower development cost, and higher product qualities (Austin, 2001; Harter, Krishnan, & Slaughter, 2000; Jiang, Klein, Hwang, Huang, & Hung, 2004). The process of open-source software development differs fundamentally from that of commercial software. The OSS practice eschews all the traditional software engineering and project management principles including plans, system-level design, schedules, and defined process management. Yet the OSS community has been consistently producing high quality products. A number of researchers have begun to
investigate how the process of open-source software development differs from that of commercial software development and what process attributes have contributed to the success of open-source projects.
Development Process: Effective coordination is critical in software development and researchers have been intrigued by how OSS developers coordinate with each other in such distributed and decentralized environments. While roles are not clearly defined, researchers suggest that OSS members voluntarily assume different roles in the development process, including passive users, active users (peripheral developers), core developers, and group/project leaders (Mockus et al., 2002; Xu, Gao, Christley, & Madey, 2005). They further highlight that a balanced structure with participants assuming a variety of roles is critical to the success of the open-source development model. Other researchers have focused on the significance of technologies (e.g., version control systems, collaborative editors, chat, and online meetings) in the collaborating members’ participation in the community (Cubranic & Booth, 1999).
Innovation Process: The OSS movement provides us with a wonderful example of innovation communities where users of the software system play an important role in creating important innovations, attracting an increasing level of academic interest in the investigation of the community innovation process. Some researchers have focused on the knowledge sharing process in some open-source projects, seeking to explain why much of the OSS development is carried out by a small percentage of developers and the effect of such participation inequality. They find that participation inequality may not necessarily be bad for knowledge sharing as too many developers will not benefit knowledge sharing by increasing the coordination and communication cost. However, participation inequality has a positive effect on knowledge sharing only up to a certain level. The authors find that extreme concentration of participation limits knowledge sharing by increasing the cognitive strain on a few developers (Kuk, 2006).
Outputs
A number of studies have devoted their attention to proposing various outcome indicators of OSS project performance, including code quality (Stamelos, Angelis, Oikonomou, & Bleris, 2002), technical improvements (Crowston, Annabi, & Howison, 2003; Grewal et al., 2006), commercial success (Crowston et al., 2003; Grewal et al., 2006), and team effectiveness (Stewart & Gosain, 2006). Nevertheless, very few have empirically evaluated the actual outcome of open-source software projects. Among the few exceptions, Stamelos et al. (2002) measure quality characteristics (testability, simplicity, readability, and self-descriptiveness) of 100 applications written for Linux. Their findings indicate that these applications demonstrate quality that is lower than the industrial standard.
Environment
The outcome of an open-source project cannot be isolated from the environment within which it operates. A fourth general area addressed by open-source researchers is how the macro-environment influences and is influenced by open-source projects.
This stream of research examines the process by which the rivalry between commercial software and their open-source variants evolves and the degree to which this rivalry influences both the open-source community and the proprietary software companies. Some researchers focus on how the commercial software companies adapt their technology strategies in response to the threat and opportunities created by the OSS movement. An observable trend identified is the emergence of hybrid strategies that proprietary software firms pursue to exploit the advantages of open-source software while retaining control and differentiation (West, 2003). Using the cases of IBM, Sun Microsystems, and Apple Computer, a number of studies have discussed different strategies employed by different for-profit firms at different timeframes. Other studies have
examined how the presence of network effects influences the diffusion of open-source software. Bonaccorsi and Rossi (2003) posit that in an environment previously dominated by established proprietary standards, open-source products are subject to the influence of negative network externality. They develop an agent-based simulation model to identify and evaluate the relevant factors that facilitate or prohibit the diffusion of open-source software. The simulation results indicate that the diffusion of open-source products depends heavily on the agents’ perception of open-source software. There is a clear negative impact of installed base of commercial software although a moderate negative network effect is not sufficient to block the diffusion of open-source software. On the other hand, given a distribution of beliefs among agents and in the absence of incumbent advantages over open-source software, the only way commercial software can dominate the market is fierce competition and massive investment in research and development (Bonaccorsi & Rossi, 2003).
Figure 2.1. Categorization of Topics in OSS Literature
- Motivation
- Licensing
- Modular Design
- Innovation
Process
- Development
Process
- Quality
- Technical
Improvements
- Commercial Success
INPUTS
PROCESS
OUTPUTS
Table 2.1 Summary of Articles
Topics Authors Summary Methodology
Motivations
Lerner & Tirole, 2002
Private reputation plays an important role in motivating developers.
Case Illustration (Apache) Hertel, Niedner, &
Herrmann, 2003
Identification as Linux developers, need to improve their own software, and perceived indispensability of the team are three key factors influencing contributors’ involvement with the Linux Kernel.
Survey (141 contributors to Linux Kernel)
(Franke & von Hippel, 2003)
Developers contribute and use open source software because they are not satisfied with “closed” product and they want to be able to customize the
software based on their own needs.
Case Study (Apache Security Software)
Roberts, Hann, & Slaughter, 2006
Extrinsic incentives such as pay and career concern do not compromise contributors’ intrinsic gratification from OSS participation and egocentric rewards such as public recognition and status positively influence OSS participation and performance.
Archival data and survey on the Apache Projects
Licensing Välimäki & Oksanen, 2005
The authors argue that open source and free software licensing has been one of the most important factors of change in the microcomputer operating systems markets, including new entrants in the relatively closed market and renewed business models by incumbents.
Case Illustration (Microsoft Windows, Apple OS X, GNU/Linux) Modularity MacCormack, Rusnak, & Baldwin, 2006
Open source products are found to
be significantly more modular than
their commercial counterparts.
Case Study (Linux and Mozilla)
Development Process
Mockus et al., 2002
Devotion from both core and peripheral developers are critical for the success of Apache and Mozilla.
Case Studies (Apache and Mozilla) Xu et al., 2005
A balanced structure with
participants assuming a variety of
roles including passive users, active
users, core developers, and
group/project leaders is critical to the
success of open source development
model.
SourceForge.net Panel Data
Innovation Process
Kuk, 2006 The author focuses on the knowledge
sharing process in some opens source projects, seeking to explain why much of the OSS development is carried out by a small percentage of developers and the effect of such participation
inequality. The study finds that participation inequality has a positive effect on knowledge sharing, but only up to a certain level. KDE Developers’ mailing list, Content Analysis Bonaccorsi & Rossi, 2003
They developed an agent-based simulation model to identify and evaluate the relevant factors that facilitate or prohibit the diffusion of open source software. The simulation results indicate that the diffusion of open source products depends heavily on the agent’s perception of Open Source, the effect of network
externality, and the competition reaction of commercial software firms.
Agent-based Simulation
Outputs Stamelos, Angelis, & Oikonomou, 2002
The authors measured quality
characteristics (testability, simplicity, readability, and self-descriptiveness) of 100 applications written for Linux and find that these applications demonstrate quality lower than the industrial
standard.
Case Study of 100 Applications Written for Linux
Environment West, 2003
Proprietary software firms are
pursuing hybrid strategies to exploit
the advantages of open source
software while retaining control and
differentiation.
Bonaccorsi and
Rossi, 2003
In an environment previously
dominated by established proprietary
standards, open source products are
subject to the influence of negative
network externality.
Agent-based Simulation
26
Chapter 3
Conceptual Framework and Research Hypotheses
OVERVIEW OF THIS CHAPTER
Researchers suggest that OSS projects are interconnected with each other through a wide array of social and professional relations and exchanges, each of which can constitute a social network. Figure 3.1 gives an example of two types of social networks in the OSS community. In an affiliation network, projects are connected through the overlap in developers’ membership with multiple projects (Grewal et al., 2006). As shown in Figure 3.1, Projects A, B, and C form a social network because Developers 3 and 5 are members of multiple projects. In a collaboration network, projects are connected through the cross-collaboration among their members. Take Figure 3.1 as an example, although Developer 3 is not a member of Project A, s/he collaborates with Developer 1 and 2 who are members of Project A. In this case, Project A and B form a social network not because of the overlap in their developers’ membership, but because of the collaborations between their developers.
Sociologists and scholars in organizational behavior and strategy have convincingly demonstrated that the social structure of ties has significant behavior and performance implications for both individuals and organizations (Collins & Clark, 2003; C. Lee et al., 2001; Lynall et al., 2003; McDonald & Westphal, 2003). Similarly, social networks could also be of strategic importance to the OSS projects (Grewal et al., 2006; Kuk, 2006). In many respects, a network perspective will provide new insights into the role of network ties and relationships in the success of OSS systems.
In this chapter, we develop a network perspective of OSS project success and build our conceptual framework on the notion that the success of OSS projects is influenced by OSS project conduct and the networks of social ties in which the projects are situated. Our proposed
framework emerges at the crossroads of four areas of inquiry: social network theories, IO economics, Strategic Choice Perspective, and RBV, each of which furnishes conceptual tools to address the research question we are pursuing. Figure 3.2 illustrates how these four perspectives fit together. Social network theories undertake a systems view and provide insights on the relevance and importance of a network perspective in our understanding of OSS project success. IO economics and the RBV provide us with theoretical explanations on the role of differing levels of network structure in relation to the performance variation observed in OSS projects (Arrow 1 and 2 in Figure 3.2). The Strategic Choice Perspective raises our awareness of the important role played by OSS projects’ strategic choices/conduct in their performance advantages and success (Arrow 3 in Figure 3.2). In what follows, we discuss how IO economics, the Strategic Choice Perspective, and RBV explain the differing forces that shape the success of OSS projects.
Why is the success or performance of OSS projects influenced by their social networks and strategic conduct? Research in the strategic management literature on the sources of differential firm performances provides insights into this issue. In the literature, the Industrial Organization (IO) economics, the Strategic Choice Perspective, and the Resource-based View (RBV) have provided competing theoretical bases into the determinants of firm performance. IO economists consider industry as the main unit of analysis and focus on the effect of industrial structures on firm performance. This stream of research proposes that the structural characteristics of industries could either enable or constrain the strategies/conduct of the member firms, which in turn influences their performances. Although the primarily focus of IO economics is on studying industrial-level market structural patterns in relation to behavior and performance, we believe that this framework could be extended to the study of network structural characteristics and their impact on performance. Hence, viewed from the IO economics standpoint, one way to understand the performance implication of social networks for OSS
projects is to focus on the macro-level network structures, i.e., the overall network structure of the macro-environment which can be the industry or the larger social network within which OSS projects are situated, and explain how the network structure patterns at the macro-level may provide both opportunities and constraints for the member projects and influence their conduct and performance. Just as the structure of certain markets is more favorable for higher firm profitability than others, it is equally likely that the structures of some networks are more conducive to success than others and embedding in such conducive networks may provide OSS projects with an advantage over those which are members of less conducive networks. Take the illustrative example shown in Figure 3.2 as an example, Project A and B could be located in different social networks, thereby with one facing more conducive macro-network structures than the other. The differences in their macro-network structure may eventually lead to the variations in their performance (Arrow 1 in Figure 3.2).
While the IO economics furnishes us with a potent research tool for conceptualizing the role of macro-level network structural patterns in the success of OSS projects, one problem remains. Based on the underlying assumption of the IO economics, OSS projects within the same industry or the same social network should demonstrate similar performance since they operate under identical network structural conditions. Nevertheless, this assumption conflicts with the widely observed within-industry or within-network differential performance, i.e., some OSS projects within the same industry or the same network still perform better than others. For example, suppose that Project A and B in Figure 3.2 are embedded in the same social network. It is still possible that one outperforms the other although they face the same macro- network structure. Apparently, this intra-network heterogeneity is beyond the explanation provided by the traditional IO framework.
To understand the intra-industry or intra-network differential performance, we draw on two strands of research: the strategic choice perspective and the resource-based view (RBV) of the firm. Unlike the IO economics which views firms as identical black boxes operating in a given macro-environment structure, the strategic choice perspective and RBV focus on the firm itself as the main unit of analysis. They argue that organizations can make discretionary strategic choices/conduct and can accumulate and develop path-dependent resources and capabilities that are specific to the firm. Such conduct, resources, and capabilities are not identical across all firms within a given macro-environment and hence are sources of explanations for the commonly observed firm performance variations within the same environment.
The RBV focuses on firm-specific resources and capabilities as an explanation for the observed intra-industry heterogeneity in firm performance. The RBV posits that firms’ idiosyncratic resources and capabilities rather than industrial structures are the explanation for heterogeneity in firm performance (Barney, 1991). Drawing upon this perspective, an alternative way to understand the effects of social networks on OSS project performance is to think of social networks as bestowing the projects with “social capital” which can be an important resource and capability basis for a project’s competitive advantage. The RBV allows us to shift our focus from the macro-level network structural characteristics to the micro-level network structural patterns, i.e., the structure patterns that are specific to each OSS project. Even within the same industry or the same social network, projects differ in their positions on the network and such project-specific structural features could be important sources of performance variation for OSS projects (Arrow 3 in Figure 3.2). Some network positions provide the projects with better access to various resources and are hence more strategic than others. The different advantages and constraints associated with projects’ network position offer an explanation to the network or intra-industry heterogeneity in OSS projects’ performance.
The strategic choice perspective focuses on firms’ intentional and discretionary strategic choices/conduct as the explanation for intra-industry differential performance. The logic here is that even within a given environment, firms can seek differing strategies to respond to and act on the (dis)advantages posed by the environment and such differences in the strategies/conduct pursued could result in performance variations across firms that operate under the same environment (Arrow 2 in Figure 3.2). Therefore, if we integrate the IO economics school with the strategic choice perspective, we could view the success and performance of an OSS project as being jointly influenced by both its strategic conduct and the macro-environment within which it operates. As suggested by the IO economics, the macro-environment and its network structures constrain or enable the strategies of the OSS projects, which in turn will influence their performances and success (Arrow 1 in Figure 3.2). On the other hand, given the constraints or opportunities provided by the macro-environment, the strategic choice perspective posits that OSS projects still have the ability to use different strategic responses to overcome the structural disadvantages or to create an environment that is conducive to their success (Arrow 2 in Figure 3.2). Therefore, a good understanding of the factors that may be important to the success of OSS projects requires that we take into consideration the significance of both the macro-environment and OSS projects’ conduct because they explain different dimensions of OSS projects’ performance variation.
THE CONCEPTUAL FOUNDATIONS
In this section, we provide an overview of the conceptual foundations on which the theoretical framework of this dissertation is built.
Social Network Theories
The notion of social networks was initially used for the study of communities where networks of interpersonal relationships develop over time and provide the basis for trust, cooperation, and collective action. The focus was on how individuals use their social networks to gain returns. More recently, researchers have applied this concept to study the performance implications of social networks for firms (Dyer & Nobeoka, 2000; Dyer & Singh, 1998; J Lincoln, Gerlach, & Ahmadjian, 1996; J Lincoln, Gerlach, & Takahashi, 1992; Nahapiet & Ghoshal, 1998; T. Rowley, Behrens, & Krackhardt, 2000). Central to the social network research is the postulation that networks of relationships constitute a valuable resource for the members of these networks.
The application of social networks to firms has its root in an open-systems perspective. Researchers emphasize that firms do not operate in a barren social context. Rather, they are embedded in social networks of relationships. A firm’s embeddedness in the web of network relations leads to asymmetric access to resources across an industry, facilitating or impeding a firm’s conduct and performance. There are two broad approaches to examining the influence of a firm’s embeddedness in its social networks. The first emphasizes the advantages bestowed by networks with differing levels of tie strengths, while the second highlights the beneficial role of the location a firm occupies in the network.
Research focusing on strengths of ties suggests that both strong and weak ties could benefit organizations. Firms embedded in networks of strong ties benefit from the exchange of high-quality information and tacit knowledge as members of the strong-tie network have a deeper understanding of each other and such knowledge is more readily transferred across organizational boundaries (T. Rowley et al., 2000; Uzzi, 1996). Strong ties also benefit firms as they serve as part of the social control mechanism by promoting trust, mutual gain, and reciprocity (Larson,
1992). For example, a number of studies have examined Japanese Keiretsu networks and the consequences of these networks for individual firms embedded in them (J Lincoln et al., 1996; J Lincoln et al., 1992). Researchers suggest that alliances like Japanese Keiretsu networks benefit the member firms through market domination, cost reduction, and coordination that groups confer on their members (J Lincoln et al., 1996). On the other hand, weak ties are more likely than strong ties to connect firms to the entities that possess unique and novel information (Granovetter, 1973, 1983). In addition to the strength of ties, researchers suggest that firms can also derive advantages from the positions they occupy in their social networks. Certain locations on the network are more advantageous than others. For example, more centrally located firms tend to have superior performance because such location facilitates the accumulation of and access to valuable resources (Powell, Koput, Smith-Doerr, & Owen-Smith, 1999). Also, if located between two other players, a firm can enjoy certain advantages by brokering tension between the other players (Burt, 2000).
Given the proliferation of social links in the OSS community and the numerous potential advantages associated with social networks, we argue in this dissertation that a network perspective is essential in our examination of factors that contribute to the success of OSS projects. In what follows, we adopt IO economics and RBV and discuss how different levels of network structures shape the success of OSS projects.
IO Economics as a Framework for the Role of Macro-level Network Structures
The industrial organizational economists posit that the structural characteristics of an industry, including the extent of concentration, industry size, market power, degree of product differentiation, and barriers to entry, exert influence on firm performance disparity (Porter, 1980) (Robinson & McDougall, 1998; Spanos et al., 2004). Since the late 1970s, this view has been the
main theoretical basis for strategic management research into the determinants of firms' differential performances. The structure-conduct-performance (SCP) paradigm, the most dominating model in the IO school, postulates that industry structure constrains the conduct (strategy) of firms that operate in this industry, which in turn results in industry-specific performance differentials between firms (Bain, 1956, 1959). Empirical results along this line of research provide evidence supporting the general paradigmatic notion of the SCP framework, i.e., industry structure determines profitability (Robinson & McDougall, 1998; Spanos et al., 2004).
Although much of the work in IO economics primarily focuses on industry effects involving market structural characteristics, in a parallel vein we could argue that the network structural characteristics of the macro-environment could play a similar role as the market structure. Just as high-performance organizations are found in an environment with favorable competitive market structure, high-performance organizations could also be found in an environment with network structures conducive to success. The general notion here is that the overall network structural features could also enable or constrain the conduct of the members that operate in this network. This in turn leads to performance differentials that are specific to the social network. At this macro-level of analysis, network structure refers to the overall pattern of relationships manifested in the macro-environment. Scholars have identified various overall structural patterns such as network density, network size, and centralization of the network, each of which can influence the performance of individuals, groups, and organizations (Gulati, Nohria, & Zaheer, 2000; Sparrowe, Liden, Wayne, & Kraimer, 2001).
In the process of cooperation, participations, and collaborations, various social networks may have emerged (Grewal et al., 2006; Kuk, 2006). Within the formulation of the SCP framework, we would expect that the structural characteristics of each of these social network environments affect the conduct and performance of each OSS project (Arrow 1 in Figure 3.2).
However, the underlying assumption of the IO economics implies that projects embedded in the same social network should demonstrate similar performance since they operate under identical network structure and conditions. But, intra-network differential performance is also widely observed in the OSS community. Therefore, the traditional IO economics framework appears inadequate in explaining the intra-network heterogeneity in OSS project performance.
RBV as a Framework for the Role of Project-level Network Structures
To understand the intra-network heterogeneity, we again turn to research on sources of firm profitability differences for insights. In the strategic management literature, the industrial organization school has also been criticized for its inability to explain the intra-industry heterogeneity in firm performance. To understand intra-industry heterogeneity, scholars have made an important attempt to shift from focusing on the industry to focusing on the firm as the main unit of analysis. One example of such an important attempt is the Resource-Based View (RBV) of the firm
The resource-based view (RBV) of the firm has its root in Penrose (1959), and more recently Wernerfelt (1985) and Barney (1991). It has been widely accepted in organization economics, information systems, and the strategic management literature. This perspective examines how competitive advantage within firms is achieved and how that advantage might be sustained over time. RBV proposes that firm-specific idiosyncrasies in the accumulation and leverage of unique and durable resources are the source of a sustainable competitive advantage. These rent-producing resources, rath
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