| 1. Introduction Inside organizations, units can learn from each other and knowledge transfer can provide new mutual opportunities for units as well as for the whole organization. New ideas diffuse rapidly when they benefit organizations adopting them, and they vanish, if otherwise (Abrahamson, 1991). Huber (1991) suggested that organizational units transfer knowledge and learn from other units, but not all units have access and capacity to learn knowledge and apply it; they require external access and internal capacity. Internal capacity can be achieved by R & D ability increase, while external access to new knowledge can be improved by networking. In this regard, Hansen (1999) introduced modelling an organization as a complex network with inter-unit links, where knowledge transfer is investigated by analyzing inter-organizational network. In regards to usage of social network analysis (SNA) in organizations, different authors focused on a wide range of network characteristics from relational (e.g. tie strength) and nodal (e.g. functional background) to positional (e.g. betweenness centrality) and structural (e.g. density), e.g. impact of size of network on innovation (Baer, 2010), relationship strength (Rost, 2011), or weak and strong tie (Nelson, 1989; Tsai, 2000, 2001). Baer, Evan, Oldham, and Boasso (2015) carried out a meta-analysis of studies on innovation and social networks and presented insights into the various trade-offs between strength of ties and bridging ties among other things. Tsai (2000) suggested that social networks facilitate the creation of new knowledge within organizations. In another study, Tsai (2001) focused on the question “How can an organizational unit gain useful knowledge from other units to enhance its innovation and performance?”, and emphasized the role of strong ties in intra-corporate and strategic alliances. Moreover, Ahuja (2000) discussed firm’s network relationship impacting the rate of innovation, where network allows for knowledge sharing and information flow. Others have studied the role of networks within the topic of knowledge sharing and innovation adoption where importance was given to the number of firm linkages and geographical proximity (Florida, 1995; Van Oort & Atzema, 2004) impacting rate of adoption. Apart from the discussion about knowledge management within and between organizations and the discussion about social network analysis in organizations on the topic of innovation, within topic of open source software (OSS) development, researchers have used social network theories to investigate the OSS phenomenon including communication among developers. The positions and relationships among developers in a social network are significant in the efficiency of the network (Jackson, 2004) using different techniques and tools such as social network analysis (SNA). Success of many OSS projects is closely related with the communication structure (see Grewal, Lilien, & Mallapragada, 2006; Singh, Tan, & Youn, 2011). One distinguished feature of the OSS development model is the cooperation and collaboration among the members, which will cause various social networks to emerge (Grewal et al., 2006). To some extent, the OSS community is a more networked world than the traditional organizational communities; where programmers can join, participate, and leave a project at any time and developers collaborate not only within the same project team but also across teams. It has also been shown that the structure of an interproject network affects knowledge sharing within and across open source projects. Montazemi, Siam, and Esfahanipour (2008) demonstrated that the market structure of embedded interpersonal ties enables participants to take advantage of information asymmetry for profit taking Singh, 2011. |