Importance of Social Networks for Knowledge Sharing and the Impact of Collaboration on Network Innovation in Online Communities [chapter]

Stefan K. Behfar
2020 Harnessing Knowledge, Innovation and Competence in Engineering of Mission Critical Systems  
Innovation results from interactions between different sources of knowledge, where these sources aggregate into groups interacting within (intra) and between (inter) groups. Interaction among groups for innovation generation is defined as the process by which an innovation is communicated through certain channels over time among members of a social system. Apart from the discussion about knowledge management within organizations and the discussion about social network analysis of organizations
more » ... s of organizations on the topic of innovation and talks about various trade-offs between strength of ties and bridging ties between different organizational groups, within the topic of open source software (OSS) development researchers have used social network theories to investigate OSS phenomenon including communication among developers. It is already known that OSS groups are more networked than the most organizational communities; In OSS network, programmers can join, participate and leave a project at any time, and in fact developers can collaborate not only within the same project but also among different projects or teams. One distinguished feature of the open source software (OSS) development model is the cooperation and collaboration among the members, which will cause various social networks to emerge. In this chapter, the existing gap in the literature with regard to the analysis of cluster or group structure as an input and cluster or group innovation as an output will be addressed, where the focus is on "impact of network cluster structure on cluster innovation and growth" by Behfar et al., that is, how intra-and inter-cluster coupling, structural holes and tie strength impact cluster innovation and growth, and "knowledge management in OSS communities: relationship between dense and sparse network structures. " by Behfar et al., that is, knowledge transfer in dense network (inside groups) impacts on knowledge transfer in sparse network (between groups). Harnessing Knowledge, Innovation and Competence in Engineering of Mission Critical Systems 2 network cluster shape (dense-sparse)) have been studied in various articles because of their significant applications including (1) network generation, design and reproduction (e.g. "Emergence of scaling in random networks" by Barabási and Albert [1], "On power-law relationships of the internet topology" by Faloutsos et al. [2] ), (2) social network analysis (e.g. "Creating social contagion through viral product design..." by Aral and Walker [3], "Optimal and scalable distribution of content updates over a mobile social network" by Ioannidis and Chaintreau [4]), (3) impact of network structure on network innovation (e.g. "Collaboration networks, structural holes, and innovation..." emphasizing the impact of direct and indirect ties on firm innovation by Ahuja [5], "Network structure of social capital" investigating the impact of sparse network structure on facilitating diffusion of ideas by Burt [6]) and (4) knowledge management among open-source-software (OSS) developers (e.g. "Location, location, location: how network embeddedness affects project success in open source systems" by Grewal et al. [7], "Knowledge transfer within information system development teams: examining the role of knowledge source attributes" by Joshi and Sarker [8]). However, to our best knowledge, there has been no study in the literature which explains impact of network structure on innovation and growth at group or cluster level. We will address this issue in this chapter, and explain the impact of group dynamics on OSS project group innovation (i.e. group intra-and inter-coupling as causal factors for group innovation and growth), also discuss knowledge management and intergroup diffusion of innovation (i.e. influence of knowledge diffusion within dense groups measured by intragroup density, degree centrality and betweenness onto knowledge diffusion between sparse groups measured by intergroup coupling). We focus on clusters or groups rather than individuals as the level of analysis for both network structure as input and innovation diffusion as output, because (1) clusters represent collective impact on network output rather than individuals' impact, (2) impact of intra cluster couplings on cluster innovation and growth is different from the impact of inter cluster couplings on cluster innovation and growth and (3) trade-offs among dense and sparse network cluster structures are different from those associated with networks of individuals. As the domain of interest, we have chosen open source software (OSS) collaboration network (or so-called OSS communities), where almost all prior works on OSS are concerned with project success measured by number of downloads or number of concurrent versions system (CVS) commits, and ignores group success measured by group growth and innovation. Group is referred to one including small or big number of developers who work on some or many project tasks. In addition, OSS developer is the unit of analysis, where two developers working on the same project task builds a tie in the network. What is a network? A network is a set of interlinked nodes, which can be simple, such as a lattice, random network or a complex network (a graph with non-trivial topological features that are not found in simple networks). However, most complex structures can be realized by networks with a medium number of interactions [9] . What is in fact a complex network? Complex networks A complex network is composed of nodes and links, or modules and dependencies, where a module is a component whose structural elements are strongly
doi:10.5772/intechopen.89605 fatcat:m6t7cplstjf37l4bcutrfbhqx4