*Names in bold indicate Presenter
Social innovation is supported and enhanced by network of individuals (or organizations) as Mulgan, Tucker, Ali, and Sanders (2007) mention that combining ideas, cutting across boundaries, and compelling new social relationships are three key dimensions of social innovation. Therefore, effective network management is necessary for successful social innovation. Having shared understating is a critical step to produce desired outcomes in network settings where individuals with diverse backgrounds work together (Gray 1999). Shared understanding develops though interaction (Monge & Contractor 2003). This study will explore how individuals in the network setting of creating social innovation (social innovation network) develop shared understanding through communication and interaction, asking questions below:
1) How do individual positions in social innovation network (i.e., structural embeddedness) affect the development of shared understanding?
2) How do relational characteristics such as trust and power (i.e., relational embeddedness) influence the development of shared understanding?
3) How do communication means used to exchange knowledge and expertise in a social innovation network – face to face communication, phone calls, or electronic means – affect the development of shared understanding?
To explore these questions, this study will use the case of I-Choose network. I-Choose network is composed of 25 researchers and practitioners with various academic backgrounds from Canada, Mexico, and the US. They collaborate to create a data interoperability framework, which provides to consumers information about how, when, and by whom products is produced (e.g., wages paid to producers or workers, working conditions, environmental impact), so that consumers can exercise their preferences for safe, environmentally sustainable, and economically just products and services. The data interoperability framework can be considered as a social innovation because it has a potential to solve information asymmetry against consumers, which has not been solved by existing policy instruments of information (c.f., Weiss 2002).
This study will use network data collected from the 25 I-Choose network members. This study will use deterministic and stochastic social network analyses to explore the correlation between two types of network data: communication networks among the members and network of the members who share understanding on the data interoperability framework.
Following a review of the literature from social innovation and network management and an outline of methods and procedure, this study will show the findings from social network analysis. This paper draws out implications for how to manage social innovation process.