Search results

This chapter explains how to design and operationalize a knowledge network analysis. The authors walk through a nine-step methodology that addresses each stage of the process. The nine-step process is the result of an in-depth review of the theoretical and applied literature. The authors explain how and why each step contributes to the quality and goodness of the analysis. The risks of skipping or sub-optimizing steps are explained. The step-by-step process highlights the dependence of a knowledge network analysis on data sources. The authors explain the importance of identifying, collecting, and curating sources.

This chapter describes capacity building as a general concept and as a networking capability specifically. There are two essential components to building capacity. The first is building the network capacity, building relational capital, and sharing knowledge. The second is building the capacity to support knowledge network analyses. The authors offer a set of key questions for determining the health of networking capacity at the strategic, operational, and individual levels. The chapter also describes the roles and competencies required for network analysts, managers, and general employees.

This study deals with the processes of innovation in the medium technology industrial sectors. First, it illustrates the differences between the linear model of innovation and the systemic and cognitive model of knowledge creation. Then, it focuses on the concepts of connectivity, creativity, and speed of change, which characterize the processes of interactive learning in the industrial clusters. Finally, it illustrates a typology of regions, where problems and policy fields are different, and it indicates the guidelines of a governance of interregional knowledge and innovation networks.

Knowledge is critical for employee and firm success. We show that being perceived as prototypical organizational members is a source of prominence in knowledge exchange that operates beyond preexisting communication or affective relationships. Self-categorization processes produce – through depersonalization – a positive attitude among the members which represents an autonomous mechanism of social attraction for knowledge exchange, while social network mechanisms are triggered by interpersonal attraction. Our findings also suggest that including perceived members’ prototypicality can avoid a potentially spurious relationship in assessing the role played by social identity and categorization theory in explaining attitude and behaviors.

This chapter explores the role of nodes in knowledge networks. The authors characterize knowledge nodes by the type of actors they represent, including individual human agents, collective human groups and teams, explicit non-human objects and resources, and non-human agents and machines. The authors define knowledge nodes by their role in the network, including producer, consumer, or broker of knowledge, and in terms of the stock of knowledge they represent and their capacity to absorb knowledge made available in the network.

This chapter focuses on scientific and research networks. All six facets of knowledge networks are described. The importance of three facets is called out, including domain, knowledge, and nodes. The authors provide profiles of five networks, including an invisible college in chemistry, a professional association network in engineering, an editorial network, a national biological observation collaboration, and a national science museum.

This chapter focuses on business and industry networks. All six facets of knowledge networks are described. Three of the six facets have particular importance for these networks, specifically domain, relationships, and how messages are managed and controlled. The authors provide six network profiles, including health care industry networks, fashion industry networks, technology industry networks, food production industry networks, building industry networks, and transportation industry networks.

This chapter focuses on emergency and hastily formed knowledge networks. All six facets of knowledge networks are described. The importance of four of the six facets is called out, including domain, topology, nodes, and relationships among the networks’ members. The authors provide four network profiles, including emergency and disaster response networks, law enforcement networks, military networks, and militia and vigilante networks.