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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.

Immersed in a global industry consolidation process, corporate managers are witnessing, in recent years, the proliferation of inter‐organizational collaborative agreements, which aim to develop, manufacture and commercialize knowledge intensive products. The decision within a knowledge management (KM) framework to collaborate in knowledge sharing networks becomes a complicated issue, since such a decision needs to be made often under conditions of uncertainty and irreversibility. The present study deals with questions such as why, how, and when to be a member of a knowledge network and provides some empirical evidence about the formation of inter‐organizational networks in knowledge intensive industries.

The purpose of this paper is to trace and understand informal knowledge sharing networks for various competencies in project teams. This will help establish a baseline and thereby enable further knowledge management interventions to be outlined.

Two project teams were identified for this study. While one of the teams had a semi‐structured knowledge management system already in place, the other had not adopted any knowledge management practices. The knowledge network analysis was rolled out for both the teams for the competencies that they were working on. This was more of an exploratory study. The results are compared across both the teams and inferences are made on the knowledge networks for the teams.

The various measures involved in social network analysis can help from a knowledge management perspective to: identify experts; provide indicators to the extent of knowledge sharing for various competencies; and baseline current knowledge management practices in a team.

This methodology would not be very feasible for large teams with more than 500 people.

This is a very useful diagnostic tool for managers to know more about the knowledge sharing dynamics in their teams. This may help them design interventions to build the capabilities of key team members along specific knowledge areas.

The paper provides indicators on the capability of the teams and their knowledge repositories based on the interactions between them.

This study aims to propose a combined effect framework to explore the relationship between research and development (R&D) team networks, knowledge diversity and breakthrough technological innovation. In contrast to conventional linear net effects, the article explores three possible types of team configuration within enterprises and their breakthrough innovation-driving mechanisms based on machine learning methods.

Based on the patent application data of 2,337 Chinese companies in the biopharmaceutical manufacturing industry to construct the R&D team network, the study uses the K-Means method to explore the configuration types of R&D teams with the principle of greatest intergroup differences. Further, a decision tree model (DT) is utilized to excavate the conditional combined relationships between diverse team network configuration factors, knowledge diversity and breakthrough innovation. The network driving mechanism of corporate breakthrough innovation is analyzed from the perspective of team configurations.

It has been discerned that in the biopharmaceutical manufacturing industry, there exist three main types of enterprise R&D team configurations: tight collaboration, knowledge expansion and scale orientation, which reflect the three resource investment preferences of enterprises in technological innovation, network relationships, knowledge resources and human capital. The results highlight both the crowding-out effects and complementary effects between knowledge diversity and team network characteristics in tight collaborative teams. Low knowledge diversity and high team structure holes (SHs) are found to be the optimal team configuration conditions for breakthrough innovation in knowledge-expanding and scale-oriented teams.

Previous studies have mainly focused on the relationship between the external collaboration network and corporate innovation. Moreover, traditional regression methods mainly describe the linear net effects between variables, neglecting that technological breakthroughs are a comprehensive concept that requires the combined action of multiple factors. To address the gap, this article proposes a combination effect framework between R&D teams and enterprise breakthrough innovation, further improving social network theory and expanding the applicability of data mining methods in the field of innovation management.

This chapter focuses on the treatment and characterization of networks as an emerging discipline. Networks are defined. The authors call out and explain the importance of network domains, network geographies and topologies, network behaviors, network nodes, network links, relationships and flows, and network messages. While network sciences provide a strong foundation for research and analysis, the authors note the lack of knowledge networks. This chapter highlights the need to expand coverage to include knowledge networks.

Networks and learning matter to small- and medium-sized enterprises (SMEs). Networks and learning are also further elaborations on the exploration–exploitation (EE) dilemma. Ambidexterity, that is, managing this apparent dilemma, can be difficult as a result of many constraints. One of these constraints is that of mutually exclusive network structures. Consequently, ambidexterity is the ability to change networks, depending on need using mixed data on four small companies formed as part of an undergraduate management class, I hypothesize how specific network properties of the advice-seeking relationship, including density, cohesion, centralization, and embeddedness, affect two outcomes. Specifically, early exploratory learning is proposed to be positively affected by less-dense networks that maintain cohesion without centralization and do not have relations embedded in other relations. In contrast, later exploitative learning should be associated with denser networks that also have higher cohesion, higher centralization, and greater embeddedness. The results provide some support for these hypotheses and suggest further research in two areas that will benefit SMEs. One, how do early networks affect learning mode? Two, how does the ability to rewire networks provide the relational infrastructure to shift from exploration to exploitation – that is, to be ambidextrous in the face of the exploration–exploitation tradeoff?

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 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.