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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.

The purpose of this paper is to provide a quantitative multicriteria decision‐making approach to knowledge management in construction entrepreneurship education by means of an analytic knowledge network process (KANP).

The KANP approach in the study integrates a standard industrial classification with the analytic network process (ANP). For the construction entrepreneurship education, a decision‐making model named KANP.CEEM is built to apply the KANP method in the evaluation of teaching cases to facilitate the case method, which is widely adopted in entrepreneurship education at business schools.

The study finds that there are eight clusters and 178 nodes in the KANP.CEEM model, and experimental research on the evaluation of teaching cases discloses that the KANP method is effective in conducting knowledge management to the entrepreneurship education.

As an experimental research, this paper ignores the concordance between a selected standard classification and others, which perhaps limits the usefulness of KANP.CEEM model elsewhere.

As the KANP.CEEM model is built based on the standard classification codes and the embedded ANP, it is thus expected that the model has a wide potential in evaluating knowledge‐based teaching materials for any education purpose with a background from the construction industry, and can be used by both faculty and students.

This paper fulfils a knowledge management need and offers a practical tool for an academic starting out on the development of knowledge‐based teaching cases and other teaching materials or for a student going through the case studies and other learning materials.

This paper seeks to present a comprehensive overview of the supply chain as an autopoietic system. The new autopoietic approach suggests a transition from traditional cognitivist epistemology to the theory of learning as a creational matter, and this type of thinking can potentially shed light on the role of knowledge creation as a part of supply chain management.

The paper is structured as follows: the first section describes the theoretical background of the concept of knowledge management in the supply chain. After that, the paper examines the general systems theory and the role of an autopoietic system within it. Then the paper addresses autopoietic epistemology. In particular, the notions of knowledge, learning, and knowledge flows are described so that the focus is on the context of the supply chain and supply chain management at operational level.

The supplier's, customer's, and firm's own organization and parts of the organization have autonomy system memories, which ultimately formulate how the intended development ideas are in fact realized and how they are adopted by the organization. Supply chain managers should take into account the fact that the routines and norms of the node are part of the system that are not controlled from outside. Instead, the system can modify its objectives internally as part of its autonomous operation, which should be taken into consideration in the knowledge sharing process.

The description of a supply chain as an autopoietic knowledge system is a new way to examine knowledge sharing in a supply chain.

Knowledge input development and innovation implementation are new features of industrial technology innovation. The purpose of this study is to find the process of coordination and ecological spiral in the ambidextrous innovation of industrial technology.

To design the model of industrial technology ambidextrous innovation based on knowledge ecology spiral, an input-output model of knowledge for ambidextrous innovation and a spiral model of knowledge ecology were constructed based on an improved Lotka-Volterra model. Then, the equilibriums in different knowledge inputs and the spiral evolution of knowledge ecology were analyzed. Finally, the ambidextrous coordination mechanism of the core organization was revealed.

By coordinating the knowledge inputs and the knowledge ecology spiral, enterprises extend the R&D investments in the innovation chain, which will facilitate the knowledge inputs of the exploitative and exploratory innovation. Implementing the ambidextrous coordination in the technology innovation chain and the knowledge ecology chain has the advantage of promoting knowledge inputs, mobility and ecological spiral. Meanwhile, it can achieve the “multi-source, integration and coordination” development of industrial technology innovation.

The two-element innovative knowledge input coordination model and the knowledge ecological spiral model based on the improved Lotka-Volterra model are constructed, which extends the modeling way of the traditional knowledge input-output profit model. It is expected to reduce the amount of knowledge input of a single member and provide theoretical reference for improving the efficiency of knowledge input by constructing the inter-dependent regenerative and inter-generative knowledge interaction.

This study aims to investigate the structural characteristics, spatial evolution paths and internal driving mechanisms of the knowledge transfer (KT) network in China’s patent-intensive industries (PIIs). The authors' goal is to provide valuable insights to inform policy-making that fosters the development of relevant industries. The authors also aim to offer a fresh perspective for future spatiotemporal studies on industrial KT and innovation networks.

In this study, the authors analyze the patent transfer (PT) data of listed companies in China’s information and communication technology (ICT) industry, spanning from 2010 to 2021. The authors use social network analysis and the quadratic assignment procedure (QAP) method to explore the problem of China’s PIIs KT from the perspectives of technical characteristics evolution, network and spatial evolution and internal driving mechanisms.

The results indicate that the knowledge fields involved in the PT of China’s ICT industry primarily focus on digital information transmission technology. From 2010 to 2021, the scale of the ICT industry’s KT network expanded rapidly. However, the polarization of industrial knowledge distribution is becoming more serious. QAP regression analysis shows that economic proximity and geographical proximity do not affect KT activities. The similarity of knowledge application capacity, innovation capacity and technology demand categories in various regions has a certain degree of impact on KT in the ICT industry.

The current research on PIIs mainly focuses on measuring economic contributions and innovation efficiency, but less on KT in PIIs. This study explores KT in PIIs from the perspectives of technological characteristics, network and spatial evolution. The authors propose a theoretical framework to understand the internal driving mechanisms of industrial KT networks.

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 study aims at analyzing the features of knowledge flow and the role-specific nodes in knowledge networks among individuals and business units of six organizations in different industries, and suggesting prescriptions to prevent the organizational knowledge sclerosis.

This research conducts multiple case studies on the organizational knowledge paths of six companies in the multiple industries through social network analysis (SNA) tool developed by the authors of this paper.

This study provides four major findings which shed a new light on how to comprehend the features of knowledge flow and the role-specific nodes in knowledge networks in organizations: the within-business unit knowledge flows are more dominant over the inter-business units knowledge flow; the downward knowledge flows are dominant over the horizontal and upward knowledge flows in the management levels; distributions of knowledge owners and providers are like L-shape and the gap between knowledge owing and providing expands as the management levels go up; and the top 20 percent people in an organization dominate over a large portion of the knowledge brokerage activities.

Cultural difference issue might arise because data collection was limited to Korean organizations. Therefore, the findings from this study needs to be cautiously interpreted considering the cultural difference/deeper understanding of the organizational knowledge paths through social network lens can make it possible for more context-specific KM strategies (e.g. suitable for a specific functional unit, management level, or industry type) to be identified and implemented.

Managers can have a solid grasp about knowledge flows and knowledge node roles in their organization through social network analysis in order to facilitate the knowledge transfer and eliminate the knowledge link lapse in organizations.

This study could be a stepping stone for further empirical research since it expanded the level of organizational knowledge network analysis from individual and team to inter-unit and inter-management level through the block modeling analysis of knowledge network.

The present paper aims to explore how to measure trust as a receptivity force in an intra-organisational knowledge-sharing network with the help of self-developed algorithms of modelling percolations.

In this paper, a completely new methodology is applied by using a sample study of an international company’s financial centre as an example. Computer software has been developed to simulate the network and calculate the percolation thresholds by combining its characteristics, thereby revealing what and to what extent connectivity and trust, respectively, influence knowledge sharing.

The application of computer modelling to build up a percolation network is useful for answering questions about the determinants of knowledge sharing. Arguably, the authors demonstrate how the applied new methodology is superior in addressing how to measure the critical values of trust, connectivity and interaction issues, as well as leading to better insights about how these can be managed. The present paper confirms that trust is an essential factor influencing knowledge sharing and that there is a reciprocal effect between social interaction and trust.

The model provides a useful tool for assessing features of the intra-organisational knowledge-sharing network and thus an important foundation for implementing actions in practice. The findings of this study imply that managers should consider the important role of task-related trust between actors and in general for knowledge sharing. With the help of percolation modelling, the degree of trust in an organisation can be computed, and this provides managers with an approach for managing trust.

The topic of “how can trust be measured” is very important and is becoming even more important now because the financial crisis and other issues are raising questions about trust and moral compass rather than financial data. A percolation-based approach to studying knowledge sharing has not been researched in depth before now, and this study attempts to fill that gap. Fundamentally, this multidisciplinary research adds value to the theoretical foundation of the percolation network and research methodology to be used in social sciences and gives an example of their potential practical implications.

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.