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This chapter discusses the empirical application of a class of strategic network formation models, using the approach to identification introduced by de Paula, Richards-Shubik, and Tamer (2018). The author emphasizes the interplay between model specification and computational complexity, and suggests tactics to make empirically realistic models become tractable. Two detailed examples, on friendship networks and coauthorship networks, are used to illustrate these issues and to demonstrate the performance of the approach with both simulation and empirical evidence. Also, the author presents extensions to the estimation method, which expand the potential range of applications, and which provide statistical inference with minimal computational burden.

This chapter provides a deep dive into knowledge networks. The authors provide an inclusive definition of a knowledge network. A knowledge network includes nodes as sources and targets of knowledge, relationships as knowledge links, and messages as knowledge transactions and flows. The authors note how knowledge networks differ from other types of networks, specifically their dynamic and chaotic state and continuous transactions. These peculiarities reflect the economic properties and behaviors of knowledge. The elements of networks described in Chapter 2 are elaborated for knowledge networks. The chapter calls out knowledge network domains, geographies, typologies, nodes, messages, and relationships.

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.

In this article, I propose a theory of network opportunity emergence. The core of the argument is that as an overall industry network structure becomes centralized, opportunities emerge for new entrants. As the institutional environment evolves toward a centralized network flow structure, innovators can identify newly emerged rich resource niches that serve as the perfect breeding ground for an entrepreneurial start-up. While the framework is an aggregate level conceptualization of market opportunities, it also identifies specific actionable opportunities at a very micro level. Examples from the networks of the airline industry illustrate the logic. I conclude by discussing the innovation and entrepreneurship implications for a wide variety of industries and network tie types, calling for utilization of the framework to answer a broad variety of research questions.

The study this chapter reports focuses on how network theory contributes to the understanding of the internationalization process of SMEs and measures the effect of network capability on performance in international trade and has three research objectives.

The first objective of the study relates to providing new insights into the international market development activities through the application of a network perspective. The chapter reviews the international business literature to ascertain the development of thought, the research gaps, and the shortcomings. This review shows that the network perspective is a useful and popular theoretical domain that researchers can use to understand international activities, particularly of small, high technology, resource-constrained firms.

The second research objective is to gain a deeper understanding of network capability. This chapter presents a model for the impact of network capability on international performance by building on the emerging literature on the dynamic capabilities view of the firm. The model conceptualizes network capability in terms of network characteristics, network operation, and network resources. Network characteristics comprise strong and weak ties (operationalized as foreign-market entry modes), relational capability, and the level of trust between partners. Network operation focuses on network initiation, network coordination, and network learning capabilities. Network resources comprise network human-capital resources, synergy-sensitive resources (resource combinations within the network), and information sharing within the network.

The third research objective is to determine the impact of networking capability on the international performance of SMEs. The study analyzes 11 hypotheses through structural equations modeling using LISREL. The hypotheses relate to strong and weak ties, the relative strength of strong ties over weak ties, and each of the eight remaining constructs of networking capability in the study. The research conducts a cross-sectional study by using a sample of SMEs drawn from the telecommunications industry in Ireland.

The study supports the hypothesis that strong ties are more influential on international performance than weak ties. Similarly, network coordination and human-capital resources have a positive and significant association with international performance. Strong ties, weak ties, trust, network initiation, synergy-sensitive resources, relational capability, network learning, and information sharing do not have a significant association with international performance. The results of this study are strong (R2=0.63 for performance as the outcome) and provide a number of interesting insights into the relations between collaboration or networking capability and performance.

This study provides managers and policy makers with an improved understanding of the contingent effects of networks to highlight situations where networks might have limited, zero, or even negative effects on business outcomes. The study cautions against the tendency to interpret networks as universally beneficial to business development and performance outcomes.

This commentary addresses the problem of interfirm network formation from the perspective of multiple types of relationship content and of network structure. The approach builds on Burt’s (1980) typology of network structures and on a range of empirical studies on interorganizational networks. The chapter by Moldoveneau, Baum and Rowley on network evolution from an information-sharing perspective captures part of this research domain. The challenges posed by network evolution research are discussed in the broader light of multilevel analysis.

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.

“Mirror Worlds” were suggested by David Gelernter based on a bold assertion: “You will look into a computer screen and see reality.” With mirror worlds, managers could be proactive, anticipating what might happen and acting accordingly, instead of waiting till events happen and then reacting. This paper extends the notion of mirrors worlds to supply chain management. In the case of supply chain management, managers could test the impact of making changes in their supply chains to study the impact.

However, mirror worlds could be extended to help actually monitor and manage supply chains to respond and adapt to changes in the world that affected the supply chain. In particular, mirror worlds could be “real” worlds if control for some of the activities between supply chain participants is in effect “turned over” to the mirror world. In that case, the mirror world would show the actual world, with the system making many of the decisions.

Inter-organizational alliances and the networks they generate have been a central topic in organization theory over the last decade. However, network analyses per se have been static. Even when information over time has been available, the temporal component has been set aside or aggregated to the end point of the study. Substantially more research has been conducted on organizations initiating inter-organizational relationships. The organization-level research has been decidedly dynamic in nature. However, organization-level research has largely examined the structural characteristics of the networks generated by organizational actions. Work combining network-level and organization-level phenomena has been rare and, to our knowledge, no research including the effects of organization-level actions on the evolution of network-level phenomena has occurred.

In this chapter we use more than 6000 R&D alliances and more than 6500 M&D alliances initiated by more than 1000 biotech firms in the U.S. over a 30 year period to construct quarterly networks. We test 13 hypotheses linking the actions of the firms to changes in network structure. Utilizing hazard-rate models we test the effects of institutional status, positional status (centrality), and structural status (coreness) of firms on their propensity to form ties with different structural consequences. Our research indicates that both R&D and M&D networks in U.S. biotechnology are developing a distinct core/periphery structure over time. Furthermore, we find support for a process of preferential attachment wherein organizations are more likely to form ties with organizations of similar institutional and structural status. Furthermore, we find evidence for cross effects, for example attachment processes that enfold across the two networks.