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Dyadic multi-dimensionality informs the variation that exists within and between network ties and suggests that ties are not all the same and not all equally strategic. This chapter presents a model of dyadic evolution grounded in dyadic multi-dimensionality and framed within actor-level, dyadic-level, endogenous, and exogenous contexts. These contexts generate both strategic catalysts that motivate network action and bounded agency that may constrain such network action. Assuming the need to navigate within bounded agency, the model highlights three strategic processes that demonstrate how dyadic multi-dimensionality underlies the evolution of strategic network ties.

This chapter aims to identify the main determinants that define the architectural properties of network emergence and significantly influence the dynamics underlying network evolution in time. The identification and analysis of these determinants, as well as the dynamic processes tied to them, allows to appreciate the competitive bases and consequences of network morphology. To this purpose, using a complex systems perspective as an integrative conceptual approach, we represent networks as complex dynamic systems of knowledge and capabilities. We perform a comparative in-depth analysis of the processes underlying the emergence and evolution of STMicroelectronic's global network and of Toyota's supplier network in the US so as to allow an elucidatory empirical assessment of the theoretical representation elaborated in the article.

This chapter addresses ambidexterity at the individual level. Ambidexterity is defined as a company's ability to guarantee both short- and long-term successes by simultaneously exploring new market or new technological paths and improving existing products. We demonstrate that this ability can result from the evolution of social networks linking individuals involved in idea development. We used a longitudinal approach that combined case study and social network structure analysis of the R&D center of a semiconductor company. Six cases have been selected according to the level of disruption of the first idea generated and the end result in terms of exploration and exploitation. For these six cases, data have been gathered from monthly project reviews, press articles and listings of patents. Seventy-four interviews with key actors in the idea-development process have also been conducted.We mapped the relationships between actors who have contributed to the development of the idea through creative thinking and/or helped it to be accepted both internally and externally over three-year windows. Consequently, two network pictures are drawn for each case, and network structure indicators are computed for these two representations. We created a description of network evolution and the consequences of this process on the level of disruption of the ideas involved. This research demonstrated that different network structures and types of connections are relied upon depending on the explorative or exploitative objectives of teams of individuals.

The chapter presents a theoretical framework that deals with the basic question of how networks and industries coevolve. We draw upon the structural and relational perspectives of networks to theorize about changes occurring in interfirm networks over time and the coevolutionary linkage of these changes to the industry life cycle. We further extend the widely accepted industry life cycle model by claiming that industry-specific evolutionary patterns impact the structure of the network’s relations, which in turn lead to diversification in the sources of innovation and to variation in the patterns of industrial evolution.

The Moldoveanu, Baum & Rowley approach highlights the importance of developing a multi-level theory of network evolution that explicitly accounts for strategic agency and (bounded) rationality. I discuss intriguing questions raised by their model about structure vs. topology, feedback loops in evolution, and network resources other than information.

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.

How do ego-networks evolve? How does such evolution affect firms’ innovation output? This chapter uses a longitudinal sample of firms in the biotechnology industry to address these questions. We use social network theory to develop a model of the structure and dynamics of firms’ interorganizational research collaboration ego-networks. Using novel longitudinal methods, this chapter demonstrates how research collaboration ego-networks in the biotechnology industry change over time and how this evolution affects focal firms’ subsequent innovative output. The model is tested on a sample of 482 biotechnology firms over a span of 17 years (1990–2006). The results indicate the significant impacts of ego-network size, ego-network growth, and the inclusion of new members in the ego-network on the innovation output of biotechnology firms. Our results also suggest that enlarging ego-networks by adding new and diverse members presents significant management challenges.

In this reply, we respond to a series of substantive comments on our work by both Madhavan and Walker. In our response to Madhavan’s comments, we consider three accounts – “weak,” “semi-strong” and “strong” – that clarify how our model “explains” and offers insights that can emerge from our modeling strategy. We also explore ways in which our explanatory strategy might be extended in light of his critique. In our response to Walker’s comments, we adopt the “semi-strong” thesis, which admits variation in network-generating mechanisms, while also recognizing that information needs to be distributed and shared in order for many types of networks to function.

Destinations have in the scholarly literature been labeled as communities of interdependent organizations that collectively coproduce a variety of products and services. The paradigm comes close to describing destinations as firms which are embedded in interfirm networks. Recent studies provide crucial insights into an understanding of destinations' orchestration and structuration as coproducing interfirm networks. However, systematic knowledge about how these systems evolve and develop is lacking. This chapter addresses this issue and elaborates how the concepts of scale-free and small-world networks together can explain the process of destination evolution. The discussion also suggests how such theorizing can spur avenues for future research.