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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.

In the network strategy view, relative competitive advantage stems not merely from opportunity structures embedded in networks but also from the distribution of ability and motivation among firms. Thus, there is a need to “bring the firm back in” to the network strategy narrative. We demonstrate that a mixed-methods design, blending large-sample data with micro-data on specific firms and their networks, can increase our understanding of the interplay of network structure and actor mechanisms, thus bridging the chasm between theory and practice in network strategy. We believe this is a critical step toward the “strategic design of networks.”

We discuss recent developments in real options theory and its applications to strategic management research, examine the potential difficulties in implementing real options in theory and practice, and propose several areas for future research. Our review shows that real options theory has provided substantial insights into investment and exit decisions as well as into the choice of investment modes. In addition, extant research studies have contributed significantly to our understanding of whether and how organizations can benefit from real options. Future research that addresses difficulties in applications will further advance both real options theory and practice in strategic management. We call for future generations of research to enhance the impact of real options as an emerging dominant conceptual lens in strategic management.

Two core assumptions set network theory apart from other perspectives and direct research into specific strategic and organizational topics.

To provide an explicit model to address the relationships between the structural characteristics of a network and the diffusion of innovations through it. Further, based on the above relationships, this research tries to provide a way to infer diffusion curve parameters (innovation coefficient and imitation coefficient) from network structure (e.g. centralization).

Based on the network and innovation literatures, we develop a model explicitly relating the structural properties of the network to its innovation and imitation potential, and in turn to the observed diffusion parameters (innovation and imitation coefficients). We first employ current theoretical and empirical results to develop postulates linking six key network properties to innovation and imitation outcomes, and then seek to model their effects in an integrative manner. We argue that the innovation and imitation potentials of a network may be increased by strategically re‐designing the underlying network structure. We validated the model by searching the published empirical literature for available published data on network properties and innovation and imitation coefficients.

We validated the model by searching the published empirical literature for available published data on network properties and innovation and imitation coefficients. The results reported from various relevant research papers support our model.

This research shows that the innovation and imitation potentials of a network may be increased by strategically re‐designing the underlying network structure; hence, provide guidelines for new product managers to enhance the performance of innovative products by re‐design the underlying network structure.

The model developed in this paper is a breaking through result of synthesizing various traditions of diffusion research, ranging from anthropology and economics to marketing which were developed independently. The research explicitly modeled the diffusion process in terms of the underlying network structure of the relevant population allowing managers and researchers to directly link the diffusion parameters to the structural properties of the network. By doing so, it added value by making it possible to infer diffusion potential from directly measurable network properties. Vis‐à‐vis the network diffusion literature in particular, we added value by “unpacking” the diffusion process into innovation and imitation processes that form the building blocks of contagion. Moreover, we developed a holistic structural model of network diffusion which integrates the several network properties that have hitherto been studied separately.

The relationship between interlocking directorates and firm performance has been increasingly debated, with a focus on whether firm's centrality in interlock networks is associated with performance. The purpose of this study is to examine not only how a firm's position in this network is associated with performance but also how the performance of network partners can impact a firm's performance. This study examines how firms effectively utilise the interlock network to achieve the goal of higher market capitalisation – termed market capitalisation rank (MCR).

The premise of the study is the UK FTSE 350 firms from 2014 to 2018. The paper makes use of a temporal network autocorrelation model to examine how firm characteristics, the structural position in the interlock network and the performance of network partners affect MCR over time.

The analysis indicates that firms with ties (via the interlock network) to firms with high market capitalisation are more likely to enhance their own MCR, highlighting network partners have the opportunity to play a critical role in a firm's dominance strategy to optimise firm value.

The value of this research is that it does not only look at the impact of a firm's position in the network on performance, but the impact of the performance of network partners on a firm's market performance as well.