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We introduce a multi-level model of the dependence of interfirm network topologies on the distribution and commonality of information in a network and the information strategies pursued by its member firms. Network topology, information properties of the network, and firm-level action within the network form dynamic, recursive, cross-level relationships – information properties in the network determine firm-level action, which in turn impacts the network topology and information properties. We derive predictions about the kinds of information strategies that firms are likely to adopt and succeed with in different information regimes, and about the kinds and short- and long-run dynamics of network topologies expected under different information regimes. Our model sheds new light on network topologies as a dependent variable that can be explained by network-level information regimes and firm-level information strategies.

This chapter highlights the importance of strategically managing knowledge networks. Strategic management is defined as being aware of current knowledge networks, understanding current knowledge stocks, and identifying gaps. It also involves assessing the knowledge needs of business units and ensuring that those needs are addressed. The chapter also highlights the importance of having a vision of a healthy knowledge network.

As a company that has continuously achieved business innovation, Apple in the United States has successfully applied strategic knowledge creation to produce a series of products that integrate various digital devices as well as diverse contents and applications, such as the iPod, iPhone, and iPad, based on a corporate vision of a digital hub concept. At the same time, the redefining of corporate boundaries that expanded Apple’s business in a horizontal direction from the Macintosh PC business to the delivery of music, smartphones, and tablets is also an indication of the evolution of a corporate vision involving Apple’s strategic transformation. This chapter presents the strategic and creative processes that enabled practitioners, including the late Steve Jobs, to demonstrate “strategic innovation capability” by “holistic leadership” at every level of management at Apple and successfully achieve a business ecosystem strategy through “creative collaboration” across diverse boundaries within and outside the company.

The research problem investigated in this paper addresses how love can intentionally be reflected in decision-making processes. The study is built on a theoretical and a practical foundation. The theoretical foundation comprises two parts with perspectives from theology and organizational theory. The practical foundation is derived from field research in the area of public administration and church leadership. Examples from field research indicate that trust and building of relationship will change adversarial behaviour into cooperative behaviour. Three network strategies are identified to make decision-making intentionally relational. The conceptual contribution is original, although the authors draw on existing insights from theology and public administration.

We develop and test a model of how a software firm's business strategy (product scope and market scope) interacts with the firm's network position (alliance degree and structural holes) to impact performance. We test the joint-effects hypotheses on a sample 359 packaged software firms that have entered into 5,489 alliances involving 2,849 distinct firms during the time period, 1990–2002. While prior studies have demonstrated the importance of network positions as a determinant of firm strategy and performance, this chapter begins to examine the performance effects of how a firm's business strategy and network positions interact. We find support for three of the four hypotheses lending empirical support for our theoretical model. We develop implications for network-based perspectives of strategy and outline areas for further research.

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.”

The chapter is structured as follows: in the first part, we provide the framework for the analysis of the formation of the born global firm, whereas the entrepreneurial, strategic, and network-based factors are conceptually linked and leading toward a global champion. The analytical model proposes the analysis of strategic choices as defining factors at the level of entrepreneurial behavior, firm strategy, and network. The case study methodology is provided in the second part of the chapter. The third part provides the empirical linkages of entrepreneurial, strategy based, and network factors’ manifestations and underpinnings in R&D intensive entrepreneurial born global firms. These are followed by discussion and conclusions enclosing empirically grounded framework that explains the emergence of R&D intensive entrepreneurial-hidden champions from the perspective of entrepreneurial firm and network theories.

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 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.