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We have started formulating and simulating the lifecycle of knowledge-driven (that would include technology-driven) ventures that can be viewed as the exercise of real options under regimes of risk and uncertainty that is modeled in the form of “happy accidents” namely, strategic knowledge serendipity, arbitrage and acquisition events that punctuate the process of the venture’s lifecycle. In practical terms, we find that the timing, selection and sequencing of key decisions pertaining to new venture formation and evolution are contingent in a non-linear manner to the breadth and depth as well as the quality and density of the network structure of the business and technology ecosystem within which a venture is situated. We find that up to a certain point of cultivating and nurturing the new firm’s “socio-economic” network, the costs outweigh the benefits but with an abrupt about-face once a critical mass in the scale, scope and quality of this “socio-economic” network or business and technology ecosystem is attained when the benefits start outweighing and exponentially exceeding the costs.

The attention given to business ecosystems and the intellectual ferment it has generated in the last decade underscore the value to develop a systematic overview of the literature so as to systematize the state of the art on entrepreneurial ecosystems. In this chapter, the authors develop a content analysis of existing studies on ecosystems to understand the main features and key elements epitomizing an ecosystem and to unravel under what boundary conditions firms can develop their entrepreneurial insights coupled with strategic thinking within the ecosystem. The authors advance a map that offers an overview of the ecosystem literature, underscore the main gaps in ecosystems research and single out the key challenges for future inquiry.

In this concluding chapter, the authors delve into the key conditions that typically epitomize a healthy ecosystem and the provision that entrepreneurial ecosystems can be observed as complex adaptive systems, where each element cannot be considered in isolation from the others. This lays the groundwork to design and formulate a cumulative knowledge and value-based theory of entrepreneurial ecosystems, which may in turn allow an appreciation of entrepreneurial ecosystems’ evolutionary paths, developing life cyles and evolving governance systems. While the chapter in this volume have collectively marked the path towards this direction, we look forward to seeing empirically grounded inquiries and case-based studies that are able to shape a more compelling and advanced dynamic understanding of entrepreneurial ecosystems.

This chapter contributes to the recent line of work examining entrepreneurs who participate in multiple ecosystems simultaneously by investigating the mechanisms and outcomes of AppCampus. The AppCampus initiative was a three-year project seeking to leverage and enhance both the entrepreneurial ecosystem around Aalto University and the Windows Phone innovation ecosystem. The authors briefly review the characteristics of both innovation and entrepreneurial ecosystems, followed by an analysis of the Windows Phone ecosystem and the Aalto entrepreneurial ecosystem. They then describe the AppCampus initiative, explaining each of its components and how these linked both the Windows Phone innovation and Aalto University entrepreneurial ecosystems, before discussing the effects that AppCampus had on these ecosystems. The chapter concludes with lessons for both entrepreneurial and innovation ecosystem leaders.

Literature on crowdfunding has highlighted the role of social capital developed within the platform (internal social capital) in determining the success of a funding campaign. However, to date, prior studies have neglected to determine whether industry specificity may influence this effect. In this chapter, the authors aim to fill this gap by investigating how social capital influences the funding of products belonging to different industries. Using a dataset of 34,121 project launched on Kickstarter during 2014, they found that the internal social capital effect varies by industry and is stronger in magnitude when the industry is characterized by high demand uncertainty and task complexity. Overall, these findings contribute to a better understanding of the role of social capital in early stage financing.