A Complexity Perspective on Innovation in Ecosystems

In this work innovation is considered from an ecosystem perspective. As much of the literature in this area is focused on empirical work, an important goal of the current work has been to underpin this by a theoretical and analytical research approach, using modeling and simulations. Here, a distinction is made between a number of more specific ecosystem perspectives. • In contest innovations, firms organize an innovation competition between their internal units or external contenders. A key result of simulating this process holds that finding a balance between too little and too much knowledge sharing is critical. • In market innovation, firms do their own innovation, but may also try to imitate innovations made by other, leading firms. Successful imitation depends on both social proximity and cognitive proximity between firms. In addition, it was found that the structure of the social network between firms is another relevant factor, with small-world networks offering the optimal balance between clustering and short distances. • In co-innovation, firms collaborate in their ecosystem to do innovation, with each firm focusing on its own components. Combining internal and external dependencies between components, leads to the emergence of different ecosystem regimes. These regimes are either rigid or chaotic, with the ‘edge of chaos’ regime in between, the latter offering optimal performance and resilience. The occurrence of these regimes is also dependent on the structure of dependencies between firms in their ecosystem. From a more integral perspective the ecosystem complexity model is introduced. This model identifies four main ecosystem roles: completers, composers, complementors and connectors. Innovation strategy, then, is very much about choosing a firm’s position in the ecosystem in terms of one of the four roles, and employing strategies to move a firm from its current to a desired position. The model can also be used to understand the lifecycle of industries. In terms of theoretical implications: simulations based on evolution theory of models based on complexity theory, have added to theoretical insights to underpin empirical work and to explain multiple stylized facts. To accomplish this, seminal work on the NK-model (Kauffman, 1993), the NKCS-model (Kauffman & Johnsen, 1991), network structures (Watts & Strogatz, 1998) and the product lifecycle (Abernathy and Utterback, 1978) has been combined and extended. More practical implications for innovation managers and policy makers are manifold. These include the need to include the ecosystem perspective in strategic decision making. Firms should build and maintain awareness about their ecosystem, its network structure, their own position and the positions of other firms. Policy makers and firms, as far as possible, should use these notions also to influence their ecosystem structure and positions. For example, by actively influencing knowledge sharing and social networks, by balancing clustering versus network short-cuts or by applying strategies such as outsourcing, intermediation or modularization, or their reverse, depending on their ecosystem-aware innovation strategy