The Dysfunctional Innovation Ecosystem

Spread the love



The Innovation Ecosystem could be defined as a dynamical system. Dynamical systems are a theory first mentioned by Jay Forrester in the 50s and applied to a wide range of disciplines such as demography, ecology, evolution, economy and sociology. It suggests that systems contain complex feedback loops, causal links, flows, stocks, delays among the agents. Because these agents influence each other with complex logic, mostly non-linear, it is very hard to predict how the system will behave. Usually the basic feedback loops consist of positive loops, that will keep enhancing itself without limitation. However, the system also holds several negative loops, that will discontinue the positive processes. Systems usually continue switching between positive flows and negative flows, making fluctuations very common. For instance, in climate, is it common to have fluctuations per the hour, per the day, per the season, per the year and over long era’s. The same holds for the economy or for rabbit populations. Complex dynamical systems can be mathematically programmed. The following example shows how a system with only two actors can even achieve chaos within a few cycles when there are small anomalies in its initial circumstances. This is called the chaos theory: imagine the long-term effect a small change can have. For instance, the effect of a local forest fire on the weather world-wide. Or losing a coin on the local economy.

Sustaining an innovation economy means evolving, adapting, re-imagining and reinventing to create and utilize new ideas and information into both existing and new products and services.  Sometimes this is undertaken to achieve higher quality and superior performance, others to respond and adapt to competition and customer demand.

Factors for the successful implementation of innovation ecosystems can be found in the areas of resources, governance, strategy and leadership, organizational culture, human resources management, people, partners, technology and clustering. These areas clarify that well-known aspects need to be addressed, thus the individuals in charge can to a certain degree built upon previous experience and existing knowledge, respectively, when setting up innovation ecosystems.
Considering the dearth of understanding, there are four issues that need more attention and development:
1) The evaluation of innovation ecosystems.
The actors concerned need to have measures at hand to better control and allocate their resources regarding different business operations. Given the scope of innovation ecosystems, these measures need to go beyond organization boundaries and to address all actors involved and their concerns. In addition, funding parties will be interested in measures as well in order to better assess the return of their investments.
2) The role of people in innovation ecosystems.
Innovation ecosystems comprise different actors with different goals, expectations and attitudes, so the authors of this paper call for more research on that topic as a deeper understanding of any supporting and hampering factors concerning the implementation of innovation ecosystems from a people-perspective.
3) The application of a variety of research designs and methods.
Longitudinal studies would enable researchers to study innovation ecosystems as they actually enfold. In addition, longitudinal studies provides the opportunity to observe whether and how innovation ecosystems change over time as they mature or face new challenges, respectively. Using mixed methods research approached would also help to obtain a more holistic understanding of the subject of innovation ecosystems than is possible using mono-methods approaches.
 4) Country-comparisons.
Our understanding would also benefit from studies that discuss innovation ecosystems taking country differences into consideration. Is it plausible to assume that innovation ecosystems will vary from country to country (even region to region), reflecting each country´s culture, individual systems and institutions. Therefore, comparative settings would clarify what factors are likely to remain constant under different conditions and what would change.