The theory of Hekkert [10] describes different changes that are necessary to make technological innovations sustainable. Not only the technology, but also social dimensions such as user practices, regulation and industrial networks are inevitable. Another important aspect is the dynamics of an innovation system. Furthermore, the central idea behind the innovation system approach is that innovation and diffusion of technology is both an individual and collective act. Therefore the micro and macro level are both important.
The technological innovation system uses a systemic approach, which means every aspect both technological and social, within the context will be used to determine the system characteristics. Nowadays a technological innovation has not a single location from which it originated, but it is often the product of different progresses made in different countries. The diffusion of an innovation is then influenced by national factors. The theory of Hekkert tries to use a systemic approach to explain how this works. Because a technology specific innovation usually has a smaller network and less relevant institutions and actors than a Network specific innovation, it is possible to create a dynamic analysis. In our case of the biomass boilers, it is indeed a smaller network with less institutions and actors.
For a systems analysis, it is important to manage all interfaces between subsystem borders and to know how the system as a whole is organised. In order to develop well, a new technology has to become part of that system, part of an incumbent regime or even overthrow that regime. This has happened in Germany in the biofuel industry, where the government subsidised the production of biofuel and the market started an active lobby to promote biodiesel. In 2003, more than 1300 commercial gas stations sold biofuel. This could also happen with
our situation of biomass boiler systems. Once the new technology ‘gets a foothold’ in the market, it might get
the momentum to grow and expand.
The whole innovation system can be described by using different functions. The paper of Hekkert uses the following seven functions and their interactions with each other to explain the system:
• Entrepreneurial activity: How actively entrepreneurs are and how risky their experiments are is very decisive for the innovation.
• Knowledge development: Without new knowledge, there are no developments in innovation
• Knowledge diffusion through networks: With more knowledge diffusion, more parties can work on the technology
• Guidance of the search: If the search for innovation is guided properly, the use of resources is more efficient. This can be done for example by setting national goals, which leads to development to reach those goals.
• Market formation: New technology is not always fully compatible with embedded technologies. Therefore the initial advantages can be small.
• Resources mobilization: Resources, both financial and human capital, are necessary as a basic input to all activities within the innovation system. How these resources are used and allocated is therefore very important.
• Creation of legitimacy / counteract resistance to change: In some cases, parties form coalitions to put a new technology on the agenda. They can counteract resistance against the change.
All these functions influence each other, and influencing one is likely to influence others as well. Therefore, the model is non-linear with multiple interactions between functions. There are many interactions possible, but most innovations have only three starting functions. These starting functions then pull the other functions, initiating a virtuous cycle. For the case of the biomass boilers, mainly the lobby for market formation, together with entrepreneurial activity are probably most important. To create a virtuous cycle, first a certain threshold of fulfilment needs to be reached.
The virtuous cycle model does explain how different functions interact, but to describe how an innovation has emerged and evolved, one can better use the process approach or sequence analysis. This approach conceptualizes the development and change by describing the sequences of events. These events explain the outcome of the model. Therefore, this not only describes the influence of a function, but also the underlying mechanisms that determine the technological change.
This method can be applied by mapping all events and linking them to the seven functions described earlier. Once these events are all known, they can be plotted on a timescale, thus showing the evolving of the technology in time. If patterns are be found in the figures, this can be used for a cross case analysis, to determine if these patterns are general or case specific. Using the insights gained from these analyses, the step towards policy
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recommendations can be made. For some projects, the threshold of continuous growth, or critical mass, is never reached, while others start to grow exponentially once the threshold is reached. Therefore it is important to determine how this threshold can be reached.
During the analysis of the different functions, one outcome may be that one or more of the functions are not relevant for the specific case. Also, some functions can negatively relate to the functioning of an innovation
system. This all together forms a ‘story line’, which can be used to describe the role of different functions within
the development.
The model that results from this method is a model based on social factors, which cannot be fully reliable. But by comparing the model with the empirical evidence, it should be able to approach different functions and their influence of the system. This insight can all be used to determine future policy and to determine how to stimulate and influence the technological change.
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