When revisiting the academic study of national innovations systems of countries, most research focuses on the volume of technological assets, such as patents, produced by a certain country. Throughout this project, in order to characterize the innovation or capability of nation, the focus relied on the usage of field related term-pairs. This allowed us to obtain a more nuanced view on how different countries and regions conduct research on a certain industry topic. Although the volume of patents is a good indicator of the intensity of the research from a certain country (Furman et al., 2002), it fails to describe exactly how this country innovates. Furthermore, the analysis of industry terms allows for a quantitative comparison of countries, which the volume of patents does not. For these reasons, it is fair to deduce that in order to fully understand the nuances of a country’s research ecosystem, more focus should be put on industry specific terminology.
The literature review clearly showed that researchers have realized that innovation capability is related to a number of external indicators: GDP per capita, R&D spending, openness to international trade etc. (Filippetti, 2010). Although this project does not refute this in any way, it does allow for a simple verification of the relationship between external factors and the national innovation of countries. Just as was done with the GDP per capita and its relation to the capability correlation matrix, this project provides a framework which allows the usage of any external indicator and the consequential visualization of its influence in the research ecosystem. By doing this, the technological capability framework provides an additional tool that can help to find the perfect national innovation capacity indicator.
Most NIC studies focus on the evolution of the patenting volume over the course of a number of years. (Furman et al., 2002) It is worth noting that by combining the macro and meso levels of the analysis, it is possible to understand how the capabilities of countries change over time. For example, just as the capability correlation matrix of countries was produced, one could create one of these for each year, thus understanding in a deeper manner, the behaviour of the system.
Finally, the last area of the literature review related to the macro and meso level touched on Hidalgo’s view on economic complexity (Hidalgo, Hausmann, 2009). This project comes to validate the importance of the complexity of an ecosystem as a way of understanding its behaviour. Furthermore, just as the economic complexity view, this thesis focused on understanding the innovation systems as a network and the study of its characteristics. This could mean that the economic complexity methodology and way of thinking are applicable to other areas, such as the study of technological capability and innovation. The value of the system lies in its complexity, not in its volume or size.
In what concerns the micro or organizational level, the main focus of the literature review relied on the understanding of how open innovation is expressed in the ecosystem of research.
The first part of the analysis touched on open strategy as a crucial part of the open innovation process (OECD, 2008). When looking at the correlations in the industry and the collaboration landscape between organizations, one cannot help but state that open strategy seems to be, at least at this moment, a utopian view. Although universities are intensely collaborating and seeking knowledge outside their premises, businesses are focused on targeted innovation topics and are very skeptical to the idea of open collaboration with universities. This would mean that at least in the biofuels ecosystem, businesses are betting on other types of open strategies, or simply not interested in pursuing that type of strategy.
In what regards patenting and appropriation of intellectual property (Penin, Wack, 2008), this project has shown that although patents influence scientific research, the number of patents has decreased over the last 4-5 years. However, the project does not seek to understand if this is due to a smaller rate of patenting or simply the expression of the maturity of a certain technology.
Finally, the study of the nature of the collaborations in the micro level presents a simple proof of concept that aims at quantifying exactly the triple helix relationships (Etzkowitz, 2001). Researchers have stressed the importance of industry-academia-government relationships through case studies and examples. This project directly quantifies these relationships as a quota of the collaborations of every organization, thus quantifying the helix. However, in the biofuel ecosystem, this helix seems to be still in its embryonic phase, at least from the industry side.
This project serves as the direct application of the engineering systems perspective (Weck et Al., 2011) to different levels of analysis of the biofuel research ecosystem.
The first implication that this project has towards theoretical studies on the topic is the fact that the engineering systems approach is a valid approach to describe, understand, and study the behaviour of a research ecosystem. Through the application of a network or graph based model, visualizations and quantitative studies (Barabási, 2014) allowed to deepen the knowledge of the system.
A second implication that this project might have is the demonstration of the scalability of the engineering systems perspective. By establishing a rigid framework of term pairs, and filtering the input of documents, the same set of quantitative tools can be applicable to different levels of the system.
The third and final implication is the under explored relationship between big data analytics and the engineering systems view. The access to a large amount of information and the understanding of it, is a very serious modern challenge. By combining the raw information of big data (EURITO) and the structured thinking of an engineering systems perspective, several advances can be made in understanding how the world works.