There are multiple possible ways and angles of studying all of the underlying factors that appear when studying knowledge as a system. In order to stay in scope of the goal of the project, three main areas will be covered in this literature review. The first area, corresponding to the macro and meso levels focuses on 1) the study of innovation and innovation measurement at the global and national level, which is, taking countries as the units or entities of the system and studying the global system over time. One should point out the intense relationship between the macro and meso level in terms of literature. Most macro level analysis also focus on the evolution of the global system, and for this reason, these levels will be merged for the sake of the literature review.
The second area of the literature review chapter focuses on 2) open innovation as a core strategy in organisations. The third and final part of the analysis focuses on the 3) engineering systems perspective, as a core part of the analysis, this perspective could be applicable to both organisations and countries. These three areas have some intersecting concepts, for example, many regional innovation studies focus both in countries and organisations.
The macro level of this literature review starts by focusing on the field of research of National Innovation Systems (NIS) as the more classical approach of understanding innovation as a quantifiable characteristic of a certain region, in this case, a country. Although a lot of research has been developed on this topic, one might argue that its pillars come from three distinct areas of research: growth theory (Romer, 1990) which states that an increase in productivity (due to innovation) leads to growth of a country’s economy; the cluster based theory (Porter, 1998) which argues that in a global economy “one would expect location to diminish in importance. But the opposite is true.” and finally, research on the innovation systems (Nelson, 1993) of nations carried by Richard.R.Nelson where he famously describes the innovation systems of 15 both high and low income countries. Nelson argues, for example, that differences between countries in terms of innovation systems are closely related to the differences in economic and political circumstances of those same countries.
Taking these three perspectives, a new field emerges that wishes to further understand the underlying factors between divergences in innovation across countries and at the same time its quantification it in a satisfactory way. This is done by the introduction of the concept and framework of National Innovative Capacity (NIC) (Furman et al., 2002) as “the ability of a country to produce and commercialize a flow of innovative technology over the long term”. By using international patenting data from 17 countries members of the Organisation for Economic Co-operation and Development (OECD) the authors managed to determine that the patenting volume was well characterized by a set of indicators. First, that while the discrepancy between countries is due to the difference between research and development (R&D) resources and spending, another very important factor is the R&D productivity. By R&D productivity it is meant policy choices, share of academic and private research, as well as specialization of a certain country. Moreover, other general indicators such as population, gross domestic product (GDP) per capita, and openness to international trade and investment also proved to be highly related to this volume of patenting. This paper and research is considered to be the core of the NIC area and gave birth to a number of derived studies on this same topic.
The dynamic innovation system framework (Castelacci, Natera, 2011, 2013) showed that absorptive (human capital, trade and infrastructure) and innovative capabilities drive the NIC of a certain country. Studies such as “Innovation capabilities of European nations” (Faber, Hesen, 2004) test the framework on 14 European Union countries and show that patents depend on the sales of product innovations but that at the same time, while some innovation indicators depend on the same economic indicators, governmental regulation and firm specific conditions may also affect innovative output. Some other studies (Filippetti, A., Peyrache, A., 2011) work on developing a composite indicator that consists of patents, R&D resources, personal computers, internet users and others to find that there is a convergence in technological capabilities is occurring (Filippetti, 2010).
Although the classical view has certainly produced more than enough interesting studies on how to measure, characterize, and quantify the innovation capacity of a certain country or group of countries, one should not discard the critical role that the economic dimension might play in the understanding of a country’s productivity.
Economic complexity (Hidalgo, Hausmann, 2009) is a framework that interprets economic growth and development that stresses the importance of the complexity of the country’s economy. It does so by utilizing a network structure and connecting countries to products they export. The characteristics of this network are highly correlated with the GDP of a certain country or even its potential economic growth. Moreover, the authors also argue that the more economically complex, or product diverse, a country is, the more economically complex it becomes. This view is particularly interesting not because of the economic growth per se, but because of the different methodology in the quantification of the assets of a country. For example, instead of taking the volume of patents (such as in the classical view in the beginning of the segment), this view emphasizes the idea of a network structure.
In what regards cluster based theory and the proximity of countries, recent studies have also taken advantage of a network based view. Recent work (Bahar, D., Hausmann, R., & Hidalgo, C. A. 2014) has shown for example that a country is 65% more likely to export a certain product if it possesses a neighbour country that exports that same product, or that the growth of exports is 1.5% per year if its neighbour has a comparative advantage in this product. However, as distance increases, this probability tends to decrease as well. Hence the title ‘Evidence of knowledge diffusion?’. This study also shows the importance of interpreting countries and products as pairs and not as isolated entities, and the value of network interpretation.
Finally, one can conclude that the research field of regional studies and national innovations systems is very heterogeneous. On the one side, the classical view tries to approach a perfect indicator that can perfectly describe the innovation capability of a certain country and all of the external and internal factors that might influence it. On the other side, the economic view, where there seems to be an increasing importance in the network view of the international innovation system, and indicators that economic development and innovation are highly related to the characteristics of this network.
Innovation is not a new field, particularly in what regards scientific research. The dictionary defines it as simply being the “introduction of something new” (Merriam, Webster 2018), others (Freeman, 1982) define it as the design manufacturing, management and commercialization of a new process or equipment. However, when observing the extensive list of possible definitions on classical literature, a pattern emerges: they all give equal importance to not only invention, but to the exploitation and development of unseen knowledge.
One particular field of innovation that quickly became of interest to a wider audience and that is also a special focus of this project is the field of so-called “open innovation”. It started by being described as a model where companies commercialize both their own innovations and innovations from other firms. Moreover, in this model, companies should find ways of bringing in-house ideas to market by building bridges outside their current business (Chesbrough, 2003). This field evolved over time into what can be described as 8 main areas of research (Giannopoulou et al. 2010): the concept of open innovation, organizational design and boundaries of the firm, open strategy, the human factor in open innovation, communities and distributed co-creation, patenting and appropriation, the innovation intermediaries’ model, and the triple helix model. In the scope of this project, it was deemed appropriate to focus on three of these fields, which will be reviewed below.
Open strategy is one of the main fields of research on open innovation which has highly impacted the way firms and organizations operate. For example, companies are increasingly adopting innovation ecosystems across countries in order to match the escalating demand for innovation from customers (OECD, 2008). Other findings in this OECD report come to find some characteristics of firms that are highly connected to the scope of this project such as the fact that “large companies are four times more likely to collaborate”, that geographic proximity plays a crucial role in the development of these networks, or that firms usually rely on external sources to develop technologies and processes outside their core competencies. Furthermore, it was also discovered that universities and public research institutions have an increasingly important role in this strategy, that staff mobility is crucial, and that national R&D programmes should be as open as possible.
In this scope, some work (Morgan, Finnegan, 2008) in the field shows the importance of collaboration with universities, research institutes, communities and governments in order to create and exploit knowledge. Other works (Bessant 2008) demonstrate other strategies firms use such as scout sending, web usage, and work with customers (in this case active users), to achieve a higher level of innovation.
In the same OECD report cited earlier, research shows that industries where intellectual property rights (IPR) are highly protected, companies mainly look outside of the business to keep up to date with research. On the other hand, industries where IPR are “softer”, companies mainly employ collaboration as a way of attaining that same objective. Although normally one possibly think of patents as something that might throttle open innovation and the sharing of knowledge, and that they directly contrast with something like open source software development, research (Penin, Wack, 2008) has shown that adequate use of this system of IPR protection can positively impact the preservation of freedom of access to research tools. Pénin and Wack do this by giving examples on how a certain patent might require a certain technology to be used by all but only under certain special conditions, thus leading to the creation of an environment where: “a material or invention can be improved by the ideas of many, but access is maintained for all who agree to the terms, without exclusive capture by anyone”.
The triple helix is a non-linear innovation model that describes an industrial society as something that has shifted from industry-government to an industry-government-university relation (Triple Helix Research Group). It was first developed by Etzkowitz in the 1990s. Etzkowitz states that knowledge and universities play an increasingly important role in the development of technology and technology based firms (Etzkowitz, 2001). Moreover, he argues that this interaction is crucial in order to improve conditions for innovation. With the emergence of globalization and its decentralization, regional university networks will fuel innovation by creating “discrete pieces of intellectual property”.
Perman and Walsh described the university-industry relationships by establishing a list of possible links between them that include research partnerships, research services, academic entrepreneurship, human resource transfer, informal interaction, commercialization of property rights and scientific publications (Perman, Walsh, 2007). These are also organized into a hierarchy of relational involvement that ranges from high to low. The researchers also point to the crucial importance of these relationships in the context of open innovation.
On a final note, there is evidence (Léon, 2007) that countries and international organizations such as the European Union have already realized the importance of these relations, by creating specialized knowledge transfer structures such as research centers to foster the exchange of knowledge and catalyse innovation. Léon also illustrates the benefits of government-industry-relations by taking the example of grid service deployment and the long and short term instruments developed by the EU to foster these same relationships.
Engineering Systems is undeniably growing as a research field, the term was reportedly born in the Bell Laboratories in the 1940s, ten years later, G.W.Gilman - then director of systems engineering at Bell - made the first attempt at teaching it in the Massachusetts Institute of Technology (Hall, 1962). During the next decades, systems engineering, its tools, definition and implementation “continued to evolve” (Brill, 1998). Among a vast number of definitions, Olivier L. de Weck (Weck et Al., 2011) defines an engineering system as:
“A class of systems characterized by a high degree of technical complexity, social intricacy, and elaborate processes, aimed at fulfilling important functions in society."
Piaszczyk (Piaszczyk, 2011) developed a conceptualization for engineering systems, where he argued that some domains are common to almost all engineering projects:
Environmental: the external drivers or consequences of the engineering system.
Social: the human components of the system.
Functional: the objectives and goals that the system wishes to achieve.
Technical: the non-human components of the system (assets, information, infrastructure).
Process: the processes that take part in the core of the system.
Temporal: how the system develops or changes over time.
These five domains are critical to be understood in the presence of any engineering system and served as a point of departure that the author used to develop his own conceptual model of engineering systems, known as the Engineering Systems Multiple Domain Matrix (ES-MDM). This is not the first conceptual model that was created to categorize engineering systems, other frameworks include the Design Structure Matrix (Browning, 2001), the popular House of Quality (Guinta and Praizler, 1993), and CLIOS (Complex, Large-Scale, Interconnected, Open, Sociotechnical System) (Dodder et al., 2005).
Diving deeper, the notion of complex system emerges, de Weck (Weck et Al., 2011 ) defines a complex system as a system where the components, interconnections, interactions or interdependencies are particularly difficult to describe, understand, predict, manage, design or change. In his work, the author also states that a complex system has not only a technical dimension but also a management and social dimension. Furthermore, he establishes two types of complexity: behavioral complexity - where the difficulty lies in the prediction, analysis, description and management - and structural complexity where the number of elements and the nature of their relationship are intricate.
However, engineering systems are not only objects of study, engineering systems are also an approach that is used to solve or understand complex problems; it is a “technique for the application of a scientific approach to complex problems” (Miles, 1973) that takes a holistic view. This perspective is highly related to the work that will be developed in the forthcoming project. One should stress the different methodologies that have been used to study these systems, in particular, two popular ones.
The first approach that is widely used is the graph-based or network approach where system components and relationships are described as networks would be. While the network approach to a system analysis can be strong in its “ability to visualize and perform statistical analysis on the properties of the network and isolate particularly interesting or important system elements or clusters of elements that may be present” (Wick et Al., 2011), this approach can also become quickly overwhelming. For example, Eppinger states that “A boxes-and-arrows depiction of the design process for a car’s suspension, for example, would run to more than 30 pages.” (Eppinger, 2001). This approach allows the study of systems as diagrams, flows, and essential visual representations but also allows the study of a system through network analysis in some systems, through different network indicators such as degree centrality, clustering coefficient, degree, and others (Barabási, 2014). One popular application of this approach is the Program evaluation and review technique (PERT) diagrams, which can be used for example, to visualize a power system restoration (Mota, 2007).The second approach is the matrix-based approach. One can generally represent a network as a matrix using the adjacency matrix of a network, or its co-occurrence matrix, where Aij is equal to 1 if node i and j are connected. One popular application of this approach is the Design Structure Matrix (DSM) where each task or element is laid out in rows and columns, and one can visualize the information and sequential dependencies of the entire project (Eppinger, 2001). Others such as the derived domain mapping matrix (DMM) can combine domains to show interdependencies across domains and synchronize several inter domain dependencies (Danilovic, M., & Browning 2006). This matrix representation is convenient because of the easy “manipulation by the tools of linear algebra” (Weck et Al., 2011) and allows the understanding of the more general characteristics of the engineering system.