تاثیر سیاست های نوآوری بر عملکرد سیستم های ملی نوآوری : تجزیه و تحلیل پویایی های سیستم

There has been a growing significance for the National Innovation System (NIS) and its use as a tool for the competitive advantage of a country to date. In this paper, an NIS model has been developed with the use of system dynamics (SD) methodological approach. The objective of this model is to integrate the systemic approach, the computer modelling and the simulation discipline into a holistic dynamic consideration of the NIS. From this central structure, the paper analyzes the impact of innovation policies on the NIS performance. In particular the SD model is used as an “experimental tool” to conduct extensive what-if analysis scenarios with regard to alternative innovation policies. The effectiveness of policies is investigated through the dynamic behaviour of product innovation and process innovation which are obtained by simulation results. By using data from a European Union country with innovation performance below that of the EU27 average, the analysis of results reveals insights over a strategic time horizon.

Innovation is a complex phenomenon involving the production, diffusion and translation of technological knowledge into new products or new processes. The concept of innovation has undergone several transformations due to the evolution of models aiming at better understanding of the innovation process (Rothwell, 1997). After the Second World War the linear model was the generally accepted model. In this model, new technology begins with basic research and advances through applied research, invention, commercial market test, and is eventually led to diffusion. Innovations are seen as the end result of a linear process that consists of different steps performed in a sequential, hierarchical, and unidirectional order. Later on, Kline and Rosenberg (1986) mentioned the conversion from the linearity of the innovation process to its dynamic-systemic behavior. The innovation process involves interactive relations among different actors, it follows a non linear path and is characterised by complicated feedback mechanisms (Kline and Rosenberg, 1986 and Edquist, 1997). The systemic approach to innovation is grounded on the presumption that innovation processes cannot be decomposed into several isolated phases that take place in a strictly proceeding sequence. The growing relevance of the interactive, collaborative and inter-disciplinary character of innovation, together with the rejection and obsolescence of linear innovation processes has been described as the transition from mode 1 to mode 2 processes in the creation of knowledge (Gibbons et al., 1994) and from ‘close’ to ‘open’ innovation in the field of exploration and exploitation of innovation (Huizingh, 2011 and Chesbrough, 2003). Ultimately, this perspective of innovative processes serves as a valuable starting point for the definition and empirical application of the innovation systems approach in general (Balzat, 2006). It also constitutes the foundation for the National Innovation System (NIS) approach, in particular if the distinctive national content and dimension of innovative processes and of institutional settings are considered. In the last decade, NIS is the most frequently used approach to understand the complex relations that constitute the innovation process (Carlsson et al., 2002). An NIS can be described as the set of institutions, which jointly and individually contribute to the development and diffusion of new technologies and which provide the framework, within which governments form and implement policies to influence the innovation process (Metcalfe, 1995). From this perspective, the innovative performance of an economy depends not only on how the individual institutions perform in isolation, but on how they interact with each other as elements of a collective system of knowledge creation and use (Rycroft and Kash, 2004 and Calia et al., 2007), which is subject to dynamic processes (Smith, 2001). Understanding these dynamics is one of the central topics in the studies of NIS, mainly under macroeconomic concept. Yet, the concept calls for a decomposition into different elements because of manifold activities which are carried out by different type of actors within an NIS. The NIS approach stresses that understanding the linkages among the actors involved in the innovation process is the key to improve innovative performance of a country (Lundvall, 1992 and Nelson, 1993). Under this perspective, the innovation process is the result of a complex set of relationships among actors which are producing, distributing and applying various types of knowledge—tangible and intangible. Innovative performance, however, relies more frequently on intangible assets as the economy moves to specialised non-repetitious activities (Kramer et al., 2011). NIS concept has emphasised the importance of systemic co-operation in innovation processes. NISs have already been analysed for different countries resulting thus in a rich sample of variety of participating institutions and organisations and their networks of interrelations (Lundvall, 1992 and Nelson, 1993). Additionally, empirical research underlines the differences and functional equivalents between countries in the organisational configuration of an NIS and its impact on a country's economic performance (Nelson, 1993, Lundvall, 1992 and Harding, 2003) and vice versa (Filippetti and Archibugi, 2011). Despite the systemic nature of innovation, the non-linearity of processes has been identified as a realistic concept of innovation as well (Kline and Rosenberg, 1986). This non-linearity directs attention to the interaction of the actors in an NIS, since it involves communication and feedback. An example of these complicated feedback mechanisms is presented in Fig. 1. In particular, the causal loop diagram shown in figure, presents the working mechanism for Research Activities, a variable of special interest for many empirical studies dealing with national innovative strength (Archibugi and Michie, 1995). The diagram consists of two negative feedback mechanisms, Loop 1 and Loop 2. In Loop 1, the actual level of Research Activities is influenced by Research Activities' Increasing Rate; the higher the rate is, the higher the Research Activities become. However, high levels of actual Research Activities, limit the increasing rate in producing new activities due to saturation phenomena (negative influence). On the other hand, the literature review proposes the expenditures on R&D activities as a basic indicator of innovative input in imprinting the research activities of an NIS' actors ( Martínez-Román et al., 2011, European Innovation Scoreboard, 2008 and European Innovation Scoreboard, 2009). Loop 2 shows this dependency; Available Expenditures in R&D increase when New Expenditures increase, but again saturation phenomena limit the rate of increase of new expenditures, creating the negative Loop 2. Available Expenditures in R&D then positively influence Research Activities Formation Rate which, in its turn, shapes Research Activities' Rate.In early 1990s, many studies have been carried out by using the NIS approach as an analytical frame, in order to reveal the structure of national innovation processes and the main involved actors. These studies can be listed in three categories. The first category contains policy-oriented studies that combine the NIS approach with the terminology of corporate benchmarking. A collection of these studies can be found in Nelson (1993). The early and descriptive studies have not been aimed at providing a formalized methodology or a clearly delineated structure of the NIS concept. These limitations have simulated research efforts to carry out system-level comparisons as well as to formalize the NIS concept. These efforts have led to the introduction of descriptive frameworks and to the development of analytical models. The studies of the second category focused to develop models to carry out international comparisons of innovative strength (Liu and White, 2001;Chang and Shih, 2004;Marklund et al., 2004). Cluster analysis techniques and factor analysis methods constitute the main methodological approaches used in these studies. The third category of studies is related with the mathematical modelling of NIS (Janszen and Degenaars, 1997 and Lee and Tunzelmann, 2005). Mathematical models from one hand can provide significant insights about the dynamics of the innovation process and from the other hand can be tools to study the impact of innovative policies on the performance of an NIS. However, the limited number of such models is remarkable and more importantly, there are still interactions that have not been studied providing thus, only information for likely directions for future research. Towards to this direction, the need for holistic dynamic mathematical models which can be used as a tool to describe and to analyze the complex and dynamic nature of NISs is obvious. The contribution of our research is two-fold. First, we provide a holistic modelling approach to the NIS concept, in order to reveal the functions of its actors and the mechanisms which lead to the innovative performance of a country. The modelling approach is based on the system dynamics (SD) theory and constitutes a tool for the analysis of relations between the actors of an NIS as well as the dynamic study of the innovative performance. Second, we investigate the impact of innovation policies on the NIS's performance by studying the case of Greece. In particular, the developed SD model is used as an “experimental tool” to conduct dynamic what-if analyses, which aim to investigate the effectiveness of innovation policies on the performance of innovative actions at a national level. Extensive simulation results exhibit the dynamic behaviour of product innovation and process innovation for a time horizon of ten years. The observations obtained by the results are organized under alternative scenarios with regard to innovation policies and reveal insights for the innovative performance of Greece. According to the European Innovation Scoreboard (European Innovation Scoreboard (EIS), 2008), the current level of innovation performance of Greece is ranked under the group of “moderate” innovators. Consequently, the observations obtained for the case of Greece can be generalized at least as likely directions for the rest of European Union countries which belong to the group of “moderate” innovators (like Cyprus, Estonia, Slovenia, Portugal). The SD model is based on the basic beginnings introduced by Lee and Tunzelmann (2005). This work is one of the first SD-based NISs which study the interactions between its structural elements. However, the absence of governmental macroeconomic policy and governmental legal and financial systems are important limitations of the proposed model. In our modelling approach, the boundaries of the NIS are expanded constituting macroeconomic conditions and financial system as additional structural elements of the system. With regard to innovation process, our study is based on the product-process life cycle theory of Utterback and Abernathy (1975). The Utterback and Abernathy approach succeeds in encompassing the mutual relationships between the stages of a product's life cycle, the related production process' stages of development and competitive strategy. Using this approach, Milling and Stumpfe (2000) developed a simplified SD model that portrays the relation between product and process innovation but they define the R&D capacity exogenously. The endogenous definition of R&D capacity is an additional innovative element of our modelling approach. The rest of the paper is organized as follows. Section 2 presents the systemic perception of the innovation process. Section 3 provides the generic structure of NIS, while the modelling of the innovation process is given in details in Section 4. In particular Section 4 contains the causal loop diagram, the mathematical formulation, the structural validity, the initial conditions and the units of measurement. Based on simulation results, Section 5 provides the innovative performance of Greece for a period of 10 years under what-if scenario analysis with regard to innovation policies. Finally, Section 6 provides a brief summary and potential future research aspects.

In this paper, we have developed a SD-based model that provides the dynamic analysis of the impact of innovation policies on the performance of an NIS. Formulating innovation performance as the output of many independent variables, the model supports policy makers to reveal structures for generating change and also develop and test strategies for the government. Simulation results under alternative policies and scenario settings show that a combination of sustained efforts is needed to strengthen innovative behaviours. Based on these results policy makers are suggested to formulate a strategy characterized by symmetry in its objectives, aiming at improving a set of performance indicators even in a moderate rate compared to targets set by isolated aggressive upgrading policies. Simulation results also reveal that improvement of innovation performance is a long-term target that requires continuous and systematic efforts, since many policies that affect the system interrelate with the national, economic, institutional and social environment. Hence, governments and regulators should not expect the improvement to appear over short-term cycles. The presented model establishes SD as an appropriate method for the development of dynamic models to deal with the structural and functional complexity of the innovation processes at a national level. This study integrates the simulation discipline and the theory of nonlinear dynamics and feedback control into a dynamic consideration of the NIS. The approach provides an “experimental” tool to reproduce historic results, test the efficiency of alternative innovation-policy scenarios both individually and in combination, and reveal effective policy suggestions. The paper also presents an application of this experimentation for the case of Greece. The key finding is that the institutional conditions have shown the greatest impact on innovation performance; however, aggressive upgrading policies still result in partial innovative behaviors. The conclusions of this paper may be inappropriate for other European countries, since they are based on the Greek case. However, for countries like Czech Republic, Cyprus, Estonia, Italy, Portugal, Slovenia and Spain, which according to EIS categorization belong to a group of countries exhibiting the same SII score as Greece, the results of such analyses might be similar. Hence, an interesting direction for future research is to conduct comparative studies between E.U. countries that belong to groups with significantly different SII scores. Furthermore, the results do not exhaust the possibilities of investigating all the effects of innovation policies on the performance of an NIS. The versatility of the model, allows for suitable modifications to investigate other effects. For instance, it would be useful to study the effect due to the existence and operation of clusters that function in sectoral and regional levels.