… the complexity of these world problems disarmed us.

Because of that we have to rearm ourselves to think about this complexity in terms of set

EDGAR MORIN “The Wandering Star”. La Nación 28/19/91 1. AUTHOR: KELLY TATIANA GIRALDO PARDO 2. TITLE: Construction Methodology Assessment for the Sustainability of Investment in Innovation

Policies 3. ABSTRACT This paper aims to identify,on the light of science and technology indicators, what factors determine the sustainability of investment policies in Science, Technology and Innovation in Latin American countries. In this way, it´s usedthe figures of the indicator RICYT (Latin American Network of Science, Technology and Innovation)and based on it´soutcomes, two models of sustainability are constructed, well-grounded on two parallel equations. The first model is according to the sustainability and relevance of science and technology activities. In the second model, the sustainability depends on the social responsibility and the initiative depends on structure. It is expected that the results produced by this "doublemodel”are more robust than those taken from separatelymethods. 4. INTRODUCTION In recent years, academic interest has been moved from the technical and economic feasibility studies, as the principal criterion of performance evaluation, to implement more complex terms such as social responsibility, supportability and later sustainability. Therefore, this work aims to make a wider consideration of the variables that determine the performance of investment in innovation activities in order to establish a methodology that can use for it´s sustainability evaluation, which lets make country classifications in terms of social responsibility, supportability and sustainability, inherent in country policies and activities to promote science, technology and innovation. However, despitethe proliferation of innovation and sustainability literature, at the beginning the contribution of this study is to establish a synergy between these two major areas, considering that just few studies had worked on this. Nevertheless, as has mentioned before, the importance of this work lies in the necessity of decision-making in public policy issues, and thereforethe entities that integrated the national innovation systems of Latin American countries. In this sense, the point of departure to establish the importance of decision-making on investment in innovation is that it is not only an economic problem, because it incorporates a diffuse content about the institutional growth and evolution. In this way, since Bowles (2005) argued that innovation can´t be cover by linear indicatorsdue to the fact that it requires mathematical expressions reflecting the holistic-cultural dimension of the process assimilating a control system decision based on dynamic criteria.

To begin, it is possible to find a system todetermine the problem of investment in innovation through the scenarios of institutional productivity, competitiveness and sustainability. Under this analytical framework, it is emphasized that information and knowledge are the cornerstones of the institutional production function, carrying out research and development activities expressed in a higher percentage of human capital and technology. From the perspective of competitiveness, knowledge is the main factor, not in linear order but in a higher complex order and every significant advance involves transcendent redefinitions in many areas of the urban production structure, productive sectors and society itself. With this, like to express that the cornerstone of competitiveness is perhaps the human capital, making it a sustainable project (Henderson and Cockburn, 1994) in the sense of constant innovation (Schumpeter, 1950). However, based on the social spaces definitions in where the fundamental relationship of productivity is derived from the interception of the individual capacities, and not on their potential to change the primary sources, typically in industrial economy, is that innovation can be thought as a probability concrete in the range and social domain. In other words, innovation as a culture could be found throughout the whole society thanks the individual innate curiosity. The question is if the societies mechanisms (institutional, market and non-market) are enable and promote itself structure or rather hinder it.This is the central hypothesis: Society doesn’t produce a significant innovation and it´s institutional mechanisms don´t contribute in transcendental way, more than this,in the cities that situationjustify its actual term of development. For that reason, we conclude that one of the main determinants in innovation process, according to Nonaka, Toyama and Nagata (2000), is the organizations autonomous capacity to create their own competitive advantages in response to the market dynamics,because the ability to create and use knowledge is their most important sourcebased on the knowledge and personal skillsof the agents involved in the innovation process. Therefore, through this synergy between knowledge and skills, organizations can efficiently and effectively innovate and improve their products, processes and services. Moreover, to understand the investment in innovation process as organizational development is obligatory to study it within the environment in which it develops,is vital to analyze the innovation ecosystem, including the inputs or sources innovation, the outputs and their impact on society as a whole the political conditions and infrastructure that cover investment process. Thereby, the incentives to allocate resources in innovation on the services areas are tied to competition between firms, thus maintaining the already established marketing channels and the need to be complementary to manufacturing sector. This study aims to identify which factors determine the sustainability of investment in innovation activities, and based on this design a methodology to evaluate this sustainability, offering a special input for policy and institutional public decisions. In this sense, the results are expected to provide elements for the construction of public policy in order to build higher levels of innovation in developing countries. On this paper are confronting systemic to analytical (inferential)aspects, for that reason it could be said that has a constructivist nature because its framework is two different epistemological paradigms. However, on

this occasion the systemic aspect only has been used as a mental map, just for realize the study, which is focus on the analytical aspects. Despite the apparent simple logic of the "linear" paradigm, the situation is more complex when the matter is investment in innovation to a particular context as education,because when the decision-making process takes place in the reality, takes into account the risk or performance, necessarily.Also comes to be relevant technical and operational capabilities of the organization, and other variables of the external environment. Then, ¿ How to make a wise investment decision, avoiding the risk of losing the investment opportunity?. After a revision of econometric models, is found that the Cartesian paradigm is implicit in there, given to them an ontological reductionism,and leads to fragment econometrics representations of reality, and in this way playingto the detriment of the models explanatory capacities. During the implementation an econometric models by policy-makers, it´s leaving out of considerationrelevant aspects at the moment to choose between several options. On the other hand, the Systemic Thinking as a method for building knowledge is continuously driven by aholistic effort

1and through the recognition of different phenomenon perspectives its understanding

become more complete. On the systemic paradigm, knowledge is constructed within the framework of a global and comprehensive vision of the phenomena, denoting by these changes observed in there as the result from the influence of dynamics relations between elements of the system that represents the phenomenon under study. This new paradigm meets the Kuhnian criteria to validate a new paradigm to attempts to replace the older in the next specific fields:

The theory is accurate in its predictions for there to be a greater connection between reality and theory.

The theoretical consistency.

The broad scope and simplicity in the theory. Apparently, all these criteria are satisfying in a high degree by the systems theory and dynamics system, though, its principal focus is to make a representation of the reality that bears the interactions between its different components or elements, giving a more exact description of the phenomena that tries to represent. Kuhn, indicates that finally the accumulation of abnormalities makes change the paradigm and observations that do not have content and can´t be explained by the current paradigms. These anomalies have implications to the case of the models econometrics, with the excessive reductionism implicitly in its formulation. The linear models to evaluate the economic viability of the investment models have developed based on hard suppositions as well as on the correlation to profit assets. Itonly considers the first two moments of the returns distribution: the average and the variance. This simplification does not suppose disadvantages when the returns of the assets present normal distribution, but unfortunately this in the practice does not happen.

1 Andrade, Dyner, Espinosa, López and Sotaquirá. Systems thinking: diversity in search of unity. page 35

In the analytical part, will use Harrod's Framework Spaces, complementing it with a graphical analysis that departs from the variables involved in some of the most relevant definitions of the term "sustainable", so will develop later in the theoretical frame. To carry out this exercise, as area of observation, will use the set of Latin-American and inter-American countries, in terms ofscience and technology indicators, which will be taken from the RICYT (Network(Net) of Indicators of Science and Technology), during the year 1997, 2002 and 2007. 5. IDENTIFICATION OF THE PROBLEM 5.1 Theoretical Frame and State of the Art 5.1.1. Sustainability, Supportability And Social Responsibility In agreement with Lopez-Ricalde (2005), the definition of sustainable development has been adjusting gradually to the social conditions that have been increased where the human being is a partof a system and not owner of the same one. At the first, the sustainability means the capacity of a project to continue being viable in a long-term. Therefore, is directly related to the organizational capacity of leading, it means, to the supportability (Letelier, 2002) and with relevancy, understood as public usefulness activities (in this case economic). The conditions of supportability are associated to the variables like financial viability and budgetary stability, which they allow the effective and efficient execution of the company or organization processes (Letelier, 2002). For that reason, the financingmust cover the necessary expenses, in order the productive activities to cover the environmental demandsand to the expectations of the population. Also are necessary resources as physical infrastructure, technology and information services. In addition, has to be demonstrated that these activities or projects have resources, and that the sourceswill be kept or change positively in the time.

Figure 1. Sustainability, supportability and social responsibility: Concepts

Source: Author’s elaboration. The second condition of the supportability,is the management of the human resources establishing the competences that are needed to shape every team, as well as the period and the strategies to reach the intentions proposed (Letelier, 2002). Because that, the supportability conditions don´t be related only to the efficiency in the use of the resources, but also to ethical topics. The supportability relates also to the organizational capacity for adapting to the changeable conditions of the environment (political, economic, cultural, educational, demographic, etc).Nevertheless that the companies’supportability depends on its social relevancy and the internal aptitude to support adverse economic situations by effective management, there can be alternative ways of conceiving the supportability (see figure 1). In the equation number two, the supportability is conceivably as the result of the management exercise, in the terms of Robbins (2004) definition, it put attention in the dimensions of the human resource and structural initiatives. In agreement with the fourth equation of the graph, sustainability is the result of structural orientation, principally from the economic aimsand goals of the company, and from the social responsibility, which is the combination betweenhuman resource consideration, bearing in mind clearly, the ethical component, with social and environmental relevancy of the company actions. 5.1.2. The Dual Character of the Innovation On the other hand, it is important to remember that any innovation has a dual character. In agreement with the model " dual - core " (Daft, 1982), inside the organization (or system in this case), two differentiated areas coexist where there can arise the innovation, technological and manager, though each one is characterized for having aims, and human differentiated activities

2. According to the perspective of the

system socio-technical, the good functioning organization needs these both systems in balance. A company must not introduce innovations of a type, if does not adopt also changes in another system, since this imbalance would redound to a minor performanceof both. This notionattends a lot to the different processes of generation and adoption innovationon the nature innovation of the organization, in which it is possible to find two differentiated areas. From this it can be concluded that variables exist (or actors, when are speaking of Innovation System) that are more directly related to a one type of innovation or another, or at least facilitate or inhibit a particular process toadopting innovation. Structuration Giddens (1998) theory, interpret situations of duality in organizations and systems. In fact, the resolution of the dichotomy between structure (organizational) and action (innovation, in this case), is the motivation for the theory of the structure, which is based on the idea that the structure, considered as a feature unexpected social interactions, is the input and output of human actions. This framework has been used for example to describe the interaction of advanced information technologies and social structures (Chanal, 2002). In the context of design and organizational innovation is particularly relevant to interpret the dilemma action / structure and the dilemmas of exploitation / exploration (Chanal, 2002). The structure of

2 Giddens, A. (1998). The constitution of society. Polity Press. Cambridge.

the organization (or system) can be considered then as a resource and a restriction on innovation practices, the concepts of organization and innovation are linked in a recursive manner or structural (Chanal, 2002)

3:

Figure 2. Dual Nature of Innovation

Source: Chanal, 2002

This idea of the recurrent nature of innovation can be extended to all levels that integrate the National Innovation System. Therefore, it’s possible to expected that the extent innovations are one of the levels, including for example micro aspects, the Innovation System taken understood as a set of actors interacting, could tends to change its structure or composition. 5.1.3 Sustainability in the Field of Innovation: The sustainability and relevance Taking into account the equation 1 in Figure 1, to evaluate the project´s sustainability of the IES investment in innovation, it needs to assess two aspects: 1. The sustainable development means "meets the needs of the present development without compromising future generations to meet their own needs." In that sense, sustainable development tends to create internal capacity through learning in the systems and institutions of science and technology, which can be appropriated by successive generations of innovators in a process that could be categorized as interactive. 2. The relevance of the project, is associated with question´s response, "Who cares about the project development? Therefore, it is intended that the project was formed with some degree of compliance with the conditions of society, the market competition, the state of the art technology, resources and opportunities to develop a project. The project relevance is also considered in relation to the objectives, interests and motivations of the various agents to carry out its development. Also, the project justification must be taken in relation to development priorities

4.

Regarding the first aspect of sustainability, innovation in Schumpeter became the source of capitalist dynamics and explained clearly serves as an endogenous phenomenon. Thus, growth and business cycles are merged into a single concept. Dependence of technological progress on economic factors such as demand conditions, research and development costs and effects of "learning by doing", implies that changes in the supply are not independent from those that occur on the demand. Therefore, the innovation sustainability over time is an effort in terms of organizational learning, considering that this is needed to adapt innovations to the structure of productive organizations, in turn, that subsequent innovations generated

3 Chanal, V. (2002). "How to design innovative organizations: a focus on practice and conversations" . EGOS conference,

2002. 4 Crespo., Marco Antonio (2009).Guide for design community social projects under the logical framework approach, free

electronic edition. Full Text www.eumed.net/libros/2009c/575/

that the innovation could be effective and to generate successive cycles of continuous improvement. For that reason, the sustainability of an investment can be seen as a direct result of organizational (or institutional) learning cycles. In this way, Argyris and Schon, basically argue that organizations that develop organizational learning start from simple anti-monotonous processes, they do not question the organization structure, its interaction with the environment, their values or decision-making processes. Then enters a second level that seeks to organizational restructuring, always working on the individual learning, and it could be queried the rationale behind the actions. Given that investment is an economic variable, economic development is sustainable when it can "hold" a certain crucial variable in the sense that not diminish in the future as a result of growth. In this sense, sustainability referrers to economic growth based on output growth and social with increasing employment, and all based on industry growth (Kaldor). However, the literature on the subject of sustainable development approaches can be classified into three groups according to the crucial variable or objective function is to maximize welfare (or utility), consumption, or capital (manufactured or natural). The choice of the crucial variable has important implications, since their sustainability often involves the unsustainability of other possible candidates for this role (Vercelli, 1998). Vercelli move away from these classifications and proposed that the basic variable to be stored over time through economic development should be freedom for future generations. Regarding the second aspect, in the field of science and technology, relevance can be divided in two main areas: educational relevance and social relevance. The relevance is defined as Latapí (1995), cited by Tünnermann (2006), as the dimension which finds that the targets proposed by the institution and those required from an external perspective. This definition of relevance "scientific" gives rise to other conceptualizations commensurate with the nature of this external perspective with which they are related educational goals. In that sense, it speaks of relevant social, political, economic, cultural and even educational ways, depending on whether the research purposes are linked or not with the needs and demands to a society, politics, industry sector, culture or institution. Therefore, Malagon (2003) proposed the concept of integral relevance in the curriculum is presented as a centerpiece of the relationship between school and the socioeconomic, political and cultural environment. Tünnermann (2006) asserts that "the assessment of materiality is not an easy task especially if takes the broad concept of social relevance. Curriculum relevance and flexibility required to shift the emphasis in the transmission of knowledge, learning processes of learning, focusing on the student "who is the learner and must" build knowledge and incorporate it into their cognitive structure. " For multilateral agencies and education authorities, the relevance is reduced to a productive relationship. This utilitarian view has reached from the various stackeholders (mostly businesses) who value research primarily in terms of its practical potential. Utilitarian bias tends to prioritize the knowledge, and in this way learning, in two basic types: at first, those that build capacity and skills applicable, and at second, rest of knowledge. This approach has other implications, manifested in patterns of research and development based on competencies.

In this sense, Francisco Cajiao (2008)5 provides the difference in the concept of relevance, understood from

the functional viewpoint R & D on the production system and from the perceptions and needs of citizens’ access to educational institutions. In the first perspective, which is usually the most common in literature, relevance relates to the needs of society, especially in the production side, trying to adapt what is available from formal education to the potential demand of the labor market. Away from many public policy guidelines related to the design of academic programs of a more technical and technological as well as guidance to be given to certain areas of knowledge like mathematics, English language learning and the emphasis on so-called job skills. Therefore, social relevance is defined as the ratio between science and technology to society and requires institutions and research centers to respond to what they generate as "products" and services in relation to social expectations. Effective dialogue enables S & T institutions with the actors of their environment, providing practical answers, appropriate and timely to your needs. The social relevance creative means the ability of research to respond proactively to the environmental needs. Because that, researchers need to take as a study of its programs and projects, the problems of local realities at regional and national levels, thereby committing themselves to finding solutions and alternatives for generation a productive and social development; these approaches and solutions will be converted in turn to be systematized knowledge, contribute as raw material for the readjustment of plans and training programs for researchers, which again involves a learning process

6.

5.1.4. Sustainability in the Field of Innovation: Social Responsibility And Initiative to he Structure. 5.1.4.1 Social Responsibility. The design of public innovation policies has become vital in the sense that it contains a number of structural changes in the national economy, which affect in different ways the institutions of higher education in different regions of the country. This is of particular interest is the formation of innovation networks in these institutions, which is directly associated with building and social capital formation around the IES and innovation practices. Thesediversity factors from the various levels of national competitiveness model (micro, meso, macro and meta), strengthen and complement the construction of an environment favorable to innovation, communication and the qualification necessary to attach to international competitiveness. Hence the need to promote collaboration among actors (individuals and institutions) from local areas to create innovators networks, who through their interaction and coordination mechanisms create synergy and economies of scale on enabling environment for industrial development. Thus, for the evolutionary approach, innovation has a high degree of insecurity by increasing density of knowledge, the formalization of research and development (R & D) firm-level and the complementary role

5 Renowned educator and researcher. From his experience in coordinating the national debate on the evaluation, sponsored

by the Ministry of Education and conducted in 2008, wrote this note on Educational Relevance. 6 Arellano Cartagena, William, "Higher Education and Social Relevance," Strategic Plan century Cartagena, March 2003,

Colombia.

of informal learning processes and effects. In the process of innovation in organizations, these factors depend on the dynamic interaction skills; they are essential because they emphasize the tacit knowledge and informal learning processes, these specificities depend on the technology and corporate organizational culture. The information, knowledge and skills arise from local accumulation of know-how, specific and formal knowledge generated in formal and informal learning,develop opportunities between the companies and organizations that make up the institutional framework of national and regional IES. This is over the time a wealth of skills that determine the innovative capacity of the organization As already mentioned, in the process of innovation are complementary factors like skills (human resources) and organization (the inclusion of technology in social and production processes). Hence, the technology cannot be separated from the environment in which it arises or organizational structures where it is used. The social capital of IES, is then formed by the set of associations or networks, which are the bonds that bind distinct sets of actors through implicit or explicit relationships ranging from simple awareness to cooperation. 5.1.4.1 Initiative to the Structure According to Winter (2000), the accumulation of capacities is a prerequisite to achievement specific objectives which are related to the future performance of technology implementations, inside the teaching-learning cases. Organizational capacities are associated to the potential of organizations to address satisfactorily solution of problems arising in the process of introducing and using new technology. In this sense, ensures the existence of capacities to overcome the problems that emerge during the process, resulting in the successful incorporation of technology. To tackle this matter, is using the approach suggested by Bell (1984), in the sense of varying operational capacities operating -those that can keep operating a technological capacities- these capacities make possible management and change. It permits to identify evolutionary scenarios of the process of introducing new technology, according to the level of capacities that organizations are able to accumulate in terms of different technological learning processes, as will discuss in the next section. As a result of the implementation of the cumulative capacity is obtained, mainly, an increase in operational efficiency or technological institution and therefore more profitable in medium and long term, associated to the simple circuit learning processes (if concerned purely operational learning) or double circuit (if concerned technological learning in the sense noted by Freeman and Perez (1988)). 5.2 Approach of the Research Questions or Problems and Needs in Terms of Justification and Relevance The central question is: Is it possible to establish sustainability criteria for evaluating potential projects of investment in innovation in HEIs? To this, the following questions will be supporting: 1. What is the determinants (both internal and external) sustainability of a proposed investment in

innovation in universities? 2. What is the relevance criteria that could be evaluated on these projects?

However, the research problem of methodological points could be summarized in the following question: Is it possible to determine sustainable levels of innovation investment in policy, science and technology? This last question is relevant in terms of both organizational policy and public policy 6. PRELIMINARY RESULTS 6.1 Model System Dynamics To study in depth the understanding of the relationships between variables that are associated to investment decisions in science and technology, initially proposed a causal model of system dynamics. That is based as follows: First. Organizational capacities (operational and technological) are accumulated as a result of learning processes, which in turn are possible by social and human capital of the community. The social component of this capital provides networks and connections, formal organizations, relations of trust, reciprocity and exchange that make the community in a social system in which its agents are in mutual interaction with outside entities. Meanwhile, human components are on the basis of aptitudes, knowledge and professional skills. In turn, social and human capital that the institutions of science and technology required to sustain learning processes are the result of adequate government social expenditure and the efficiency of the community leading to skills, knowledge and skills of individual labor networks, connections, organizations and relationships of trust, reciprocity and exchange, suitable for a successful introduction of the technologies in question. In the case of institution with sufficient capital will be better to interact with the central government and other entities to achieve greater support (in terms of social investment, for example). Second. Depending on the technologies involved in the innovation process, their scores are determined by operational difficulty and technological complexity. The operational difficulty is associated to the frequency and severity of operational problems in technological implementation. The emergence problems and its solution are essentially as they appear will guarantee the stable operation of the production process, as it achieved a good performance of the technology, therefore, adequate levels of electrical power available for community, for example. In turn, the technological difficulty demands higher exigencies for organization and the community to improve significantly, innovate and expand the technological developments, increasing the capacity of the process. Both, the solution of operational problems as opportunities for improvement, innovation and expansion depend on the capacities that the organization has accumulated, in the first case of operational, and the second of technology.

Figure 3: Causal Diagram of the Microsystem Innovation

Source: Authors' calculations based on Velasquez and Ceballos (2008)

Third. The physical and technological infrastructure available to the processes and activities of teaching as research and extension, in this case is taken as a given parameter. However, the physical and technological infrastructure available can be increased as a result of investment, innovation and the consequent expansion of the process. This depends on the technological performance of research units. Fourth. Access to other resources like economic, information and knowledge funds, depends on factors such human capital (in terms of operational and technological capacities, as noted in point 1), as well as social capital. The last one is shared with other research units, when are sometimes other similar innovation processes within. To measure social capital, can use the Giraldo-Pardo (2007)proposes, which integrates the systemic competitiveness model for HEIs in Colombia, as a social network, making measurements of this capital from the centrality network measures. 6.1.1 Scenarios Simulation Model Following Velasquez and Ceballos (2008), the simulation scenarios for the introduction of new technology, are formed by the dynamic between organizational capacities and technological performance, this dynamic has studied widely in the literature of technology management, like that: a. The pessimistic scenario, in which capacities are insufficient to generate some kind of learning and performance time, b. The intermediate scenario, in which technologies are supported on the basis of minimum capacity operation and maintenance, and c. The optimistic scenario, in which learning processes ensure superior skills accumulation,with continuous technologies improvement and innovation. These scenarios were determined based on the causal diagram above:

Pessimistic Scenario Basic learning. Low value of social and human capital that is determinate by strong learning obstacles preventing the research unit to internalize the learning process required to build the necessary skills for

troubleshooting and maintenance operation, also obstructs the functioning of the technology until it falls into disuse recovery possibility of the investment implementation process. Losses.

Intermediate Scenario Operational learning. The levels of social and human capital are not enough to make a jump to the significant accumulation of technological capacities, anyway some degree of accumulation of operational capacities which ensures that the performance achieved with time, the parameters by the technology suppliers to satisfy the basic requirements of the research unit. Equilibrium.

Optimistic Scenario Technological learning. Thanks to the highest levels of social and human capital achieved, there is a significant accumulation of operational and technological capacities. This is manifested in the development and adaptation of new technologies, reducing the cost of implementation and increasing profitability, thanks to the launch of new services like on-line courses and videoconferencing (for example). Even leads to the possibility of "exporting" technology or related services to other research units, which dramatically increases the trade surpluses of the institution concerned. Profits. 6.2 Econometric Model 6.2.1 Model Specifications The objective of this section is to analyze different regimes of science and technology, reflected in structural changes in national systems of innovation, considering the fluctuations of investment and productivity as determinants. It should be noted that these structural changes involve learning processes, both organizational and individual environment to the whole systems. Subsequently, identifying and qualifying different growth phases or ways to describe a long-term trajectory. The oscillatory behavior of this trajectory has to be attributed to different regimes, determined by changes in innovation policy and its effects on the production structure through induced structural changes. Supported by the neo-Schumpeterian and kaldorianavision Space Framework as a tool for analyzing the dynamics of cognitive growth path on the Institutions of Higher Education. The working hypothesis is that the policy of promoting science and technology plays an important role in the process of growth of capital (social and human), influencing the long-term trajectory. The dynamic interactions between short and long term growth trajectories are non-linear, i.e. the path does not necessarily tend to equilibrium positions pre-determined (as seen in the approach to the conventional theory). These initial assumptions lead us to consider discontinuous changes in the path of growth that reflect or impact to the productive structure, causing structural changes and forming a growth regime. In summary, our understanding of the dynamics of organizational learning of economics learning trajectory associated to structural change which, in turn, will condition future trajectories qualitatively learning. The design of Space Framework starts by taking output indicators (in this case, technology), and the change of investment (i) in real terms of the human resource (e). Well defined like:

Explanation: gv is the rate of growth of output per researcher (a measure of productivity growth) and gi is the growth rate of investment per researcher. So, gv and gi variables provide the coordinates of the trajectory dynamics of a particular institution in the plane (Figure 1). Thus, a spatial coordinate (gv, gi) is the analytical Space Framework apparatus. Alterations in the levels of the coordinates (gv, gi) represent changes in the dynamic unit (groups) relationship analyzed and may set or alter the intensity of the variables or qualitative changes of regime. Each path is associated with a behavior pattern of growth and treated with regime. The scheme is a set of possible growth paths that behave according to the dynamics of a behavior pattern that is followed by a canonical model. Then structural change coincides to the change of model growth. Figure 3 illustrates how to interpret growth trajectories and phases in FS apparatus. In this structure are treated six regimes and more special treatment. The last one tangent space around the line cuts coordinates (gv, gi) in 45-degree angle is called Harrodian Corridor

7 separate the regime above the line VI. The regime I,

below that line. The two regimes VI (accumulation) and I (innovation) are in the first quadrant, since the regimes are grouped into the background following clockwise. In the first quadrant is where organizational learning occurs (or growth), that is, the rate of productivity growth (gv) and investment per researcher (gi) grow at positive rates. The regime II (Restructuring), is in the second quadrant, the gigv grows as decreases (ie negative values). The other schemes, III, IV and V are in the analytical structure of FS treated as reflections of the above schemes before. These schemes are mirrors behavior in terms of growth rates inversely schemes VI, I and II. In regime III, and IV growth rates gv and gi are both negative, and left in the third quadrant (economic recession). In the fourth and last quarter, which coincides with the regime V, gigv is decreasing and increasing, which describes a situation, for example, where the growth of the intensity of investment in innovation and research is not reflected in an increased productivity per researcher. That is the opposite of any prediction of the various growth theories.

Figure 4: Space Framework and Growth Regimes

7 Harrodian behavior is typically represented by the path of steady state. In other words, the reasons for what gv and gi

are constants. The coordinates (0.0) are associated with the exogenous growth path (Böhm and Punzo, 2001).

Source: Punzo (1995). Some complex dynamics for a multisectoral economy model

One interpretation for this argument is that the FS is equipped with three patterns or growth regimes: i) steady state, ii) ii) accumulation, focusing on the intensity change of investment, and iii) iii) innovation, functionally independent of the accumulation capital. These three states can be associated with learning scenarios seen before. The growth path is traced by the sequence formed by the pairsgv and gi, distributed at the level of FS. Each point in the FS (defined by the pair of coordinates) in a growth phase, is associated to growth trajectory. To formalize the trajectories interpretation in the FS, must start from the neoclassical growth theory which assumes that the prediction of economy long-term behavior, on certain conditions, is based on the properties of a production function, although a capital accumulation equation can be found a constant value for the growth rate per-capita, commonly known as the growth rate of steady state. This rate is found when per capita rate of investment required (required to maintain a constant capital-labor ratio) is equal to the actual investment per capita, or the Solow model condition for equilibrium. At least the neoclassical theory assumes that growth rate of CNG, in steady state, is given by:

N is considered, the rate of population growth as exogenous and growth rate of technical progress. The observed growth rate of economies is not exogenous. This difference helps to explain the nature of FS. Thus, the FS tries to explain the endogenous growth rate given by deviations GEN observed rate g of the rate growth of steady state.

FS will consider the CNG rate as the origin of the diagram in Figure 1, the coordinates (0.0), and define what is relevant to be investigated, ie the analysis of endogenous growth rate . In that sense, we assume that the behavior of the effective rate g influences the long-term trajectory and thus we assume that the endogenous growth rate

8 can be explained by two regimes or growth models: capital accumulation and

8 Romer (1986), Lucas (1988), Romer (1990) among others, sought to endogenize the growth rate by introducing

knowledge, human capital and new ideas through research and development in the structure of the production function of Solow model. This evolution of neoclassical theory is known as "new growth theory."

innovation. This is because both, the rate of accumulation of capital incorporated in the economy innovation influence to the actual growth rate, consequently, their trajectory over time. At the FS two-dimensional space, all theories of long-term are present. The Solow model (1957) observed points out the origin indicate that endogenous growth can be explained by kaldoriana accumulation theory of capital, as the Schumpeterian theory, system innovation. In this regard, the elucidation of exogenous or endogenous growth can use two kinds of models (conventional and neo-Schumpeterian or kaldorian) the latter two endogenous, study the long-term dynamics. In the case of Latin-American countries studied, most of them are in a state II (restructuring) during the years 1997-2002, Colombia for example, had growth at rates gigv negative, located in the third quadrant (the quadrant of the economic recession, of course in research), as well as Venezuela. It is important to mention that gvcalculateshas been based on the number of SCI-SEARCH publications for every 100 researchers (individuals), whereas gi, was calculated taking into account spending on research Science and Technology (individuals).

Figure 1: FS Growth Regimes for the Years 1997-2002

Source: Authors' calculations based on Ricyt data

For the next five years, between 2002 and 2007, the situation changed for many countries, for example, Colombia went from being in quadrant III to be in the positive horizontal axis. This implies a significant improvement in both policies to promote innovation, as well as in research and development activities in the country. Venezuela, meanwhile, continued in a recessionary trend.

Figure 2: FS Growth Regimes for the Years 2002-2007.

Argentina

Bolivia

Brasil

Canadá

Chile

Colombia

Costa Rica

EspañaMéxico

NicaraguaPortugal

ParaguayTrinidad y Tobago

Estados Unidos

Uruguay

Venezuela

-0,8

-0,6

-0,4

-0,2

0

0,2

0,4

0,6

0,8

1

-0,6 -0,4 -0,2 0 0,2 0,4 0,6 0,8 1

Source: Authors' calculations based on Ricyt data

In the U.S. case, there was an effect of payback, since while invested in the 1997-2002 period to a decrease in the productivity rate, this rate increased again between 2002-2007, possibly as result of previous development policies. 6.2.2 Methodology for Assessing the Sustainability of Investment in Innovation Projects for IES The previous methodology was exposed to analyze different regimes of science and technology; we must now evaluate the various components of the "sustainability" such as “supportability" and "relevance" (equation 1 of Figure 1) innovation policies. In the case of supportability, this is obtained from a ratio of two basic variables that compose it, that is, the product (an initiative of the structure) and investment per researcher (consideration) (see table equation 2 Figure 1). In order to evaluate this component in different countries, scatter plots are made which related the account

9 (expenditure per researcher in thousands of dollars PPP) on the horizontal axis, with the lead

structure10

(average variables, invention coefficient and publications in science11

) on the vertical axis. The

9 Consideration, understood as the act of inclusion of actors or ideas into something broader. In this case, precisely the

inclusion of internal actors in the processes of research and innovation, as an active part of each of these processes in relation to the responsibility between each of them. To estimate this variable, could be spending per researcher, taking into account that as increasing, researchers have more resources, not only to carry out their duties, but also to more qualify. This could also be seen as an indicator of "living", such as output per capita, only that framed in the field of science and technology. 10

As explained in the theoretical framework, the initiative to the structure directly related to the achievement of process results. 11

The adjustment is applying to the following factor, which allows units standardization, but also helps to visualize the

order of the countries surveyed: x-m/M-m

Where m is the minimum, M is the maximum x value of the variable for the country concerned.

Argentina

BoliviaBrasil

CanadáChile

Colombia

Costa Rica

Ecuador

España

México NicaraguaPanamá

Portugal

Paraguay

El Salvador

Trinidad y Tobago

Estados Unidos

Venezuela

-0,8

-0,6

-0,4

-0,2

0

0,2

0,4

0,6

0,8

1

-1 -0,8 -0,6 -0,4 -0,2 0 0,2 0,4 0,6 0,8 1

results are shown in Figures 1.1, 1.2 and 1.3 of the Annex. Accordingly, countries with more sustainable policies for research and development for 1997 were Argentina, Venezuela and the U.S. (see Chart 1.1). Other countries, except Canada, Spain and Brazil, had very low levels of supportability. In the same way, we can establish similar relationships for other equations in Figure 1. In the case of equation 3, social responsibility is the result of the conjunction between consideration concepts and relevance. In the case of the consideration, a new spending per researcher (in thousands of dollars PPP), while the application, shall be the average (arithmetic mean) of the next variables adjusted

12:

S & T spending by Type of Activity

S & T expenditure by Financing Sector

S & T expenditure by sector of performance.

Expenditure on S & T by Socio-Economic Objective

Government budget appropriations on R & D by socio-economic objective

S & T spending by scientific discipline 13

. The results for social responsibility can be seen from figures 2.1, 2.2 and 2.3 of the Annex. Overall, levels of relevance of S & T policies remain high and stable between 1997 and 2007, while on the other hand, increases the component of consideration during this period, showing an evolution in terms of social responsibility for most of the countries. In the case of equation 1, sustainability is defined in terms of supportabilityand relevance. Take exactly the same indicators that were developed for each of them in equations 2 and 3. The results are in figures 3.1, 3.2 and 3.3. Throughout the study period, are kept almost the same level of relevance to all countries, but there is an evolution in terms of supportability. Moreover, to strengthen the conclusions it´s following the same procedure with equation 4 in Figure 1, expressed sustainabilityin terms of social responsibility and initiative to the structure. The results are in figures 4.1, 4.2 and 4.3 of the Annex. Most countries still have average levels of social responsibility throughout the entire period, except for some countries like Nicaragua and Honduras, which have high

12

In the case of relevance, will take into account the variability of each two variables mentioned above, so that the

lower accumulation in a few items for each of these variables, the greater the relevance, more satisfying actors. For each of these variables (taking its variance), the adjustment factor will be the following:

M-x/M-m Where m is the minimum, M is the maximum x value of the variable for the country concerned.

13

To select these variables, is important to take into account that the relevance is defined as "the dimension that proves targets proposed by institutions required from an external perspective" Latapí (1995). On the other hand, González-Guitián and Molina-Piñeiro (2009), define the following indicators of relevance in the fields of science and technology in Cuba:

• Percentage of projects related to national projects, branch, territorial, business and science and Technology University. • Project plans and provincial branches generalization. • Funding of research projects in Cuban convertible peso (CUC). Guitián and Molina González Piñeiro: The evaluation of science: a review of indicators, in Contributions to Social Science, November 2009, www.eumed.net/rev/cccss/06/ggmp.htm

levels of "social responsibility" extremely low or Venezuela, which has always boasted a high level "social responsibility". For each of the three years studied, the only country with a high degree of initiative to the structure was the United States, and therefore, the more sustainable from this perspective, along with Venezuela. 7. FINAL RESULTS AND CONCLUSIONS Based on a multidimensional view of the issue of sustainability in the field of science, technology and innovation, we can make detailed diagnoses on the effectiveness and efficiency of development policies, when you have multiple objectives and stackeholders. Moreover, as have been mentioned from the beginning of this article, this vision is important for the complexity implicit in the innovation process, although this is often not the result of a planned process but something spontaneous. However, all governments worldwide are increasingly interested in developing strategies to promote the development of science and technology as part of their economic development programs. Another important aspect which is implicit in the methodology designed, is the dual nature of innovation which is fertile ground for the study of sustainability, unlike other areas of public or private decision, because they are comparable in terms of complexity. This dual character is also compatible with the dual character of the two definitions of sustainability developed in this work, which brings us to an evolution of economic and organizational aspects in parallel with a greater understanding of themselves, which are emerging in jargon and current theories on growth and development. This complexity and consciousness of it, is the bases of the systemic paradigm, which is shown again as an indispensable tool for understanding current events, includes not only the recursion and recurrence phenomena implicit in the construction of concepts (in this case sustainability and innovation), but also in the dynamic relationship of the same, what we know in real life as "tendencies" or developments. " Finally, we expect that the diagnostics provided throughout this paper, one day become anspecial input to strategic making-decision in the field of science and technology, as well as to formulate policies aimed at promoting innovation. 8. APPENDIXES Graph 1.1: YEAR 1997: supportability = consideration (horizontal axis)+ initiative of the structure

(vertical axis)

Source: Authors’ calculations based on Ricyt data

Grafico 1.2: YEAR 2002: supportability = consideration (horizontal axis)+ initiative of the structure

(vertical axis)

Source: Authors’ calculations based on Ricyt data

ArgentinaBolivia

Brasil

Canadá

ChileColombiaCosta Rica

España

GuatemalaHonduras MéxicoNicaraguaPanamáPerúPortugalParaguayEl Salvador

Estados Unidos

Uruguay Venezuela0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1

0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1

ArgentinaBolivia

Brasil

Canadá

ChileColombiaCosta Rica

España

GuatemalaHonduras MéxicoNicaragua PanamáPerúPortugalParaguayEl Salvador

Estados Unidos

Uruguay Venezuela0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1

0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1

Grafico 1.3: YEAR 2007: supportability = consideration (horizontal axis)+ initiative of the structure

(vertical axis)

Source: Authors’ calculations based on Ricyt data

Graph 2.1: YEAR 1997: social responsibility = pertinencia (horizontal axis)+ consideration (vertical

axis)

ArgentinaBolivia

BrasilCanadá

ChileColombiaCosta Rica

España

GuatemalaHonduras MéxicoNicaragua PanamáPerúPortugal

Paraguay El Salvador

Trinidad y Tobago

Estados Unidos

Uruguay Venezuela0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1

0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1

Source: Authors’ calculations based on Ricyt data

Graph 2.2: YEAR 2002: social responsibility = pertinencia (horizontal axis)+ consideration (vertical

axis)

Argentina

Brasil

CanadáChile

Colombia

Costa RicaCuba

México

PanamáPerúPortugal

ParaguayEl Salvador

Trinidad y Tobago

Estados Unidos

Uruguay

Venezuela

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1

0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1

Source: Authors’ calculations based on Ricyt data

Graph 2.3: YEAR 2007: social responsibility = pertinencia (horizontal axis)+ consideration (vertical

axis)

Argentina

Bolivia

BrasilCanadá

Chile

ColombiaCosta Rica

Cuba

EcuadorEspaña

Guatemala

México

Panamá

Perú Portugal

ParaguayEl Salvador

Trinidad y Tobago

Estados Unidos

Uruguay

Venezuela

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1

0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1

Source: Authors’ calculations based on Ricyt data

Graph 3.1: YEAR 1997: sustainability = supportability (horizontal axis)+ pertinencia (vertical axis)

Argentina

Bolivia

Brasil

Canadá

Chile

ColombiaCosta Rica

Cuba

EcuadorEspaña

Guatemala

México

Panamá

Perú

Portugal

Paraguay

El Salvador

Trinidad y Tobago

Estados Unidos

Uruguay

Venezuela

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1

0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1

Source: Authors’ calculations based on Ricyt data

Graph 3.2: YEAR 2002: sustainability = supportability (horizontal axis)+ pertinencia (vertical axis)

Argentina

Brasil

Canadá

Chile

Colombia

Costa Rica

Cuba

Ecuador

España

Guatemala

MéxicoPanamá

Perú

Portugal

Paraguay

El Salvador

Trinidad y Tobago

Estados Unidos

Uruguay

Venezuela

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1

0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1

Source: Authors’ calculations based on Ricyt data

Graph 3.3: YEAR 2007: sustainability = supportability (horizontal axis)+ pertinencia (vertical axis)

ArgentinaBrasil

Canadá

Chile

Colombia

Costa Rica

Cuba

Ecuador

España

Guatemala

MéxicoPanamá

Perú

Portugal

Paraguay

El Salvador

Trinidad y Tobago

Estados Unidos

UruguayVenezuela

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1

0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1

Source: Authors’ calculations based on Ricyt data

Graph 4.1: YEAR 1997: sustainability = social responsibility (horizontal axis)+ initiative of the

structure (vertical axis)

Argentina

Bolivia

Brasil

Canadá

Chile

Colombia

Costa Rica

Cuba

Ecuador

España

Guatemala

México

Panamá

Perú

Portugal

Paraguay

El SalvadorTrinidad y Tobago Estados Unidos

Uruguay

Venezuela

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1

0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1

Source: Authors’ calculations based on Ricyt data

Graph 4.2: YEAR 2002: sustainability = social responsibility (horizontal axis)+ initiative of the

structure (vertical axis)

ArgentinaBolivia

Brasil

Canadá

ChileColombiaCosta Rica

CubaEcuador

España

GuatemalaHonduras MéxicoNicaragua PanamáPerú PortugalParaguayEl SalvadorTrinidad y Tobago

Estados Unidos

Venezuela0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1

0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1

Source: Authors’ calculations based on Ricyt data

Graph 4.3: YEAR 2007: sustainability = social responsibility (horizontal axis)+ initiative of the

structure (vertical axis)

ArgentinaBolivia

Brasil

Canadá

ChileColombiaCosta Rica

CubaEcuador

España

GuatemalaHonduras MéxicoNicaragua PanamáPerúPortugal

ParaguayEl SalvadorTrinidad y Tobago

Estados Unidos

Venezuela0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1

0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1

Source: Authors’ calculations based on Ricyt data

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