Int. J. , Vol. x, No. x, xxxx
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A NEW METHODOLOGY FOR REGIONAL FORESIGHT C. Roveda*, R. Vecchiato**, R. Verganti**, P. Landoni** * Fondazione Rosselli and Dipartimento di Ingegneria Gestionale, Politecnico di Milano, Italy **Dipartimento di Ingegneria Gestionale, Politecnico di Milano, Italy corresponding author:
[email protected] Abstract Regional foresight is receiving a fast growing attention and interest from public policy makers and other regional stakeholders. Until recently, regional foresight has been implemented in most cases as a scaling down of national studies, by applying approaches and methods of national foresight to specific regional themes. The purpose of this paper is to present and discuss the new regional foresight methodology that the authors have developed when carrying out the RISE (Research, Innovation, Economical Development) project under the sponsorship of the government of Lombardy Region. This methodology basically aggregates and integrates a set of indicators (qualitative and quantitative) defined in order to evaluate the attractiveness of a technology for the long term competitiveness of the main sectors of the regional manufactoring industry and the feasibility for the regional technical and scientific system to develop this technology. In this way, it was possible to provide a sound basis for S&T policy making.
Copyright © 200x Inderscience Enterprises Ltd.
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1. Problems in the application of the “key technologies list” method This paper describes the foresight methodology that has been designed within a recently completed project, (RISE - Research, Innovation, Economical Development) which was promoted and sponsored by the regional government of Lombardy and had the objective of identifying the emerging horizontal (critical) technologies, for which the scientific institutions and the industrial firms of Lombardy could gain a strong competitive position in their development, from research to applications. On the basis of the results of this prospective analysis
the regional
government and large firms could allocate resources to R&D in a selective way, in order to optimize their contribution to the regional economic development. At the end an Action Plan was designed in order to implement the innovation strategies with a cooperative approach involving all interested actors.
The methodology for the evaluation of the “criticality” of the technologies is similar to the one used in many national foresight studies1: it scores a (long) list of technologies by means of a set of appropriate criteria. The evaluation is performed by experts, the views of whom are collected in a variety of ways (direct interviews, panels, questionnaires, etc.).
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This method, known as “key technologies list”, usually unfolds through the following stages:
− selection and classification of the emerging technologies; − definition of the “criticality/priority” criteria; − application of the “criticality/priority” criteria to the selected emerging technologies; − definition of a priority list of the technologies.
The criticality/priority criteria take into account features of both the attractiveness of a technology for the future competitiveness of the concerned industrial system, and of their feasibility. Attractiveness refers to the capability of a technology of fostering beneficial impacts both on industry (mostly) and on society. Feasibility refers to the capability of the S&T system of developing a technology from R&D to industrial applications. The notions of attractiveness and feasibility are translated into appropriate sets of mostly qualitative indicators. Regarding attractiveness, they can be the fundamental requirements of the country; the effects and potential benefits on the national production (industrial) system; the intrinsic relevance. Regarding feasibility, they can be the resources and the performance of the private and public research sectors2.
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Even if the “key technologies list” methodology has been widely used and has provided many useful information, it didn’t always support the selection of R&D themes in an effective way. The results of the assessment process have critical limits that lower their usefulness as a cognitive basis for supporting decision making. These limits come mainly from the qualitative nature of the indicators; the experts are not requested to give account of their evaluations by using quantitative data. As a consequence it is very difficult to compare and sum up the different evaluations even for a specific technology. More than this the attractiveness and feasibility indicators are evaluated with reference to a socio-economic system (national or regional) as a whole. In the case of nations or regions (such as Lombardy) with a complex socio-economic system, this leads to overwhelming difficulties in defining priorities. It’s almost impossible to develop indicators capable of taking into account and summarising the specificities of the many components (sectors, areas, etc.) of such a system. In order to overcome some of these limits it can be worth making use of a larger set of indicators; in this way the different features of the scientific, technical and industrial systems can be grasped. But one needs to keep the number of indicators low: too many indicators, especially when there are many technologies, can hamper the process of comparing the technologies and of creating a priority list 3.
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In order to cope with these constraints an original methodology has been designed: it is based on a wide variety of indicators that are in a second stage summarised in a small number of parameters, which allow to compare the technologies and to obtain a priority list. This methodology is presented in the following paragraphs and its results are described with reference to the RISE project.
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2. Methodology The new methodology overcomes many limits of the “key technologies list”as: -
at the first stage of the evaluation process it makes use of a wide set of indicators in order to grasp the many diverse features of a complex socio-economic system ;
-
it gathers, whenever possible, quantitative information, so that it’s possible to compare the experts’ evaluations;
-
it aggregates the first level indicators into a smaller set of parameters that can support the decision making process;
-
it correlates these indicators and so provides an easy and contextualized picture of attractiveness and feasibility of all the technologies.
2.1 The basic (first level) indicators
Given the socio-economic system of a region and a set of technologies (with an appropriate level of granularity4), a number of indicators (qualitative and quantitative)
are defined in order to evaluate the
attractiveness and the feasibility of any technology.
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First of all, the stage of the technology within its life cycle is assessed, as it strongly affects the possibility of entry for new competitors, the intensity of competition, the available market share and the growth opportunities: for instance, if a technology is in an early stage of development there are more market opportunities for new and existing firms. According to the stage, one can classify technologies in the following way: -
Embryonic technologies: they are in the first stages (fundamental and applied research) so their potential benefits are still uncertain. If the industrial firms of a region want to become leaders in such a technology, they must make investments at this stage, when competition is not high yet, but uncertainty and technical risks are maximum.
-
Growing technologies: their economical relevance is unambiguous, but if one starts investing in a technology at this stage only, following the first investors, it may be very difficult to become a leader (it may be feasible in some niches which are still free).
-
Mature technologies: they have already reached the upper limit of growth: competition is high, strong opportunities are few.
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Within any family there are usually technologies at different stages; however, it is possible to assess an average stage as an indicator for the whole family.
The attractiveness and feasibility indicators may be defined as shown in the following. In the first place some fundamental categories are identified (Table 1): with reference to them a set of specific indicators is built, which enables an in-depth evaluation of the technologies.
Table 1: Macro categories ATTRACTIVENESS A.1 A.2 A.3 A.4 A.5
Economic and industrial impacts Time horizon of expected impacts Uncertainty of expected impacts Intensity of international competition in R&D Social impacts
FEASIBILITY F.1 F.2 F.3 F.4
Available scientific and technological resources and competences Needed scientific and technological resources and competences Lead-markets Congruence with industrial and infrastructural factors
2.1.1 Attractiveness A.1 Economic and industrial impacts The indicators relate to:
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-
the potential capability of the technology to give rise to relevant product and process innovations (“Creation of competitive advantages by product innovation” and “Creation of competitive advantages by process innovation”);
-
the size and growth potential of the sectors that use the technology (“Size of sectors of exploitation” and “Dynamics of sectors of exploitation”);
-
the pervasiveness of the technology (“Pervasiveness”);
-
the potential capability of the technology to give rise to new firms (“Start-up companies potential”).
The indicators are evaluated as the average of the scores provided by the experts on a Likert scale, from 1 (e.g low capability of the technology to generate innovative products) to 5 (e.g. relevant capability of the technology to generate innovative products).
A.2 Time horizon of expected impacts This type of indicators points out the time periods needed by the technology to have impacts on the economy and industry. For instance: -
“First Impacts” provides an indication on when there will be the initial impacts.
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“Time horizon” (T) is defined as the time span in which impacts of the technology on economy and industry are expected to be relevant.
The indicators are evaluated as the average of the values given by the experts. In the case of a significant variance among these values, another time span is considered as the minimum of the shortest time horizons and the maximum of the longest time horizons given by the experts.
A.3 Uncertainty of expected impacts This type of indicator measures how much the development of the technology and its expected impacts on economy and industry are uncertain. In particular Uncertainty (U) is a summarising indicator evaluated as the average of the scores provided by the experts, on a Likert scale, from 1 (low level of uncertainty) to 5 (high level of uncertainty).
A.4 Intensity of international competition in R & D This type of indicator points out the countries and regions which are investing heavily in R & D for the technology, and their main research centres, firms, infrastructures, etc. In particular Intensity of Competition (C) is a summarising indicator that evaluates the level of international competition, based on the numbers of
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research centres and firms engaged in the development of the technology and the size of their efforts. It is evaluated as the average of the scores provided by the experts on a Likert scale, from 1 (low international competition) to 5 (high international competition).
A.5 Social impacts: main opportunities and criticalities The main impacts of the technology are assessed with reference to: -
environment
-
health
-
security
-
mobility.
The indicators are evaluated as the average of the scores provided by the experts: each parameter was evaluated on a Likert scale, from 1 (limited or negative impacts) to 5 (very positive impacts).
2.1.2 Feasibility
F.1 Available scientific and technological resources and competences Among the main indicators there are: -
Knowledge (K): it evaluates the level of the competences and the know-how available in the region compared at the international
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situation. The indicator is measured as the average of scores provided by the experts on a Likert scale, from 1 (low level of knowledge in comparison to other areas) to 5 (high level of knowledge in comparison to other areas). -
Regional actors: it points out the main public and private centres of the region which carry on research activities for the development of the technology.
-
Available researchers (AR): it measures, approximately, the number of researchers currently involved in R&D activities related to the technology. The indicator is evaluated as the average of the values provided by the experts.
F.2 Needed scientific and technological resources and competences Among the main indicators there are: -
Target number of researchers (TR): it measures the approximate number of researchers that the R&D system of the region needs to reach a position of excellence at international level in the development of the technology. The indicator is evaluated as the average of the values provided by the experts.
-
Critical mass (CM): it measures the approximate number of researchers
needed
to
reach
an
“acceptable”
level
of
competitiveness at international level in the development of the
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technology. The indicators is evaluated as the average of the values provided by the experts. -
Technological investment (TI): it measures the approximate amount of financial resources that need to be invested in the next three years in order to provide the region with the competences and the infrastructures required to make the region an international leader in the development of the technology. The indicator is evaluated as the average of the values provided by the experts.
F.3 Lead-markets This type of indicator relates to the lead-markets for the technology and their characteristics. In particular “Access to lead-markets” (L) is a summarising indicator that identifies the capability of the region to have access to these markets. The indicator is evaluated as the average of the scores provided by the experts on a Likert scale, from 1 (low access of the region to lead markets) to 5 (high access of the region to lead markets).
F.4 Congruence of the technology with the industrial system and the infrastructures of the region. This congruence is evaluated by means of the following indicators: -
availability in the region of competences in related technologies (“Competencies in related technologies”);
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possibility of having access to complementary resources (“Resources”);
-
existence of networks of national and international partners (“Partnership”);
-
structure of the industrial system of the region (“Industrial structure”);
-
structure of the regional system of regulations (“Regulations”);
-
structure of the system of supporting infrastructures both in the region and in the country (“Infrastructures”).
The indicators are evaluated as the average of the scores provided by the experts on a Likert scale, from 1 (e.g. limited access to complementary resources) to 5 (e.g. high level of access to complementary resources).
2.2 The aggregate (second level) indicators
The previous set of indicators provides a lot of detailed information that highlights the main elements of interest and the criticalities of any technology. However, it proves to be very difficult to grasp from such indicators an overall view of the feasibility of generating the scientific knowledge at the
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basis of a technology and of transferring it into industrial applications, both in absolute terms and in comparison with other technologies. In order to achieve such a view these indicators can be aggregated into a smaller set of indexes: they are listed in the following together with their meaning and computational formula (formulas in some cases refer to the definitions given in the previous paragraph)
2.2.1 Attractiveness
1
Economic and industrial impacts (I): it takes into account many aspects, as the size and dynamics of the markets for the technology, its relevance and pervasiveness and its potential for creation of new firms (start-ups). Formula: I = 0.8 * Market potential category + 0.2 * Start-up companies potential Market potential categories are defined as follow: Market potential 249=5 Market potential = [Size of sectors of exploitation + pervasiveness] * [MAX (Creation of competitive advantages by product innovation; Creation of competitive advantages by process innovation) * Dynamics of sectors of exploitation]
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Intensity of competition (C) at an international level in the field of scientific and technological research. This indicator provides a measure of the possibility or of the risks to obtain a significant position in the market exploitation of the technology (especially in the case of a late entrance) as explained before.
3
Time horizon (T), as explained in the previous paragraph, is the time period the technology needs in order to make a significant impact.
4
Expected attractiveness (EA): it measures the attractiveness that results from the joint evolution of “Economic and industrial impacts” and the possibility for the region of being a main player in the field (the opposite of “Intensity of competition”). Formula: EA = 0.7 * I + 0.3 * (6 - C)
5
Uncertainty (U), as explained in the previous paragraph, measures how much the development of the technology and its expected economic and industrial impacts are affected by uncertainty.
6
Risk (R): it measures the risk associated with developing the technology. Formula: R = T * U * C [ Note: all values in the formula are previously normalized; R ranges between 0 and 1 ]
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Social impacts (SI): it measures the overall social impacts of the technology. Formula: SI = 0.25 * impacts on environment + 0.25 * impacts on health + 0.25 * impacts on security + 0.25 * impacts on mobility
8
Attractiveness (A): it measures the overall attractiveness of the technology, by taking into consideration its main potentialities and the probability of success. Formula: A = I * R
2.2.2 Feasibility
1
Scientific capabilities - Knowledge (K) of the regional Science and Technology system, in comparison with the international state of the art. As previously seen, this index provides a measure of the knowledge the system can rely on in order to face the international competition in developing the technology.
2
Needed Researchers (∆R): it is the absolute number of new researchers needed in the region in order to achieve a level of excellence in developing the technology.
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Training of new researchers has to be related to the regional (and national) higher education system and to the time needed for the process. Formula: ∆R = TR – (AR in public research centres + AR in industrial research centres)
3
Congruence (G) of the technology to the regional socio-economic system. It takes into account the leads markets, complementary technologies, infrastructures and partnerships, available in the region, together with the industrial structure and regulations. Formula: G = 0.2 * Access to lead-markets + 0.15 * Competencies in related technologies + 0.1 * MAX (Resources; Partnership) + 0.3 Industrial structure + 0.1 Regulations + 0.15 Infrastructures
4
Minimum Needed Researchers (min∆R): it is the absolute number of new researchers needed in the region in order to reach the critical mass, i.e. an “acceptable” level of competitiveness at international level. Formula: min∆R = CM – (AR in public research centres + AR in industrial research centres)
5
Technological investment (TI): it measures the amount of financial resources necessary in the next three years to provide the region with the competences and infrastructures needed to reach a position of excellence at international level.
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6
Overall Resources (O): it measures synthetically the effort the region has to undertake in order to become an international leader in the development of the technology. Formula: O = 0.33 * min∆R + 0.33 * ∆R + 0.33 * TI [ Note: all values in the formula are previously normalized; O ranges between 1 and 5 ]
7
Position (P): it measures the overall capability of the region in developing the technology and achieving an acceptable competitive position. Formula: P = 0.33 * K + 0.33 * G + 0.33 * O
2.3 Cross- analysis
It’s apparent that the aggregation procedure of the indicators, particularly the weights, is arbitrary to a certain extent, even if there are some rational and reliable assumptions at its basis. Validation of these assumptions has been done by carrying out a sensitivity analysis of the results of the procedure.
At the end for every technology some cross-analysis among some of these indicators are carried out in order to obtain a synthetic and contextualized picture of attractiveness and feasibility, which points out the real opportunities of developing a technology in a region. Thanks to this
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process it is possible to compare the different technologies and to set the investment priorities.
The following cross-analysis have proven able to provide the most interesting insights:
a) Intensity of competition - Economic and industrial impacts; b) Intensity of competition – Time horizon; c) Knowledge - Needed researchers – Congruence; d) Needed researchers – Congruence – Intensity of competition; e) Needed researchers – Knowledge – Intensity of competition.
The first correlation allows to check if the experts’ evaluation on the expected economic and industrial impacts of a technology is shared by the international players with their research resources allocation. Furthermore it allows a clearer evaluation of the attractiveness of a technology. If two technologies generate comparable economic and industrial impacts, but they are characterised by different levels of intensity of international competition, the one with the lower level should be considered more
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“attractive” in the sense that it is easier to gain a strong position in the market; this is more difficult when the international competition is high.
The second correlation provides information complementary to the former one and it completes the attractiveness evaluation. If two technologies generate comparable economic and industrial impacts, the one with a longer time horizon allows to catch up with the leaders. On the contrary technologies with a short time horizon are a strategic option only for regional systems that already hold a leadership position in these fields.
The third correlation links indicators of attractiveness and feasibility together, so providing a syntethic evaluation of the technologies. The indicators of the level of available knowledge and of congruence highlight for any technology the actual position of a social-economic system both in terms of pre-requisites and technical and scientific capabilities (human resources, technical instruments, etc.) and in terms of capacity of the regional firms to exploit the potential research outcomes.
At the same time the needed researchers indicator measures the resources a system has to add in order to reach a leadership position in developing a technology at international level. In other words, it evaluates how the system is far from reaching the objectives given actual state of its
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resources. A technology can be considered more feasible if the system has an initial comparative advantage over its international competitors. On the contrary, technologies for which the system is behind and a lot of effort has to be done in order to catch up with the leaders, are less likely to produce interesting results: these technologies have to be considered less feasible and not a priority, even if they might generate high socioeconomic impacts.
The last two correlations compare the scientific and industrial “quality” of the regional system in relation to a technology with the “quantity” of the human and financial resources needed to achieve industrial excellence. This analysis aims at checking the existence of both factors, focusing, on one hand, on the needed incremental resources and on the present and future intensity of competition and, on the other hand, on the congruence of the technology and on the available knowledge. The final output is a synthetic insight of the system competitive advantage that can be reached and of the required effort. This competitive advantage will be more relevant and sustainable if the socio-economic system holds a stronger initial position (congruence and knowledge indicators) compared to the other systems, if the level of
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competition is low and if only “few” additional human resources are needed. Finally the importance of this competitive advantage in terms of potential economic value (and thus in terms of relevance of the technology) will be related to the size of the economic impacts as highlighted by the first correlation.
3. A case of application of the new methodology: the RISE project When carrying out the foresight exercise within the RISE project, mentioned before, the emerging technologies were selected within the following
areas:
Advanced
Materials,
ICT
(Information
and
Communication Technologies), Biotechnologies, Energy Technologies and Nanotechnologies, through survey of the scientific literature and of other recent foresight studies. The “granularity” of the selected technologies was set at “family” level, i.e. rather aggregate, given the exploratory feature of the process: as an example, in the “Advanced Materials” area the following families were selected: Superconductors, Semiconductors and Metal Matrix Composites; Structural Ceramics and Ceramic Matrix Composites; Polymers and Polymer Matrix Composites; Materials for Photonics and Magnetism; Modelling, Material Engineering & Material Recycling.
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At the end of the evaluation process, the following correlations crossanalysis among the aggregate (second level) indicators were built and provided useful insights for the selection of the critical technologies for Lombardy, in terms of their attractiveness and feasibility. 3.1 Correlation: Intensity of competition - Economic and industrial impacts The distribution of the various technology families in terms of economic and industrial impacts and of intensity of competition in the research arena (Fig. 3.1) shows clearly a tight correlation between the two indicators: so when the technologies are supposed to have relevant economic effects, all industrialised countries invest heavily in the related research, thus increasing the intensity of competition. The distance from the diagonal in the diagram allows to pinpoint the relative advantage of each technology: if a technology is set above the diagonal, it turns out to be more attractive, as its potential economic impacts go beyond the medium-risk, due to the competition and efforts of the main international players in the development of technology, associated with a general level of potential economic impacts. Vice versa, if a technology is set down the diagonal, this mean that the potential economic impacts of the technology do not deserve to run the risk associated with its development, taking into
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account the international level of competition, when compared with other technologies (laying in the diagonal or above).
It is interesting to note that technologies like ICT, Biotechnologies and especially Nanotechnologies are considered to have potentially high economic impacts, but there is a very high international competition in the related field of research and there is the need of a very large increase of the numbers of researchers in Lombardy: so it seems very difficult for Lombardy to achieve an excellence position in these areas. There is a concentration of research efforts at international level also, excellence on this technologies it’s not easy to achieve.
3.2 Correlation: Intensity of competition – Time horizon As shown in figure 3.2., the technologies for which the intensity of competition is higher, have a longer time horizon. In the diagram, technology families are ordered by growing level of competition; the column highlights the time span in which impacts of the technology on economy and industry are expected to be relevant, calculated as the average of the values given by the experts; the thin lines above and beneath the columns show the minimum of the shortest time horizon and
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the maximum of the longest time horizons given by the experts, in case of a significant variance.
However, there is no significant correlation between these two indicators and no clear pattern emerges (only Energy Technologies, ICT and Nanotechnologies as a whole are going to be exploited in the mediumlong term).
3.3 Correlation: Knowledge - Needed researchers – Congruence Figure 3.3. describes the position of any technology in terms of level of technical and scientific knowledge available in Lombardy and of its congruence with the regional socio-economic system. The diameter of a circle shows the amount of additional researchers needed to achieve an excellence position in the international arena; for instance, the diameter for the Telecommunications Family (ICT-TLC) shows a need for nearly 3.000 more researchers and technicians. It’s apparent that Lombardy has the capability of pursuing effective development strategies in some technology areas: for instance, in the ICT area there are a high level of scientific competence and a strong congruence with the regional socio-economic system, but a large
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investment in new researchers needs to be done in order to pursue a strategy for excellence. For the Nanotechnology area the situation is more critical because of a lower level of scientific competitiveness and a larger requirement of additional human resources, even if the congruence with the regional socio-economic system is good. The situation for the Biotechnology area is even more difficult: there is a followership level of scientific knowledge and a strong lack of human resources and, more than this, there is a low congruence with the regional industry, especially in terms of capacity of transferring research results into industrial applications. Innovation
strategies
for
the
Advanced
Materials
and
Energy
Technologies areas appear likely to achieve good results, even if the technical and scientific knowledge is average, while the congruence with the socio-economic system is above average and, more important than this, only a small investment for new researchers is required in order to achieve a competitive position in the international arena.
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3.4 Correlation: Needed researchers – Knowledge – Intensity of competition The information from figure 3.4 provides a better understanding of the feasibility of pursuing a strategy of excellence in Lombardy. It confirms that in the areas where there is a high level of international competition in research (important economic and industrial impacts are expected and Lombardy has an above average level of scientific knowledge) there is also a very large need of additional researchers. Thus it turns out that Lombardy, if pursuing a leadership strategy on these technologies as a whole, finds many difficulties and risks; in this areas niche strategies on specific technologies are likely to be more feasible and effective.
3.5 Correlation: Needed researchers – Congruence – Intensity of competition The information provided by figure 3.5 highlights the existence of a negative correlation between the level of competition (which is itself positively correlated with the amount of additional researchers needed to pursue a strategy of excellence) and the level of congruence with the socio-economic system of Lombardy. By analyzing this information in conjunction with that of figure 3.3, which links the level of the knowledge available for the development of a technology with its congruence, it
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comes out that Lombardy adopted a strategy that is contradictory and antithetic to some extent with the one of the other developed countries. In fact, in the technology areas with a high level of competition, such as ICT, Nanotechnologies and Biotechnologies, Lombardy, on one hand, has a lot of relevant expertise and scientific competence; on the other hand, Lombardy has not enough industrial capabilities for exploiting the scientific knowledge produced by its research system. The concrete and significant opportunities of competitive developments in the areas of Advanced Materials and Energy Technologies are confirmed.
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4. Concluding remarks The methodology described in this paper can be an useful tool for defining priorities in the field of S&T, and setting up the research agenda for the innovation system of a region. In particular, the use of a two stage process in the evaluation of the criticality of the technologies allows to take into account: -
a large array of features marking the attractiveness and the feasibility of a technology;
-
the different features of the relevant sectors of the region
More than this, the integration of all these parameters into aggregated indexes (second stage indicators) and their correlations provide a synthetic overview of the relevant features of a technology, in terms of attractiveness and feasibility. All of this makes the comparison of the technologies and therefore the setting of priorities easier.
However, there are some negative aspects in the use of this methodology, such as : -
experts are inclined to focus their attention on a specific technology, with which they are most familiar, and to give less
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attention to the interactions between a technology and the other ones of the same or other areas; -
experts are inclined to focus their analysis on the technologies with high potential impacts;
-
experts do not always have all the required information: this is the case of the academicians when they have to provide quantitative information., on market potentials;
-
qualitative evaluation are usually biased. Anyway discussion within a panel of experts allows to compare the different metrics and therefore to reduce misalignments or incongruence among individual evaluations.
Anyway, the proposed methodology is suitable to provide useful outcomes if there is a participative approach, the experts have a broad experience and are able to interact positively and to integrate their knowledge and to mutually validate their views.
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Figures Figure 3,1: Correlation: Intensity of competition - Economic and industrial impacts
Impatti Economici
Economic and industrial impacts
(Dimensione e Dinamiche del mercato, Rilevanza, Pervasività; Start Up)
5,5 T ES
T LC
GEN
5,0 5,0
BIO 4,5 DAT
NIC
CT C
4,0 AMB INT
3,5
ICT
ELE
MAT
IDR
BIO
SOF
3,0
MAT
PO L
4,5
NAN
RET
DIS
ENE
2,5 RIN
MOD SUP
2,0
F OT ALI; F ER
AUT 1,5
1,0 CER
4,0 0,5 1,5
2,0
2,5
3,0
3,5
4,0
Intensità competiz ione Intensitydella of competition
4,5
5,0
5,5
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Figure 3.2: Correlation: Intensity of competition – Time horizon
2016 2014 2012
Serie1 IC
2010
BI Serie2
2008
MA Serie3
2006
Serie4 EN
NA
BIO (5,0)
FER (5,0) AMB (5,0)
ALI (5,0)
GEN (5,0)
ELE (5,0)
TLC (5,0) CTC (5,0)
NIC (4,5)
SOF (4,7)
POL (4,5)
RET (4,0)
MAT (4,2) INT (4,5)
TES (4,0)
DAT (4,0)
SUP (3,7) DIS (3,7)
FOT (3,5)
IDR (3,0)
MOD (3,2)
RIN (2,0)
2002
CER (3,0) AUT (3,0)
2004
Famiglie (ordinate per(with Competizione crescente) Technology families level of competition)
Figure 3.3: Correlation: Knowledge - Needed researchers – Congruence
4,5 TLC
State-ofthe-art
CTC
DAT RET INT SOF
3,5
ICT
Followership
BIO
3,0 2,5
GEN
FOT
ELE
IDR
MAT
RIN
DIS
2,0 1,5 2,50
SUP TES
NIC
BIO NAN MOD
3,50
ENE MAT
POL 1000
AUT AMB (ENE)
Low
Knowledge Conoscenza
4,0
100
4,50
Congruence Congruenza (Lead Markets, Tec. Complementari, Risorse e Partnership, Struttura Industriale, Regolamentazione e Infrastrutture)
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Figure 3.4: Correlation: Needed researchers – Knowledge – Intensity of competition
5,5 BIO
ELE
AMB
CTC
5,0
GEN
SOF
NIC
4,5 Competition
TLC
POL
INT TES
4,0
MAT SUP
DIS
3,5 MOD
ICT
DAT RET
BIO NAN ENE MAT
FOT IDR
3,0
AUT (ENE)
2,5
RIN
1000
2,0
100
1,5 1,5
Low
2,5
Followership
Knowledge
3,5 State-of-the-art
4,5
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Figure 3.5: Correlation: Needed researchers – Congruence – Intensity of competition 5,5 GEN
BIO
5,0
Competition
4,5
TLC
AMB
ELE
SOF
CTC
INT
POL
TES RET DIS SUP
DAT
NIC
ICT
MAT
4,0 3,5
NAN
FOT IDR
3,0
RIN
2,0 1,5 2,50
MOD
ENE MAT
AUT (ENE)
2,5
BIO
1000 100
3,50 Congruence
4,50
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References Roveda C. (1996) Le priorità nazionali della ricerca industriale Primo rapporto, Franco Angeli. Gavigan J. P and Scapolo F. (1999) “A comparisation of national foresight exercises”, Foresight Vol. 1, pp. 491-513. Gavigan J., Scapolo F., Keenan M., Miles I., Farhi F., Lecoq D., Capriati M. and Di Bartolomeo, T. (2001) A Practical Guide to Regional Foresight, European Commission, DG JRC-IPTS, Seville, EUR20128en. Georghiou, L. (2001), “Third Generation Foresight - Integrating the Socio-economic Dimension”, Proceedings of the International Conference on Technology Foresight - the approach to and potential for New technology Foresight, NISTEP. Grupp, H., (1994) “Technology at the beginning of the 21st century”, Technology Analysis & Strategic Management ,Vol. 6, No. 4, pp 379–408. Irish Council for Science and Innovation (1999) Technology Foresight Ireland: An ICSTI Overview. Lempert, R.J., Popper, S.W. and Bankes S.C. (2003) “Shaping the Next One Hundred Years: New Methods for Quantitative”, LongTerm Policy Analysis, The RAND Pardee Center. Linstone, H. A. (1999) “Foresight Activities Around The Globe: Resurrection and New Paradigms”, Forward Thinking Proceedings-Report, GR Print+ Mail GmbH, Hamburg. Martin, B. R. (1995) “Foresight in Science and Technology”, Technology Analysis & Strategic Management, Vol. 7., No. 2, pp. 139-168 . Miles I. and Keenan M. (2002) “Bringing It All Back Home: Linking National and Regional Foresight”, IPTS Report no. 61, pp. 29-35.
Miles, I., Keenan, M., Fahri, F. and LeCoq, D. (2001) “Creating Vision in the Regions: a framework for organising Regional Foresight, 2001”, IPTS Report no. 59 pp. 6 -12. Miles I. and Keenan M. (2000) Foren issue paper - From national to regional Foresight: experiences & methods , workshop 1, Manchester. Ministry of Economics Finance and Industry, General Direction for Industrial Strategies (1995) Les technologies clés pour l’industrie française à l’horizon 2000. Ministry of Economics Finance and Industry, CM International, (2005) Technologies clés 2005.
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National Institute of Science and Technology Policy & Science and Technology Agency (1992) The Fifth Technology Forecast Survey: Future Technology in Japan. Office of Science & Technology Policy (1995) National Critical Technologies Report. Office of Science and Technology (1995) Technology Foresight: Progress through Partnership. Observatorio de Prospectiva Tecnologica Industrial (1995) Primer Informe de Prospectiva Tecnologica Industrial: Futuro Tecnologico en el horizonte. US Council on Competitiveness (1996) Endless Frontier, Limited Resources: US R&D Policy for Competitiveness. US Council on Competitiveness (1995) National Critical Technologies Report. Notes 1
For example, the Dutch ‘98 “Technology Radar”, the US ‘98 “New Forces at Work – Industry views critical technologies – Office of Science Technology Policy, RAND Critical Technologies Institute”, the French 2000 “Technologies clés 2005”, the Italian “Priorità Nazionali della Ricerca Industriale” 2nd Report 2004, and finally the Greek ‘02 “Technology Foresight in Greece” 2
For example, the US used five criteria relating to Economic Prosperity, and three for National Security, with a technology being deemed critical if it met any one of the criteria. The French key technologies 2000 study used 25 criteria (in five sets of 5) to evaluate the attractiveness of the technologies (industrial and economical impacts, ambient impacts, social impacts, impacts on the national autonomy from foreign sources, dynamics of technologies) and 10 to evaluate the feasibility (4 for the scientific and technological feasibility and 6 for the industrial and commercial feasibility). In the Italian study ’96 the technologies were scored against criteria sets on a highmedium-low scale. One set of 13 criteria gave a measure of the ‘attractiveness’ of the technology for Italy, and a second set of 13 for the ‘feasibility’ of developing the technology in Italy. In the second Italian exercise, the criteria of attractiveness which have been used are 16, the Feasibility ones are 11. In the UK95a exercise were used criteria sets for ‘attractiveness’ (11 relating to economic and social benefits, 10 to the ability of the UK to capture these benefits) and ‘feasibility’ (four relating to the likelihood of scientific or technological breakthrough, four to the strengths of the UK science and technology base, one each for the cost of required investment and the time required for the technology to mature). 3
Only the Dutch “Technology Radar” project conducted an even more sophisticated criteria/index-based scoring elaboration. A specially weighted summing over the scores was used to calculate an index representing the relative contribution of a technology field to improve the competitive advantage of the Dutch economy. A second economic value index to represent the economic importance of a given technology ‘j’ to the Dutch economy (EV(j)) was also calculated as follows:
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EV (j) = [·iGDP (i)] * [·i R&D (i)] where GDP(i) is the contribution (percentage share) of the business segment ‘i’ to the Dutch GDP, and R&D(i) is the expenditure on R&D (percentage share) made by business segment ‘i’, the sum being taken over all business segments ‘i’ for which technology ‘j’ is important. Plotting the resulting indices ‘competitive advantage’ and ‘economic value’, against each other on a two dimensional plot, resulted in the earmarking of 15 technology fields as ‘strategic’ for the Dutch economy – i.e. those that scored highest on both counts. Each of these was explored in detail, comparing the state of knowledge supply and demand in the Netherlands. 4
In the following “technology” and “technology family” are often used as synonymous. Properly, a technology family is a group of strictly related technologies (i.e., DSL technologies in the telecommunication field).
