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Abstract
Nowadays the governments of industrialised countries, in the presence of reduced public resources, have to assign clear objectives to public research laboratories to increase the competitiveness of firms. The purpose of this article is to analyse the public research bodies of the National Research Council of Italy in order to pinpoint the main typologies operating in the national system of innovation (NSI). This research shows four main types of research institutes as drivers of NSI. The results can supply useful information to policy makers on the behaviour of these structures and on their strengths and weaknesses.
Notes
The present work, a continuation of the research which began in 1998, analyses the activities of the public research institutes within the Italian national research council (CNR). While I alone am responsible for any errors or omissions to be found in the text, I wish to thank those who offered a contribution in scientific terms and in personal support, amongst them Professor Secondo Rolfo (Ceris‐CNR), for the intense scientific cooperation and Mrs Silvana Zelli of Ceris‐CNR for the meticulous computer elaboration of the CNR database. Particular thanks go to Professor Luca Gnan of the Bocconi University in Milan (Italy), for his useful tuition in SPSS software in economic modeling and Professor Donald Lamberton of the Australian National University in Canberra, for useful comments and suggestions. I also am indebted to all the staff of Ceris‐CNR for their research assistance.
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The principal component analysis (PCA) or Karhunen–Loeve transform is a mathematical way of determining that linear transformation of a sample of points in N‐dimensional space, which exhibits the properties of the sample most clearly along the co‐ordinate axes. Along the new axes the sample variances are extremes (maxima and minima), and uncorrelated. The name comes from the principal axes of an ellipsoid (e.g. the ellipsoid of inertia), which are just the co‐ordinate axes in question. PCA extracts components to maximise the proportion of variability explained by each component, subject to the orthogonality constraint. R. K. Back, The Data Analysis Briefbook, Springer, Berlin, 1998. The objectives of the PCA are: (a) to discover or to reduce the dimensionality of the data set; (b) to identify new meaningful underlying variables. By their definition, the principal axes will include those along which the point sample has little or no spread (minima of variance). Hence, an analysis in terms of principal components can show (linear) interdependence in data. A point sample of N dimensions for whose N co‐ordinates M linear relations hold, will show only (N–M) axes along which the spread is non‐zero. Using a cut‐off on the spread along each axis, a sample may thus be reduced in its dimensionality. (See C. M. Bishop, Neural Networks for Pattern Recognition, Oxford University Press, Oxford, 1995.) The principal axes of a point sample are found by choosing the origin at the centre of gravity and forming the dispersion matrix. The principal axes and the variance along each of them are then given by the eigenvectors and associated eigenvalues of the dispersion matrix. Principal component analysis has in practice been used to reduce the dimensionality of problems, and to transform interdependent co‐ordinates into significant and independent ones. An example used in several particle physics experiments is that of reducing redundant observations of a particle track in a detector to a low‐dimensional subspace whose axes correspond to parameters describing the track. In practice, non‐linearities of detectors, frequent changes in detector layout and calibration, and the problem of transforming the co‐ordinates along the principal axes into physically meaningful parameters, set limits to the applicability of the method. A simple programme for principal component analysis is described in M. J. O'Connel, ‘Search program for significant variables’, Comp. Phys. Comm., 8, 1974, p. 49 . Although this is an efficient way of reducing and/or recognising the dimensionality of the data, it may not produce the best projection for interpretation purposes. It may be possible to simplify the interpretation if axes are rotated so that a view of data, the projection, is obtained which is easier to interpret. The problem now becomes one of selecting between all of the possible projections. As the axes are rotated the variable loadings change. One criterion that could be used is to find the ‘simplest’ combination of loadings. One commonly used method is the Kaiser's Varimax method: (H. F. Kaiser, ‘The varimax criterion for analytic rotation in factor analysis’, Psychometrika, 23, 1985, pp. 187–200) which uses the variance of the loadings to obtain a solution in which each loading is as close as possible to either 0 or 1. Recall that factor loadings are correlation coefficients, thus if a variable has a large (absolute) loading it is highly correlated with a factor, while a small loading indicates no correlation. The aim of the Varimax rotation is to remove, as far as possible, loadings in the mid range, e.g. 0.3–0.7. Ideally, each variable will have a large loading for only one factor.
The term cluster analysis (first used by Tryon, 1939; R. C. Tryon, Cluster Analysis, McGraw Hill, New York) encompasses a number of different algorithms and methods for grouping objects of a similar kind into respective categories. A general question facing researchers in many areas of inquiry is how to organise observed data into meaningful structures, that is, to develop taxonomies. In other words, cluster analysis is an exploratory data analysis tool which aims at sorting different objects into groups in a way that the degree of association between two objects is maximal if they belong to the same group and minimal otherwise. Hierarchical cluster analysis is a statistical method for finding relatively homogeneous clusters of cases based on measured characteristics. It starts with each case in a separate cluster and then combines the clusters sequentially, reducing the number of clusters at each step until only one cluster is left. When there are N cases, this involves N–1 clustering steps, or fusions. This hierarchical clustering process can be represented as a tree, or dendrogram, where each step in the clustering process is illustrated by a join of the tree. The joining or tree clustering method uses the dissimilarities (similarities) or distances between objects when forming the clusters. Similarities are a set of rules that serve as criteria for grouping or separating items. These distances (similarities) can be based on a single dimension or multiple dimensions, with each dimension representing a rule or condition for grouping objects. The most straightforward way of computing distances between objects in a multi‐dimensional space is to compute Euclidean distances. This is probably the most commonly chosen type of distance. It simply is the geometric distance in the multidimensional space. It is computed as: distance(x,y) = { i (xi–yi )2}1/2. You may want to square the standard Euclidean distance in order to place progressively greater weight on objects that are further apart. This distance is computed as: distance(x,y) = i (xi–yi )2. Ward's method within the cluster analysis is distinct from all other methods because it uses an analysis of variance approach to evaluate the distances between clusters. In short, this method attempts to minimise the Sum of Squares (SS) of any two (hypothetical) clusters that can be formed at each step. Refer to J. H. Ward, ‘Hierachical grouping to optimize an objective function’, Journal of the American Statistical Association, 58, 1963, pp. 236–44 for details concerning this method. In general, this method is regarded as very efficient, however, it tends to create clusters of small size.
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