of many variables, above all the variable used as proxy of human capital), partly because it would mean contradicting the results of the surveys (e.g. a considerable bias has been created by respondents that most likely have inserted 0 as value in their answers instead of putting a missing value, or not available). A possible aim for further research could be changing the nature of the questionnaire from the perception form to an objective nature.

Alternatively, it would be advisable to link the survey to other sources of information on the firms that are of quantitative nature. However, from current knowledge, it seems that the firms of the survey do not have an identifier, i.e. a unique code that could link the firm of the survey with other databases in which it could be possible to find, for example, accounting data. If this were possible, it would allow us to control for very important variables, for example the capital/labor ratio of each firm, the level of foreign ownership (if any), or whether they received any fiscal incentive that could help them to invest in R&D or training.

Finally, spatial analysis and the analysis of clusters (linkages between firms, their sectors and their geographical location), could also help us to understand if there are “trends” in innovation and if these trends are lead by centers of excellence or by main innovators, or both. This view is particularly important in developing countries, since it would allow us to explore the sustainability of a process of continuous innovations in clusters based on traditional industries.

All industrial clusters can in fact be characterized in terms of transaction costs and relation characteristics (classical industrial clusters theory) and also in terms of technological regimes and knowledge characteristics (evolutionary approach). It would be interesting to empirically analyze whether the nature of agglomeration effects is sensitive not only to the industrial structure but also to the stage of product life cycle and to changes in the underlying technological base _._ Technological and knowledge features alone are not a sufficient guide to the types of clusters that are likely to emerge, nor are industry characteristics. Knowledge and innovation processes, organizational, firm and industry-specific characteristics, and institutional and governance setting all play a role in explaining the diversity of industrial clusters and their evolutionary trajectories. Understanding this diversity, and in particular both the transaction costs features and the knowledge features of any particular cluster, is the base for any policy action aimed at improving regional industrial characteristics and development. Building regional innovation systems requires the formulation of regional economic development strategies, and it requires engaging universities and the productive sector in the formulation and implementation of these.

Further analysis should focus on the geographical aspects, and the possible consequences of knowledge spillovers on the innovation process of firms within clusters. The framework could be the same as the one underlying this paper, where the definition of innovation extends beyond formal R&D activities to include continuous improvement in product design and quality, the ability not only to generate but also to absorb and adapt new knowledge. In such analysis, the role of human capital would appear in a wider context, and, after controlling for as many explanatory variables as possible, given the 17