For descriptive purposes, this section compares the sectorial specialization of the districts with that of the areas in which they are located, and it also studies the sectorial variety within the districts.The first exercise aims to evaluate to what extent the technology districts are grounded on the specialization of the area in which they are located. The correspondence between district and area sectorial specialization was a requirement of the public program and was supposed to affect the effectiveness of the policy. i In Table A2 the sectorial distribution of all the firms and the sectorial distribution of district firms are reported for each geographical area. Both are calculated over the number of firms using a 4-digit sector of activity of the enterprises. A sectorial diversity index is reported in the last five columns, calculated as the difference between the sectorial share of district firms and the sectorial share of all firms within the area: [(Number of district firms in sector j, area i)/(Total number of districts firms in area i)] -[(Number of firms in sector j, area i)/(Total number of firms in area i)]. For each area, values greater (smaller) than zero indicate that districts are more (less) specialized in sector j compared to the area; values close to zero mean that the specialization of the districts reflects the specialization of the area. From the Table A2 it is possible to find some interesting information.
In this paper we study a policy tool called technology districts, implemented in Italy over the last decade to foster local innovation activity. First, we examine the characteristics of technology districts and those of the firms within them. Next, we assess the performance of district firms. We find that in the southern regions technology districts are more numerous but smaller than those located in the Centre-North, are poorly diversified from a sectorial point of view and more distant from the economic structure of the area. We find that the firms that did join a district had previously been, on average, larger, more innovative and profitable, and also show higher leverage than the others. Our results show that overall after the birth of a district the performance of the firms that joined it did not differ significantly from that of similar firms that did not.
This article presents a methodological framework developed to monitor the evolution of virtual learning communities intended as open communities of peers, tutors, and mentors from industry and academia. The proposed framework is described through an explorative case. It has been applied to observe and supervise a virtual learning community built around a Master's program intended to create "e-Business Solutions Engineers." The framework is based on two dimensions of analysis: individual growth and team growth. The first is a function of personal development and satisfaction, and the second depends on social networking dynamics and cooperative content creation. The analysis of data, collected during 10 months of exchanged e-mails and five monthly Web surveys, has been validated through interviews with key informants and through the involvement of academic and industrial partners in the formal assessment of the learners' performance. C 2010 Wiley Periodicals, Inc.
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