Since they are both state Universities, UniTO and PoliTO share the same organizational structure. Table 6 reports some relevant data on the two Universities.Footnote 7 They are both presided over by a Rector, elected by professors and researchers and by technical staff and students’ delegates. The Rector is assisted by an Academic Senate, made up of representatives of professors, technical staff, and students. The Administrative Director, hired through a public competition, looks after the economic management of the University, assisted by a Management Board, partly elected by University personnel and partly appointed by the Academic Senate. Teaching is organized in Faculties, each offering Bachelors and Masters Courses, and Doctorate Schools, offering Ph.D. programs. Research is performed in Departments, which can be structured in research groups, research centers, or laboratories. Faculties are presided over by a Dean, assisted by the Faculty Council (made up of all the Professors and Researchers, and delegates of technical personnel and students), and the Departments are led by a Director, assisted by a Department Council. Professors and Researchers belong to both a Faculty (as for teaching activities) and a Department (as for research activities), and are formally considered to spend half of their time teaching at their Faculty and half doing research in their Department.
Reale and Seeber (2011, pp. 5 and hereafter) and Reale et al. (2007) describe comprehensively the case of the Italian University.
This situation is the result of a hybrid system since, despite several reform attempts in the last decades, the national central government has actually never given full autonomy to Universities (which, as a result, are all organized in the same way) but professors’ lobby groups have managed to keep their privilege of democratic self-government. Nonetheless, thanks to the presence of strong personalities in leading positions (rectors), the internal community’s professional culture, and strong relations with their region, changes have indeed occurred in some Universities (Reale and Potì 2009).
Several sets of data have been studied in order to assess how research and teaching activities performed in the two Universities overlap.
The first two sets of data are those on scientific publications and patents. We analyzed data from the ISI Thomson database for both PoliTO and UniTO and we took into account the first 25 subject categories in the database for each University. Among these categories only 4 are common to both Universities. “Physics, particles & fields” is the second most represented category for UniTO, with 7.02 % of total publications (17,408), and the 21st for PoliTO, with 2.33 % of the total (5,706). “Physics, multidisciplinary” is the 7th for both, with 4.06 % for UniTO and 5.35 % for PoliTO. “Chemistry, physical” is the 9th for UniTO (3.73 %) and the 10th for PoliTO (4.40 %). Finally, “Materials sciences, multidisciplinary” is the 19th for UniTO (1.94 %) and the third for PoliTO (9.15 %). These are the four areas in which researchers of materials sciences mostly publish.
Patents data were retrieved from the EPO online database, Espacenet.Footnote 8 We considered the patents for which the applicant was either UniTO or PoliTO. The database includes 87 patents for PoliTO and 49 for UniTO, of which 29 UniTO patents and 58 PoliTO patents contain International Patent Classification data. This subset shows marked differences in the production of patents by the two Universities. In the case of UniTO the most represented categories are A61 (Medical or Veterinary Science; Hygiene) and C12 (Biochemistry; Beer; Spirits; Wine; Vinegar; Microbiology; Enzymology; Mutation or Genetic Engineering), while for PoliTO they are A61 (as above), G01 (Measuring; Testing), B60 (Vehicles in General), and H01 (Basic Electric Elements). It must also be noted that several PoliTO patents are in the B (Performing Operations; Transporting) and F (Mechanical Engineering; Lighting; Heating; Weapons; Blasting) classes, while there are no UniTO patents in these two classes. Conversely, the C class (Chemistry; Metallurgy) is much more represented in UniTO than in PoliTO.
Data on patents and scientific publications match the analysis performed on one of the datasets exploited in the present paper for the case study, FIRP. In fact all the files in the database were carefully studied and a keyword was assigned to each described research project. Keywords were based—with some modifications—on the classification of the Italian University SystemFootnote 9 provided by the Ministry of University and Research. Main modifications applied are: “Architecture” was separated from “Engineering”; “Engineering” (originally split into three sectors) was considered as a single sector; two extra sectors—particularly representative for their high rate of innovativeness—were added, “Electronics” and “Materials”.
A further classification of database files was then made assigning a second keyword to each one of them, in order to better define each research project and describe the application sector of the research. “Materials” were subdivided according to the preparation-characterization-development-application research path.
38 files (7.08 % of the total) in UniTO and 44 files (6.54 % of the total) in PoliTO belong to art/humanities/economics subjects, showing a limited contribution of these sectors to the database.
The rate of overlap between research areas was measured calculating for each sector the percentage of files belonging to the two Universities and assigning: “Very high” when files belonging to one of the two (and conversely to the other one) range from 50.00 to 62.50 %; “High” when the range is 62.50–75.00 %; “Low” when the range is 75.00–87.5 %; and “Very low” when the range is 87.50–99.99 %. Overlap is “Very low” in 3 areas, “High” and “Low” in 1 area, “Very high” in 3 areas. 4 of the 12 main sectors show no overlap. Thus, the number of sectors where no overlap occurs is higher than the number of areas where overlap is “High”. Table 7 reports the data of the analysis.
When overlap is “High” or “Very high”, a further second-keyword analysis shows that: in “Earth sciences” only one sector has “High” overlap (39 vs. 14 files), while the other sectors show little or no overlap; in “Physics” 2 subsectors out of 4 have no overlap and only one subsector (“Applied”) displays “High” overlap; in “Informatics” and “Materials” all subsectors are overlapped. For what about “Materials” the classification, based on the research activities path, shows that “Preparation” is roughly equally subdivided (13 vs. 10 files), “Characterization” (i.e. the step more linked to basic research) is mostly performed at UniTO (8 vs. 20), “Development” at PoliTO (14 vs. 7), while “Applications” is almost the exclusive domain of PoliTO (10 vs. 1).