The paper applies a relational perspective to patent data in order to investigate the characteristics of innovation flows within and across 103 Italian NUTS3 regions (province). In this way it is possible to use the CRENoS database on regional patenting built on EPO data spanning from 1978 to 2003to investigate the scientific and technological orelationso among invention-creatingo and coinvention-adoptingo territories. In particular, patents are used as relational data connecting inventors and applicants along a dual interpretation of a knowledge production and a knowledge utilization function. In addition a gravity model is used to identify frictions and attractions of the Italian innovation system. Analytical tools, such as social network analysis, spatial econometrics and negative binomial estimation procedures, are used to map and measure the structure and the evolution of a series of innovation sub-systems, both at territorial level (i.e. province) and at the industry level (i.e. five specific industries, chosen according to the Pavitt's taxonomy, Footwear, Textiles, Machinery, Personal Computers and Chemicals).
Maggioni, M. A., Uberti, T. E., Usai, S., Treating patent as relational data: Knowledge transfers and spillovers across Italian provinces, <<INDUSTRY AND INNOVATION>>, 2011; 18 (Gennaio): 39-67. [doi:10.1080/13662716.2010.528928] [http://hdl.handle.net/10807/16510]
Treating patent as relational data: Knowledge transfers and spillovers across Italian provinces
Maggioni, Mario Agostino;Uberti, Teodora Erika;
2011
Abstract
The paper applies a relational perspective to patent data in order to investigate the characteristics of innovation flows within and across 103 Italian NUTS3 regions (province). In this way it is possible to use the CRENoS database on regional patenting built on EPO data spanning from 1978 to 2003to investigate the scientific and technological orelationso among invention-creatingo and coinvention-adoptingo territories. In particular, patents are used as relational data connecting inventors and applicants along a dual interpretation of a knowledge production and a knowledge utilization function. In addition a gravity model is used to identify frictions and attractions of the Italian innovation system. Analytical tools, such as social network analysis, spatial econometrics and negative binomial estimation procedures, are used to map and measure the structure and the evolution of a series of innovation sub-systems, both at territorial level (i.e. province) and at the industry level (i.e. five specific industries, chosen according to the Pavitt's taxonomy, Footwear, Textiles, Machinery, Personal Computers and Chemicals).I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.