
In this article I analysed the trend of innovation in the production system in the Italian regions using ISTAT-BES data. After presenting a static analysis and innovation trends of the production system, I present a clustering with a k-Means algorithm optimized with the Silhouette coefficient. Subsequently, an econometric analysis is presented for estimating the determinants of innovation in production systems. Finally, the results are critically discussed with economic policy recommendations.
O30 - General, O32 - Management of Technological Innovation and R&D, O33 - Technological Change: Choices and Consequences ; Diffusion Processes, O34 - Intellectual Property and Intellectual Capital, O31 - Innovation and Invention: Processes and Incentives
O30 - General, O32 - Management of Technological Innovation and R&D, O33 - Technological Change: Choices and Consequences ; Diffusion Processes, O34 - Intellectual Property and Intellectual Capital, O31 - Innovation and Invention: Processes and Incentives
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
