Abstract
Information and Communication Technologies (ICTs) impact the communities in which we live and the way individuals, business, government and civil society interact and develop. Simultaneously, all sectors have shown increased interest in the concept of social capital and the role it could play in building stronger communities, increasing economic productivity and contributing to regional rejuvenation. Thus, ICTs and social capital concept interlink with each other in a variety of ways. The purpose of this paper is to investigate the relationship between ICTs and social capital through the study of the relative’s disparities among Italian regions. This paper provides an operational definition of the concepts of ICT and social capital and presents consistent evidence on the geography of this relationship in Italy. The statistical and geographical analysis, based on non-linear clustering with self-organizing map (SOM) neural networks, are performed to analyse the performance of Italian regions in the period 2006–2013. The results show the isolation of Southern Italian regions. In particular, we found that ICTs may not promote social capital, that is, ICTs could not play a decisive role in creating and developing social capital. These results prompt the formulation of new policies for Italian regions.
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Notes
- 1.
For a large scale study see [7].
- 2.
- 3.
During this study, social capital is considered to be an attribute of networks (or societies, regions, countries, etc.).
- 4.
If we refer to these forms of social capital under functional aspects, information and trust are vital for a network since they represent most of the network’s ‘intangible’ resources, which help the society to achieve either economic and social outcomes (like well-being and higher employment rate) or intangible outcomes (such as sense of social security). All of these aspects occur if information represents the primary resource that individuals or groups wants to achieve through the available of social connections.
- 5.
The properties of the algorithm leads this map to represent the statistical property of the original dataset used to create it.
- 6.
In this context, statistical dissimilarity is translated into space distance and vice versa. This feature is obtained due to the learning nature of the algorithm. Also, it was possible because the dataset has been mapped onto the surface of the SOM by adding each neuron up to color gradient. This mapping help us to define the agglomeration of the regions. In this case, the color of the cells on the maps represents the intra cluster homogeneity degree: the cold color reveal high intra-cluster homogeneity, while the warm colors represent a lack of homogeneity.
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Cialfi, D., Colantonio, E. (2019). Do ICTs Matter for Italy?. In: Bucciarelli, E., Chen, SH., Corchado, J. (eds) Decision Economics. Designs, Models, and Techniques for Boundedly Rational Decisions. DCAI 2018. Advances in Intelligent Systems and Computing, vol 805. Springer, Cham. https://doi.org/10.1007/978-3-319-99698-1_18
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