Unsupervised Neural Networks for the Analysis of Business Performance at Infra-City Level

Renata Paola Dameri (), Roberto Garelli () and Marina Resta ()
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Renata Paola Dameri: University of Genova
Roberto Garelli: University of Genova
Marina Resta: University of Genova

A chapter in Organizational Innovation and Change, 2016, pp 203-215 from Springer

Abstract: Abstract The goal of this paper is using Neural Networks (NN) to analyze business performance and support small territories development policies. The contribution of the work to the existing literature may be basically summarized as follows: we are focusing on the application of an unsupervised neural network (namely: on Self-Organizing Maps—SOM) to discover firms clusters on micro-territories inside city’s boundaries, and to exploit possible development policies at local level. Although since early ’90 of the past century NN have been widely employed to evaluate firms performance, to the best of our knowledge the use of SOM of that specific task is much less documented. Moreover, the main novelty of the paper relies on the attention to data at “microscopic” level: data processing in an infra-city perspective, in fact, has been neglected till now, although recent studies demonstrate that inequalities in economic and well-being conditions of people are higher among neighbourhoods of the same city rather than among different cities or regions. The performance analysis of a large set (7000 environ) of companies settled in Genova, Italy permits to test our research method and to design further applications to a large spectrum of territorial surveys regarding both economic and social well-being conditions.

Keywords: Neural networks; Self organizing maps; Knowledge management; Business performance; Territorial development; Inclusive growth (search for similar items in EconPapers)
Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnichp:978-3-319-22921-8_16

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DOI: 10.1007/978-3-319-22921-8_16

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