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A Technology-Based Classification of Firms: Can We Learn Something Looking Beyond Industry Classifications? †
In this work we use clustering techniques to identify groups of firms competing in similar technological markets. Our clustering properly highlights technological similarities grouping together firms normally classified in different industrial sectors. Technological development leads to a continuous...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7512469/ https://www.ncbi.nlm.nih.gov/pubmed/33266611 http://dx.doi.org/10.3390/e20110887 |
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author | Gkotsis, Petros Pugliese, Emanuele Vezzani, Antonio |
author_facet | Gkotsis, Petros Pugliese, Emanuele Vezzani, Antonio |
author_sort | Gkotsis, Petros |
collection | PubMed |
description | In this work we use clustering techniques to identify groups of firms competing in similar technological markets. Our clustering properly highlights technological similarities grouping together firms normally classified in different industrial sectors. Technological development leads to a continuous changing structure of industries and firms. For this reason, we propose a data driven approach to classify firms together allowing for fast adaptation of the classification to the changing technological landscape. In this respect we differentiate from previous taxonomic exercises of industries and innovation which are based on more general common features. In our empirical application, we use patent data as a proxy for the firms’ capabilities of developing new solutions in different technological fields. On this basis, we extract what we define a Technologically Driven Classification (TDC). In order to validate the result of our exercise we use information theory to look at the amount of information explained by our clustering and the amount of information shared with an industrial classification. All-in-all, our approach provides a good grouping of firms on the basis of their technological capabilities and represents an attractive option to compare firms in the technological space and better characterise competition in technological markets. |
format | Online Article Text |
id | pubmed-7512469 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-75124692020-11-09 A Technology-Based Classification of Firms: Can We Learn Something Looking Beyond Industry Classifications? † Gkotsis, Petros Pugliese, Emanuele Vezzani, Antonio Entropy (Basel) Article In this work we use clustering techniques to identify groups of firms competing in similar technological markets. Our clustering properly highlights technological similarities grouping together firms normally classified in different industrial sectors. Technological development leads to a continuous changing structure of industries and firms. For this reason, we propose a data driven approach to classify firms together allowing for fast adaptation of the classification to the changing technological landscape. In this respect we differentiate from previous taxonomic exercises of industries and innovation which are based on more general common features. In our empirical application, we use patent data as a proxy for the firms’ capabilities of developing new solutions in different technological fields. On this basis, we extract what we define a Technologically Driven Classification (TDC). In order to validate the result of our exercise we use information theory to look at the amount of information explained by our clustering and the amount of information shared with an industrial classification. All-in-all, our approach provides a good grouping of firms on the basis of their technological capabilities and represents an attractive option to compare firms in the technological space and better characterise competition in technological markets. MDPI 2018-11-18 /pmc/articles/PMC7512469/ /pubmed/33266611 http://dx.doi.org/10.3390/e20110887 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Gkotsis, Petros Pugliese, Emanuele Vezzani, Antonio A Technology-Based Classification of Firms: Can We Learn Something Looking Beyond Industry Classifications? † |
title | A Technology-Based Classification of Firms: Can We Learn Something Looking Beyond Industry Classifications? † |
title_full | A Technology-Based Classification of Firms: Can We Learn Something Looking Beyond Industry Classifications? † |
title_fullStr | A Technology-Based Classification of Firms: Can We Learn Something Looking Beyond Industry Classifications? † |
title_full_unstemmed | A Technology-Based Classification of Firms: Can We Learn Something Looking Beyond Industry Classifications? † |
title_short | A Technology-Based Classification of Firms: Can We Learn Something Looking Beyond Industry Classifications? † |
title_sort | technology-based classification of firms: can we learn something looking beyond industry classifications? † |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7512469/ https://www.ncbi.nlm.nih.gov/pubmed/33266611 http://dx.doi.org/10.3390/e20110887 |
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