Study of the relationship between competitiveness and digital footprint indicators in Valencian wineries
[EN] The digital footprint of the Spanish wine sector is a valuable resource for predicting real-time indicators, enabling companies to anticipate their competitors and devise effective digital transformation strategies using emerging technologies. With advances in computation and web-scraping techn...
| Autores: | , , |
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| Formato: | capítulo de livro |
| Fecha de publicación: | 2023 |
| País: | España |
| Recursos: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglés |
| OAI Identifier: | oai:riunet.upv.es:10251/201684 |
| Acesso em linha: | https://riunet.upv.es/handle/10251/201684 |
| Access Level: | acceso abierto |
| Palavra-chave: | Digital footprint Web scraping Competitiveness Supervised and unsupervised learning Wineries |
| Resumo: | [EN] The digital footprint of the Spanish wine sector is a valuable resource for predicting real-time indicators, enabling companies to anticipate their competitors and devise effective digital transformation strategies using emerging technologies. With advances in computation and web-scraping techniques, it is now possible to approximate competitiveness indicators using real-time information from company websites. Given this context, the general objective of this work is to analyze the relationship between the digital footprint and competitiveness of Valencian wine companies. To this end, it is proposed to use financial variables obtained from the Sistema de Análisis de Balances Ibéricos (SABI) and indicators extracted from the companies' websites. Unsupervised learning techniques will be implemented to find groups or clusters of companies based on their economic performance. Subsequently, digital footprint indicators will be used to create a supervised learning model to predict the above classification of companies based solely on digital footprint indicators to identify the most significant indicators for predicting competitiveness. |
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