Digital footprint approach for the study of competitiveness in wineries
[EN] Advances in computation and web-scraping techniques allow us to use real-time information from company websites and social media platforms, namely digital footprint indicators. Additionally to their real-time availability, these indicators are easily accessible, making them a potentially practi...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglés |
| OAI Identifier: | oai:riunet.upv.es:10251/214803 |
| Acceso en línea: | https://riunet.upv.es/handle/10251/214803 |
| Access Level: | acceso abierto |
| Palabra clave: | Digital footprint Web scraping Competitiveness Unsupervised learning Supervised learning Wineries ECONOMIA APLICADA ESTADISTICA E INVESTIGACION OPERATIVA |
| Sumario: | [EN] Advances in computation and web-scraping techniques allow us to use real-time information from company websites and social media platforms, namely digital footprint indicators. Additionally to their real-time availability, these indicators are easily accessible, making them a potentially practical tool for monitoring a company¿s level of competitiveness. Therefore, this article aims to obtain a multivariate analysis method to accurately predict the wineries¿ competitiveness group, applying the methodology to a sample of Valencian wineries to explore the association between digital footprint indicators and competitiveness. Unsupervised learning techniques were implemented to detect outliers and clusters in observations using financial variables obtained from the Sistema de Análisis de Balances Ibéricos (SABI). Thus, clustering was used to identify groups of wine companies differentiating the sample of companies according to their competitiveness characteristics. Subsequently, footprint indicators were used to create multivariate models to predict the above classification of companies. Also, this methodology permits the study of which digital indicators are essential in this prediction, specifically the presence of words on the web and others regarding online company activities on social networks associated with competitiveness. This research provides practical guidance for developing and incorporating the essential digital indicators, which could be applied in wineries and any study of companies¿ competitiveness. |
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