Innovative Enterprises in Europe -a Country Cluster Analysis using R Shiny
For EU countries, keeping and growing innovative business is vital in order to be globally competitive. Innovation in enterprises, as a cornerstone for future production, is of great significance for all countries depending more and more on services. Due to the way innovation is understood...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Editura ASE
2023-06-01
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| Series: | Proceeding Papers (BASIQ International Conference) |
| Online Access: | https://conference.ase.ro/papers/2023/23031.pdf |
| _version_ | 1797403836436971520 |
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| author | Andrei Tudorel Andreea Mirica Ionela-Roxana Petcu |
| author_facet | Andrei Tudorel Andreea Mirica Ionela-Roxana Petcu |
| author_sort | Andrei Tudorel |
| collection | DOAJ |
| description | For EU countries, keeping and growing innovative business is vital in order to be globally competitive. Innovation in enterprises, as a cornerstone for future production, is of great significance for all countries depending more and more on services. Due to the way innovation is understood within enterprises, such process no longer is bound to single entity. It easily exceeds the sphere of one organisation rapidly evolving to an ecosystem of many actors and 3rd party suppliers and different shareholders across different countries. All these aspects reveal the link between business development, effective created value for the customers and the potential to innovate. The role of information technology both from operational perspective and in the development of innovative potential and new products is currently acknowledged both by enterprises and by small start-up companies. In such context, Eurostat introduced a new tool, “Innovation profiles”, to monitor enterprise innovation level. Thus, our paper explores such data analysing the similarities between 17 European countries. The source is Eurostat and latest data available from 2020, is used. The results of the Cluster Analysis performed in R Shiny app are obtained employing Pearson Correlation coefficient, measuring the Squared Euclidian distance, and the Ward linkage method. Positive moderate to low correlation is obtained in terms of innovation for the three obtained clusters, emphasizing the link between Romania, Poland, and Bulgaria. |
| first_indexed | 2024-03-09T02:45:09Z |
| format | Article |
| id | doaj.art-58f8d90986ff4e94b8a75e94ff9366bc |
| institution | Directory Open Access Journal |
| issn | 2457-483X |
| language | English |
| last_indexed | 2024-03-09T02:45:09Z |
| publishDate | 2023-06-01 |
| publisher | Editura ASE |
| record_format | Article |
| series | Proceeding Papers (BASIQ International Conference) |
| spelling | doaj.art-58f8d90986ff4e94b8a75e94ff9366bc2023-12-05T18:41:01ZengEditura ASEProceeding Papers (BASIQ International Conference)2457-483X2023-06-0126026610.24818/BASIQ/2023/09/031Innovative Enterprises in Europe -a Country Cluster Analysis using R ShinyAndrei TudorelAndreea MiricaIonela-Roxana Petcu For EU countries, keeping and growing innovative business is vital in order to be globally competitive. Innovation in enterprises, as a cornerstone for future production, is of great significance for all countries depending more and more on services. Due to the way innovation is understood within enterprises, such process no longer is bound to single entity. It easily exceeds the sphere of one organisation rapidly evolving to an ecosystem of many actors and 3rd party suppliers and different shareholders across different countries. All these aspects reveal the link between business development, effective created value for the customers and the potential to innovate. The role of information technology both from operational perspective and in the development of innovative potential and new products is currently acknowledged both by enterprises and by small start-up companies. In such context, Eurostat introduced a new tool, “Innovation profiles”, to monitor enterprise innovation level. Thus, our paper explores such data analysing the similarities between 17 European countries. The source is Eurostat and latest data available from 2020, is used. The results of the Cluster Analysis performed in R Shiny app are obtained employing Pearson Correlation coefficient, measuring the Squared Euclidian distance, and the Ward linkage method. Positive moderate to low correlation is obtained in terms of innovation for the three obtained clusters, emphasizing the link between Romania, Poland, and Bulgaria.https://conference.ase.ro/papers/2023/23031.pdf |
| spellingShingle | Andrei Tudorel Andreea Mirica Ionela-Roxana Petcu Innovative Enterprises in Europe -a Country Cluster Analysis using R Shiny Proceeding Papers (BASIQ International Conference) |
| title | Innovative Enterprises in Europe -a Country Cluster Analysis using R Shiny |
| title_full | Innovative Enterprises in Europe -a Country Cluster Analysis using R Shiny |
| title_fullStr | Innovative Enterprises in Europe -a Country Cluster Analysis using R Shiny |
| title_full_unstemmed | Innovative Enterprises in Europe -a Country Cluster Analysis using R Shiny |
| title_short | Innovative Enterprises in Europe -a Country Cluster Analysis using R Shiny |
| title_sort | innovative enterprises in europe a country cluster analysis using r shiny |
| url | https://conference.ase.ro/papers/2023/23031.pdf |
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