Identification of innovation drivers based on technology-related news articles
Innovations contribute to economic growth. Hence, knowledge about drivers of innovation activities is a necessary input for economic policy making when it comes to implement targeted support measures. We focus on firms as potential drivers of innovation and use a novel data-driven approach to identi...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Journal of Open Innovation: Technology, Market and Complexity |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2199853125000101 |
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| _version_ | 1832595740256370688 |
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| author | Albina Latifi David Lenz Peter Winker |
| author_facet | Albina Latifi David Lenz Peter Winker |
| author_sort | Albina Latifi |
| collection | DOAJ |
| description | Innovations contribute to economic growth. Hence, knowledge about drivers of innovation activities is a necessary input for economic policy making when it comes to implement targeted support measures. We focus on firms as potential drivers of innovation and use a novel data-driven approach to identify them. The approach is based on news articles from a technology-related newspaper for the period 1996–2021. In a first step, natural language processing (NLP) tools are used to identify latent topics in the text corpus. Expert knowledge is used to tag innovation-related topics. In a second step, a named entity recognition (NER) method is used to detect firm names in the news articles. Combining the information about innovation-related topics and firms mentioned in news articles linked to these topics provides a set of firms linked to each innovation-related topic. For each entity (firm), we identify the specific innovation fields in which it is involved. Similarly, for each innovation field, we determine the companies that are driving innovation within that area. The results suggest that the approach helps identifying drivers of innovation activities going beyond the usual suspects. However, given that the rate of false alarms is not negligible, at the end also human judgement is needed when using this approach. |
| format | Article |
| id | doaj-art-2228fafab4de42d9b4ec1540555a7ace |
| institution | Kabale University |
| issn | 2199-8531 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Open Innovation: Technology, Market and Complexity |
| spelling | doaj-art-2228fafab4de42d9b4ec1540555a7ace2025-01-18T05:04:26ZengElsevierJournal of Open Innovation: Technology, Market and Complexity2199-85312025-03-01111100475Identification of innovation drivers based on technology-related news articlesAlbina Latifi0David Lenz1Peter Winker2Corresponding author.; Department of Economics, Justus Liebig University Giessen, GermanyDepartment of Economics, Justus Liebig University Giessen, GermanyDepartment of Economics, Justus Liebig University Giessen, GermanyInnovations contribute to economic growth. Hence, knowledge about drivers of innovation activities is a necessary input for economic policy making when it comes to implement targeted support measures. We focus on firms as potential drivers of innovation and use a novel data-driven approach to identify them. The approach is based on news articles from a technology-related newspaper for the period 1996–2021. In a first step, natural language processing (NLP) tools are used to identify latent topics in the text corpus. Expert knowledge is used to tag innovation-related topics. In a second step, a named entity recognition (NER) method is used to detect firm names in the news articles. Combining the information about innovation-related topics and firms mentioned in news articles linked to these topics provides a set of firms linked to each innovation-related topic. For each entity (firm), we identify the specific innovation fields in which it is involved. Similarly, for each innovation field, we determine the companies that are driving innovation within that area. The results suggest that the approach helps identifying drivers of innovation activities going beyond the usual suspects. However, given that the rate of false alarms is not negligible, at the end also human judgement is needed when using this approach.http://www.sciencedirect.com/science/article/pii/S2199853125000101Innovation driversTopic modelingEntity recognition |
| spellingShingle | Albina Latifi David Lenz Peter Winker Identification of innovation drivers based on technology-related news articles Journal of Open Innovation: Technology, Market and Complexity Innovation drivers Topic modeling Entity recognition |
| title | Identification of innovation drivers based on technology-related news articles |
| title_full | Identification of innovation drivers based on technology-related news articles |
| title_fullStr | Identification of innovation drivers based on technology-related news articles |
| title_full_unstemmed | Identification of innovation drivers based on technology-related news articles |
| title_short | Identification of innovation drivers based on technology-related news articles |
| title_sort | identification of innovation drivers based on technology related news articles |
| topic | Innovation drivers Topic modeling Entity recognition |
| url | http://www.sciencedirect.com/science/article/pii/S2199853125000101 |
| work_keys_str_mv | AT albinalatifi identificationofinnovationdriversbasedontechnologyrelatednewsarticles AT davidlenz identificationofinnovationdriversbasedontechnologyrelatednewsarticles AT peterwinker identificationofinnovationdriversbasedontechnologyrelatednewsarticles |